Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits
Smart Electrical Vehicle
Development of Smart Electronic System to Implement Smart Home
Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment
Photographing a Black Hole
Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
Fetal ECG Extraction from Maternal ECG using MATLAB
Detection of Phase to Phase Faults and Identification of Faulty Phases in Series Capacitor Compensated Six Phase Transmission Line using the Norm of Wavelet Transform
A Novel Approach to Reduce Deafness in Classical Earphones: MUEAR
A novel mathematical ECG signal analysis approach for features extraction using LabVIEW
Filtering of ECG Signal Using Adaptive and Non Adaptive Filters
Application of Polynomial Approximation Techniques for Smoothing ECG Signals
A Novel Approach to Improve the Wind Profiler Doppler Spectra Using Wavelets
Wearable Health Monitoring Smart Gloves
The connections between circuit components in VLSI design are made by using a liquid crystal polymer substrate. Due to mistakes made by manufacturing machines and design engineers, there is a possibility of getting faulty connections in very large integrated circuits (VLSI). The faults in printed circuit boards (PCB) can be identified by using advanced technologies like computer vision and deep convolutional neural networks. In this paper, a model based on neural networks to detect faultiness of design has been proposed. The 3-4 Convolutional Neural Networks (CNN) model in deep learning can analyze the patterns in images and gives accurate results when the model is tested against new data. In our proposed model, an auto encoder based neural networks has been used for the detection of faults in printed circuit boards (PCB). An Artificial Intelligence (AI) camera to scan circuit design has also been used. The AI camera scan images at a higher resolution rate and at a higher dynamic range. The images taken by the camera are tested by the model to detect whether the image is faulty or not. The model is trained on a different set of circuit images and tested against validation data. Accuracy of 98.6% has been obtained by this approach.
The primary objective of this paper is to design a feasible yet highly adaptable E-BICYCLE, as number of motor vehicles on the roads all-over the world increases. The worlds’ car usage is booming. Cars are polluting the cities as they consume large quantity of Petroleum and the amounts of carbon dioxide released increases which in turn pollutes the green house gases causing climate change. Huge amount of money is being spent on the development and manufacturing of electrical vehicles. This paper presents the study of design of Electrical Bicycle. The aim of this paper is to design a simple, cost effective model of Electrical Bicycle with intelligent control system. The materials used are mostly environmental friendly and cost is much lower than the existing electric power bike. This can be implemented by connecting an electrical motor, a controller and battery pack, cabling and monitoring instruments.
Smart homes and home automation are analogue terms used in reference to a wide range of solutions for controlling, monitoring of internal environmental parameters and automating various functions of the home. Deploying novel embedded technology, the smart system is designed, with AVR ATmega 32 microcontroller. The research is specifically focused on the development of a precise and stable system for monitoring and controlling the temperature, light intensity, humidity, human interference, etc. for home automation. The system provides controlling of cooler fan and heaters, measurement of light intensity and controlling of both internal and external lights of garden or strait light, measurement of humidity and domestic gas leakage detection system. A highly precise humidity sensor SY-HS-220, temperature sensor LM 35, LDR, MQ-6, and PIR are employed for this purpose. The signal conditioning circuit is wired with single power supply operated CMOS operational amplifier TLV 274. The firmware is designed in embedded C, using CodeVisionAVR, the Integrated Development Environment (IDE). The designed system is implemented to monitor and control the parameters and the results are interpreted in this paper.
The Multilingual Speech Processing is the field of speech technology in which the speech signal of multiple languages of a speaker has been analyzed to observe the effect of the language on the speech features. On the basis of observation, a Multilingual Speaker Identification system can be designed for identification of the speaker in multilingual environments. For present study Multilingual Speech Processing database of different speakers has been recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The sentences consist of consonants, i.e., “Cha”, “Sha” and “Jha”. Total numbers of speakers involved are 30 including males and females. The basic features of the speech signal: Pitch and first three Formant F1, F2 and F3 are calculated through PRAAT software where as cepstral features like Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) has been extracted from MATLAB software. A model is proposed to identify the speaker by multi language speech signal of a speaker using MFCC, GFCC and combined features as acoustic features. For training and testing, it is performed using neural network function Resilient Back Propagation Algorithm and Radial Basis Functions and results are compared. In this experiment accuracy of multilingual speaker identification is 94.77% using BPA and 96.52% using RBF neural network.
Photographing a black hole was a fiction for a long time. The Event Horizon Telescope (EHT) project achieved photographing the black home by the rational study on signal processing from different radio telescopes using technique called Very Long Baseline Interferometry (VLBI). The outcome as a photograph (the black hole) is the output of combinations of signals processed through several algorithms to sort and synchronize volumes of data from various radio telescopes.