BASIC_PRO MARIA PREFER
and JESI ISE
when the Artificial intelligence and machine learning and deep learning systems for all electronic machine machines are needed a program that aims to add a coordinate image system where when the object is not known, all electronic machine tool systems have provided image responses that provide the right decision for action for AI, Machine Learning and Feel learning for Deeper in an electronic machine integration of policy makers so that 150% decisions can be proven:
1. Maneuvers
2. Shooter
3. Leading
4. Lagging
5. Struggle
6. Defense
7. Shadow force
7 actions Integrated electronics engine is stated absolutely integrated and moving.
✍️📩🇺🇸⚛️🔛
( Gen.Gate )
Actvision AI , ML , DL , CV
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Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
How AI and Machine Learning Can Improve Robotics
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Artificial intelligence (AI) and machine learning — which is a subset of AI — are opening new opportunities in virtually all industries, plus making frequently used equipment more capable.
Not surprisingly, then, AI and machine learning are often applied to robots to improve them. Here are some examples of why an AI robot could be superior to those without the technology.
Industrial Robots With AI Become More Aware of People and Surroundings
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Robots deployed in the industrial sector can help companies get more things done with fewer errors. Of course, safety is key when adding robots in the workplace which is why some AI robotics companies are developing offerings where robots can understand what’s in their environment and react accordingly.
Modern Robotics has an industrial robotics system that combines computer vision, AI and sensors. This setup allows the machines to work at full speed unless humans get too close.
As such, robots are no longer confined behind cages, but human safety is still a priority. Veo Robotics’ technology enables a robot to dynamically assess how far it must remain from a person to avoid hitting it.
There are also autonomous mobile robots (AMR) equipped with AI technology to help the machines learn the layout of a warehouse and steer safely around warehouse obstacles in real time. Those vehicles transport parts and finished products, saving humans from a task that may otherwise cause them to take thousands of steps per day.
Machine Learning Allows Robots to Learn From Mistakes and Adapt
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People get smarter through experience. Through technology such as machine learning, robotics applications may have the same ability. When that happens, they might not need continual time-intensive training from humans. Instead, learning would happen through ongoing use.
An example of how you would train a robot via machine learning can be found from the Shadow Robot Company and our work with OpenAI, founded by business tycoons, Elon Musk and Sam Altman. When OpenAI researchers took our hardware, they explored machine learning by creating a robotic system called DACTYL in which a virtual robotic hand learns through trial and error. These human-like strategies were then transferred to the Shadow Dexterous Hand in the natural world enabling it to grasp and manipulate objects efficiently. This shows the feasibility and success of training agents in simulation, without modelling exact conditions so that the robot can gather knowledge through reinforcement and make better decisions intuitively.
Researchers at the University of Leeds are working on a robot that uses AI to learn from mistakes too and evaluates its data gathered over time to make better decisions.
The process involves training the bot with approximately 10,000 trial and error attempts, letting it discover which methods are most likely to succeed.
Similarly, Australian researchers depended on machine learning to teach humanoid robots to react to unexpected changes in their environment. Simulations indicated that the machine learning algorithm allowed the biped robot to remain stable on a moving platform.
Due to machine learning applications like these, the robots of the near future may be more adaptable. If so, they’ll be more valuable to companies that want robots for tasks or environments with high levels of variability.
AI Robotics Companies Make Manufacturing More Efficient
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Manufacturers are figuring out how to rely on AI to improve their workflows. There’s no single way to use AI to help, however. For example, some companies depend on AI to assist with creating components that eventually end up in robots, such as printed circuit boards (PCB). The process of creating a multilayered PCB is exceptionally complex, with each hole in the component requiring a 20-25 micron layer of conductive electro-deposited copper on its walls.
As early as the 1990s, products used neural networks to design PCBs. Applying AI during PCB design or manufacturing could bring new robots to the market faster, even if the finished products don’t always use artificial intelligence to work.
Some AI robotics companies are also speeding up manufacturing by shortening the time required for robots to learn their tasks. FANUC recently announced a faster way for users to train industrial robots, such as those that pick products from bins. It uses AI to substantially simplify the process of getting robots ready for the warehouse floor. The people involved in the training only need to click images on a screen to teach the bot what to pick up and what to ignore.
Then, what if machine learning applications helped an AI robot know when something was wrong with it? Unplanned downtime can be costly and inconvenient for companies, disrupting workflows and restricting profitability. OMRON debuted a self-diagnosing robot that can tell when it needs repairs or routine maintenance. That machine could help manufacturing become more efficient, too, by staving off disruptions caused by failing equipment.
AI and Machine Learning Applications Give Robots Greater Potential
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Progress in AI and machine learning robotics is happening quickly. This overview is only a sample of how the two technologies could benefit the robots of the future.
People who specialize in robotics, engineering or related fields should stay abreast of developments like these and strive to understand how such advancements could affect their work soon or over the long-term.
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AI , ML , DL , CV for electronic true interest future :
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1. "Artificial Intelligence in Communication Systems"
2. "Microwave and Wireless Communication "
3. cyber-security;
4. Internet of Things;
5. artificial intelligence and machine learning;
6. wireless communication .
7. identyfying for unidentyfying Flying Object vector .
Artificial intelligence (AI) has proven its worth in the last decade in solving complex and/or poorly structured problems in a diverse array of applications. Wireless communications has experienced extraordinary growth since the 1990’s, to the extent that it is almost taken for granted today. However, the application of AI in the design, analysis, and maintenance of wireless communications networks is still in its infancy, though in a rapid growth phase. Many papers have been written on the use of AI in both physical and network layers, but there has so far been few convincing arguments for the practical use of AI in wireless communications. In this Special focuss we aim to explore the practical applications of AI within the lower layers of the protocol stack of wireless communications systems. Cross-layer designs will be of particular interest, and trade-offs between complexity and performance will be emphasized. Test-bedding and field-trial descriptions are especially welcome. focuss of interest include, but are not limited to, the following:
1. Non-linear effects in wireless transceiver design;
2. Wireless network resource allocation;
3. IoT and other specialized network design;
4. Localization of wireless devices;
5. Detection and prevention of cyber-security attacks at the wireless network edge;
6. Military communications;
7. AI techniques suitable for online training;
8. Systematic design and adaptation of AI parameters in a dynamic setting;
9. practical applications of AI in wireless communications.
10. controlling dynamic devices from the other place at long distance .
Aviation Industry using AI , ML ,DL ,CV
aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment.
1. Introduction
Reliability and availability of aircraft components have always been an important consideration in aviation. Accurate prediction of possible failures will increase the reliability of aircraft components and systems. The scheduling of maintenance operations help determine the overall maintenance and overhaul costs of aircraft components. Maintenance costs constitute a significant portion of the total operating expenditure of aircraft systems.
There are three main types of maintenance for equipment: corrective maintenance, preventive maintenance, and predictive maintenance . Corrective maintenance helps manage repair actions and unscheduled fault events, such as equipment and machine failures. When aircraft equipment fails while it is in use, it is repaired or replaced. Preventive maintenance can reduce the need for unplanned repair operations. It is implemented by periodic maintenance to avoid equipment failures or machinery breakdowns. Tasks for this type of maintenance are planned to prevent unexpected downtime and breakdown events that would lead to repair operations. Predictive maintenance, as the name suggests, uses some parameters which are measured while the equipment is in operation to guess when failures might happen. It intends to interfere with the system before faults occur and help reduce the number of unexpected failures by providing the maintenance personnel with more reliable scheduling options for preventive maintenance. Assessing system reliability is important to choose the right maintenance strategy.
Machine learning is a rising technology that is supposed to develop in the future. Machine learning methods are applied in prediction/preventive systems, communications, security, energy management, and so on . The data preparing level is the core module of machine learning and the decision making system. It manages the data to make it useful for decision. The decision making depends on future forecasting, failure event, and availability of equipment . Data mining is a way of classifying and clamping data into comprehensible information. It comprehends the applicable models from a mass of information and adopts different approaches to uncover secret data. Data mining can be defined as knowledge derivation from raw data .
Feature selection is a fundamental issue in data mining and machine learning algorithms that focus on the features which are the most relevant to the intended prediction . Features collected from the observation of a circumstance are not all equivalently significant. Normally, operational data tend to be incomplete, insufficient, or partially meaningful or not meaningful at all. Some of them may be noisy, redundant, or irrelevant. Feature selection aims to choose a feature set that is relevant to a specific duty. This problem is a complex and multidimensional issue . Hsu proposed a novel feature selection algorithm based on the correlation coefficient clustering method. It focused on reducing noisy, repeated, or redundant features. The performance in the computational speed and the classification accuracy can be improved through the removal of the irrevelant features. Methods of data processing helps improve the quality of the data and increase the accuracy of data mining, thereby making it more efficient. Data quality is important for the process of information discovery, checking data anomalies, and predicting and analyzing for decision making [9]. Predicting equipment failures are essential to reduce repair and equipment costs and to assess equipment availability .
Mass data can be useful for businesses and can guide systems to follow right paths. To boost performance in machine learning algorithms, it is critical that meaningful information be gathered from the dataset. To eliminate noisy and irrevelant data during data preparation, we used K-means clustering algorithm, which is one of the popular unsupervised machine learning algorithms. It defines k number of centroids and allocates every case to the nearest cluster while keeping the centroids small . The “means” in the K-means refers to the averaging of the dataset to find the centroid. This algorithm assigns each case to only a single set. The purpose is to accomplish a high level of similarity within the clusters and low similarly across them . It is used for more effective and better clustering with decreased complexity.
There are many studies on maintenance data and forecasting failure rates. Data preparation is a critical step in the feature selection process, and it has a major effect on the success of a machine learning algorithm.
Satellite with AI and CV
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Artificial intelligence has been making waves in recent years, enabling us to solve problems faster than traditional computing could ever allow. Recently, for example, Google’s artificial intelligence subsidiary DeepMind developed AlphaFold2, a program which solved the protein-folding problem. This is a problem which has had baffled scientists for 50 years.
Advances in AI have allowed us to make progress in all kinds of disciplines – and these are not limited to applications on this planet. From designing missions to clearing Earth’s orbit of junk, here are a few ways artificial intelligence can help us venture further in space.
**What are the uses of artificial satellites?**
1. They are used in communication.
2. They are used in weather forecasting system.
3. They are used in GPS (Global Positioning System)
4. They are used to transport instruments and passengers to the space to perform experiments.
several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms.
PSK = Phase Shift Keying
QAM = Quadrature Amplitude Modulation
this picture explain difference phase of PSK and QAM .
Model HUB electronic Block
Model Remote Station Block
VSAT Networking
Data Card Electronic Diagram
AI Going to CV
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Computer Vision can be extensively used to automate space exploration. It can be used in planet tracking, satellite imagery, heavenly body detection, obstacle detection for aircraft navigation and most importantly it reduces the magnitude of risks faced by astronauts during human-space missions.
Artificial Inteligence use neural
network concept
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Neural Networks find extensive applications in areas where traditional computers don’t fare too well. Like, for problem statements where instead of programmed outputs, you’d like the system to learn, adapt, and change the results in sync with the data you’re throwing at it. Neural networks also find rigorous applications whenever we talk about dealing with noisy or incomplete data. And honestly, most of the data present out there is indeed noisy.
With their brain-like ability to learn and adapt, Neural Networks form the entire basis and have applications in Artificial Intelligence, and consequently, Machine Learning algorithms. Before we get to how Neural Networks power Artificial Intelligence, let’s first talk a bit about what exactly is Artificial Intelligence.
For the longest time possible, the word “intelligence” was just associated with the human brain. But then, something happened! Scientists found a way of training computers by following the methodology our brain uses. Thus came Artificial Intelligence, which can essentially be defined as intelligence originating from machines. To put it even more simply, Machine Learning is simply providing machines with the ability to “think”, “learn”, and “adapt”.
With so much said and done, it’s imperative to understand what exactly are the use cases of AI, and how Neural Networks help the cause. Let’s dive into the applications of Neural Networks across various domains – from Social Media and Online Shopping, to Personal Finance, and finally, to the smart assistant on your phone.
You should remember that this list is in no way exhaustive, as the applications of neural networks are widespread. Basically, anything that makes the machines learn is deploying one or the other type of neural network.
RADAR and Electronic Warfare
future platform
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We need confirm BASIC_Pro of Data flowing from radar and electronic warfare (EW) systems to the analyst's screen will determine the course of action in any given mission. Bearing in mind that decisions need to be made, at times in seconds, it's critical for radar and EW systems to quickly sift through that data and turn it into actionable intelligence. To achieve this goal, the defense industry is using artificial intelligence (AI), machine learning (ML), and deep learning (DL) techniques to program these systems and make them into smarter, more autonomous tools.
CV ( Computer Vision ) super up
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computer vision is defined as “a subset of mainstream artificial intelligence that deals with the science of making computers or machines visually enabled, i.e., they can analyze and understand an image.” Human vision starts at the biological camera’s “eyes,” which takes one picture about every 200 milliseconds, while computer vision starts by providing input to the machine. This makes it the best case for a class of algorithms called the Convolution Neural Network.
The basic building block of a neural network is a neuron, which loosely models the biological neuron. Similar to a biological neuron, an artificial neuron has input channels, a processing body, and output channel .
Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, or medical scanning device. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.
Sub-domains of computer vision include scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration.
Computer vision is a sector of Artificial Intelligence that uses machine and Deep Learning to allow computers to “see” and analyze their surroundings. the formulation :
1.Customer Tracking.
2.People Counting.
3.Theft Detection.
3.Waiting Time Analytics.
4.Social Distance.
5.Productivity Analytics.
6.Quality Management.
7.Skill training.
8.Flying Object form RADAR
9.GPS Locater
10.Sattelite Loading Tracking Analytics.
this now advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.
One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better.
Computer Vision for Autonomous Robots and AI-ML-DL network as BASIC_PRO amplifier.
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Exploration and engineering of extraterrestrial life is an important and active field of research in the future survival of humans and the life of robotics projects in the field, because the concept of a vehicle, both robotic and human drift, must be able and able to carry out autonomous exploration and engineering that has the potential to impact It is significant in various applications of life on earth and extraterrestrial such as search and rescue operations, detection of undefined objects on earth, monitoring of the earth and extraterrestrial environment, and exploration and enhancement of life on planets other than earth. Such autonomous exploration capabilities are desirable for the Moon and Mars missions , as well as Pluto because remote operations are impractical due to the large transmission delays so new models of transmissions as well as moving materials are required to deliver the transmissions . This kind of work, we can define as an exploration and engineering problem by simultaneously covering the unknown environment, mapping the area, and detecting objects of interest. There are three main challenges present in a complete solution to an exploratory problem. First, the approach must maintain globally consistent maps that move uniformly and possibly with irregular patterns over long distances with intermittent relative and absolute measurement information, such as GPS and magnetometers. Second: the solution must reliably identify potential objects of interest over the widest possible range to minimize time spent sweeping the environment for candidate objects, as well as identify objects of interest in various lighting and undefined and undefined environmental conditions using high-resolution robotic speed cameras and AI. , ML and DL awareness, Improvement and Monitoring as well as real-time maintenance.
Multi-Object Detection
Along with a tremendous amount of visual data (more than 3 billion images are shared online every day), the computing power required to analyze the data is now accessible. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification.
Below I give some examples of network forms that exist in the development of electronic engineering and is also one of the circuits I used when I did my electronics thesis, namely the electronic network of the grid-spot monitor as the origin of the Television monitor as well as the thesis on measuring instruments for observing the potential difference of a car accumulator, namely spots. LM 3914 bars, so I also put a picture here of how Neuro networks in humans are imitated into electr
onic networks or artificial intelligence, let's see some of the forms.. God Bless.
The picture above is a block diagram of a tracking system on a moving satellite. the explanation of the block diagram is that the magnitude of the signal received by the antenna is not the same at any time. The received signal shows the modulation amplitude, where the speed of the transmitting wave will be equal to the speed of the antenna beam around the rotating axis. then this modulating amplitude is detected by the tracking receiver which will generate ripple voltage. in this phase comparator ripple compared to AZ ( Azimuth ) and EL ( Elevation ) reference frequency . The resulting output will be a control signal from the servo system which will move the rotary axis in the direction where the satellite is located. In fact, to rotate the antenna beam, it is not the main reflector that is driven, but the sub reflector which is rotated at a speed of approximately 8.5 to 9.6 revolutions / sec.
lets go we look Hybrid Matrix Amplifier
( HMA network forming signal )
letsgo and come on to some examples of networks that move and pay close attention to azimuth and elevation.
🖕 WWW and NewsGroup tracking 🎅
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POWER
(Principal_Orientation_Winner_Energie_
Response )
+
Awareness_Improvement_Maintenance
to be
Good Habits and Good Faith.
=
1. Education for Awareness
2. Training for Improvement
3. Work for Maintenance
4. Research for MONEY
( Mine_On_Natural_Energy_You)
5. Continouse R & D for long Timing
Technical .
6. Faith Support for Strenghtness
7. Blessing From Lord for Long Life
Scientific .
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