Control of Objects from a remote distance became an important factor for mankind about decades ago. Since then there has been a tremendous amount of improvements in the field of remote control. Once the “Internet of Things(IoT)” had been introduced, remote control technology across various applications has also opted for this method of data transmission from device to device over the internet. Through this work, a new way of controlling a robotic arm that can mimic/repeat human hand gestures and operate in a remote location through the use of IoT technology is proposed. The robotic arm design involves generative designing for weight reduction over a five end-effector model for cost and weight reduction purposes, also for the cause of hand gesture recognition a Neural Network has been trained and implemented behind an application that has been completely programmed in python. As compared to traditional methods of controlling a robotic arm the proposed method provides better cost efficiency, data transmission, data collection, and accessibility due to the abundant availability of computers, and adaptability towards the growing technology.

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

Many technological advancements in the modern era have made actual use of electrical power and the constrained operating of power systems within stability limits. Some expeditious load variations and rising power demands initiate complications in voltage stability and can put stress on performance, leading to voltage instability. Voltage Stability Indices can be used to perform voltage stability assessment. This review evaluates various VSIs based on mathematical derivations, assumptions, critical values, and methodology. VSIs determine the maximum loadability, voltage collapse proximity, stability margin, weak areas, and contingency ranking. Stability indices can also specify the optimal placing and sizing of Distributed Generators. Thus, VSIs play a vital role in power system voltage stability. This review is a comprehensive survey of various indices and analyses their accuracy in determining the instability of power systems. Voltage stability is a crucial concern in operating a reliable power system, and the systematic evaluation of voltage stability is essential in a power system. This review considered and analyzed 34 indices from 138 articles from the literature for their significant performance in various power system stability problems. Of 33 indices, were 22 derived from transmission line parameters, referred to as line indices, and 12 from bus and line parameters, referred to as bus indices.

Easy modular I action is one of the benefits of a cascaded H-bridge (CHB) inverter. This study proposes an only one multilevel inverter comes with a unique H-bridge unit. The structure of the proposed topology is then enhanced in order to make use of switching devices and DC-link voltage inputs while creating a massive number of voltage steps. A cooperative active and reactive power control strategy is offered to earn a better real and reactive power management for every DC voltage source of a photovoltaic (PV) module, as well as boost systems power quality and reliability. A unique control approach and proportional pulse width modulation (PWM) modulation are described for the cascaded H-bridge multilevel inverters for grid-connected systems. Each Hbridge module can give different power levels thanks to this control. To supply the DC source, use the system's proportional, integral and derivative (PID) controller. The functionality and achievements of the proposed scheme with its associated algorithms in production of all operating voltage have been proven using experimental data from a nine-level single-phase inverter. Finally, to construct a cascaded H-bridge nine-level inverter, the proposed control strategy is developed and implemented in MATLAB software.

The system is novel approach to combine wireless power transmission system (WPT) and automated guided vehicle (AGV). The wireless power transfer for charging the mobile robot is implemented using inductive coupling method. The system setup consists of transmitter coils whose switching action is controlled through transistors, receiver coil connected to full ridge rectifier and mobile robot. The track consists of number of transmitter coil which transmits power in form of electromagnetic waves. The receivers in the robot, which receives these waves and converts it back to electric power to charge the battery. The robot tracks its target destination based on the user command from the smart phone through Bluetooth. Very few theoretical researches are available on this field. A prototype was developed and tested based on the researches. The system achieves good range but falls short in efficiency to charge a battery, charging of battery takes longer time than regular charging time. Further research and extensive exploration can bring this technology from theory to practice.

Chronic kidney disease (CKD), also referred to as chronic kidney failure, is a medical condition defined by a gradual loss of kidney function over a period of time. It is characterized by conditions that inhibit or worsen the ability of the kidneys to filter wastes from human blood. If the disease worsens, wastes could build up to alarming levels in our blood and lead to organ failure, after which dialysis or kidney transplant would be needed to maintain life. It could further cause complications such as high blood pressure, low blood count (also known as anaemia), reduced bone strength, poor health and nutrition, and neurological damage. Since these symptoms usually slowly develop over time, early diagnosis can help chronic kidney disease from getting worse. This paper aims to use a set of medical attributes and analyse different machine learning algorithms to develop a model that would help predict whether or not a patient has chronic kidney disease. It also aims to perform a comparative analysis of multiple classification algorithms along with ensemble stacking method by testing their performance.

Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.

Modern Alternating Current (AC) microgrids (ACMGs) are highly complex because of the indeterminate nature of distributed energy generation and load causes frequency fluctuations. It is essential to maintain the constant power amongst load and generations by the proper design of an efficient controller to stabilize frequency fluctuations. In this paper, the type-2 fuzzy fractional-order tilt integral derivative controller structure is designed and analyzed for frequency control of an ACMG by using an enhanced Harris hawks optimization (EHHO). The modified optimization technique is analyzed employing test functions and compared with similar methods. It is noticed that the modified optimization technique is outperforming the other methods in most of the test functions. The frequency control performance of an ACMG is demonstrated and compared with the original Harris hawks optimization (HHO) and other optimization methods. The proposed type-2 fuzzy fractional-order tilt integral derivative controller tuned with EHHO technique is demonstrated to be effective and superior to any of the alternatives by application to a typical two-area test system.

Sorting of objects is extensively used in many industries like food processing industries, toy industries, etc. To ensure that the quality of the product is up to the mark, this process is simplified by the use of automation. Automation is the use of control systems like computers or robots for handling different process and machineries to replace a human being and provides mechanical assistance. Automated systems generally use more complex algorithms which increase the cost of the design and the power consumed. This not only reduces manual efforts, time consumed, gives more time for marketing, but also prevents danger which might occur when human beings work in hazardous environments. Automation greatly improves the productivity and is highly scalable. Here, OpenCV is used which is a computer vision library used extensively in the industry. The Python programming language is used as it is easy to use and has sufficient speed for our task. A simple USB camera is used to capture the real time video. The camera is engaged in sensing the object for shape and colour by means of image processing and after this the conveyor moves and the flaps are actuated by means of servomotor in order to sort the objects based on their respective shape and colour.

Recently, activated carbon derived from different agricultural by-products or bio-waste is receiving a great deal of attention due to its low or zero cost and environmental friendliness. In this work, flowers obtained from Borassus flabellifer (BFL) is used as a carbon source and potassium hydroxide (KOH) as activation precursor to produce activated carbon with high specific surface area and predominant micropore. The obtained carbon material was activated at 650 °C. The as-prepared sample had a specific surface area of 930.3 m2/g and pore size distribution of 1.96 nm. The carbon material exhibited high electrochemical performance with a specific capacitance of 247 F/g at 0.5 A/g in 1 M H2SO4 electrolyte and an excellent cycling stability of 94% after 2500 cycles. A specific energy of 101.1 Wh/kg and a specific power of 4500 kW/kg were obtained. Based on the electrochemical properties exhibited by BFL, it could be used as an excellent electrode material for supercapacitor applications.

The source current harmonics reduction techniques were found to be unpredictable and disparity under different loading conditions. The presence of uncertainity issue in harmonics elimination is due to nonlinear loads. Filters can be used to eliminate the harmonics and power quality issues. But these filters are not cost effective to provide dynamic performance under various loading conditions. The target of this paper is to minimize source current harmonics with optimum voltage stability under different loading conditions. A new Unified Power Flow controller is developed whose series compensator is replaced by modular multilevel converter to achieve high modular level with reduced harmonics and fast current limiting during the fault short circuit and shunt compensator is replaced with four switches and one capacitor combination to achieve the twin benefit of more reliable power system and good voltage stability for different loadings. DDSRF (Decoupled Double Synchronous Reference Frame) theory is utilized in the proposed converter for generating the reference current from the AC supply. DDSRF theory generates sinusoidal harmonics with the opposite phase to the load current. The UPFC can suck or injects the responsive power in the PCC. After DDSRF theory, hysteresis controller is used to produce PWM pulse for the shunt and series compensator. The proposed DDSRF theory is compared with existing dq theory to show its effectiveness in terms of THD analysis. The PI and fuzzy logic methodology is utilized to control the capacitor DC rail voltage. The proposed approach is simulated using Matlab under various loading condition and hardware is developed using Spartan 6E FPGA Controller.

Received Oct 20, 2021 Revised Aug 3, 2022 Accepted Sep 6, 2022 Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.

In modern era, in electric vehicles charger regularization, the following phase to make the charging procedure more convenient is to eradicate the usage of wired cable sandwiched by linking the electric vehicles and charger to accomplish wireless charging of electric vehicles, and a wireless power transfer (WPT) system ought to be depicted with respective ground clearance of electric vehicle. It is an innovation technology which can be applied for all electric vehicles (EVs) as it helps to get rid of user involvement. The crucial impediment for acquiring wireless charging is ground clearance which downgrades the power transfer efficiency. The theory of WPT for different ground clearance is elucidated, and the corresponding cordless charger device is analysed. The portrayed cordless charger device has capacity to distribute the power of about 45v utmost ground clearance of 20 cm. The battery designed is 4.5KWh and the super capacitor (SC) of 3.8KWh which is sufficient to charge electric vehicle.

Photovoltaic (PV) systems exhibit non-linear output power voltage (P-V) characteristics for varying input irradiation and temperature. Due to this, the Maximum Power Point Tracker (MPPT), a widely used optimization technique is indispensable in PV power systems. This non-linearity gets more complicated when PV panels experience partial shading in an array. During partial shading, the panels receive non-uniform irradiance and, due to this the current-voltage (I-V) curve is multimodal and so are P-V curves. Therefore, PV array which is partially shaded will exhibit multiple power peaks which are termed global maxima and local maxima. The most prevalent algorithms like Perturb and Observe (P&O) and Incremental Conductance (INC) experiences a unique issue by getting stuck at local maxima and not grasping the global power peak. This research article proposes a new Grasshopper Optimization Algorithm (GOA) which is capable of extracting maximum power even during terrible shading conditions. This algorithm is validated by comparing its performance with both conventional and other most prominent global search counterparts. The results demonstrate the effectiveness of the proposed algorithm.

This paper discusses the development of intelligent smart metering infrastructure for smart buildings based on information and communication technologies. The system collects electrical parameters, like real power, reactive power, voltage, current, power factor, and energy consumption, and physical parameters, such as temperature, humidity, and occupancy of the building. The electrical data are collected using Raspberry Pi 3B along with Smart Pi module. The physical parameters are collected by using a wide range of sensors communicating with the data acquisition module wirelessly by Wi-Fi technology. The monitoring platform is developed both in the customer end for the energy user and remote end for monitoring and control by the system operator. The developed system also has a parallel mode of communication based on LoRa technology, which will come into the picture if the primary mode of communication fails, ensuring there is always a continuous transfer of data under natural disaster situations. In addition, the system has an occupancy detection–based Internet of Things control module through which the system operator can remotely control selected appliances in the customer side based on the occupancy of the building as well as during disaster situations. The developed system also paves the way for secured smart buildings by continuous tracking of the building physical parameters, like temperature, humidity, and occupancy of the building during emergency situations like floods, earthquakes, and terrorist attacks, by which relief operations can be provided.

The progress of automobiles for transportation has been intimately associated with the progress of civilization. The main aim of this article is to develop a vehicle that can run on internal combustion engine (ICE) and electric motor efficiently and lower the fuel usage for a trip. The goal of this article is to analyze the driving speed of a vehicle and switch the energy sources based on the prediction given by a neural network. This ultimately reduces the fuel consumption when compared with a regular vehicle that is powered entirely by fuel. The neural network used in this paper is built using TensorFlow, which is considered one of fastest machine-learning libraries ever, which in turn helps in switching, thus leading to efficiency. The outcome of progress in the automobile sector in the present day is the accumulation of many years of pioneering research development. The usage of battery during low torque helps in reducing the heat dissipation in peak times; furthermore, the usage of ICE during high torque balances the economy of the vehicle.

To empower the abstraction of maximum power from the solar panel using MPPT with P&O algorithm, this proposed work enlightens the practice of single-axis solar tracker exploitation by Arduino Uno. The introduction of IoT into the project helps in remote monitoring of the entire system and also the analysis with the neat graphical representation of the data over a course of time. The significant characteristic for monitoring, supervising and performances estimations of integration of Internet of Things (IoT) technology which is an efficient wireless communication technology is considered which disables the difficulties faced in physical monitoring of solar photovoltaic (PV) and getting the values of voltage and current.

Object identification is the one of the most developing area in image processing. In this paper proposed a neural network based object identification for aerial images. The computer is able to understand high level image are video for processing. Convolution neural network is detected the single object first and then detects the multiple objects. The input to an R-CNN is an image consisting of various instances, and output will be bounding boxes with the class label of the object. The input images are passed through discrete wavelet transform for pre processing. In this paper, it is mainly focused on object detection with segmentation.

The study of wavelet transform and its application to image processing are being incorporated in undergraduate and postgraduate syllabuses. This article discuss the basic concepts of image processing and its importance in real time application. The content of this work elaborates the importance of the image denoising and what is the necessity of image denoising in transmitted images in image enhancement topic among the final year project students as well as an emerging research topic among the research scholars worldwide. Hence this article is prepared in order to trigger those students’ knowledge in basic steps involved in image processing techniques in detail. This study highly focuses on how wavelet transform is involved in denoising and impact of denoising in an image. In general, the basic transforms like Fourier transform and discrete cosine transforms are used to transform the image from one domain to another domain. Further it does not require a priori knowledge of noisy image over a transmission time. Hence the proposed method can provide a satisfactory improvement in image enhancement, assured reduction in root mean square error and improvement in the peak signal-to-noise ratio. The peak signal-to-noise ratio value of noisy image to denoised images is improved more in different wavelet transform methods. Thus, this paper can help the students who are working with visual perception of images and machine learning algorithm under image processing environment.