International Journal of Reconfigurable and Embedded Systems (IJRES)
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Fuzzy-based MPPT algorithm implementation on FPGA chip for multi-channel photovoltaic system
Get PDFMicroprocessors and microcontrollers are mostly used to control electrical systems. These chips front into problems while monitoring systems that need heavy computing and important processing. Likewise, they fail while handling inputs and outputs speeds, especially with multi-channel photovoltaic (PV) systems. In comparison to a digital signal processor (DSP) and microcontroller implementations, field programmable gate array (FPGA) device is able to integrate a great number of PV channels and to achieve short development time, cost less and more flexible operation. As well, new control algorithms are increasingly complex; using new performing technologies is very motivating. Mainly, FPGA technology is adopted thanks to its ability to control complex applications and intelligent laws. In opposition to traditional controls, fuzzy logic based control presents more efficiency and reliability response for non-linear systems. Therefore, this paper deals with the execution of the fuzzy-based maximum power point tracking (MPPT) technique by the means of the FPGA chip for a multi-channel photovoltaic system. A multi-channel photovoltaic system is designed. Then, the FPGA circuit is investigated to get benefits from this hardware solution. Since software implementation way integrates a limited number of PV panels, hardware implementation is a promising solution that reduces execution time and therefore controls a huge number of photovoltaic channels. Finally, results of simulation of the fuzzy technique implementation on FPGA chip show that the proposed PV system controls more than 4400 channels. Therefore, the system output power is increased and the system profitability is improved
CEAZA mega board: an open-source data logger for scientists
Get PDFOver the last decade many researchers have taken advantage of the technology boom related to the launch of the Arduino platform to make their own datalogging devices. Many of these developments ended with the first functional prototypes in which multiple electronic boards are mixed by wiring/soldering and then used in datalogging activities. In this study we present a new, simple, robust, and expandable datalogger board based on maker’s community integrations. Our datalogger board extends previous work in this area as we designed an Arduino Mega 2560 derivative integrated board that is compatible with existing developments but was also designed and implemented considering requirements such as low power consumption, expandability, and integration. Different tests were made so reliability in low temperatures and low energy needs are satisfied. Is expected that the scientific community can add this board to their tool set, as this board solves the energy problem and present an easy transition from handmade logger integrations
COVID-19 paediatric cavity telecare system: a novel chain key generation and encryption scheme
Get PDFIn this unprecedented coronavirus crisis, telehealth had emerged as a substitute way of treatment. More specifically, paediatric children are at high risk of outside exposure now. Non critical children must be treated remotely through the tableware system. A key based secured online transmission of an intraoral image of the paediatric cavity has been proposed in this manuscript. A cavity is a dental disease occurring in children. It is mainly caused due to prolonged bacterial infections. Secured online transmission with respect to medical transactions is immensely required in telecare information systems (TIS). Data confidentiality factor is preserved with preference in this proposed technique. A parity based novel chain key (NCK) has been generated and diffused inside the intraoral paediatric cavity image. NCK generation scheme is so highly robust that it gives different combinations after each bit altering. Initial seeds are kept at the dentist and patients, to resist myriad attacks inside the wireless channel, especially during this COVID-19 period. Histogram, floating frequency, and autocorrelation were obtained with accuracy using the proposed technique. Effects were observed by flipping simultaneous bits of the initial key and results were highly acceptable. The time for the proposed key generation has been found to be 514.61 ms. The total cryptographic time has been noted as 3.5983 ms in this technique
Automatic generation of user-defined test algorithm description file for memory BIST implementation
Get PDFMemory built-in self-test (BIST) is a widely used technique to allow the self-test and self-checking of the embedded memories on chips after the fabrication process. It can be used by implementing a standard testing algorithm available in the EDA tool library or a user-defined algorithm (UDA). This paper presents the development of software that automatically generates a description file of a UDA to be deployed for memory BIST circuit implementation using Tessent memory BIST software. It comprises the test setup and also the microprogram coding for each instruction to be executed when performing tests on embedded memories. The proposed automation software was tested by using March SR as the input algorithm and the results obtained from the simulations show that the output test patterns generated by the implemented memory BIST match the expected patterns and passed all the tests, which validated the correct functionality of the UDA description file generation. The proposed automation software also fast generation the UDA description file, which was completed in less than 500 ms
Comparison of breast cancer classification models on Wisconsin dataset
Get PDFBreast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis
A design methodology for approximate multipliers in convolutional neural networks: A case of MNIST
Get PDFIn this paper, we present a case study on approximate multipliers for MNIST Convolutional Neural Network (CNN). We apply approximate multipliers with different bit-width to the convolution layer in MNIST CNN, evaluate the accuracy of MNIST classification, and analyze the trade-off between approximate multiplier’s area, critical path delay and the accuracy. Based on the results of the evaluation and analysis, we propose a design methodology for approximate multipliers. The approximate multipliers consist of some partial products, which are carefully selected according to the CNN input. With this methodology, we further reduce the area and the delay of the multipliers with keeping high accuracy of the MNIST classification
Smart metering system data analytics platform using multicore edge computing
Get PDFThe smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing
FPGA implementation of Lempel-Ziv data compression
Get PDFWhen transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs
An internet of things belief rule base smart system to predict earthquake
Get PDFAn intelligent belief rule base (BRB) based system with internet of things (IoT) integration can evaluate earthquake prediction (EP). This ingenious and rational system can predict earthquake by aggregating changed animal behavior combined with environmental and chemical changes which are taken as real time inputs from sensors. The BRB expert system blends knowledge demonstration criterion like attribute weight, rule weight, belief degree. The intelligent BRB system with IoT predicts the probable occurrence of the earthquake in a region based on the sign and symptoms culled by the persistent sensors. The final result taken from Intelligent BRB system with IoT integration is compared with expert and fuzzy-based system. The projected method gives a better prediction than the up-to-date expert system and fuzzy syste
Implementation of video surveillance system using embedded Blackfin processor
Get PDFThe video surveillance is critical system to track the people at the various places and to track and monitor the nuciencess bound to be happened. On the other side several studies have proved and showed the hit and miss nature of human intervention to spot change in a surrounding environment which increasing the designer challenges for the development of video surveillance system with the help of embedded processor. The designer faces a greater challenge to apply the principle of embedded systems and develop the system smart features with low power and cost for the required applications of VSS. System requirement specification (SRS), Hardware design document (HDD), Software design document and test procedure has been arrived and developed to achieve VSS system. Blackfin processor has high end video engines and is more suitable for development of video surveillance system (VSS). The VSS is designed and developed using ADSP BF533 Ez-kit lite board. Peripheral like parallel peripheral interface (PPI) is used to interface between camera and processor. Also, it is used for interfacing processor and TV. The master-slave communication is established between two Blackfin processors through SPORT to transfer the captured frame from camera to display on TV. Power management is also implemented to save the power of the system