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Quantum entanglement in the einstein ones call phenomenon by Agustinus Manguntam Siber Wiper G- Lock ______________________________________________________

The quantum world is the subatomic world or the world within the atom itself, which is the smallest element that makes up this universe. Water, soil, air, ice, magnets and all elements on earth including humans are composed of atoms and within atoms there are still smaller ones, namely particles. Einstein's latest research, apart from explaining relativity and particles and gravitational space in black holes, also explains quantum entanglement, analyzing data in Quantum spoky action at a distance and Einstein describes it in mathematical form as well as the physics of space and time which is called the Einstein Onescall phenomenon. research and development of the Einstein Ones Call phenomenon is also found in human brain networks where humans can connect with each other even though they are at different distances in space and time. Quantum entanglement science is a futuristic physics science because we move in space within the atom itself as a constituent of matter in the universe. The science of the Einstein OneCall phenomenon will experience increasingly rapid development due to increasingly advanced and rapid electronic technology in the physics of electronic instrumentation connected in artificial neural networks as well as in machine learning and deep learning systems as well as developments in chip making both from semiconductors and other elements. arranged in a network of electronic machines, many programs are being developed into the future to form superhuman technology and the natural world.

The nature of the structure of substances and their quantum effects are caused by the arrangement of these atoms, we call them lattices, which become molecules, and in solid substances they are called crystals and amorphous substances, the processing of their forms into tools and materials in instrument physics and control of electronic machines in networks that show good performance. has long-range regularity and relies on the effects of quantum physics.

1. The logic behind quantum entanglement : The state of a composite system is always expressible as a sum, or superposition, of products of states of local constituents; it is entangled if this sum cannot be written as a single product term. Quantum systems can become entangled through various types of interactions. 2. quantum entanglement at interaction with nature : The instantaneous nature of the interaction between particles seems to work faster than light. And yet, as “spooky” as it may be, in the around 100 years since its inception, entanglement has been proven to be a real aspect of the Universe . 3. Quantum entanglement theory : Quantum entanglement is a bizarre, counterintuitive phenomenon that explains how two subatomic particles can be intimately linked to each other even if separated by billions of light-years of space.In the simplest terms, quantum entanglement means that aspects of one particle of an entangled pair depend on aspects of the other particle, no matter how far apart they are or what lies between them . 4. real example quantum rntanglememt : The following are examples of quantum entanglement: An electron and positron both originate from a decaying pi meson. The two particles are entangled because their spins must add up to the spin of the pi meson. Observing one particle's spin reveals the other particle's spin . 5. Quantum communication : The end result is always the same, though: While it's one of the weirdest and coolest phenomena in physics, there is no way to use quantum entanglement to send messages faster than the speed of light. 6. Quantum entanglement mean time travel : Physicists have described using quantum entanglement to simulate a closed timelike curve—in layman's terms, time travel. Before we proceed, I'll stress that no quantum particles went back in time. 7. quantum entanglement prove parallel universe : In each branch, a parallel reality is created, where the measured quantity takes on a specific value. Quantum entanglement plays a crucial role in this interpretation, as entangled particles exist in a superposition of states until a measurement occurs, leading to the creation of parallel worlds. 8. Quantum entanglement is a bizarre, counterintuitive phenomenon that explains how two subatomic particles can be intimately linked to each other even if separated by billions of light-years of space. = Research and Development = ________________________ An example of quantum entanglement that I work with involves a light source that emits two photons at a time. Those two photons of a pair can be entangled so that the polarizations of the individual photons can be any orientation (i.e., random), but photons of a pair always have matching polarizations. What is polarization? The polarization of light depends on the electric field of the light wave. As the light travels from point one point to another, its electric field will oscillate transversely to that propagation direction. It might oscillate in the vertical plane, in the horizontal plane or any direction in between. Back to those entangled pairs. So, if I measure the polarization of photon A to see if it is polarized horizontal or vertical, I get an answer and find it to be, this time, vertical. Entanglement means that when I measure whether its twin is horizontal or vertical, I find that its polarization is vertical too. If I do that experiment many times, I will always find that the two photons' polarizations match, even if I find that the result of which polarization they match to is random. (Think a pair of magical loaded dice.) So, a key point is that the measurement result will be random, but if I make the same measurement on the twin, I will get that same random result. (Again, as a normal human being, that should bother you.) Entanglement is at the heart of quantum physics and future quantum technologies. Like other aspects of quantum science, the phenomenon of entanglement reveals itself at very tiny, subatomic scales. When two particles, such as a pair of photons or electrons, become entangled, they remain connected even when separated by vast distances. In the same way that a ballet or tango emerges from individual dancers, entanglement arises from the connection between particles. It is what scientists call an emergent property. When researchers study entanglement, they often use a special kind of crystal to generate two entangled particles from one. The entangled particles are then sent off to different locations. For this example, let's say the researchers want to measure the direction the particles are spinning, which can be either up or down along a given axis. Before the particles are measured, each will be in a state of superposition, or both "spin up" and "spin down" at the same time. If the researcher measures the direction of one particle's spin and then repeats the measurement on its distant, entangled partner, that researcher will always find that the pair are correlated: if one particle's spin is up, the other's will be down (the spins may instead both be up or both be down, depending on how the experiment is designed, but there will always be a correlation). Returning to our dancer metaphor, this would be like observing one dancer and finding them in a pirouette, and then automatically knowing the other dancer must also be performing a pirouette. The beauty of entanglement is that just knowing the state of one particle automatically tells you something about its companion, even when they are far apart. Are particles really connected across space? But are the particles really somehow tethered to each other across space, or is something else going on? Some scientists, including Albert Einstein in the 1930s, pointed out that the entangled particles might have always been spin up or spin down, but that this information was hidden from us until the measurements were made. Such "local hidden variable theories" argued against the mind-boggling aspect of entanglement, instead proposing that something more mundane, yet unseen, is going on. Thanks to theoretical work by John Stewart Bell in the 1960s, and experimental work done by Caltech alumnus John Clauser (BS '64) and others beginning in the 1970s, scientists have ruled out these local hidden-variable theories. A key to the researchers' success involved observing entangled particles from different angles. In the experiment mentioned above, this means that a researcher would measure their first particle as spin up, but then use a different viewing angle (or a different spin axis direction) to measure the second particle. Rather than the two particles matching up as before, the second particle would have gone back into a state of superposition and, once observed, could be either spin up or down. The choice of the viewing angle changed the outcome of the experiment, which means that there cannot be any hidden information buried inside a particle that determines its spin before it is observed. The dance of entanglement materializes not from any one particle but from the connections between them. Relativity Remains Intact A common misconception about entanglement is that the particles are communicating with each other faster than the speed of light, which would go against Einstein's special theory of relativity. Experiments have shown that this is not true, nor can quantum physics be used to send faster-than-light communications. Though scientists still debate how the seemingly bizarre phenomenon of entanglement arises, they know it is a real principle that passes test after test. In fact, while Einstein famously described entanglement as "spooky action at a distance," today's quantum scientists say there is nothing spooky about it. "It may be tempting to think that the particles are somehow communicating with each other across these great distances, but that is not the case," says Thomas Vidick, a professor of computing and mathematical sciences at Caltech. "There can be correlation without communication," and the particles "can be thought of as one object." Networks of Entanglement Entanglement can also occur among hundreds, millions, and even more particles. The phenomenon is thought to take place throughout nature, among the atoms and molecules in living species and within metals and other materials. When hundreds of particles become entangled, they still act as one unified object. Like a flock of birds, the particles become a whole entity unto itself without being in direct contact with one another. Caltech scientists focus on the study of these so-called many-body entangled systems, both to understand the fundamental physics and to create and develop new quantum technologies. As John Preskill, Caltech's Richard P. Feynman Professor of Theoretical Physics, Allen V. C. Davis and Lenabelle Davis Leadership Chair, and director of the Institute for Quantum Information and Matter, says, "We are making investments in and betting on entanglement being one of the most important themes of 21st-century science."

Quantum entanglement’s long journey from ‘spooky’ to law of nature ÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷÷ the phenomenon of entanglement, he famously referred to it as "spooky action at a distance". Even to him - the genius behind the theory of relativity - the concept seemed way too crazy to be real.But fast forward to today, the entanglement isn't just accepted, but actually it's crucial to our understanding of the quantum world and it is key element in development of quantum computing. But - even though we accept it - we have no idea about the real mechanics that stands behind it.Another concept for 'visualizing' entanglement (a little bit more easier to digest): 1. Imagine you and your collegue go for a lunch. 2.You order pizza, and your friend orders hot-dog. 3.Then, you receive them in two identical boxes (one contains pizza, second hot-dog). 4.You select one box randomly (without knowing what is inside) and second box it taken by your friend. 5.After that, you both split and each of you go home to eat your lunch, but you cannot open the box until you are back home. 6.When you are back home - you look into your box In esence, you have 50% chance to have a pizza in your box (pizza and hot-dog are somehow in superposition state, in your box). So what happens when you open the box? Two things: 1.You know what you will eat - meaning you will collapse superpostion of your box into one defined state (pizza or hot-dog) 2. You instantaneously know what your friend has in their box - if you have a pizza, they must have a hotdog, and vice-versa. This 'information' about what your friend has in their box was instant, no matter the distance between both of you - whether your collegue is in the room next door, whether he/she traveled 50km to their parents home or even went to Mars on Musk's starship. Why? Simply because your lunch boxes were "entangled". Not so spooky after all, right?

I. Quantum Superposition explain Quantum entanglment <<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>> The Nature of Quantum Particles: Quantum particles, such as electrons or photons, behave in ways that challenge our classical intuition. One of the most confounding aspects is the misconception that they exist in multiple states simultaneously. In reality, at any given moment, a quantum particle exists in one specific state. However, what makes quantum physics both fascinating and puzzling is that these states can be unlike anything we encounter in our everyday experiences. For example, particles can exist in states where their properties, like spin or position, are not definite until measured. Abstract Vector Space: Quantum mechanics employs a mathematical framework that includes abstract vector spaces to describe the state of quantum particles. This mathematical approach allows for a more comprehensive representation of a particle’s properties. Importantly, even when particles are in a state of superposition, where they seem to exhibit multiple contradictory properties, they are still fundamentally in a single state. The abstract vector space provides a way to handle these complex and often counter intuitive states. The Measurement Process: One of the fundamental principles of quantum mechanics is that measurement fundamentally alters the state of a quantum particle. When we measure a property of a particle, such as its position or momentum, the particle’s state collapses into one of the possible outcomes dictated by the probabilities defined by the state vector. This change in state upon measurement is a central aspect of quantum mechanics and contributes to the unique behavior of quantum particles. Infinite Basis States: Superposition, a cornerstone of quantum physics, allows particles to exist in a combination of multiple basis states. The number of basis states can be infinite, depending on the specific quantum system and the properties being measured. This concept emphasizes the incredible flexibility and complexity of quantum states, where particles can exhibit a wide range of behaviors and properties simultaneously. quantum superposition challenges our classical understanding of particles existing in one definite state at all times. Instead, it reveals a world where particles can exist in states that are simultaneously strange and fascinating. The use of abstract vector spaces and the influence of measurement on a particle’s state are key elements of quantum mechanics that help us navigate and understand the mysteries of quantum superposition.

II . Science Quantum Entanglement Directly Observed at the Macroscopic Scale >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< Quantum entanglement is when two particles or objects are linked, even though they may be far apart; their properties are connected in a way that isn’t possible with the rules of classical physics. When two particles are entangled, they are correlated in a way that classical physics can’t describe, leaving physicists using mathematics for an explanation. This special connection plays a big part in many areas of quantum science, like keeping information safe, moving information from one place to another, and using particles to do calculations. Quantum Entanglement Observed at Larger Scales Quantum entanglement is an odd phenomenon that Einstein called “spooky action at a distance,” but scientists find it fascinating because of how strange it is. Quantum entanglement was directly inspected and recorded at the macroscopic scale in a 2021 study. This is a much larger scale than the subatomic particles that are usually associated with entanglement. The experiments used two tiny metal drums one-fifth the width of a human hair – which are still very small from our perspective but are enormous in quantum physics. Although there is no reason to believe that quantum entanglement cannot occur with macroscopic objects, it was previously thought that quantum effects were not observable at bigger scales or that the macroscopic scale was subject to other laws. However, the 2021 study reveals that is not the case. In actuality, the same quantum laws also apply here and are visible. How Did They Record Quantum Entanglement? The researchers used microwave photons to vibrate the small drum membranes, and these laser beams kept them synced in position and velocity. The drums were cooled, entangled, then measured in stages within a cryogenically chilled container to avoid outside interference, a common issue with quantum objects. The states of the drums are subsequently encoded in a radar-like reflected microwave field. While other studies have reported on quantum entanglement on a macroscopic scale, the research from 2021 took things further by actually recording all the necessary measurements of these entangled pairs rather than assuming them and by generating the entanglement in a predictable rather than random manner. Separate but related experiments have demonstrated the ability to simultaneously measure the position and momentum of the two drumheads using macroscopic drums (or oscillators) in a state of quantum entanglement The findings are noteworthy because they circumvent Heisenberg’s Uncertainty Principle, which states that momentum and position cannot be accurately measured simultaneously. According to the principle, recording either measurement will cause interference with the other due to a phenomenon known as quantum back action. Back action in quantum mechanics refers to a measurement device’s impact on entangled particles being measured. In addition to supporting the previous study’s demonstration of macroscopic quantum entanglement, this particular investigation used that entanglement to avoid the adverse effects of quantum back action, thus exploring the boundary between classical physics (where the Uncertainty Principle holds) and quantum physics (where it appears to no longer apply). Potential Applications for Both Findings Both sets of findings have the potential to be used in quantum networks, which can manipulate and entangle things on a macroscopic scale to power next-generation communication networks, for example. “Apart from practical applications, these experiments address how far into the macroscopic realm experiments can push the observation of distinctly quantum phenomena.

III . The world of quantum intelligence at circuit network Quantum Entanglment ______________________________________________________________________________ superposition as like as intelligence circuit ; Intelligent beings have the ability to receive, process, store information, and based on the processed information, predict what would happen in the future and act accordingly. super computer in superposition circuit as like as We, as intelligent beings, receive, process, and store classical information. The information comes from vision, hearing, smell, and tactile sensing. The data is encoded as analog classical information through the electrical pulses sending through our nerve fibers. Our brain processes this information classically through neural circuits (at least that is our current understanding, but one should check out this blogpost). We then store this processed classical information in our hippocampus that allows us to retrieve it later to combine it with future information that we obtain. Finally, we use the stored classical information to make predictions about the future (imagine/predict the future outcomes if we perform certain action) and choose the action that would most likely be in our favor. Such abilities have enabled us to make remarkable accomplishments: soaring in the sky by constructing accurate models of how air flows around objects, or building weak forms of intelligent beings capable of performing basic conversations and play different board games. Instead of receiving/processing/storing classical information, one could imagine some form of quantum intelligence that deals with quantum information instead of classical information. These quantum beings can receive quantum information through quantum sensors built up from tiny photons and atoms. They would then process this quantum information with quantum mechanical evolutions (such as quantum computers), and store the processed qubits in a quantum memory (protected with a surface code or toric code). It is natural to wonder what a world of quantum intelligence would be like. While we have never encountered such a strange creature in the real world (yet), the mathematics of quantum mechanics, machine learning, and information theory allow us to peek into what such a fantastic world would be like. The physical world we live in is intrinsically quantum. So one may imagine that a quantum being is capable of making more powerful predictions than a classical being. Maybe he/she/they could better predict events that happened further away, such as tell us how a distant black hole was engulfing another? Or perhaps he/she/they could improve our lives, for example by presenting us with an entirely new approach for capturing energy from sunlight? One may be skeptical about finding quantum intelligent beings in nature (and rightfully so). But it may not be so absurd to synthesize a weak form of quantum (artificial) intelligence in an experimental lab, or enhance our classical human intelligence with quantum devices to approximate a quantum-mechanical being. Many famous companies, like Google, IBM, Microsoft, and Amazon, as well as many academic labs and startups have been building better quantum machines/computers day by day. By combining the concepts of machine learning on classical computers with these quantum machines, the future of us interacting with some form of quantum (artificial) intelligence may not be so distant.

Quantum entanglement is the core of quantum physics, which is a part of theoretical physics. This theory is once assumed to be the hope of faster-than-light communication. If the technique is achievable, it would be a great breakthrough in the field of physics. IV . New Electronic State of Matter May Lead to Quantum Teleportation _____________________________________________________________________ The University of Pittsburgh Scientists have discovered a new electronic state of matter that could lead to quantum computing and even the ability for quantum teleportation. Normally, electrons in semiconductors or metals move and scatter, and eventually drift in one direction if you apply a voltage. But in ballistic conductors, the electrons move more like cars on a highway. The advantage of that is they don’t give off heat and may be used in ways that are quite different from ordinary electronics. Researchers before us have succeeded in creating this kind of ballistic conductor. The discovery we made shows that when electrons can be made to attract one another, they can form bunches of two, three, four and five electrons that literally behave like new types of particles, new forms of electronic matter.

Now in the 21st century, we’re looking at all the strange predictions of quantum physics and turning them around and using them. When you talk about applications, we’re thinking about quantum computing, quantum teleportation, quantum communications, quantum sensing—ideas that use properties of the quantum nature of matter that were ignored before. V . Quantum entanglment application in 2024 __________________________________________ Various industries are trying to solve time and processing power consuming problems using quantum computers to unlock valuable applications of quantum computing. The phenomena of quantum entanglement comes useful to cut down on the time and computing power to process information transfer between qubits. Entanglement enables tasks such as quantum cryptography, superdense coding, and teleportation. Quantum entanglement is the state where two systems are so strongly correlated that gaining information about one system will give immediate information about the other no matter how far apart these systems are. This phenomena baffled scientists like Einstein who called it “a spooky action at a distant” because it violates the rule saying that no information can be transmitted faster than the speed of light. However, further research validated entanglement using photons and electrons. How is entanglement used in quantum computing? In quantum computers, changing the state of an entangled qubit will change the state of the paired qubit immediately. Therefore, entanglement improves the processing speed of quantum computers. Doubling the number of qubits will not necessarily double the number of processes since processing one qubit will reveal information about multiple qubits (i.e. the entangled qubits). According to research, quantum entanglement is necessary for a quantum algorithm to offer an exponential speed-up over classical computations. Applications of entanglement in quantum computing Simple 2-qubit entanglement pairs (EPR) have a few identified applications in quantum computing, including: Superdense coding In simple words, superdense coding is the process of transporting 2 classical bits of information using 1 entangled qubit. Superdense coding can: Allow user to send ahead of time half of what will be needed to reconstruct a classical message ahead of time, which let’s the user transmit at double speed until the pre-delivered qubits run out. Convert high-latency bandwidth into low-latency bandwidth by sending half of the information over the high latency channel to support the information coming over the low latency channel. Double classical capacity in one direction of a two-way quantum channel (e.g. converting a 2-way quantum channel with bandwidth B (in both directions) into a one-way classical channel with bandwidth 2B). a. Cryptography is the process of exchanging information between two parties using an encrypted code and a deciphering key to decrypt the message. The key to cryptography is to provide a secure channel between 2 parties. Entanglement enables that. If two systems are purely entangled that means they are correlated with each other (i.e. when one changes, the other also changes) and no third party shares this correlation. Additionally, quantum cryptography benefits from the no-cloning theorem which states that: “it is impossible to create an independent and identical copy of an arbitrary unknown quantum state”. Therefore, it is theoretically impossible to copy data encoded in a quantum state.

b. Quantum teleportation is also the process of exchanging quantum information such as photons, atoms, electrons, and superconducting circuits between two parties. Research suggests that teleportation allows QCs to work in parallel and use less electricity reducing the power consumption up to 100 to 1000 times. The difference between quantum teleportation and quantum cryptography is: quantum teleportation exchanges “quantum” information over a classical channel quantum cryptography exchanges “classical” information over a quantum channel Challenges that currently face quantum teleportation are: the volume of teleported information the amount of quantum information shared between the sender and receiver has before teleportation. The sender should have one of the qubits of the pair and the receiver the other qubit of the pair The strength of prior correlation between the sender and the receiver qubits increases the capacity of a quantum channel teleportation circuit noise acting on the quantum channels.

quantum entanglement used in everyday life : Reliable timekeeping is about more than just your morning alarm. Clocks synchronize our technological world, keeping things like stock markets and GPS systems in line. Today, the most precise clocks in the world, atomic clocks, are able to use principles of quantum entanglement to measure time.Entanglement can enable quantum cryptography, superdense coding, maybe faster than light speed communication, and even teleportation. Improved Sensing: Quantum entanglement can also be used to create more sensitive sensors. Entangled particles can be used to create sensors that are more precise than classical sensors, as the state of one particle can be used to determine the state of another particle, even if they are separated by a large distance. Entanglement is also a key resource for quantum error correction, which is necessary to protect quantum information from decoherence and other errors. By creating and manipulating entangled states, quantum computers can detect and correct errors in a way that is not possible for classical computers. "Everything is connected" in quantum physics refers to the principle of entanglement. In quantum mechanics, particles can be entangled, meaning that their properties are correlated in a way that cannot be explained by classical physics. C . Quantum neural network quantum neural networks involves combining classical artificial neural network models (which are widely used in machine learning for the important task of pattern recognition) with the advantages of quantum information in order to develop more efficient algorithms. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer is still in a premature stage, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments. Sample model of a feed forward neural network. For a deep learning network, increase the number of hidden layers. Most Quantum neural networks are developed as feed-forward networks. Similar to their classical counterparts, this structure intakes input from one layer of qubits, and passes that input onto another layer of qubits. This layer of qubits evaluates this information and passes on the output to the next layer. Eventually the path leads to the final layer of qubits.The layers do not have to be of the same width, meaning they don't have to have the same number of qubits as the layer before or after it. This structure is trained on which path to take similar to classical artificial neural networks. Quantum computth classical data, classical computer with quantum data, and quantum computer with quantum data and quantum computer with quantum data. ≈======≈≈==≈≈≈≈≈≈≈===============≈≈≈≈≈=============================================

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Real life of quantum entanglement : Clocks synchronize our technological world, keeping things like stock markets and GPS systems in line. Today, the most precise clocks in the world, atomic clocks, are able to use principles of quantum entanglement to measure time. Quantum entanglement can cause particles to collapse instantaneously over long distances, we can't use that to transport information faster than the speed of light. It turns out entanglement alone is not enough to send data . VI.Quantum Entanglement Meets AI: A Ecosystems in Quantum Machine Learning __________________________________________________________________________ Embracing QML in an industrial setting requires careful preparation to ensure successful implementation. Here’s a guide on how to get ready for QML test cases: 1. Understand the Basics: Familiarize yourself with the fundamental principles of quantum mechanics and machine learning. Grasp the concepts of qubits, quantum gates, superposition, entanglement, and the basics of classical machine learning algorithms. 2. Learn Quantum Computing: Develop a foundational understanding of quantum computing. Study quantum algorithms like Grover’s and Shor’s algorithms, and quantum programming languages such as Qiskit, Cirq, or QuTiP. This knowledge forms the basis for integrating QML into industry test cases. 3. Identify Appropriate Cases: Identify industrial challenges where QML could make a significant impact. Consider scenarios with complex data analysis, optimization problems, cryptography, simulation, or pattern recognition. 4. Data Preparation: Ensure you have well-structured and relevant datasets. QML’s efficacy depends on the quality of data. Clean, preprocess, and format data to suit the quantum algorithms and machine learning techniques you intend to use. 5. Collaborate Across Disciplines: Quantum machine learning often demands collaboration between quantum physicists, data scientists, domain experts, and software engineers. Foster interdisciplinary cooperation to approach challenges comprehensively. 6. Access to Quantum Hardware or Simulators: If possible, secure access to quantum computers or simulators. Experimenting with real quantum hardware provides insights into its behavior, limitations, and potential. Cloud-based platforms from IBM, Google, and others offer access to quantum resources. 7. Learn QML Algorithms: Study quantum machine learning algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms. Understand how these algorithms differ from classical counterparts and how they apply to your chosen test cases. 8. Experiment and Test: Start with small-scale test cases to validate QML’s potential benefits. Experiment with various quantum algorithms and machine learning techniques. Compare results with classical approaches to understand QML’s value proposition. 9. Quantum Error Correction: Quantum hardware is susceptible to errors due to noise and decoherence. Familiarize yourself with quantum error correction techniques to enhance the reliability of your QML solutions. 10. Stay Updated: QML is a rapidly evolving field. Stay current with the latest research, developments, and tools. Attend conferences, webinars, and workshops to network and learn from experts. 11. Collaborate with Quantum Computing Providers: Establish partnerships with quantum computing providers and research institutions. Collaborations can offer access to cutting-edge technologies, expertise, and resources for implementing QML in the industry. 12. Scalability Considerations: As QML evolves, ensure that your test cases and solutions are designed with scalability in mind. Quantum computers are growing in scale, and your solutions should be adaptable to larger and more powerful hardware. Detailed Overview of Quantum Machine Learning Platforms QML platforms provide a crucial bridge between quantum computing and machine learning, enabling researchers, developers, and businesses to experiment with and harness the power of quantum algorithms in various applications. Here’s an in-depth look at some prominent QML platforms: IBM Quantum: IBM Quantum is a comprehensive platform that provides access to quantum hardware, simulators, and essential tools. It offers the IBM Quantum Experience, granting users the capability to experiment with real quantum computers and simulations. One of its notable features is the Qiskit framework, which enables users to delve into quantum programming. This platform is ideal for those interested in quantum machine learning (QML) algorithm development, quantum simulations, and hybrid quantum-classical experiments. Google Quantum AI: Google Quantum AI focuses on both building quantum processors and facilitating quantum computing research. Their platform offers access to quantum processors, such as Sycamore, and utilizes the Cirq framework for quantum programming. It’s designed for researchers and developers seeking to explore quantum algorithm research, conduct QML experiments, and delve into quantum simulations. Rigetti Quantum Cloud Services: Rigetti Quantum Cloud Services is a cloud-based platform that extends access to quantum processors and simulators. With features like Quantum Virtual Machine (QVM) and the Forest quantum programming framework, it’s suited for those interested in QML algorithm development, quantum chemistry simulations, and tackling optimization problems. Microsoft Quantum Development Kit: The Microsoft Quantum Development Kit serves as a bridge between quantum and classical programming. It supports the Q# language, making it easier to work with quantum operations. Offering quantum simulations and integration with Visual Studio, it’s ideal for researchers and developers who want to engage in quantum algorithm research, build quantum applications, and explore hybrid quantum-classical experiments. Xanadu’s PennyLane: PennyLane from Xanadu is an open-source quantum machine learning library. It’s designed to work with various quantum computing platforms, allowing users to explore hybrid quantum-classical algorithms, quantum neural networks, and quantum optimization. The integration with popular machine learning frameworks like TensorFlow and PyTorch makes it attractive for those interested in combining quantum and classical machine learning techniques. AWS Braket: Amazon Web Services (AWS) Braket is a cloud-based platform offering access to quantum processors and simulators. It supports both gate-based and annealing quantum processors, providing a platform for quantum algorithm development, hybrid quantum-classical experiments, and solving optimization problems. Quantum Inspire: Quantum Inspire provides cloud-based access to quantum processors and simulators. With a user-friendly interface, it’s suitable for beginners and those interested in education. This platform is an entry point for quantum algorithm development, education, and small-scale QML experiments. The fusion of quantum computing and machine learning has birthed Industry-Ready QML. QML can solve complex problems, optimize processes and transform data analysis. It promises quantum-speed computational advantages and advanced machine learning insights. Embracing QML is an investment in redefining industry boundaries and shaping the future of technology. QML represents a journey of innovation and progress, unlocking unprecedented industrial possibilities and creating a dynamic landscape that pioneers are set to shape. ======================================================================================

Amnimarjeslow Government 1512 universal defense protection system network in space and time moves 020 made in and created in the era of artificial intelligence Thankyume in King 3 Space and time 💫 written by ; Agustinus Manguntam Siber Wiper G-lock

Shield weapons used to protect the universe's defense system currently use many sophisticated electronic and telecommunication techniques including: 1. Iron dome, 2. Arrow, 3. David's sling, 4. Iron Beam, 5. V-Shield , 6.Sky sonic. technical review is certainly needed to study it, especially regarding space and time synchronization with the function of target locking and protection of the universal air. This locking technique numerically follows many arithmetic and geometrical series functions implemented in complex telecommunication electronics circuits in an integrated Artificial Intelligence network. Air Defense or Air Superiority is carried out with interceptors or fighter aircrafts that take off from airbase a few minutes after the AEW alert. They use their nose-mounted radar first to detect the target in the search domain designated by the Air Defense system at a distance ranging from 30 to 100 NM. 1 . IRON DOME -------------- is a mobile all-weather air defense system . The Iron Dome missile defense system has achieved a 97 percent success rate intercepting incoming rockets, amid almost non-stop . Iron Dome is composed of three fundamental elements, a detection and tracking radar, a battle management and weapon control system (BMC) and a missile firing unit (MFU). The radar system has been developed by Israeli defence company Elta . technology iron dome use Missile Firing Unit: the unit launches the Tamir interceptor missile, equipped with electro-optic sensors and several steering fins for high maneuverability. The missile is built by Rafael. A typical Iron Dome battery has 3–4 launchers (20 missiles per launcher).In recent years, Iron Dome has been upgraded with new capabilities, including the use of artificial intelligence (AI). AI is being used to improve the accuracy and efficiency of the system, and to make it more effective against a wider range of threats.Considered among the most advanced defence systems in the word, the Iron Dome uses radar to identify and destroy incoming threats before they can cause damage. The all-weather system was specially designed to help combat shorter-range rudimentary weapons like the rockets fired . Ten Iron Dome batteries protect the citizens and infrastructure of Israel, with each battery comprising three to four stationary launchers, 20 Tamir missiles and a battlefield radar.

Benefits of Iron Dome: It helps combat shorter-range rudimentary weapons like the rockets fired. It can differentiate between missiles likely to hit built-up areas and those that would not. Static and mobile units only launch interceptor missiles to shoot-down anything interpreted as dangerous. 2. ARROW --------- is a family of anti-ballistic missiles designed to fulfill an Israeli requirement for a missile defense system that would be more effective against ballistic missiles than the MIM-104 Patriot surface-to-air missile. Jointly funded and produced by Israel and the United States, development of the system began in 1986 and has continued since, drawing some contested criticism. Undertaken by the MALAM division of the Israel Aerospace Industries (IAI) and Boeing, it is overseen by the Israeli Ministry of Defense's "Homa" (Hebrew: חומה, pronounced [χoma], "rampart") administration and the U.S. Missile Defense Agency. It forms the long-range layer of Israel's multi-tiered missile defence system, along with David's Sling (at medium-to-long range) and both Iron Dome and Iron Beam (at short ranges).The Arrow system consists of the joint production hypersonic Arrow anti-missile interceptors, Arrow 2 and Arrow 3, the Elta EL/M-2080 "Green Pine" and "Great Pine" early-warning AESA radars, the Elisra "Golden Citron" ("Citron Tree") C3I center, and the Israel Aerospace Industries "Brown Hazelnut" ("Hazelnut Tree") launch control center. The system is mobile and can be moved to other prepared sites. Following the construction and testing of the Arrow 1 technology demonstrator, production and deployment began with the Arrow 2 version of the missile. The Arrow is considered one of the most advanced missile defense programs currently in existence.It is the first operational missile defense system specifically designed and built to intercept and destroy ballistic missiles . IAI, the prime contractor of the Arrow system, is responsible for integration and the final assembly of the Arrow missile in Israel. Boeing also coordinates the production of Arrow missile components manufactured by more than 150 American companies located in over 25 states. Arrow Weapon System (AWS) Manufacturer: Boeing [BA] and Israeli Aircraft Industries (IAI) signed a strategic teaming agreement that will lead to Boeing building parts of the Arrow missile system. Characteristics: The AWS uses interceptors, a fire control radar and Arrow fire control radar and battle management command center. Combat Use: Arrow, an upper tier defensive system, is able to take multiple shots in a combat scenario to defend against short and medium range incoming enemy missile targets. Foreign Users: Arrow is an Israeli system co-produced with the United States.

3. David Slings --------------- DAVID’s SLING's advanced, multi-mission interceptor, also known as the Stunner™ and the SkyCeptor™, a joint endeavor of RAFAEL and RAYTHEON - two world leaders in advanced weapon systems development - provides an affordable, lethal hit-to-kill solution for the huge volume of asymmetric threats . DAVID'S SLING™ system is modular, scalable, and flexible to tailor-fit the area and topology to be defended. The David’s Sling interceptor (Stunner/ SkyCeptor ) delivers superior kinematics, maneuverability and lethality by combining novel innovative steering control, multi-pulse propulsion and a next-generation seeker into a lightweight airframe. The Stunner/ SkyCeptor is an advanced multi-mission interceptor designed also for "plug-and-play" insertion into the fielded air and missile defense systems. Integrate easily into a variety of engagement scenarios. Benefits : Innovative technologies, lethal hit-to-kill interceptor High probability of kill against a broad spectrum of current and projected air and missile defense threats Designed for "plug and play" insertion into fielded air and missile defense systems – open architecture Next-generation multi-sensor seeker Cost effective. Capabilities : Large interception envelope Effectively intercepts threats during saturation attack Precision hit-to-kill aim point selection at end game The launcher carries up to 12 stunner interceptors, launched in a near-vertical orientation. Multi-pulse propulsion ,next-generation seeker . INTELLIGENCE CYBER & SECURITY SUPPORT ------------------------------------- Intelligence to cyber defense, border control and protection of critical infrastructure facilities and big data, Rafael principal provides governments, law enforcement, security forces, and global enterprises with the highest level of safety and security. In a country known for its cyber security expertise, It was selected to head Israel’s national defense . Intelligence ; Turn images into insights with RAFAEL’s ImiSight™ multi-sensor visual, data acquisition/processing, GIS solutions and automated/semi-automated image exploitation capabilities. The Challenge : To make intelligent, timely decisions, defense and civilian organizations need a consolidated solution that can collect and process data from various sources, transforming it into meaningful insights. The Solution : The cost-effective, multi-source ImiSight™ Intelligence System & Service accelerates intelligence production, collecting, processing and creating reports from big data sources: e.g., satellite imagery, airborne sensors, UAVs and aerial imagery. Benefits : Obtain meaningful information through advanced image exploitation processes Use for both real-time and offline research/monitoring Automate processes for rapid big data management and analysis Easily integrate with your own -3rd - party sensors Scale to meet specific requirements Receive reports on demand, in various formats. Capabilities : ImiSight speeds up the intelligence production cycle via automated/semi-automated image exploitation capabilities, such as image enhancement tools, object and anomaly recognition, geo-location and special orientation tools. Targeting and ISR End-to-End Situational Awareness RAFAEL offers a full set of adaptive, robust and comprehensive aerial surveillance solutions for unmatched situational awareness and high-precision strikes. Targeting and ISR RAFAEL offers unmatched situational awareness, real-time intelligence and long-range targeting.

4. IRON BEAM ------------ Called the Iron Beam, the system is designed to neutralize a range of incoming targets — including unmanned aerial systems, rockets, artillery and mortar rounds — using a 100-kilowatt or more directed energy weapon . Benefits : Neutralizes a wide range of threats with pinpoint accuracy Protects military forces and civilian populations Uses an unlimited magazine Causes limited collateral damage Costs almost nothing per intercept Integrates with a variety of platforms and systems Light Shield", is a directed-energy weapon air defense system which was unveiled at the Singapore Airshow on February 11, 2014 by Israeli defense contractor Rafael Advanced Defense Systems.The system is designed to destroy short-range rockets, artillery, and mortar bombs; it has a range of up to 7 km (4.3 mi), too close for the Iron Dome system to intercept projectiles effectively. In addition, the system could also intercept unmanned aerial vehicles (UAVs). Iron Beam will constitute the sixth element of Israel's integrated missile defense system,in addition to Arrow 2, Arrow 3, David's Sling and Iron Dome. Iron Beam Type Laser air defense system Place of origin Israel Service history Used by Israel Production history Designer Rafael Advanced Defense Systems Designed 2010-2015 Manufacturer Rafael Advanced Defense Systems Development : The system is based on five years of research and development in solid-state lasers and is developed by Rafael, funded by the MoD, and underwritten by the United States. An Iron Beam battery is composed of an air defense radar, a command and control (C2) unit, and two HEL (High Energy Laser) systems. It was intended to be mobile and to be able to be used stand-alone, but was later rendered non-mobile to address weight and power availability concerns and integrated into Iron Dome to reduce complexity. It is intended for two laser guns to initially produce 100–150 kW of power. In April 2022, the Israeli Ministry of Defense and Rafael announced that in a series of experiments the system successfully shot down drones, rockets, mortar bombs, and anti-tank missiles. The military pushed for an earlier deployment, possibly due to concerns that there would not be sufficient Iron Dome projectiles to combat attacks; Prime Minister Naftali Bennett said in February 2022 that Israel would deploy the system within the year. However, in October 2022 Rafael said it expects to take "two to three years" to deploy the 100+kW weapon operationally. In May 2023, Rafael unveiled the Naval Iron Beam meant for installation on ships. The system is designed to emit 100 kW out to "several kilometers" to protect vessels against drone swarms and anti-ship missiles. The naval version maintains the same turret external dimensions and can be configured to be integrated onto ship superstructures or in containerized modules to be embarked when needed. The Naval Iron Beam is planned to be operational within 4-5 years and first be fitted to the Israeli Navy's Reshef-class corvette.

V. V ( SKY ) - SHIELD --------------------- Sky shield independently emphasizes air defense with a suite of air electronic warfare solutions. fiber optic technology in telecommunications electronics and supersonic technology are used to ensure aircraft endurance in combat.

VI . SKY SONIC -------------- In response to what it describes as the “geopolitical reality” of hypersonic weapons, Rafael Advanced Defense Systems today announced that it is developing a new interceptor missile called Sky Sonic geared specifically at the hypersonic threat. In a statement, the company described the missile as a “groundbreaking defensive response to the growing threat of hypersonic missiles.” The firm plans to show the weapon design off at next week’s Paris Air Show, aiming squarely at the European market. The missile is currently being developed and has not undergone live testing yet. The multi-stage interceptor, developed for several years in secret, uses a Hit-to-Kill system that Rafael has used in other interceptors, per a company briefing held today. It is a distinct system, but in line with Rafael’s other air defense systems and missile interceptor it is designed with an open architecture to allow maximal flexibility, according to the company. Rafael has presented the project to the US and, the company said, feedback from has been positive . A “successful defense against hypersonic threats requires a multifaceted approach that involves not only countering their speed but also effectively tracking, detecting, and intercepting their unpredictable flight paths . hypersonic” threats describe missiles that are not merely travelling fast (ballistic missiles travel past Mach 5) but also involve missiles that glide or maneuver. This means they bring together the threat of speed and also the kind of difficulties involved in intercepting low-flying cruise missiles that may maneuver to fly up valleys or change direction. “Developing a comprehensive defensive response to hypersonic threats presents numerous complex challenges, including detection and tracking difficulties that necessitate a synchronized sensor system capable of accurately identifying and locating the threat throughout its trajectory . An interceptor launched against a hypersonic threat needs to be able to fly “swiftly” toward the target and also “the interceptor must exhibit exceptional maneuverability and operate on a non-ballistic trajectory to effectively pursue and neutralize the hypersonic threat. This system is supposed to be operation in the near-term. The company says the “system delivers unparalleled accuracy in intercepting rockets, mortar projectiles, missiles, unmanned aerial vehicles (UAVs), and UAV swarms from several kilometers to a few hundred meters away. As an integral part of the comprehensive Iron Dome air defense system, the Iron Beam significantly enhances its defensive capabilities.” Sky Sonic, as a groundbreaking defensive response to the growing threat of hypersonic missiles.The Sky Sonic interceptor represents a major technological leap in hypersonic missile defence. Designed with exceptional manoeuvrability and high-speed capabilities, it effectively neutralises hypersonic missiles, which travel at ten times the speed of sound, with unmatched precision and stealth .

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Amnimarjeslow Goverment to open flash connecvtivity electronic machines ; 91220017 and 02096010014 relationship ; Gen . Mac Flash on the way of speed star create machine paths through past and future space and time .

Digital communication technology in wise control space and time must be to have got Quality efficiency of IPOTimer machines (Input, Process, Output, Transfer Function setup and Timer) Electronic computing tools to other electronic devices such as satellites, TVs, electronic cars and others (Smart home, Smart Office, Smart City): 1. Parallel Port , 2. Serial Port , 3. ISA , 4. PCI , 5. USB and HDMI , 6. Wireless , 7. IOT ( Internet of Things ) , 8 Cloud and Machine Learning by AI 9. Metaverse Speed ​​, 10. Inteligent Machine integrated , All possible equipment connectivity needs for synchronization and computational analysis with integrated electronic machines continue to increase not only computers with printers but also with other external devices. Usually this connectivity is through an electronic communication device which we call an interface. There are many methods in electronics to connect the 4th and 5th generation electronic machines with the host system connected to external devices using an interlock, that is, each control signal transition will be recorded and analyzed at the opposite end of the interface. the interface works like a transistor system in analog circuits, namely electronic switches which are divided into 2 groups, namely data communication equipment and data terminal equipment. all electronic communication systems and their controls must be accompanied by improvements in IC (integrated circuit) technology, namely the IC input, processor, output and memory devices which we usually call Chips and their installation can be plug and play, making it easier to use and apply, connectivity and spectrum fast analysis in an integrated electronic machine network is necessary for the efficiency and effectiveness as well as the quality of a service product and high technology industry . allows integrating intelligent electronic machines . a series of electronic machines is an arrangement of many modules, namely software modules, hardware modules, analog and digital communication modules, brainware modules, modules for the relationship between electronic machines and the environment and humans as well as integration modules between electronic machines. Electronic equipment must be controlled both from the input, process and output as well as the setting and timing. In the current era, electronic machine tools must be integrated with artificial intelligence systems and the internet of things so that electronic systems are integrated and smart inteligence . Smart inteligence meaning smart control automation ; Automation in control is an important moment in various fields of human science in the form of science and technology, the science of control and automation is a science that learns for the stages of the human productivity process, humans develop from work productivity processes with manual control then develop into control of electric machines and now with digital electronic control with advanced development of microcontroller control and cloud engine based, namely the IOTX network. Control systems and automation models then and now are a comparison of human life in the era of electric machines into the era of digital electronic machines that integrate feedback networks with a collection of neural networks in Artificial intelligence. the manual control system is an open control system, while the automatic control system is a closed control. closed control system, namely automation control or networked digital electronic control that uses digital sensors and transducers in its technological progress, digital era technology is widely applied to automation products in manufacturing with Robotic PID control (ie a mixture of manual, setting and timer control). control automation systems in integrated network digital electronics produce many 21st century products such as drones, driverless cars and efficiency and effectiveness in working capital and capital investment, in other words automation control enters the era of Smart home, Smart Manufacture, Smart System, Smart Production , Smart City , Smart Planet .

I. Smart Description Electronic Machines Networking ___________________________________________________ Exposure to future technology synchronization which synchronizes Artificial Intelligence with Machine Learning and Deep learning with various forms of good, efficient and high quality smart electronic networks. Machine learning Machine learning is a branch of computer science with a focus on developing a system that is able to learn on its own without having to be repeatedly programmed by humans. However, before producing a data result from object behavior, Machine Learning requires initial data as material to be studied. Machine learning (ML) is a learning machine designed to be able to learn without human direction. Machine learning is a branch of artificial intelligence (AI) or artificial intelligence. Machine learning is often used for various purposes. Machine learning also has the ability to be able to obtain its own data and then study it so that it can perform certain tasks. This machine learning is based on the sciences of mathematics, statistics, data mining, and others The initial role of data is very important as the first step in Machine Learning to produce output. It is used as an initial exercise or trial of Machine Learning. After passing the initial trials, Machine Learning will be able to solve problems without being explicitly programmed. Deep Learning Deep Learning is a part of machine learning where the algorithm is able to understand patterns with high accuracy based on very large data through various complex variables. Deep Learning on the other hand is one of the implementation methods of Machine Learning which aims to imitate how the human brain works using Artificial Neural Network or artificial reasoning network. Deep Learning with a number of algorithms as "neurons" will work together in determining and digesting certain characteristics of a data set. Programs in Deep Learning usually use more complex capabilities in learning, digesting, and also classifying data. One of the main differences between Machine Learning and Deep Learning is performance as the amount of data increases and how to solve problems. Deep Learning algorithms are used to create artificial neural networks that are not capable of optimally processing small amounts of data. This is because Deep Learning algorithms require large amounts of data and are able to solve the problem as a whole from start to finish without the need to separate it into several parts. Meanwhile, Machine Learning algorithms are capable of processing smaller amounts of data. And to solve the problem, it is recommended to break it into several parts so that it can be solved separately, and the solutions are combined to get a complete result.

Every technological sophistication is designed to make human work easier. Likewise with machine learning, machine learning has its own way of working that varies according to the technique to be used. The main concept of machine learning remains the same, which includes data collection, data cleaning, data exploration, data selection, technique selection. provide training on models, and evaluate Machine learning results. We often encounter the application of machine learning in everyday life for various purposes. Some examples of the application of machine learning include: marketplace recommendations in the online shopping system, where one of the data is obtained from search history categorization of email, whether it is included in the category of updates, social, promotions, spam, and others. facial recognition, often used in security systems search engine, provides search suggestions in the google search engine Machine learning in its application has penetrated into various fields. Things like transportation applications, financial services, education, health, and social media are examples of machine learning in everyday life. https://youtu.be/bkqcKJHE7mw

Life is inseparable from production , production produces what we call goods , services and one more quality . The three spheres of added value in this life are mutually synergistic, each other must exist and each other is interrelated and needed in human social life. The internet of things increases the ability of the human senses to be able to remotely monitor and control goods and services and quality products, we know this as the increase in industry 4.0. The programming stages in Industry 4.0 are supported by the availability of appropriate smart electronic technology to support IOT programming in human social life in the future, where many analysis and data collection processes can be accessed anywhere and under any conditions. on the internet of things programming and planning starts from everything based on connected microcontroller electronics based on IOT systems with Cloud engines or in other words the machine learning process on smart electronics and deep learning analysis in the cloud engine learning stage like what happens on social media: Google , Facebook , Whatsapp , Instagramm , Truthleak , YouTube , TikTok , Chat AI bot and others , of course the artificial intelligence program in the form of machine learning will add added value to goods and services as well as the quality of human life in the future . welcome we studying to make machine learning simplicity programme look like google engine : Electronic Machine Learning and deep learning processing : machine learning is a subset of artificial intelligence that involves the development of computer algorithms that access large amounts of data to create models for information. These models are then used to predict specific behavior. The three machine learning types are supervised, unsupervised, and reinforcement learning. Machine learning with Artificial inteligent a science discipline group in Artificial intelligence moving, it is where electronics and computer science meet. It involves custom-designed hardware with complex algorithms and software. On this base brainware , we 'll design computer systems that can learn from data, recognise speech and images, and solve problems. The 7 Steps of Machine Learning 1. Data Collection. → The quantity & quality of your data dictate how accurate our model is. 2. Data Preparation. → Wrangle data and prepare it for training. 3. Choose a Model. . 4. Train the Model. 5. Evaluate the Model. 6. Parameter Tuning. 7. Make Predictions Human life in the era of timelines continues to experience changes both in terms of financial transactions and technology transfer as well as energy storage techniques that are not easily stolen or destroyed in terms of material but can be stored virtually. This increase in change is made possible by the development of IOT technology and machine learning as well as artificial intelligence era future line . IoT devices are built with software that contains instructions for them and is coded using programming languages. They might seem like devices, but they're essentially computers, and every computer needs to be instructed, and programming language is the way to do it. IoT is a digital technology revolution that is even bigger than the industrial revolution. The Internet of Things is one of the most palpable consequences of the Fourth Industrial Revolution, of which we are currently in the early stages. Just as it happened during the previous revolutions, early adopters, professionals are able to create or adapt their business around the new technologies, will ensure their competitive edge for the following decades. As always, knowledge is power. The Ultimate Guide To Implementing IoT and Challenges : Requirements Implementation steps : Step 1: Clearly set your business objectives Step 2: Research tested IoT use cases Step 3: Decide on the correct hardware Step 4: Selecting IoT tools Step 5: Selecting an IoT platform Step 6: Prototyping and implementing Step 7: Gather useful data Step 8: Apply cold and hot path analytics Step 9: Implement Machine Learning Step 10: Think about security, security, security ( Privacy ) risks Risk 1: Failure of Implementation Risk 2: Internet Failure Risk 3: Security Risk 4: Doing nothing IoT is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled. https://youtu.be/usSPMfyc2So Let's break that down: First, IoT is about connectivity. All your things are connected via the internet. Things refer to any physical object that can be uniquely identified (by URI or Unique Resource Identifier) ​​and that can send/receive data by connecting to a network. Examples are buildings, vehicles, smartphones, shampoo bottles, cameras, etc. They can be connected among themselves, with a central server, with a network of servers, with the cloud, or a mix of all this and more. Second, IoT is about information and communication. Everything is sharing information to their designated endpoints either other things or servers. They are constantly sending information about status, actions, sensor data and more. All of them with their unique ID attached, so that it is possible to know where the data came from. And finally, IoT is all about action and interaction. These last two concepts define the core of what IoT is: connection and information sharing. However, all that data isn't generated just to be stored somewhere and forgotten. It has to be used for something. And that use can be automation: computers using the data to automatically (or even autonomously) make decisions and, for example, with the help of Machine Learning, act. And that usage can also be monitoring: letting people know the state of something or some process. The people may be the users of a product or the overseers of for example a production line.

IOT program security and processing techniques: 1. Challenge: Data processing The volume of data collected through IoT presents challenges for rapid cleaning, processing and interpretation. Edge computing addresses this challenge by shifting most of the data processing away from centralized systems to the edge of the network, closer to the devices that need the data. However, the decentralization of data processing presents new challenges, including the reliability and scalability of edge devices and the security of data in transit. 2. IoT security, safety and privacy IoT security and privacy are important considerations in any IoT project. While IoT technology can transform your business operations, IoT devices can pose a threat if not properly secured. Cyber ​​attacks can compromise data, damage equipment, and even cause harm. 3. Strong IoT cybersecurity (IOTX) goes beyond standard secrecy measures to include threat modeling. Understanding the different ways an attacker can harm your system is the first step to preventing attacks. 4. When planning and developing an IoT security system, it is important to choose the right solution for every step of the platform., from OT to IT. A software solution that provides the necessary protection for a given system. II . IOT Application to Proof _____________________________ IoT applications of today's technology AI and IoT IoT systems collect large amounts of data, so it's often necessary to use AI and machine learning to sort and analyze that data so you can detect patterns and take action based on the insights. For example, AI can analyze data collected from manufacturing equipment and predict maintenance needs, reducing costs and downtime from unforeseen breakdowns. Blockchain and IoT Currently, there is no way to confirm that data from IoT has not been manipulated before being sold or shared. Blockchain and IoT work together to break down data silos and build trust so data can be verified, tracked, and relied on. Kubernetes and IoT With a zero-downtime deployment model, Kubernetes helps IoT projects stay updated in real time without impacting users. Kubernetes scales easily and efficiently using cloud resources, providing a common platform for edge deployments. Open source and IoT Open source technologies accelerate IoT, enabling developers to use the tools of their choice in IoT technology applications. Quantum computing and IoT The massive amounts of data generated by IoT are naturally suitable for quantum computing capabilities to accelerate heavy computing. Additionally, quantum cryptography helps add a level of security that is needed but is currently hindered by the low computational power inherent in most IoT devices. Serverless and IoT Serverless computing allows developers to build applications faster by removing the need for them to manage infrastructure. With serverless applications, cloud service providers automatically provision, scale, and manage the infrastructure needed to run code. With the variable traffic of IoT projects, serverless provides a cost-effective way to scale dynamically. Virtual reality and IoT Used together, virtual reality and IoT can help you visualize complex systems and make decisions in real time. For example, using a form of virtual reality called augmented reality (also known as mixed reality), you can display important IoT data as a graphic on top of real-world objects (such as your IoT devices) or workspaces. This combination of virtual reality and IoT has inspired technological advances in industries such as healthcare, field services, transportation, and manufacturing. Digital Twins and IoT Testing your system before execution can be a dramatic cost and time saving measure. Digital Twins take data from multiple IoT devices and integrate it with data from other sources to offer a visualization of how the system will interact with devices, people, and spaces. IoT data and analytics IoT technologies generate such high volumes of data that special processes and tools are needed to turn data into actionable insights. Typical IoT technology applications and challenges: Application: Predictive maintenance IoT machine learning models designed and trained to identify signals in historical data can be used to identify similar trends in current data. This allows users to automate preventive service requests and order new parts early so they are always available when needed. Application: Real time decisions A variety of IoT analytics services are available, designed for real-time and end-to-end reporting, including: High-volume data stores use formats that can be queried by analytical tools. Processing of high volumes of data streams to filter and aggregate data prior to analysis. Low latency analytics turnaround using real time analytics tools that report and visualize data. Use of real time data using message intermediaries. Challenge: Data storage Large data collection implies large data storage requirements. Several data storage services are available that have varying capabilities in organizational structure, authentication protocols, and size limits. Data link layer The data layer is part of the IoT protocol that transfers data within the system architecture, identifies and corrects errors found at the physical layer. IEEE 802.15.4 Radio standard for low power wireless connections. It is used with Zigbee, 6LoWPAN, and other standards for building wireless embedded networks. LPWAN A low power wide area network (LPWAN) allows communications across a range of 500 meters to over 10 km in some places. LoRaWAN is an example of an LPWAN optimized for low power consumption. Physical layer The physical layer is a communication channel between devices in a given environment. Bluetooth Low Energy (BLE) BLE dramatically reduces power consumption and costs while maintaining the same range of connectivity as classic Bluetooth. BLE works natively across mobile operating systems and is quickly becoming a favorite with consumer electronics due to its low cost and long battery life. Ethernet This wired connection is a less expensive option that provides a fast data connection and low latency. Long term evolution (LTE) A wireless broadband communication standard for mobile devices and data terminals. LTE increases the capacity and speed of wireless networks and supports broadcast and multicast streaming. Near field communication (NFC) A collection of communication protocols using electromagnetic fields that allow two devices to communicate within four centimeters of each other. NFC-enabled devices function as identity key cards and are commonly used for contactless mobile payments, tickets, and smart cards. Power Line Communication (PLC) Communications technology that allows sending and receiving data over existing power cables. This allows you to power and control IoT devices over the same cable. Radio frequency identification (RFID) RFID uses electromagnetic fields to track unsupported electronic tags. Compatible hardware provides power and communicates with this tag, reading its information for identification and authentication. Wi-Fi/802.11 Wi-Fi/802.11 is standard in homes and offices. While an inexpensive option, it may not suit all scenarios due to limited ranges and 24/7 energy consumption. Z wave The grid network uses low energy radio waves to communicate from appliance to appliance. zigbees IEEE 802.15.4-based specification for a suite of high-level communications protocols used to create personal area networks with small, low-power digital radios. IoT technology stack part 3: IoT Platforms The IoT platform makes it easy to build and launch IoT projects by providing a single service that manages your deployments, devices and data. The IoT platform manages hardware and software protocols, offers security and authentication, and provides user interfaces. The exact definition of an IoT platform varies as more than 400 service providers offer features ranging from software and hardware to SDKs and APIs. However, most IoT platforms include: IoT cloud gateways Authentication, device management and APIs Cloud infrastructure Third party application integration Managed service IoT managed services help businesses proactively operate and maintain their IoT ecosystem. Various IoT managed services, such as Azure IoT Hub, are available to help simplify and support the process of creating, deploying, managing, and monitoring your IoT projects. IoT protocol: How IoT devices communicate with the network IoT devices communicate using IoT protocols. Internet protocol (IP) is a set of rules that define how data is sent across the internet. The IoT protocol ensures that information from one device or sensor is read and understood by other devices, gateways and services. Different IoT protocols have been designed and optimized for different scenarios and uses. Given the wide array of IoT devices available, it's important to use the right protocol in the right context. What IoT protocol is right for the required situation? The type of IoT protocol you need depends on the layer of the system architecture through which the data will be traversed. The Open Systems Interconnection (OSI) model provides a map of the different layers that transmit and receive data. Each IoT protocol in the IoT system architecture enables device-to-device, device-to-gateway, gateway-to-data center, or gateway-to-cloud communication, as well as inter-data center communication. Application layer The application layer works as an interface between users and devices in a given IoT protocol. Advanced Message Queuing Protocol (AMQP) The software layer that creates interoperability between messaging middleware. This helps a range of systems and applications work together, making messaging the industry standard. Restricted Application Protocol (CoAP) Limited bandwidth and limited network protocol designed for devices with limited capacity to connect in computer-to-machine communication. CoAP is also a document transfer protocol that runs over the User Datagram Protocol (UDP). Data Distribution Services (DDS) Versatile peer-to-peer communication protocol, from running small devices to connecting high-performance networks. DDS simplifies deployment, increases reliability, and reduces complexity. Message Queuing Telemetry Transport (MQTT) A messaging protocol designed for lightweight computer-to-machine communication and primarily used for low-bandwidth connections to remote locations. MQTT uses a publisher-subscriber pattern and is ideal for small devices that require efficient bandwidth and battery usage. Transport layer In any IoT protocol, the transport layers enable and protect data communication as it moves between layers. Transmission Control Protocol (TCP) The dominant protocol for most internet connectivity. The application offers host-to-host communication, breaks large data sets into individual packets, and resends and reorders packets as needed. User Datagram Protocol (UDP) A communications protocol that enables process-to-process communication and runs over IP. UDP increases the data transfer rate over TCP and best suited applications that require lossless data transmission. Network layer The network layer of the IoT protocol helps each device communicate with the router. IP Many IoT protocols use IPv4, while newer executions use IPv6. This recent update to IP routes traffic across the internet and identifies and discovers devices on the network. 6LoWPAN This IoT protocol works best with low-power devices that have limited processing capabilities. IoT X2 technology stack: IoT protocol and connectivity Connecting IoT devices A key aspect of planning an IoT technology project is determining the device's IoT protocol—in other words, how the device connects and communicates. In the IoT technology stack, devices are connected via gateways or built-in functionality. What are IoT gateways? Gateways are part of IoT technology that can be used to help connect IoT devices to the cloud. While not all IoT devices require a gateway, they can be used to establish device-to-device communications or connect devices that are not IP based and cannot connect to the cloud directly. Data collected from IoT devices moves through gateways, is pre-processed at the edge, and then sent to the cloud. Using an IoT gateway can lower latency and reduce transmission size. Having a gateway as part of the IoT protocol also allows you to connect devices without direct internet access and provides an additional layer of security by protecting data moving in both directions. How to connect IoT devices to the network? The type of connectivity you use as part of the IoT protocol depends on the device, its functionality, and the user. Typically, the distance that data must travel—both short and long range—determines the type of IoT connectivity required. IoT network type Low power and short range networks Low-power, short-range networks are perfect for homes, offices, and other small environments. Such networks tend to require only small batteries and are usually inexpensive to operate. Typical example: bluetooth Good for high-speed data transfer, Bluetooth transmits voice and data signals up to 10 meters. NFC A collection of communications protocols for communication between two electronic devices that are 4 cm (1 ⁄2 in) or less apart. NFC offers a low-speed connection with a simple setup that can be used to bootstrap a more supportive wireless connection. Wi-Fi/802.11 Wi-Fi's low operating costs make it standard throughout homes and offices. However, it may not be the right choice for all scenarios due to its limited range and 24/7 energy consumption. Z wave The grid network uses low energy radio waves to communicate from appliance to appliance. zigbees IEEE 802.15.4-based specification for a suite of high-level communications protocols used to create personal area networks with small, low-power digital radios. Low power and wide area network (LPWAN) LPWAN allows communication between a minimum of 500 meters, requires minimal power, and is used for most IoT devices. Common examples of LPWANs are: IoT LTE 4G With high bandwidth and low latency, this network is a great choice for IoT scenarios that require real time information or updates. IoT 5G While not yet available, 5G IoT networks are expected to enable further innovation in IoT by providing significantly faster download speeds and connectivity to more devices in a given area. Cat-0 This LTE based network is the lowest cost option. This network laid the foundation for Cat-M, the technology that will replace 2G. Cat-1 This standard for cellular IoT will replace 3G eventually. Cat-1 networking is easy to set up and offers a great solution for applications that require a voice or browser interface. LoRaWAN Long-term wide area networks (LoRaWANs) connect mobile devices, devices that are secure and battery operated two-way. LTE Cat-M1 The network is fully compatible with LTE networks optimizing cost as well as power on the second generation of LTE chips specifically designed for IoT applications. Narrowband or NB-IoT/Cat-M2 NB-IoT/Cat-M2 uses direct sequence spread spectrum modulation (DSSS) to send data directly to servers, eliminating the need for gateways. While NB-IoT costs more, not requiring a gateway makes it cheaper to run. Sigfox This global IoT network provider offers a wireless network to connect low-power objects that transmit continuous data. IoT technology and protocols The Internet of Things is the convergence of embedded systems, wireless sensor networks, control systems, and automation that makes industrial manufacturing factories, smart retail, next-generation healthcare, smart homes and cities, and connected wearables possible. IoT technology empowers you and me to transform business with data-driven insights, refined and controlled operational processes, new business lines, and more efficient and effective use of quality materials. IoT technology is constantly evolving, with countless service providers, multiple platforms, and millions of new devices emerging every year, leaving developers with many decisions to make before entering the IoT ecosystem. Understand common IoT protocol, power and connectivity requirements. The IoT science and technology ecosystem consists of the following layers: device, data, connectivity, and technology users. 1. Device layer A combination of sensors, actuators, hardware, software, connectivity, and gateways which are the devices that connect to and interact with the network. 2 . Data layer Data collected, processed, transmitted, stored, analyzed, presented and used in a business context. 3. Business layer and R&D IoT technology business functions, including billing management and marketplace data. 4. User layer ( share ) People interacting with IoT devices and technologies. X1 IoT technology stack: I . IoT devices 1. Actuators Actuators perform physical actions when the control center gives instructions, usually in response to changes identified by sensors. They are a type of transducer. 2. Embedded system Embedded systems are microprocessor or microcontroller based systems that manage specific functions within a larger system. The system includes both hardware and software components. 3. Smart device Devices that have capabilities for computing. These devices often include microcontrollers and cloud engines that can best spread a given workload across devices. 4. Microcontroller unit (MCU) This small computer is embedded on a microchip and contains a CPU, RAM, and ROM. Although they contain the elements necessary to carry out simple tasks, microcontrollers have more limited power than microprocessors. 5. Microprocessor unit (MPU) The MPU performs CPU functions on one or more integrated circuits. Although a microprocessor requires peripherals to complete tasks, it greatly reduces processing costs because it contains only the CPU. 6. Non-computing devices A device that only connects and transmits data and has no computing capability. 7. Transducer In general, a transducer is a device that converts one form of energy into another. In IoT devices, this includes internal sensors and actuators that transmit data when the device engages with its environment. 8. Sensors Sensors detect changes in their environment and create electrical impulses to communicate. Sensors usually detect environmental shifts such as changes in temperature, chemicals, and physical position and are a type of transducer.

III . How AI is changing IoT ** _______________________________ Just when we needed it most, the internet of things is delivering gobs of data and remote device control across almost every industry : Electronic Industry , healthcare industry , Agriculture Industry , Inteligence Industry and Military Industry . Today’s growing multitude of IoT endpoints is tying the digital and physical worlds ever closer together, improving the accuracy of predictions and delivering event-driven messages that can be acted on without human intervention. To examine the impact of the IoT and provide implementation advice, Network World, Computerworld, CSO, CIO, and InfoWorld each bring their own view of the most pervasive trend in tech. IoT + Artificial intelligence unlocks the true potential of IoT by enabling networks and devices to learn from past decisions, predict future activity, and continuously improve performance and decision-making capabilities. Businesses have been built or optimized using IoT devices and their data capabilities, ushering in a new era of business and consumer technology. Now the next wave is upon us as advances in AI and machine learning unleash the possibilities of IoT devices utilizing “artificial intelligence of things,” or AIoT. Consumers, businesses, economies, and industries that adopt and invest in AIoT can leverage its power and gain competitive advantages. IoT collects the data, and AI analyzes it to simulate smart behavior and support decision-making processes with minimal human intervention. ***Why IoT needs AI ?*** IoT allows devices to communicate with each other and act on those insights. These devices are only as good as the data they provide. To be useful for decision-making, the data needs to be collected, stored, processed, and analyzed. This creates a challenge for organizations. As IoT adoption increases, businesses are struggling to process the data efficiently and use it for real-world decision making and insights. This is due to two problems: the cloud and data transport. The cloud can’t scale proportionately to handle all the data that comes from IoT devices, and transporting data from the IoT devices to the cloud is bandwidth-limited. No matter the size and sophistication of the communications network, the sheer volume of data collected by IoT devices leads to latency and congestion. Several IoT applications rely on rapid, real-time decision-making such as autonomous cars. To be effective and safe, autonomous cars need to process data and make instantaneous decisions (just like a human being). They can’t be limited by latency, unreliable connectivity, and low bandwidth. Autonomous cars are far from the only IoT applications that rely on this rapid decision making. Manufacturing already incorporates IoT devices, and delays or latency could impact the processes or limit capabilities in the event of an emergency. In security, biometrics are often used to restrict or allow access to specific areas. Without rapid data processing, there could be delays that impact speed and performance, not to mention the risks in emergent situations. These applications require ultra-low latency and high security. Hence the processing must be done at the edge. Transferring data to the cloud and back simply isn’t viable. ***Benefits of AIoT *** Every day, IoT devices generate around one billion gigabytes of data. By 2025, the projection for IoT-connected devices globally is 42 billion. As the networks grow, the data does too. As demands and expectations change, IoT is not enough. Data is increasing, creating more challenges than opportunities. The obstacles are limiting the insights and possibilities of all that data, but intelligent devices can change that and allow organizations to unlock the true potential of their organizational data. With AI, IoT networks and devices can learn from past decisions, predict future activity, and continuously improve performance and decision-making capabilities. AI allows the devices to “think for themselves,” interpreting data and making real-time decisions without the delays and congestion that occur from data transfers. AIoT has a wide range of benefits for organizations and offers a powerful solution to intelligent automation. ***Avoiding downtime *** Some industries are hampered by downtime, such as the offshore oil and gas industry. Unexpected equipment breakdown can cost a fortune in downtime. To prevent that, AIoT can predict equipment failures in advance and schedule maintenance before the equipment experiences severe issues. Increasing operational efficiency AI processes the huge volumes of data coming into IoT devices and detects underlying patterns much more efficiently than humans can. AI with machine learning can enhance this capability by predicting the operational conditions and modifications necessary for improved outcomes. Enabling new and improved products and services Natural language processing is constantly improving, allowing devices and humans to communicate more effectively. AIoT can enhance new or existing products and services by allowing for better data processing and analytics. ***Improved risk management*** Risk management is necessary to adapt to a rapidly changing market landscape. AI with IoT can use data to predict risks and prioritize the ideal response, improving employee safety, mitigating cyber threats, and minimizing financial losses. ***Key industrial applications for AIoT *** AIoT is already revolutionizing many industries, including manufacturing, automotive, and retail. Here are some common applications for AIoT in different industries. ***Manufacturing*** Manufacturers have been leveraging IoT for equipment monitoring. Taking it a step further, AIoT combines the data insights from IoT devices with AI capabilities to offer predictive analysis. With AIoT, manufacturers can take a proactive role with warehouse inventory, maintenance, and production. Robotics in manufacturing can significantly improve operations. Robots are enabled with implanted sensors for data transmission and AI, so they can continually learn from data and save time and reduce costs in the manufacturing process. ***Sales and marketing*** Retail analytics takes data points from cameras and sensors to track customer movements and predict their behaviors in a physical store, such as the time it takes to reach the checkout line. This can be used to suggest staffing levels and make cashiers more productive, improving overall customer satisfaction. Major retailers can use AIoT solutions to grow sales through customer insights. Data such as mobile-based user behavior and proximity detection offer valuable insights to deliver personalized marketing campaigns to customers while they shop, increasing traffic in brick-and-mortar locations. ***Automotive*** AIoT has numerous applications in the automotive industry, including maintenance and recalls. AIoT can predict failing or defective parts, and can combine the data from recalls, warranties, and safety agencies to see which parts may need to be replaced and provide service checks to customers. Vehicles end up with a better reputation for reliability, and the manufacturer gains customer trust and loyalty. One of the best-known, and possibly most exciting, applications for AIoT is autonomous vehicles. With AI enabling intelligence to IoT, autonomous vehicles can predict driver and pedestrian behavior in a multitude of circumstances to make driving safer and more efficient. ***Healthcare*** One of the prevailing goals of quality healthcare is extending it to all communities. Regardless of the size and sophistication of healthcare systems, physicians are under increasing time and workload pressures and spending less time with patients. The challenge to deliver high-quality healthcare against administrative burdens is intense. Healthcare facilities also produce vast amounts of data and record high volumes of patient information, including imaging and test results. This information is valuable and necessary to quality patient care, but only if healthcare facilities can access it quickly to inform diagnostic and treatment decisions. IoT combined with AI has numerous benefits for these hurdles, including improving diagnostic accuracy, enabling telemedicine and remote patient care, and reducing the administrative burden of tracking patient health in the facility. And perhaps most importantly, AIoT can identify critical patients faster than humans by processing patient information, ensuring that patients are triaged effectively. ***Prepare for the future with AIoT *** AI and IoT is the perfect marriage of capabilities. AI enhances IoT through smart decision making, and IoT facilitates AI capability through data exchange. Ultimately, the two combined will pave the way to a new era of solutions and experiences that transform businesses across numerous industries, creating new opportunities altogether. IV . IoT, AI, and the future battlefield ________________________________________ Powered by artificial intelligence (AI), a massive military Internet of Things (IoT) promises a host of battlefield benefits in such areas as unmanned surveillance and targeting, situational awareness, soldier health monitoring, and other critical applications. However, major data and communications challenges must be overcome first. Future conflicts will require critical decisions made within hours, minutes, or seconds – not days – that entail analyzing an operating environment and issuing commands, according to a Congressional Research Service publication on the Joint All-Domain Command and Control (JADC2) initiative. One way the Department of Defense (DoD) aims to speed up and automate decision-making is through a massive military Internet of Things (IoT) and artificial intelligence (AI). A major DoD initiative, JADC2 aims to collect data streams from thousands of battlefield vehicles, environmental sensors, and other intelligent devices across every military branch. AI and machine learning (ML) can then be used to deliver relevant information enabling quick decision-making at the front lines – even down to identifying military targets and recommending the optimal weapon to engage them. Military IoT includes many different “things” – everything from battlefield sensors and weapons systems to tracking devices, communications equipment, wearables, drones, ships, planes, tanks, and even body sensors. Together they stream unprecedented volumes of real-time information to the battlefield. Each branch of the military has its IoT-related initiatives. For the Air Force, IoT is an essential component of its evolving Advanced Battlefield Management System (ABMS). For the Army, it’s the Army Futures Command, and for the Navy, Project Overmatch. The overall goal of JADC2 is to tie all these initiatives together and make them work as a single force successfully on the battlefield. ***Big challenges ahead*** The success of this massive IoT initiative depends of course on the ability to collect and store huge volumes of streaming data from thousands of “things” in real time. A much greater challenge, however, is actually making sense of all that information instantly and getting the results to warfighters fast enough that they can use it to their advantage. The technical obstacles are formidable and include: Merging, integrating, and sharing huge volumes of streaming IoT data generated from devices residing in siloed military branches with scores of different data formats and communications networks. Ideally, the goal is a single data format and data store that can be processed rapidly. Deciding on a common high-bandwidth, low-latency network to serve as the connective tissue between military IoT devices and edge and cloud processing and AI environments. There are numerous possibilities, including satellite and specialized proprietary military network solutions, but 5G is envisioned by many as the eventual connective-tissue solution. Dividing data processing and storage intelligently between a massively scalable centralized environment such as the cloud when feasible, and fast-performing systems lying at the network edge. These solutions get systems much closer to the battlefield where data connections can deliver the fast network performance, low latency, and availability to enable quick decisions on the front lines. Resilient data storage, communications, synchronization, and processing at the network edge, even in remote locations or at times when there are no traditional communication capabilities such as 5G available, often for weeks. Battlefield personnel can’t be forced to rely on less-than-reliable distant cloud connections, plus critical data can’t be lost due to a connection or power lapse, even if it’s just for a few minutes. Airtight cyberattack prevention, detection, and remediation for all this data communications and storage. ***Compelling military IoT use cases*** The DoD is in the very early stages of planning and implementing JADC2 and IoT, with many of these decisions still to be made and only a few limited demonstrations of IoT’s potential to date. Assuming most of these IoT challenges can be met, the use cases for manned and unmanned applications are compelling and many. Following are several examples. Autonomous weapons systems: Human beings continue to be the principal battlefield agents and drivers of success. However, autonomous surveillance and weapons ­systems such as military drones, smart missiles, and unmanned ground vehicles can conduct advanced battlefield surveillance, enhance battle intelligence, and even engage targets to preserve soldiers’ lives. They can also bring precision to the battle via AI and technologies such as facial recognition that can target enemy combatants more accurately than humans and avoid friendly fire and civilian casualties. Deciding on the division between human and autonomous decision-making will be one of the big moral and technical challenges linked to the success of autonomous systems. Soldier-borne sensors and devices: Often called the Internet of battlefield things, a network of intelligence-gathering and biometric body sensors embedded in soldiers’ combat uniforms, helmets, weapons systems, and transports can convey valuable battlefield information together with soldier location, health stats, and mental state. This knowledge can be used to decide when to move soldiers out of the battlefield in the most adverse situations or administer medical aid proactively on a timely basis to reduce casualties. Situational awareness: Situational awareness is critical for quick and effective decision-making on the battlefield. Not only is merging IoT with AI a way to enhance and automate situational awareness – including battleground layout, squad and enemy locations, assets, and objectives – it has the potential to provide that awareness faster than ever before without having to rely on centralized command and control. Leveraging resilient connections and the power of network edge processing, unmanned systems and other IoT surveillance devices can share and merge data to deliver superior intelligence, surveillance, and reconnaissance (ISR) information directly to the front lines. The use of AI to assist and automate many surveillance functions can lighten the stress and cognitive load on soldiers on the battlefield . Connecting drones, sensors, and other devices to local edge database/AI/ML servers via 5G or another common fabric makes information available when the cloud is not accessible or too distant to deliver information quickly. When cloud connections are feasible, IoT can take advantage of the cloud’s massive scalability and processing power. Even in remote situations where 5G is not available or cyberattacks render it infeasible, alternative available peer-to-peer networks such as WiFi, Bluetooth, or private proprietary communications solutions can synchronize distributed databases and ­provide the network and data resiliency needed on the battlefield. A solution is ­available for harnessing peer-to-peer connections and synchronizing data across them, then ­connecting and synchronizing data with local, regional, and cloud servers when they are available. There are numerous other IoT use cases, such as supply-line vehicle monitoring, military-­base security, preventive maintenance on the battlefield, and even inventory management. As the battlefield becomes more complex and unpredictable, IoT and AI will become an increasingly valuable strategy for accelerating and automating critical decision-making, outthinking the enemy, and minimizing combat and civilian casualties. V . energy storage on the IOT and AI network using a battery with controlled free energy . Today, increasing numbers of batteries are installed in Automatic car ( example tesla cars ) , telecommunication operation ( Sattelite operation ) ,residential and commercial buildings; by coordinating their operation, it is possible to favor both the exploitation of renewable sources and the safe operation of electricity grids. However, how can this multitude of battery storage systems be coordinated? Using the Application Programming Interfaces of the storage systems’ manufacturers is a feasible solution, but it has a huge limitation: communication to and from storage systems must necessarily pass through the manufacturers’ cloud infrastructure. Therefore, this schematic concept presents an IoT-based solution which allows monitoring/controlling battery storage systems, independently from the manufacturers’ cloud infrastructure. More specifically, a home gateway locally controls the battery storage using local APIs via Wi-Fi on the condition that the manufacturer enables them. If not, an auxiliary device allows the home gateway to establish a wired communication with the battery storage via the SunSpec protocol. Validations tests demonstrate the effectiveness of the proposed IoT solution in monitoring and controlling . In the absence of free APIs, the adoption of the SunSpec protocol is a valid option, but SunSpec alliance members (Tesla, General Electric, Jinko, LG, SMA, Sonnen, Solax Power, to name a few) must define a standard for the messages’ bodies, sent via the SunSpec protocol. The integration of the IoT in power systems is rapidly growing today as IoT supports measurement, communication, data processing and command implementation in smart grids. However, the literature is not very generous with contributions on IoT applications in battery storage systems monitoring and control, at residential and commercial levels . the battery storage system is part of a microgrid that also includes a photovoltaic system, loads and a hydrogen-based storage system. For that microgrid, the authors proposed an innovative multi-layered architecture to deploy heterogeneous automation and monitoring systems. A Controller Area Network (CAN) bus was used to interconnect the battery management unit with a central controller which acts as a data and command exchanger. How does energy storage use the IoT? Large-scale battery storage facilities are becoming a widespread solution to energy storage challenges. Digitalised battery storage solutions, connected via the IoT, can store and dynamically distribute energy exactly as it is needed, either locally or from a central distribution hub. Battery storage enables consumers and businesses to store and consume what they generate. It can also serve as a primary or backup power source at industrial/commercial sites or hospitality events. Secure, resilient cellular connectivity enables service providers to remotely control and monitor battery assets for operational, safety, environmental and efficiency reasons. The IoT collects and communicates real-time data, giving asset managers unparalleled visibility into devices and operations. For the energy sector, the IoT exchanges data to assist with asset monitoring, metering measurement, equipment maintenance, performance optimisation, demand and capacity management and identifying cost saving opportunities.

Conclusion : the internet of things is also projected to work on an operating system, a cloud base visual work operating system that consists of modular building blocks used ans assembled to create software applications and work management tools. IOTX can also function privately as state and international cyber security, namely for early detection with singular communication formulas, as well as quick responses for cyber handling, by sending information on artificial neural network analysis from cyber data so that IOTX plus AI can be used for cyber crime prevention and response. fast handling by checking and analyzing organizational data and cyber crime work areas both nationally and internationally to cloud workloads which enables seamless and automatic protection against a spectrum of a cyber threats. ex problem sample cyber security + AI + IOTX = https://youtube.com/shorts/L_yb7S9Irb4?feature=share

_______________________________________________________________________ Created , structured and analyzed in a thinking structure by Agustinus Manguntam Siber Wiper Glock as Thinker , providing investments in time and space in the Smart Electronics business for a more capable life across Earth and space .

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