Secret Image Sharing Scheme Based on Compressed Sensing
Implementation of HOG Based Feature Extraction Method
Soldier Health and Position Monitoring System using GPS and IOT Technology
A Review on the Closed-Loop Strategy for Speed Control of BLDC Motors
V/F Based Speed Controller of Induction Motor using Ann Controller - A Review
Blockchain 3.0: Towards a Secure Ballotcoin Democracy through a Digitized Public Ledger in Developing Countries
Fetal ECG Extraction from Maternal ECG using MATLAB
Brief Introduction to Modular Multilevel Converters and Relative Concepts and Functionalities
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
Implementation of HOG Based Feature Extraction Method
Speaker recognition system automatically recognizes who the speaker is by using the speaker's speech features included in speech signal. After verifying the speaker claimed to be, it allows and enable access control of various voice services. The main applications of speaker recognition are in the field of forensic and providing additional security layer where security is the primary concern. The aim of this work is to verify a speaker with the approach of MFCC and Back Propagation Neural Network. Training function Lavenberg-Marquardt is used to train the network. Voice samples from a group of ten people uttering the same sentence five times repeatedly are collected to train the neural network. The testing of the network for verifying the speaker is done with new data set with the same utterance spoken once. A specific target or speaker ID is assigned to each speakers and verification is based on how close the network output is to the assigned code for each speaker. Verification method depends on the minimum positive error generated between the code and the actual network output. If the error is below the threshold value, the speaker claimed to be is accepted otherwise rejected. The tool for simulation is MATLAB.
Ultrasound images used for medical applications are generally found in low contrast and high noise, generally caused by the environment while capturing the image. Compared to other medical images, denoising an ultrasound image is challenging. The Bayesian shrinkage method has been selected for thresholding based on its sub-band dependency property. The spatial domain based de-noising filtering techniques, using soft thresholding method are compared with the proposed method using Genetic Algorithm (GA). A proposed technique includes GA and results are compared with existing spatial domain based denoising filtering techniques. The proposed algorithm provides enhanced visual clarity for diagnosing the medical images. The proposed method based on GA assesses the better performance on the basis of the quantitative metric like Peak Signal-to-Noise Ratio (PSNR) and Fitness value. The overall simulated result shows that proposed technique outperforms the prevailing denoising filtering methods in terms of preservation of the edges and visual quality of the image.
The rapid spread of the internet, along with the comprehensive development of digital technologies and easily reproduced digital media, has increased the popularity of media. Nowadays, exchange and transmission of digital images through internet is increasing and thus the protection of image is critical. This has initiated more researchers to develop efficient methods in digital image content protection. Digital watermarking is one of the ways to achieve protection in images. Digital watermarking is a process by which secret data is encrypted into the image without affecting the visual quality of the copyrighted image. This paper presents a block-based image watermarking technology using Discrete Wavelet Transformation (DWT) that does not affect the human visual system. The host images with digital watermark are exposed to cyber attacks when accessible through the open domain in the internet. The experimental results of this study shows that the proposed image watermarking method protects the invisibility of the watermark.
Most of the present literature on speech enhancement focus totally on existence of noise in corrupted speech which is way from real-world environments. In this project we choose to enhancing the speech signal from the noise and reverberant using RNN and CNN. We trained separate networks for both RNN and CNN with noise, reverberation and both combination of reverberant and noise data. A simple way to enhance the quality of speech is raise the quality of the previous recordings by using speech training with speech enhancement methods like noise suppression and dereverberation using Neural Networks. The quality of voices trained with lower quality data that are enhanced using these networks was significantly higher. The comparison of RNN and CNN is shown and the experimental results are performed using MATLAB tool.
All around us, every day, hundreds of radars are at work in planes, ships, airports and even in cars. Invisible radio waves complement our vision efficiently. The history of radar began relatively recently, but it is difficult to imagine life without this technology. This article reviews the science behind the technology and how this technology evolved as what is available in the present.
