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
Video compression is reducing and removing redundant video data so that a digital video file can be effectively sent and stored. The compression process is used to create a compressed file for transmission or storage by applying an algorithm. To play the compressed file, an inverse algorithm is applied to produce a video that shows virtually the same content as the original source video. In this paper, different wavelets like Haar, Daubechies, Biorthogonal, Symlet were used to perform video compression for the given video input. These wavelets were compared with different input video formats like MPEG, AVI, and WMV formats and the output was observed using the parameters Peak Signal to Noise ratio, Retained Energy and Compression Ratio in MATLAB.
This paper explains the tuning of analog PID controller for superheated steam temperature system of 500 MW boiler using Particle Swarm optimisation Algorithm based on ITAE as the objective function. The analog controllers have large applications in industrial control. The fifth order model of superheated steam temperature system of 500 MW boiler is taken for study. Most of the available tuning algorithms are based on FOPTD models which is given by G(s) = Ke –τs/(Ts+1). If the plant is not approximated well to good FOPTD model, good controllers may not be designed using existing algorithms. Genetic algorithm is a stochastic algorithm based on principles of natural selection and genetics. To tune the PID controllers using Particle Swarm optimisation Algorithm , it does not require any FOPTD model. Hence the analog PID controller is tuned using Particle Swarm optimisation algorithm for superheated steam temperature system and the results are compared.
Real time video surveillance is becoming increasingly important in the security of forest nowadays. Many efforts have been made in this field, to detect and track the human and human activities in scene and recognize simple motion such as walking and running. Real time Video surveillance is an alternative attempting to prevent the events to happen (or reduce their chances). This paper presents a digital real time video surveillance system with distributed IP-cameras. In this paper we present a real-time surveillance  system framework for recognizing complex activities in the defined area. In our system we detect moving objects in the scene [III] and use tracking algorithm [IV] to record activities for further analysis. Compared with the traditional surveillance system, an IP based distributed system surveillance offers much better flexibility in video content processing and transmission . At the same time, it, also, can easily implement advanced features such as real time transmission of video and audio if connected through known ip address. Real time videos from distributed cameras are transmitted.
Image and video compression is one of the major components used in video-telephony, video conferencing and multimedia-related applications where digital pixel information can comprise considerably large amounts of data. Management of such data can involve significant overhead in computational complexity and data processing. Compression allows efficient utilization of channel bandwidth and storage size. Typical access speeds for storage mediums are inversely proportional to capacity. This paper aims at implementing a wavelet transform and neural network based model for image compression which combines the advantages of both wavelet transformations and neural networks. Images are decomposed using Haar wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Scalar quantization and Huffman coding schemes are used for different sub bands based on their statistical properties. The coefficients in low frequency band are compressed by Differential Pulse Code Modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network.
In mobile radio communication environment, transmitted signal might undergo several reflections and local scatterings depending upon various multipaths available in the channel before reaching at the receiver. These scattered signals are subjected to random delay, fluctuations and phase shifts. Multipathseffects are reduced using various diversity schemes including Raking. The Rake signal can be viewed as the sum of the multipath signals with received signal from each path being scaled by a factor related to the strength of that path. The operation is an equivalent operation to the multiplication of the received signal by the time reversed channel impulse response. We consider the Design of ultra-wideband (UWB) systems that enable high data rate communications for short range wireless applications. In particular we propose two pre-equalization filter (PEF) schemes for multiple-input-single-output (MISO) direct-sequence UWB (DS-UWB) system with Pre-Rake and symbol-by-symbol detection. The first scheme minimizes the total transmitted power from the base station to achieving physical layer quality of service (QoS) for different users. In the second scheme, we consider the minimization of a weighed sum of user’s mean-square-error. In order to obtain a computational tractable solution for this scheme the channel duality is considered. Our approach is sufficiently general to include reduced-complexity version of Pre-Rake that employs a limited number of Rake fingers. Our simulation results show that the proposed PEF schemes with Pre-Rake achieve significant performance over Pre-Rake without equalization.