With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enable automatic and remote sensing, processing, and execution. IoT ensures that information is easily available and accessible from any location around the world. The IoT applications improve the visibility, scalability, and cost-effectiveness of solar energy generation and service. A bibliometric analysis of scientific publications in the field of solar PV and IoT applications was conducted using the Scopus database between the years 2011 and 2023. Many studies of technological development have been discovered, and some insights can still be approached in such a way that the practical implementation of photovoltaic solar systems is improved. Since 2013, there has been an increase in the rate of publications. The majority of these studies were conducted in India, and the most common IoT applications reported were in the fields of computer science and engineering. This article identifies knowledge gaps to inform the community, industry, and government officials about IoT research directions in the solar energy field.
KEYWORDS: Signal filtering, Tunable filters, Control systems, Electronic filtering, Nonlinear filtering, Engineering, Optical engineering, Digital filtering, Signal processing
KEYWORDS: Field programmable gate arrays, Tunable filters, Signal filtering, Image processing, Signal processing, Control systems, Artificial intelligence, Electronic filtering, Design and modelling, Robotics
Field programmable gate arrays (FPGAs) are increasingly popular due to their customizability, which enables them to be tailored to specific applications, resulting in minimal resource usage that saves energy and space. In this work, we used an FPGA with a Z-board from Xilinx to simulate the application of the sliding innovation filter (SIF) to a robotic arm. SIF is a predictor-corrector filter used for both linear and nonlinear systems to estimate states and/or parameters. It shares similar principles with sliding mode observer and smooth variable structure filter (SVSF) and uses a correction gain derived to satisfy Lyapunov stability, keeping the estimates near the measurements. We tested SIF on a manipulator with two joints (rotational and prismatic), using FPGA to run the simulation while tracking resource utilization. We compared the results with those of SVSF.
Sleep apnea is a disorder that has the potential to be life-threatening, that is characterized by irregular breathing patterns. In order to improve the diagnosis and prediction of sleep apnea, a study was conducted to develop a high-accuracy detection method using machine learning. This method involved the use of a convolutional neural network classifier, which was trained using public data sets of ECG signals from both apnea patients and healthy volunteers. The CNN model was able to attain a level of accuracy of 94.12% using the Xception model and 91.18% using the ResNet50 model. According to the study’s findings, using deep learning techniques can be a helpful strategy to enhance sleep apnea diagnosis and prediction.
Epilepsy is a neurological condition caused by sudden onsets of electrical activity in the brain. This results in frequent, uncommon seizures, which can lead to severe physical consequences. In a clinical setting, data recorded using EEG (Electroencephalogram) is used to help diagnose the condition. This research focuses on the use of Short-Term Fourier transform (STFT) and feature extraction in the EEG data for the use in a majority voting model using logistic regression (LR) to detect the presence of epileptic seizures in the five EEG frequency bands ( i.e. Alpha, Beta, Gamma, Delta, and Theta). To quantify, a number of evaluation metrics have been calculated. Overall, the model was able to achieve an accuracy of up to 92%.
The most notable advancement in the 21st Century has been in artificial intelligence (AI). Despite how far AI has progressed, how it applies to healthcare remains a significant challenge for brilliant minds all over the world. A neurological condition known as epilepsy can strike a person at any time in their life. An individual with epilepsy therefore experiences frequent to infrequent seizures, which can occasionally result in death. Electroencephalogram (EEG) signals aid in the diagnosis of this condition. However, lengthy EEG signals frequently take a day or longer to detect this disorder, even for trained neurologists, and may even cause human error. Therefore, it is essential to create a reliable and computationally efficient system. This study aims to classify seizures by creating Convolutional Neural Network (CNN) Inception ResNet V2 and short-time Fourier transform (STFT) to extract the time-frequency plane from time domain signals. This study helped to better classify health and seizures by achieving up to 100% the highest classification accuracy.
Plastic pollution has emerged as one of the biggest environmentally threatening issues. Using image classification, the proposed study aids in categorizing the level of marine pollution in ocean underwater regions. This study classified the amount of pollution in the ocean using the two variants of Inception Convolutional Neural Network (CNN) models i.e., Inception- ResNet V2, and InceptionV3. High accuracies of up to 96.4% have been reported. This study will help researchers working in the field of water quality detection.
Marine pollution is a major environmental hazard and a serious healthcare, economic, and social issue. Machine learning (ML) and deep learning (DL) techniques can be used to automate marine waste removal and make the cleanup process more efficient. The proposed study uses image classification to help categorize the level of marine pollution in ocean underwater regions. The performance of two deep convolutional neural networks (VGG19 and ResNet50) is investigated in this study and VGG19 reported an accuracy of 98.1%.
In modern robotics and automation systems, control and estimation techniques are essential tasks. In this paper, the mathematical model of a non-linear RR manipulator is developed. To realize it, the circuit design of the system is described first in detail, and then implemented on the FPGA (field programmer gate array) prototyping board. The results show that the implementation of the system requires a minimal amount of FPGA resources.
KEYWORDS: Field programmable gate arrays, Simulink, Filtering (signal processing), Mathematical modeling, Digital signal processing, Systems modeling, Roads, Nonlinear filtering
In this paper, the unscented Kalman filter (UKF) is used to estimate the states of a vehicle while it is moving on a road with different speed. Field programmer gate array (FPGA) prototyping board is used to implement the filter. The resources of FPGA is optimized using different techniques. The overall filter performance is examined in further details.
In this paper, the extended Kalman filter is used to estimate the states of an electro-hydrostatic actuator. The filter is then programmed using very high-speed hardware description language (VHDL) and realized using a field programmer gate array (FPGA) prototyping board from Xilinx. The design is then optimized to improve the performance using different techniques. The results show that the implementation of the system requires a minimal amount of FPGA resources.
In this paper, the Smooth Variable Structure Filter is used to extract the position and speed of a vehicle on a complex road. The filter is realized using FPGA (field programmer gate array). The FPGA’s resources and speed are examined at an optimal configuration. FPGA (Field Programmer Gate Array) Z-board from Xilinx is used in this work. The performance is examined and presented.
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