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Electrical engineering and electronics traverse many fields of technological innovations and are in the foreground for groundbreaking advancements. Over the years, electrical and computer engineering have pioneered and contributed to developing more streamlined design, development, testing and improved manufacturing processes for frontier-end electronics, devices and equipment. In the strive for streamlining and in pursuit of innovation, the electrical and electronics industry has rapidly edged into the ever-expanding applications of artificial intelligence and machine learning.With the emergence of new technologies such as IoT, artificial intelligence (AI), machine learning (ML) and deep learning, the electronics and electrical industry is adopting and embracing major changes that lead to faster workflows, through optimization, automation and removing redundancies. ML and AI in industrial setups are designed to optimize systems and improve efficiency. This is possible as these systems are equipped with sensors and analytical processes that compute and interpret the data providing useful information.For instance, engineers create networks of interconnected cameras and sensors to guarantee that an autonomous vehicle's AI can "see" its surroundings. They must also make sure that the data is transmitted from these onboard sensors as quickly as possible because any lag in processing might cause a serious mishap.The electrical and electronic industry has seen significant advancements in recent years, with the emergence of artificial intelligence (AI) and machine learning (ML) revolutionizing the way we design, build, and operate electrical systems and devices. AI and ML technologies are increasingly being used to optimize performance, reduce costs, and improve efficiency across a range of applications in the industry. From predictive maintenance and fault detection to energy management and personalization, AI and ML are transforming the electrical and electronic industry in unprecedented ways. This article provides an overview of the role of AI and ML in the industry, examining their applications, benefits, and prospects.To start with let us understand what the terms AI and ML mean and how they are different.What are AI and ML?Artificial intelligence (AI) and Machine learning are often used synonymously although there are some differences between these two terms. To understand AI, it is important to first define what is machine learning and differentiate it from artificial intelligence.Artificial Intelligence is commonly known as AI, which refers to the development of computer systems, embedded systems and logical processing that can perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language processing.  For computer systems to learn and mimic human intelligence, they need to learn from a large dataset. For computers to learn from data, adapt to changing settings, and carry out activities that would otherwise require human involvement, artificial intelligence (AI) entails the creation of algorithms, machine learning models, and other approaches. Self-driving cars, voice recognition, image and speech recognition, fraud detection, and many more uses for AI are becoming more prevalent.Machine Learning or ML for short is a basic subset of AI. Basically, it involves the design and development of simple or complex algorithms and models that enable computers to learn from data, make analyses and improve their performance on a task without being explicitly programmed. Machine learning algorithms can learn from a large dataset to identify patterns, correlations and relationships, and then use that knowledge to make predictions or decisions on predictive new data. Machine learning can thus be described as a way of predicting the future based on presented parameters. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. ML is used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive maintenance, among others.Artificial Intelligence and Machine Learning in the Electrical and Electronics IndustryThe term artificial intelligence as used in the electrical and electronics industry generally refers to a variety of systems and technologies built to imitate human intelligence by making decisions and solving related problems. In recent years, engineers and scientists have explored different applications and ways in which AI can be applied in electrical systems.Some of the most common ways in which AI is incorporated into electrical systems and consumer electronics:• Expert systems: Expert systems are a subset of artificial intelligence (AI) systems that employ an inference engine to derive conclusions from a knowledge base containing data on a particular area. If-then rules that are drawn from the knowledge of human subject-matter experts often make up the knowledge base. To offer a solution or suggestion, the inference engine applies these principles to the data or issue at hand. Since the 1970s, expert systems have been utilized for a variety of tasks, including financial planning, technical problem-solving, and medical diagnosis. They have the potential to successfully automate decision-making procedures and lessen the requirement for human expertise in specific jobs. They may not always deliver correct or timely information due to their limits in dealing with complicated or dynamic circumstances.• Fuzzy logic control: Fuzzy logic is a mathematical framework that deals with uncertainty and imprecision, and fuzzy logic control systems employ fuzzy logic to make choices and manage operations. Traditional control systems base judgments on exact numerical values, whereas fuzzy logic control systems base decisions on the degree to which linguistic variables are members of fuzzy sets. In complicated control systems, where it may be challenging to set precise numerical values for every input variable, fuzzy logic control systems are particularly helpful in cases when there is ambiguity or imprecision in the data.• Machine Learning: The creation of algorithms and models for machine learning (ML) enables computers to learn from data and enhance their performance on a job without being explicitly programmed. A huge dataset may be used by machine learning algorithms to detect patterns and correlations, which can subsequently be applied to fresh data to produce predictions or choices.• Artificial Neural Networks: A class of machine learning model known as artificial neural networks (ANNs) is modelled after the structure and operation of biological neural networks in the human brain. Artificial neurons, also known as neurons, are linked nodes that are arranged in layers to form ANNs. Each neuron in the network takes input signals from neighbouring neurons or outside sources, analyzes them using an activation function, and then generates an output signal that is sent to other neurons.• Deep Learning: To train artificial neural networks with many layers and enable them to learn hierarchical representations of the input data, deep learning is a subset of machine learning. Deep learning models are highly suited for a variety of applications, including image and audio recognition, natural language processing, and autonomous systems since they can automatically learn to discover complex patterns and correlations in the data. To alter the weights of the connections between the neurons in the network, deep learning models are often trained using a large dataset and an optimization technique, such as stochastic gradient descent.Application of AI and ML in the Electrical and Electronics IndustryThe design, construction, and use of electrical systems and devices are being transformed by artificial intelligence (AI) and machine learning (ML), which are becoming more and more relevant in the electrical and electronic industries. The industry is being impacted by AI and ML in the following ways:• Predictive maintenance: AI and ML algorithms may be used to identify when electrical equipment is most likely to fail and plan repair, cutting down on both maintenance costs and downtime.• Optimization: AI and ML models can be used to analyze large sets of data and make the decision based on the data. Machine learning algorithms can be used to optimize the performance of electrical systems and devices, ensuring that they are operating at peak efficiency. This optimizes processes, equipment and devices to perform more efficiently.• Fault detection and diagnosis: AI and ML algorithms can be used to detect and diagnose faults in electrical systems, allowing for more accurate and efficient troubleshooting and repair. The process can be based on the information collected by the sensor in systems or by predictive analysis based on previous parameters.• Energy management: Energy management is one of the most vastly used applications of AI due to its ability to compute and make a logical decision in homes and industrial setups. AI and ML can be used to optimize energy consumption in buildings and homes, reducing energy waste and saving money on energy bills.• Design optimization: AI and ML algorithms can be used to optimize the design of electrical systems and devices, improving performance and reducing costs.• Robotics and Automated Assembly: By automating the assembly of electrical components, AI and ML can increase productivity and decrease the need for manual labour. Algorithms can quickly and accurately detect and manipulate electrical components using computer vision and other approaches AI and ML are critical for developing and improving the performance of robots and automated systems used in manufacturing and other industries.• Personalization: Smart bulbs, smart homes and smart devices are based on the idea of customizing and personalizing technology. AI and ML can be used to create personalized electrical devices that adapt to individual user preferences, improving the user experience. These devices operate depending on the preferences of an individual or a particular setting thus making them customizable. • Smart Grid Management: The electricity grid can be managed more effectively with the help of AI and ML. Algorithms can find possibilities to decrease energy waste and suggest modifications to optimize the distribution of electricity by evaluating data on energy consumption trends.• Quality Control: Quality control in the traditional manufacturing process and assembly is a manual process that is often inaccurate and done through sampling. Throughout the manufacturing process, electrical component flaws may be automatically detected using AI and ML. Algorithms may find flaws and eliminate problematic parts by evaluating photos and other data, therefore raising the calibre of the final output.Overall, artificial intelligence (AI) and machine learning (ML) are revolutionizing the electrical and electronic sectors, allowing engineers to create, construct, and use electrical systems and gadgets more successfully and efficiently than ever before.FAQs1.What is the difference between AI and machine learning? AI refers to the broader field of creating intelligent machines that can perform tasks that typically require human intelligence, while machine learning is a subset of AI that involves training algorithms to make predictions or decisions based on data.2.How are AI and ML used in the electrical and electronics industry? AI and ML are used in a wide range of applications in the electrical and electronics industry, including predictive maintenance, process optimization, fault detection and diagnosis, energy management, and smart grid management.3.What are the benefits of using AI and ML in the electrical and electronics industry?The benefits of using AI and ML in the electrical and electronics industry include improved efficiency, increased productivity, reduced downtime, improved safety, and cost savings.4.What are the challenges of implementing AI and ML in the electrical and electronics industry? The challenges of implementing AI and ML in the electrical and electronics industry include the need for large amounts of high-quality data, the complexity of developing and training models, the cost of implementing new technologies, and the need for specialized skills and expertise.5.What are some popular AI and ML techniques used in the electrical and electronics industry? Some popular AI and ML techniques used in the electrical and electronics industry include artificial neural networks, fuzzy logic control systems, deep learning, reinforcement learning, and genetic algorithms.6.What are some examples of AI and ML applications in the electrical and electronics industry? Some examples of AI and ML applications in the electrical and electronics industry include energy demand forecasting, predictive maintenance of electrical equipment, automated fault detection and diagnosis, and optimization of power grids.

Overview: The classifications of wireless charging technologies for electric vehicles are covered in detail in this article. Beyond wired charging methods, wireless charging methods are currently getting a lot of attention because of their benefits.   Catalog Near-Field Charging Technologies Medium-Field Charging Technologies Far-Field Charging Technologies Summarizing with Key Points   According to the transmitted distance, wireless charging methods for battery electric vehicles (BEVs) can be classified into three categories: near-field charging, medium-field charging, and far-field charging. Near-Field Charging Technologies: Inductive, magnetic-resonant, and capacitive charging are the near-field charging technologies for BEVs. Inductive Charging An electromagnetic field is used to transfer power from a transmitter pad to a receiver pad during inductive charging, which is one of the most recent near-field charging methods for modern transportation. This process is seen in Fig. 1. In these systems, maximizing power transfer while maintaining high efficiency is one of the key factors to take into account both during the design phase and during operation. These charging solutions have a maximum efficiency of 90% for a distance of 4 cm and a power transfer capability of 3 to 60 kW over a short distance of 4 to 10 cm, respectively.   Fig. 1: Inductive charging topology for BEVs Source: IEEE Access   Also, it's crucial to control the EV power bus voltage to extend the battery's lifespan. This can be done by simultaneously controlling the switching frequency and conversion ratio of the primary-side converter (i.e., the high-frequency (HF) AC-AC converter at the transmitter pad) and the secondary-side converter (e.g., full-bridge, dual-active bridge DC-DC converter, etc., at the receiver pad).   One of the most important steps in creating a reliable and effective wireless power transfer (WPT) system for charging the batteries of BEVs is the construction of an appropriate power pad. WPT systems still face a number of difficulties despite being employed in many BEV applications. These difficulties include the designs of the power pad and the coil, electromagnetic field protection, HF power converters, metal object detection, etc. Magnetic-Resonant (MR) Charging: The resonant frequency can be increased by adding compensation capacitors, which results in a large transmission distance capability (i.e., 1 to 5 m), making MR charging, as illustrated in Fig. 2, far more efficient than inductive charging. Up to 100 kW of power can be sent over a distance via MR charging. There are four phases to these charging technologies that can be used for installation.   Fig. 2. Magnetic-resonant charging topology for BEVs Source: IEEE Access   Simple residential systems in Phase 1, parking lots in Phase 2, on-street parking in Phase 3, and dynamic charging systems in Phase 4. (future technology for highways). Phases 2 through 4 require government assistance, even though step 1 seems to be widely used in residential BEVs. For instance, the UK invests 40 million pounds in MR-based charging technology research, which includes looking into wireless charging options for street and commercial vehicles like ride-sharing vehicles, delivery vehicles, and so on.   Also recently shown by Oak Ridge National Laboratory is an MR-based wireless charging system with a 120 kW output, which is comparable to a Tesla supercharger. It has a high efficiency of 90% and can transmit a high power of 100 kW across a medium distance of 1 m. Also, Qualcomm built a 100-meter test track in France that includes a 20 kW wireless charging system. Due to the previously mentioned promising characteristics of MR charging, it has garnered greater interest than inductive charging. Capacitive Charging Unlike the inductive and MR charging technologies, capacitive charging can be produced using an electric field. For this reason, two metallic plates with integrated transmitter and receiver pads can be connected to a power source or load, as shown in Fig. 3. These two plates function similarly to two capacitors connected in parallel, which allows for the generation of an electric field between them and the induction of electrical current in the receiver pad.   The rate of change of the electric field between the transmitter and receiver pads is equivalent to this induced current. Hence, by raising the frequency given by the utility grid, power converters like resonant-based converters can be used to raise the rate of the electric field. Their maximal efficiency, transmission distance, and power transfer capacity can all exceed 7 kW, 12 cm, and 80%, respectively.   Fig. 3. Capacitive charging topology for BEVs Source: IEEE Access Medium-Field Charging Technologies Mechanical force serves as the primary energy-carrying medium in the theory behind medium-field charging technologies (also known as magnetic gear-based charging technology). They can be used in low-power charging applications with a 1.5–3 kW range. The magnetic-gear charging mechanism for BEVs is depicted in Fig. 4.   Fig. 4. Medium-field charging topology for BEVs Source: IEEE Access   According to the diagram, the mechanical interaction between two synchronized permanent magnets that are arranged side by side is the basis for this charging technology's operation. They have a medium-range power transfer capability of 3 kW (i.e., 15 cm). Magnetic gear-based charging prototypes that could transfer 1.6 kW across 5 cm with 81% efficiency had been shown as of late 2009 in a number of well-documented papers. Far-Field Charging Technologies This section covers the electromagnetic radiation (EMR)-based far-field charging methods for BEVs, including laser, microwave, and radio wave charging. Laser Charging For the past few years, laser power transmission has been employed for charging reasons in only a small number of real-world applications (such as drones, orbital vehicles, autonomous rovers, etc.). This kind of charging technique uses a distributed laser charging (DLC) transmitter to generate a resonant beam that can have a frequency as high as 3.59 x 1014 Hz, which is then picked up by a DLC receiver. The received beam is then supplied through a DC/DC power converter, as seen in Fig. 5(a), to regulate the output voltage for battery charging needs.   Fig. 5. Wireless charging topology via laser (a) Laser charging for BEVs (b) Future technology of laser charging for satellites and orbital vehicles. Source: IEEE Access   A laser-based system that can transmit 10 MW of power across a distance of up to 10 km with a maximum efficiency of 37% is being developed by the JAXA institute. The charging connection should be considered, though, as losing communication between the transmitter and receiver pads results in no charging; therefore, it is important to maintain consistent charging with good charging capability. One of the next technologies for wireless laser charging is depicted in Fig. 5(b) and might be used for BEVs, solar-powered planetary and satellite applications, orbital vehicles, etc. Microwave Charging Applications involving the transfer of power over a long distance (i.e., 100 km), including platforms based on balloons, helicopters, experimental airplanes, experimental vehicles, etc., have all been tested with microwave charging technology. The highest amount of transmitted energy was attained in an experiment conducted by the US Jet Propulsion Laboratory in 1975. The second attempt, tested by N. Kaya, successfully transmitted energy between two objects in space. The first wirelessly propelled aircraft was then launched using a ground-based microwave emitter in Canada in 1987.   Fig. 6. Wireless charging topology via microwave (a) Microwave charging for BEVs (b) future technology of microwave charging for satellites and orbital vehicles. Source: IEEE Access   An electric vehicle system is shown in Fig. 6(a) being powered up using microwaves with a maximum frequency of 2.45 GHz that are produced by Magnetron. As stated, the corresponding power, distance, and maximum efficiency are set at 10 kW, 5 m, and 80%, respectively, for such applications. Unfortunately, BEVs have not yet widely benefited from this technology. The disadvantage of this charging technique is that it stops charging when connectivity between the transmitter pad and receiver pad is lost.    Large antennas, direct line-of-sight transmission routes, and sophisticated tracking systems are also necessary. As seen in Fig. 6(b), wireless charging through microwaves may one day be utilized for applications like electric vehicles and orbital vehicles. Radio Wave Charging The radio wave charging method, which is based on electromagnetic field transmission, is another form of far-field charging technology. With this kind of charging technique, a rectenna that consists of a high frequency filter, a rectifier, and a low frequency filter can be used to capture the power transmitted from the transmitter.    Fig. 7. Wireless charging topology via radio wave for energy harvesting purposes. Source: IEEE Access   As seen in Fig. 7, the rectifier feeds a DC chopper to deliver the desired DC voltage and charging current to the battery. The efficiency of radio wave charging is currently too low in contrast to laser and microwave charging technologies, and as a result, it needs extensive research to be able to satisfy the required power efficiency for BEV charging. Also, an operator must make sure that the charging connection is not lost in order for a radio wave charging system to maintain adequate charging capabilities, as any loss of connection prevents charging.   Summarizing with Key Points: Some of the takeaways from the article are as follows:   Wireless charging methods can be categorized into three categories based on the transmitted distance: near-field, medium-field, and far-field charging. Near-field charging technologies include inductive, magnetic-resonant, and capacitive charging. Key factors to consider when designing and operating these systems include power pad design, coil design and electromagnetic field protection. Other key factors to include are high frequency power converters, metal object detection, etc. Far-field charging technologies include microwave and radio wave charging methods. Microwave charging can be used for electric vehicles as well as satellites and orbital vehicles.  Radio wave charging is based on electromagnetic field transmission and uses a rectenna to capture the power transmitted from the transmitter.    This blog post is part of a full research article from IEEE Access.   The featured image is courtesy of Midjourney.

  With the evolving times and fast-advancing technologies, smart devices, computerized systems and other industrial applications are heavily relying on miniature computing.  In today’s world, embedded systems are a critical part of the daily average person ranging from their application in homes, offices, industries and even personal gadgets.   These embedded systems have become a crucial part of real life partly due to their ease of use, minimal intervention and availability. The engineering behind these systems is to meet the requirements while being efficient, low powered and meeting essential demands. Some of the devices that we used daily with smart devices include microwaves, smart ovens, refrigerators, washing machines, and smart lighting, to mention but a few.   Artificial intelligence and machine learning in recent days have been in the limelight with many investors and a major key player in the world of technology contributing to its growth. The application of machine learning and artificial intelligence is virtually limitless. The heart of most devices using this technology are embedded systems. As the use of embedded systems continues to grow within every industry and sector, so does technology.   Embedded systems and embedded controllers are often used interchangeably and for the most part, can pass for each other. However, there is a slight difference in meaning. Embedded Systems vs Embedded Controllers An embedded system is a combination of hardware and software designed for a specific purpose, often with real-time constraints. It typically consists of a microcontroller, memory, input/output peripherals, and sometimes additional hardware such as sensors or actuators. Embedded systems are used in a wide range of applications, including consumer electronics, automotive, aerospace, and industrial automation.   An embedded controller is a type of microcontroller, often just referred to as a microcontroller, that is specifically designed for controlling a specific device or system. It is typically used in embedded systems that require precise control over the operation of mechanical or electrical components. Embedded controllers often have specialized features such as analogue-to-digital converters, timers, and communication interfaces that make them well-suited for controlling a specific system.   In general, an embedded controller is a specific type of microcontroller that is designed to perform a specific function within an embedded system. Meanwhile, an embedded system can consist of various components, including microcontrollers, and is designed to perform a specific task or set of tasks. Thus, an embedded system is the device and interface that we interact with daily while the microcontroller is the control unit that gives life to the technology.   Over the years, embedded controllers have evolved significantly with major improvements and advancements from the earliest microprocessors and iterations of a microcontroller to the advanced microcontrollers we use today. Why embedded controllers Embedded controllers are found in a wide variety of devices, products and systems from household appliances and medical devices to industrial machinery and automotive systems. Their application also can be vastly diverse from simple automation applications such as light control to entire industrial automation setups. With the rise of IoT and industrial application of IoT (IIoT), applications in the industrial sector have rapidly expanded.   Aside from their simplicity, inexpensiveness and a vast array of applications, embedded systems are chosen for their other advantages. Compared to traditional computers and microprocessors, embedded controllers are the key enablers of modern automation.   Here are a few key indicators of how embedded systems have evolved and changed the world of automation and modern miniaturized computing: Improved efficiency  Embedded controllers are helping to improve efficiency in a variety of applications, from smart homes to industrial automation. By automating routine tasks and optimizing processes, these controllers can help reduce waste, save energy, and streamline operations.   Enhanced functionality  Embedded controllers are enabling new and innovative features in a wide range of products, from cars and smartphones to medical devices and appliances. These controllers are making it possible to deliver new levels of performance, functionality, and convenience to consumers and businesses.   Increased automation Embedded controllers are helping to drive the automation of many industries, from manufacturing and logistics to agriculture and healthcare. By automating routine tasks, these controllers can help increase productivity, reduce costs, and improve quality control.   Greater precision and accuracy Embedded controllers are enabling greater precision and accuracy in many applications, from medical devices and scientific instruments to automotive systems and consumer electronics. By controlling and monitoring specific functions, these controllers can help ensure that products and systems operate reliably and accurately.   Advancements in technology Embedded controllers are driving advancements in technology, from the Internet of Things (IoT) to autonomous vehicles and smart cities. These controllers are enabling the development of new technologies and systems that are transforming the way we live, work, and interact with the world around us.   Integration of communication interfaces In the mid-2000s, microcontrollers began to integrate communication interfaces, such as Ethernet, Wi-Fi, and Bluetooth, which made it possible to connect devices to the internet and other devices. This paved the way for the development of the Internet of Things (IoT).   Advancements in power efficiency In recent years, microcontrollers have become more power-efficient, with the development of low-power processors, sleep modes, and power management systems. This has enabled the development of battery-powered devices that can operate for extended periods.   Advanced functionality and security Today's microcontrollers offer advanced functionality, such as real-time operating systems, graphics processing, and machine learning capabilities. They also incorporate advanced security features to protect against cyber threats.   Embedded controllers are shaping the world we live in, enabling new levels of efficiency, functionality, and automation across a wide range of industries and applications. As technology continues to advance, microcontrollers are likely to continue to evolve and play an increasingly important role in our lives.   Exploring Embedded Controllers in Real Life As earlier said, the application of embedded controllers has become immense and the potential of further exploration is still underway. With these advancements and vast applications, the impact of this technology is revolutionary and is shaping the future.   Embedded controllers are changing the world in several ways, thanks to their ability to improve efficiency, increase productivity, and enhance functionality in a wide range of applications. Here are a few examples:   Smart Home Automation and Home Appliances In terms of vast applications and the most widely explore uses of embedded controllers, home automation carries the day. This is perhaps due to the simplicity of using embedded controllers and embedded systems, enabling small applications, simple smart devices, DIY projects of automation and other reliable solutions to smart monitoring and even security systems. Embedded controllers are a key component of the smart home revolution, enabling homeowners to remotely monitor and control their appliances, heating and cooling systems, security systems, and more. This allows for greater energy efficiency, convenience, and comfort.   Health Management Systems Embedded controllers are playing an important role in healthcare, enabling the development of advanced medical devices that can monitor and administer medication with greater accuracy and precision. This improves patient outcomes and reduces the risk of errors.   Medical Devices Over the longest time, medical devices and other healthcare-related systems have tried to incorporate embedded systems. This allows for easier monitoring, management and even automation of simple processes. The systems can gather and collect data on a patient’s condition and monitor progress in treatment by monitoring heart rate, pulse rate and other vitals. The information can be relayed to caregivers or doctors via the cloud.   Medical devices, such as pacemakers and insulin pumps, rely on embedded controllers to monitor vital signs and even administer medication. These controllers are designed to operate reliably and accurately in a wide range of conditions Automobiles and Autonomous Vehicles With the advent of the booming exploration in autonomous and self-driving vehicles, such as self-driving cars, autonomous submarines and unmanned drones, the use of embedded controllers has played a key role. Providing navigation systems, IoT modules, battery management systems and other subsystems that relay all the needed data to the users. Embedded controllers are a critical component of autonomous vehicles, enabling them to monitor their surroundings, make decisions, and take action without human intervention. This has the potential to revolutionize transportation and make it safer and more efficient.   In modern automobiles, embedded systems are designed and fitted to provide a better customer experience whilst also providing enhanced safety on the road. The result of this has been realized with lower traffic fatalities over the years.Adaptive speed control, automobile breakdown warning, pedestrian detection, merging assistance, airbags, and other active safety systems are some prominent examples. These are a few of the characteristics that are expected to reduce the risk of accidents and increase demand for embedded systems throughout the world.   Industrial automation With Industry 4.0 on the cusp of fruition, embedded controllers are playing a vital role in its realization being the link between modern technology, IoT and industrial systems. Most industrial systems and setups are adopting machine learning and artificial intelligence to improve work efficiency, accuracy, repeatability, and safety and reduce the cost of labour. This is possible since machines using sophisticated algorithms can identify defects, reduce downtime and diagnose systems before failure.   Embedded controllers are used in industrial automation systems to control machinery and monitor production processes. These controllers can operate in harsh environments and are designed to withstand high temperatures, vibrations, and other stresses. In such applications robots are designed to perform tasks that are considered dangerous. Robots are equipped with embedded systems, employing the use of sensors actuators and feedback from other systems to perform the tasks safely.   Consumer electronics Devices like smartphones, tablets, and smart speakers use embedded controllers to manage their complex functions and interfaces. These controllers help to optimize battery life, reduce power consumption, and enhance user experiences.   Overall, the evolution of microcontrollers has enabled the development of a wide range of devices and systems, from simple household appliances to complex industrial machinery and the Internet of Things. As technology continues to advance, microcontrollers are likely to continue to evolve and play an increasingly important role in our lives.   FAQs What is an embedded controller? An embedded controller, also known as a microcontroller, is a small computer system that is designed to control and manage specific tasks within electronic devices.   Embedded controllers are changing the world in several ways, such as improving efficiency, enhancing functionality, increasing automation, and enabling new technologies and systems.   What are some examples of applications that use embedded controllers? Examples of applications that use embedded controllers include smart homes, medical devices, automotive systems, industrial automation, and the Internet of Things (IoT).   Embedded controllers are playing an important role in healthcare, enabling the development of advanced medical devices that can monitor and administer medication with greater accuracy and precision, leading to improved patient outcomes and reduced risk of errors.   Embedded controllers are a critical component of the IoT, enabling devices to communicate with each other and with the internet, and enabling the development of new technologies and systems that are transforming the way we live and work.   What are some future developments in embedded controllers? Future developments in embedded controllers are likely to include advancements in processing power and memory, integration of communication interfaces, improvements in power efficiency, and advanced functionality such as machine learning and artificial intelligence          

CatalogIntroductionComponents RequiredSoftware RequiredHardwareUltrasonic Sensor (HC-SR04)WorkingCOMPLETE HARDWARESoftwareConclusion Future Enhancement in the Project IntroductionThe aim of this undertaking is to educate ourselves on the creation of a Blind Walking Stick that utilizes an Arduino and an Ultrasonic Sensor HC-SR04. There are Billions of people who are blind in this world. These individuals require assistance from others to navigate and move around as they are unable to do so independently. To address this issue, we have developed a device called the Blind Walking Stick which enables visually impaired individuals to walk more easily without relying on others for assistance. To enhance the device's accuracy and efficiency, two or three Ultrasonic Sensors can be incorporated into the project.  Components Required: Arduino UNO BoardHC-SR04 Ultrasonic SensorBuzzer9 Volt BatterySwitch (Optional) Software Required:Arduino IDE  Hardware: Connection of Ultrasonic Sensor with Arduino.  Vcc pin of Ultrasonic Sensor  is connected to 5-volt pin of ArduinoTrigger pin of Sensor is connected to D9 pin of ArduinoEcho pin of Sensor is connected to the D10 pin of ArduinoThe ground of Sensor is connected to the GND pin of Arduino.The positive terminal of the 9-volt battery is connected to the Vin pin of Arduino and the negative terminal is connected to the GND pin of Arduino.A buzzer is connected between the D9 pin of Arduino and the GND pin Ultrasonic Sensor (HC-SR04)An electronic device known as an ultrasonic sensor is utilized to determine the distance of an object by emitting ultrasonic sound waves and then transforming the reflected sound into an electrical signal. These ultrasonic waves travel at a faster rate than audible sound, which cannot be perceived by humans. The ultrasonic sensor is comprised of two major components: the transmitter, which uses piezoelectric crystals to emit the sound, and the receiver, which detects the sound after it has traveled to and from the object. To compute the distance between the object and the sensor, the sensor calculates the time taken for the sound to travel from the transmitter to the receiver. This calculation is based on the formula D = ½ T x C, where D represents distance, T denotes time, and C is the speed of sound, roughly 343 meters/second. As an illustration, if an ultrasonic sensor is pointed at a box and it takes 0.025 seconds for the sound to return, then the distance between the sensor and the box can be calculated.D = 0.5 x 0.025 x 343  Ultrasonic sensors are used primarily as proximity sensors. They can be found in automobile self-parking technology and anti-collision safety systems. Ultrasonic sensors are also used in robotic obstacle detection systems, as well as manufacturing technology. In comparison to infrared (IR) sensors in proximity sensing applications, ultrasonic sensors are not as susceptible to interference of smoke, gas, and other airborne particles (though the physical components are still affected by variables such as heat).  Ultrasonic sensors are also used as level sensors to detect, monitor, and regulate liquid levels in closed containers (such as vats in chemical factories). Most notably, ultrasonic technology has enabled the medical industry to produce images of internal organs, identify tumors, and ensure the health of babies in the womb. WorkingThe primary aim of this project is to facilitate blind individuals in walking without difficulty and provide them with alerts whenever their path is obstructed by obstacles. The device utilizes a buzzer that emits a warning signal, the frequency of which changes based on the distance of the object. The buzzer will beep more frequently when the obstruction is closer. The core component used in the device is the Ultrasonic Sensor HC-SR04, which functions by transmitting a high-frequency sound pulse and then measuring the time taken to receive the sound echo reflection. The sensor is equipped with a transmitter and a receiver surface, with one transmitting ultrasonic waves and the other receiving the echoed sound signal. The sensor's calibration is based on the speed of sound in air, which is approximately 341 meters per second. After the distance measurement, Arduino makes a beep format using a buzzer also the led glow as well, The frequency of the beep is reduced when the distance is greater, and increased when the distance is shorter. COMPLETE HARDWARE  This is the Complete Hardware of our Project. Since this is a Prototype circuit so we used Selfie stick because it can extend and also We did not used 9V battery but instead we used 2 Lithium Ion cell and one rechargeable circuit to charge these cells, but for simple explanation of the project 9v battery can be used. We used On and Off simple switch to power On and Off the circuit and at the front of the stick we placed our Buzzer, Arduino and Ultrasonic Sensor. You can build the hardware the way you like but the circuit remains same.      Software // defines pins numbersconst int trigPin = 9;const int echoPin = 10;const int buzzer = 11;const int ledPin = 13; // defines variableslong duration;int distance;int safetyDistance;  void setup() {pinMode(trigPin, OUTPUT); // Sets the trigPin as an OutputpinMode(echoPin, INPUT); // Sets the echoPin as an InputpinMode(buzzer, OUTPUT);pinMode(ledPin, OUTPUT);Serial.begin(9600); // Starts the serial communication}  void loop() {// Clears the trigPindigitalWrite(trigPin, LOW);delayMicroseconds(2); // Sets the trigPin on HIGH state for 10 micro secondsdigitalWrite(trigPin, HIGH);delayMicroseconds(10);digitalWrite(trigPin, LOW); // Reads the echoPin, returns the sound wave travel time in microsecondsduration = pulseIn(echoPin, HIGH); // Calculating the distancedistance= duration*0.034/2; safetyDistance = distance;if (safetyDistance <= 5){  digitalWrite(buzzer, HIGH);  digitalWrite(ledPin, HIGH);}else{  digitalWrite(buzzer, LOW);  digitalWrite(ledPin, LOW);} // Prints the distance on the Serial MonitorSerial.print("Distance: ");Serial.println(distance);}   Conclusion Smart Walking Stick is very useful especially for blind people who want to go out for a walk. It helps them to walk smoothly  Future Enhancement in the Project We can add GPS in order to pinpoint the exact location of the personAlso we can add Voice recognition system which can tell where we are going and if any obstacle comes in our way it will let us know

 Overview of flicker noiseFlicker noise in oscillatorsFlicker Noise in SemiconductorFlicker Noise in op AmpHow to eliminate the flicker noise in op AmpThe working mechanism of flicker noiseEquation of flicker noiseThermal Noise vs. Flicker NoisePros of the flicker noiseCons of flicker noiseApplications of flicker noiseFlicker Noise FAQ Overview of flicker noiseElectronic noise known as flicker noise or 1/f noise happens naturally in almost all electronic parts. It can also result from contaminants in conductive channels, creation and recombination noise inside transistors due to base current, and other factors. Pink noise or 1/f noise are common names for this noise. All electrical devices commonly experience this noise, which has a variety of origins but is typically correlated with direct current flow. It is important in a variety of electronic fields and is important for oscillators used as RF sources.Because the power spectral density of this noise increases with frequency, it is sometimes referred to as low-frequency noise. Below a few KHz, this noise is generally visible. The flicker noise bandwidth ranges from 10 MHz to 10 Hz.Figure 1: The relationship between noise voltage and frequency Flicker noise in oscillatorsFlicker noise is inversely proportional to frequency, or 1/f, and in many applications, such as RF oscillators, there are parts where flicker noise, or 1/f noise, dominates, and other regions where white noise from sources like shot noise and thermal noise, or both, dominate. Within the oscillator the flicker noise expresses itself as sidebands that are near to the carrier, the other kinds of noise stretching away from the carrier with a smoother spectrum, however fading the larger the offset from the carrier.As a result, there is a corner frequency, fc, between the regions where the various types of noise predominate. It is typically discovered that the noise outside of the region where flicker noise predominates is phase noise for a system like an oscillator. As the offset from the carrier increases, this decays until flat white noise takes over.MOSFETs have a greater fc (which can reach GHz levels) than JFETs or bipolar transistors, whose fc is typically below 2 kHz. When building RF oscillators, flicker noise, or 1/f noise, is a crucial type of noise. Although it is frequently disregarded, its influence can be reduced by selecting the right gadget.Figure 2: Flicker noise in ocillators Flicker Noise in SemiconductorThe nature of semiconductor noise and how it is specified in semiconductor devices are covered in the section that follows. Since the origin of each semiconductor noise source is a random process, the noise's instantaneous amplitude is unpredictable. The distribution of the amplitude is Gaussian (normal).Figure 3: Flicker Noise in SemiconductorRemember that the RMS value of noise (Vn) equals the standard deviation (σ) of the noise distribution. A random noise source's RMS and peak voltages have the following relationship: VnP-P = 6.6 VnRMS. The crest factor of any signal is the ratio of peak-to-peak to RMS voltage (VnP-P/VnRMS). Because a Gaussian noise source statistically delivers peak-to-peak voltages that are 6.6 times the RMS voltage or higher 0.10% of the time, the crest factor in Equation 1 is 6.6. The likelihood of surpassing 3.3s is 0.001 in this shaded area under the noise voltage density curve in Figure 2. It's crucial to keep in mind that while random signals (like noise) multiply geometrically in a root sum square (RSS) way, associated signals add linearly. Flicker Noise in op AmpSince flicker noise occurs in addition to the thermal noise present in carbon composition resistors, it is frequently referred to as excess noise there. In varied degrees, other resistor types also show flicker noise, with wire coiled having the least. The type of resistor used will not impact the noise in the circuit because flicker noise is proportional to the DC current in the device, thus if the current is kept low enough, thermal noise will predominate. Scaling up resistors to minimize power consumption in an op amp circuit may result in a reduction in 1/f noise at the expense of an increase in thermal noise. Below is the formula to calculate the flicker noise:Figure 4: Flick noise formulaWhere Ke and Ki are proportionality constants (volts or amps) representing En and In at 1 Hz. fMAX and fMIN are the minimum and maximum frequencies in hertz. How to eliminate the flicker noise in op AmpWhat is the best way to deal with this loud, low-frequency noise? With the limited bandwidth, it is almost impossible to try and filter out this noise without changing the important signal. There is yet some hope, though. Although an amplifier's inherent 1/f noise is beyond the control of a system designer, this noise source can be reduced by choosing the right amplifier for the job. The best option is a zero-drift amplifier if 1/f noise is a major problem. Figure 5: zero-drift op amp chartAny amplifier that uses a constantly self-correcting architecture is referred to as "zero-drift" in the industry, regardless of whether it uses an auto-zero topology, a chopper-stabilized topology, or a combination of the two. No matter the specific architecture used, the objective of zero-drift amplifiers is to reduce offset and offset drift. Other dc features, such common-mode and power supply rejection, are also significantly enhanced during the procedure. The fact that the 1/f noise is eliminated during the offset correction procedure is another significant advantage of these self-correcting designs. This noise source occurs at the input and is relatively slow moving, hence it looks to be a component of the amplifiers offset and gets adjusted accordingly.  The working mechanism of flicker noiseBy raising the overall noise level above the thermal noise level, which exists in all resistors, flicker noise is produced. In contrast, wire-wound resistors have the least amount of flicker noise. This noise is merely present in thick-film and carbon-composition resistors, where it is referred to as surplus noise. Charge carriers that are sporadically trapped and released between the interfaces of two materials may be the source of this noise. Because instrumentation amplifiers use semiconductors to record electrical signals, this phenomena is common in those materials.This noise is merely inversely proportional to the frequency. There are various areas in many applications, such as RF oscillators, where noise predominates, and other areas where white noise from sources like shot noise & thermal noise predominates. A correctly constructed system is typically dominated by this low-frequency noise. Equation of flicker noiseSimply put, nearly all electronic components produce flicker noise. In light of this, the noise is discussed in respect to semiconductor devices, notably MOSFET devices. The formula for this noise is S(f) = K/f. Thermal Noise vs. Flicker NoiseThermal NoiseFlicker NoiseIn order to use SAR data both quantitatively and qualitatively, thermal noise must be eliminated by normalizing the backscatter signal throughout the whole SAR image.Several methods, like ac excitation and chopping, can be used to reduce this noise.The lower parasitic resistance components will result in a reduction in the intensity of thermal noise.Wherever the offset voltage of the amplifier is reduced, this noise intensity will be reduced using a chopper or chopper stabilization approach.Anytime current passes through a resistor, thermal noise results.Semiconductors used in instrumentation amplifiers to record various electrical signals typically experience this noise.Johnson noise, Nyquist noise, and Johnson-Nyquist noise are further names for this sound.1/f noise is another name for this noise.Thermal noise is the noise caused by the equilibrium thermal agitation of the electrons in an electrical conductor.Flicker noise is the sound produced by randomly trapped and released charge carriers at the interfaces of two materials. Pros of the flicker noiseAs the noise is low frequency, it will become quieter if the frequency increases.It is an innate noise present in semiconductor devices that is caused by their physics and manufacturing process.The effects are typically seen in electrical components at low frequencies. Cons of flicker noisePerformance can be hampered by this noise in any precision DC signal chain.In all varieties of resistors, the overall noise level can be raised above the thermal noise level.It is frequency dependant. Applications of flicker noiseCertain passive devices and all active electronic components contain this noise.This phenomena typically happens in semiconductors, which are primarily used to store electrical signals in instrumentation amplifiers.The amplifying capabilities of the device are limited by this noise in BJTs.In resistors made of carbon, this noise is present.This noise typically appears in active gadgets because the charge conveys unpredictable behavior. Flicker Noise FAQFlicker noise is measured in what ways?Similar to other types of noise measurement, flicker noise in current or voltage can be measured. The sampling spectrum analyzer instrument extracts a discrete sample from the noise and uses the FFT method to produce the Fourier transform. Low frequencies are beyond the capability of these sensors to accurately measure this noise. Thus, sampling equipment is wideband and has a high noise level. They can reduce the noise by averaging many sample traces. Due to its narrow-band acquisition, conventional-type spectrum analyzer equipment nonetheless have a higher SNR. What should I do to stop the flickering noise?By a chopper stabilization technique that lowers the amplifier's offset voltage, this noise can be effectively eliminated. Flicker Noise: Why Is It Pink?Pink noise, which has a spectral power density reduction of 3 dB per octave, is also known as flicker noise. As a result, the frequency has an inverse relationship with the pink noise band power. Lower power is produced at higher frequencies. Why is flickering called pink noise?One of the most frequently seen signals in biological systems is pink noise. The term originates from the pink appearance of visible light with this power range. White noise, on the other hand, has an equal strength throughout all frequency ranges. How is flicker noise measured?Flicker noise is proportional to the inverse of the frequency, i.e. 1/f and in many applications such as within RF oscillators there are sections in which the flicker noise, 1/f noise dominates and other regions where the white noise from sources such as shot noise and thermal noise dominate.

Overview: Transportation electrification began with small electric vehicles and gradually entered into medium-duty and heavy-duty vehicle electrification. In this article, we will understand the importance of commercial vehicle electrification and the challenges ahead. Significance of Commercial Vehicles Electrification Global climate change has resulted from human-caused greenhouse gas (GHG) emissions, which have raised the earth's temperature over the past century. The 2016 Paris Agreement sought to reduce global GHG emissions in order to keep the average global warming within two °C above pre-industrial temperatures in order to combat this threat from climate change. The transportation industry, which produces nearly 25% of the world's CO2 emissions, is one of the biggest sources of GHG emissions. Road vehicles account for nearly 75% of all CO2 emissions in the transportation industry among all modes of transportation. Therefore, a crucial step in reducing direct CO2 emissions is the electrification of road transportation. Many governments have therefore established transitional plans to electrify their transportation sector by 2050. Around 10 million electric vehicles (EVs) were in use worldwide as of the end of 2020, with battery electric vehicles making up two-thirds of this total. These EVs are predominantly light passenger cars. Challenges in Commercial Vehicles Electrification Nearly 40% of the world's road transportation sector's CO2 emissions in 2015 came from commercial vehicles, and under the "business as usual" scenario, those emissions are expected to at least double between 2015 and 2050. Therefore, the electrification of commercial vehicles is a crucial research area because it offers a promising chance to significantly reduce these emissions. Due to the small size of electric vehicle batteries, their low mileage, and the lack of public charging infrastructure, the majority of studies on electrifying commercial vehicles have concentrated on the hybridization of these vehicles.  Light-duty trucks (LDTs), which have been successfully electrified without significantly altering travel habits, have been the primary focus of the initial deployment of zero-emission commercial electric vehicles (CEVs), including electric trucks (ETs). Heavy-duty truck (HDT) deployment is in the pilot stage, whereas the deployment of medium-duty trucks (MDT) is still in the early stages. According to recent studies, there have been around 2,50,000 light-duty commercial electric vehicle sales, including trucks, with a stock of close to 31,000 medium- and heavy-duty vehicles. When compared to light passenger vehicles, commercial electric vehicle adoption has lagged, which has been attributed to the unsatisfactory policies implemented in this sector. With the availability of suitable charging infrastructure that meets the charging needs of these vehicles, the possibility of electrifying commercial vehicles grows. Commercial vehicle drivers are unlikely to switch to electric vehicles if the charging process is more challenging, uncertain, and time-consuming. However, as can be seen from Table 1, there are a variety of uses for commercial vehicles, which also affects the average load, trip length, and daily mileage of these vehicles. Furthermore, compared to passenger vehicles, the operational schedules of commercial electric vehicles can affect how quickly these vehicles charge up at charging infrastructure. Table 1. Different applications of commercial vehicles. Source: IEEE AccessVMTi refers to Vehicle Miles Travelled,PTOii refers to Power Take-Off,Percentageiii The percentage of the truck population by vocations depends on California truck population. Recent Advancements in Commercial Vehicles Electrification  In contrast to diesel and alternative fuel trucks, however, recent advancements in lithium battery technology have made electric trucks both technically and financially feasible. Existing studies have examined the potential advantages of ETs over diesel trucks over a vehicle's lifetime. These studies have found that, despite the high upfront costs of ETs, they can perform at least as well as diesel trucks over their entire lifecycle, particularly if the latter have long battery lives and high annual mileage. Moreover, the use of ETs, particularly MDTs, and HDTs, has increased as a result of regulations and government incentives encouraging the use of zero-emission vehicles. With battery sizes ranging from 300 kWh to roughly 990 kWh, a number of truck manufacturers, including DAF, Daimler, MAN, Navistar, Nikola, PACCAR, Volkswagen, Volvo, Tesla Inc., and Thor Trucks, have made significant plans to electrify their MDTs and HDTs. Due to their short-range needs and compact batteries, MDTs have drawn the most attention from these announcements regarding electrification. All of the announcements have a model for medium-duty trucks, and some manufacturers, like Daimler and BYD, have already released their commercial trucks for certain markets. In their announcements, some manufacturers, including Navistar, Volkswagen, Thor Trucks, Freightliner, and Tesla Inc., have mentioned the production of HDTs.  On the other hand, a lot of businesses have started incorporating ETs into their fleets or have made an announcement regarding their procurement of ETs. For instance, Walmart Inc. reported 45 class 8 Tesla Semi HDT pre-orders for the coming year. Similar orders for electric delivery trucks were made by Amazon and Rivian in 2019, and Anheuser-Busch announced plans to use 21 HDTs from BYD in California by the end of the year. In general, commercial vehicles, such as trucks, can be divided into three groups based on their gross vehicle weight (GVW). LDTs fall into this category if their GVW is less than 3.5 tonnes (t), MDTs fall into this category if their GVW is between 3.5t and 15t, and HDTs fall into this category if their GVW is above 15t. Each category has a wide range of vehicle types appropriate for their range of occupational operations, such as long-haul freight and garbage collection trucks.  Due to policies encouraging the adoption of zero-emission vehicles and advancements in battery technology, the electrification of MDTs and HDTs has been increasingly adopted in recent years. MDT models with battery bank capacities ranging from 48.5 kWh to about 350 kWh and an estimated range of up to 400 km have been produced by numerous truck manufacturers. Many models of HDTs with battery bank capacities between 120 kWh and 1000 kWh to cover an estimated range of up to 800 km have been introduced or produced. Table 2 lists the specifications of some MDTs and HDTs that are currently advertised or reported. Table 2. Specification of some commercial electric vehicles. Source: IEEE Access The estimated range of CEVs and the availability of appropriate charging infrastructure determine whether or not they can be used to cover the daily travel distance of commercial vehicles. According to surveys, most medium-duty commercial vehicles travel an average daily distance of 80 km to 250 km, while heavy-duty commercial vehicles travel an average daily distance of up to 700 km. As a result, at locations where they park overnight or in between shifts, the reported range of medium-duty CEVs can cover a sizable portion of the daily travel distance with just one charging event per day.  However, some medium- and heavy-duty CEVs require high charging rates to be met in a single charging event over the times they are parked because of high charging requirements (such as long-haul operation, multiple-shift operation, etc.). A high percentage of the daily travel distance is covered by multiple charging events per day at various locations along commercial vehicles' routes due to the constrained capacity of some electrical power infrastructure, which restricts the charging rate of charging infrastructure. Therefore, the number of times a CEV may need to be charged each day will depend on the daily mileage of commercial vehicles, the CEV's estimated range, and the infrastructure's charging rate. Summarizing With Key Points: Some of the takeaways from the article are as follows: Transportation emits nearly 25% of the world's CO2 and GHGs. Thus, many governments have transitional plans to electrify transportation by 2050. As of 2020, there were 10 million electric vehicles (EVs), two-thirds of which were battery-electric. Light passenger cars dominate these EVs.Most studies on electrifying commercial vehicles have focused on hybridization because electric vehicle batteries are small, have low mileage, and lack charging infrastructure.If charging is difficult, uncertain, and time-consuming, commercial vehicle drivers will not switch to electrifying their vehicles.Recently, MDTs and HDTs have been electrified due to policies encouraging zero-emission vehicles and advances in battery technology.  This blog post is part of a full research article from IEEE Access.*******************************************************************************************************************************************