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
In order to map the image, color intensity of the image, or for detecting the object image segmentation is used. It is one of the important procedures used by many of the algorithms. Fuzzy C Means algorithm is one of the effective and powerful image segmentation algorithms compared to all other segments. To describe or explain the dissimilarity in-between Clustered Prototype and the data acquired, FCM uses Euclidean distance to resolve (Zhao et al., 2015). Since the mean information of the cluster is only characterized by the Euclidean distance, both the cluster divergence and noise is made sensitive. Mahalanobis distance is more accurate than the Euclidean distance as a dissimilarity measure when they are used for image segmentation, and they also used to define the mean and covariance of a cluster. The final experimental results show that the Mahalanobis distance is more accurate than the Euclidean distance.
Signal Processing is used to bring out the speech in a degraded signal. Amplitude of the signal is obtained by using the SFF (Single Frequency Filtering). Spectral and Temporal resolutions are compared by using three different methods, which are discussed in this paper. Voice Activity Detection is the process in which any noise or disturbance that are made to the speech signal is detected. In this paper, the author has proposed Voice Activity Detection system with the help of Frequency based filtering method. The experimental results show that it gives better results compared to the existing systems.
In almost all the acoustic environments, noise is always considered as a ubiquitous one. The quality of the signal gets degraded and also contaminated because of the infection, which was caused by various sources when one speaks through the microphone. Here there is a possibility, where there may be a harm caused when human to machine communication happens. The digital filtering problem is considered in this paper, which is the estimation of the clean speech from Noise detection as well as Noise reduction. The estimation is done through linear filtering of noise in the speech signal. In this paper, the author has reviewed different and various speech signal processing techniques, where the noise gets affected and also how the noise gets removed.
The process of removing noise from the signal is known as Noise Reduction. Both the digital and analog recordable devices can be affected by noise. Noise can be of two variations, it can be either a coherent noise which could be introduced by the algorithm or they can be of non-coherent with white or random noise. Since the structure of the medium is a grained one, noise is introduced in both the photographic and magnetic taped scenarios accordingly. Noise can be reduced by different techniques with a corresponding algorithm or methodology, whereas in this paper, the author comprises the survey of different noise removal techniques from different authors’ point of view.