Neural Network Based Prediction of Stable Equivalent Series Resistance in Voltage Regulator Characterization

High demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR

Thermodynamic and experimental explorations of CO2 methanation over highly active metal-free fibrous silica-beta zeolite (FS@SiO2-BEA) of innovative morphology

CO2 methanation is a novel way for climate change mitigation by converting CO2 into substitute natural gas. In this study, a highly active fibrous silica-beta zeolite (FS@SiO2-BEA) catalyst was prepared for CO2 methanation by a microemulsion process, and examined by N2 adsorption–desorption, field emission scanning electron microscope (FESEM), transmission electron microscopy (TEM), and electron spin resonance (ESR) spectroscopy techniques. It was found that the FS@SiO2-BEA catalyst possessed a fibrous silica morphology, leading to high surface area (609 m2/g), oxygen vacancies, and basicity. A thermodynamic study was also carried out using Gibbs free energy minimization method, and it was found that low temperatures (25–350 °C) and high H2: CO2 ≥ 4 ratios have enhanced the CO2 methanation activity. The prepared FS@SiO2-BEA catalyst exhibited high CO2 conversion (65%), and CH4 selectivity (61%) with a space–time yield of 3.30 g gcat−1 h−1. The obtained experimental results highly followed the thermodynamic calculations

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

Modulation Techniques for Biomedical Implanted Devices and Their Challenges

Implanted medical devices are very important electronic devices because of their usefulness in monitoring and diagnosis, safety and comfort for patients. Since 1950s, remarkable efforts have been undertaken for the development of bio-medical implanted and wireless telemetry bio-devices. Issues such as design of suitable modulation methods, use of power and monitoring devices, transfer energy from external to internal parts with high efficiency and high data rates and low power consumption all play an important role in the development of implantable devices. This paper provides a comprehensive survey on various modulation and demodulation techniques such as amplitude shift keying (ASK), frequency shift keying (FSK) and phase shift keying (PSK) of the existing wireless implanted devices. The details of specifications, including carrier frequency, CMOS size, data rate, power consumption and supply, chip area and application of the various modulation schemes of the implanted devices are investigated and summarized in the tables along with the corresponding key references. Current challenges and problems of the typical modulation applications of these technologies are illustrated with a brief suggestions and discussion for the progress of implanted device research in the future. It is observed that the prime requisites for the good quality of the implanted devices and their reliability are the energy transformation, data rate, CMOS size, power consumption and operation frequency. This review will hopefully lead to increasing efforts towards the development of low powered, high efficient, high data rate and reliable implanted devices

A novel prototype and simulation model for real time solid waste bin monitoring system

This research deals with an exclusive solution to monitor the solid waste bin condition on real time. The system architecture is designed using wireless sensor networks. A set of carefully chosen sensors are used to measure the status of the bins and ZigBee and GPRS are used as communication technologies. The physical architecture of the system contains three levels such as smart bins for the measurement and transmission of bin status, gateways for storing and forwarding bin data to server and control station for storing and analyzing the data. After the framework design, a simulation is performed using Castalia to ensure the feasibility and accuracy of the system. The simulation is performed for ten bins and taking ten samples from each bin where a fi ll level threshold of 15 is considered. The simulation result shows that, the proposed system would be able to automate the solid waste monitoring process that helps to optimize waste collection route

Energy harvesting for the implantable biomedical devices: issues and challenges

The development of implanted devices is essential because of their direct effect on the lives and safety of humanity. This paper presents the current issues and challenges related to all methods used to harvest energy for implantable biomedical devices. The advantages, disadvantages, and future trends of each method are discussed. The concept of harvesting energy from environmental sources and human body motion for implantable devices has gained a new relevance. In this review, the harvesting kinetic, electromagnetic, thermal and infrared radiant energies are discussed. Current issues and challenges related to the typical applications of these methods for energy harvesting are illustrated. Suggestions and discussion of the progress of research on implantable devices are also provided. This review is expected to increase research efforts to develop the battery-less implantable devices with reduced over hole size, low power, high efficiency, high data rate, and improved reliability and feasibility. Based on current literature, we believe that the inductive coupling link is the suitable method to be used to power the battery-less devices. Therefore, in this study, the power efficiency of the inductive coupling method is validated by MATLAB based on suggested values. By further researching and improvements, in the future the implantable and portable medical devices are expected to be free of batteries

Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method

Abstract: Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits