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The identification of faults in machinery is a very emerging trend. In the last few decades, regenerative tool chatter and its adverse effects have been explored by many researchers. However, a lot of work has to be done within this domain. A new methodology has been presented in the present work to determine the chatter severity while machining. The methodology has three stages. In the first stage, numerous experiments have been carried out, and associated signals have been captured. Thereafter, in the second stage, preprocessing of the recorded signals have been done using “ensemble empirical mode decomposition” to filter out the contaminations from the signals. The intrinsic mode functions have been further evaluated using statistical indicators viz. chatter index and absolute mean amplitude. In the third stage, these statistical indicators have been examined concerning the input parameters to identify the variation in the responses and chatter severity. The proposed methodology seems helpful for the researchers to identify the chatter features concerning variation in input parameters.

Speaker identification for the speech signal processing request, determining the speaker is a challenge due to physical variation. This paper emphasizes a new algorithm based on acoustic feature analysis of text-dependent speech. In this proposed method text-dependent speech changed by ten physical variation methods. Acoustic feature of all types of voice is calculated by its arithmetical correlation coefficients and mean value. The audio characteristic is calculated with Mel-frequency cepstrum coefficient (MFCC), its derivatives and double derivatives. An acoustic characteristic is analysed by using normal voice and changed voice by different speakers, the mixed data used for test and training purpose. Passing all the training and test data through the various classifiers based on identification system. Speaker identification efficiency results are calculated from the different classifier.

Faults like regenerative tool chatter have been evaluated by several researchers in order to suppress its adverse effect. However, many facets of this domain are yet to be addressed. In the present work, a new methodology has been proposed to process the recorded regenerative chatter signals in order to extract the chatter features. In the proposed approach, experiments have been performed and signals pertaining to regenerative tool chatter have been recorded using microphone. Thereafter, the recorded signals have been evaluated and preprocessed using variational mode decomposition (VMD) in order to extract chatter features. The decomposed signals that result in variational mode functions have been further evaluated by calculating a response termed as chatter index. This response has been used to predict the chatter severity during machining at different combinations of input parameters, on verifying the obtained results it has been found that the proposed methodology is significant in identifying the chatter severity.

In this study, we have developed a fourth order compact finite difference method for finding the numerical solution of two-parameter singularly perturbed convection-diffusion boundary value problems. We have used fourth order compact finite difference method on uniform mesh which provides a tridiagonal linear system of equations. The convergence analysis of the proposed method is established through a matrix analysis approach and it is proved that present method gives fourth order convergence results. Present method is implemented on two numerical examples for checking the efficiency and precision of the method. Numerical outcomes are exhibited which supports the theoretical outcomes. Numerical outcomes are compared with other existing methods and found that present method gives more accurate approximate solution as compare to the other existing methods.

The requirement of imaging methods in the medical field is vivid. If the capturing devices are not sophisticated, the acquired images will have a significant amount of noise. These noises are hazardous and cannot be entertained. Polycystic Ovarian Syndrome (PCOS) caused the state of affairs in girls if not diagnosed and look after early stages. Tran's epithelial duct ultrasound machine could be a non-invasive technique of imaging the human ovary to show salient options necessary for PCOS identification. Numbers of follicles and their sizes area unit the most options that characterize ovarian pictures. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. conflict in medical aid is essentially created by technical advances in modalities that resulted from fruitful interactions among the essential science, bioscience, and manufacturer. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. This paper attempts to identify the noise & try to generate a noise-free image by evaluation of noise properties. The noise pattern information thus provides an upper hand in the second stage filtering with specific filters i.e. fuzzified. A median filter for salt and pepper noise; and an adaptive wiener filter for Gaussian noise. 46.2%, 15.1%, and 12.4% improvement in MSE for salt & pepper, Gaussian, and speckle noise as compared to best existing methods.

This paper provides an overall design and implementation perspective of a laboratory-scale automated visual inspection system for the beverage industry's production line. A case study has been undertaken where the image processing algorithm inspects the beverage bottle for any defects. Different defects such as improper labeling and improper liquid level can be detected using the image processing algorithm. A laboratory prototype of the conveyor belt has been built, and a prototype filling plant has been established to verify the simulation results.

An applications of signal processing are frequently used everywhere in day-to-day life. “Digital Signal Processing (DSP)” has been basic requirement of efficient and accurate arithmetic operations for performing fast and accurate signal processing. Logarithm arithmetic provides an option of that desire. In this work, it is presented an efficient VLSI implementation of an antilogarithm converter by using 10-region error correction. It provides error efficient implementation with significant hardware gain. VLSI implementation of reported and proposed antilogarithmic converters design created in Xilinx ISE 12.1. Antilogarithmic converters (reported and proposed design) are synthesized by using Synopsys design software perform the RTL Synthesis analysis by using package design compiler. This paper presents 10- region converter which considers design trade-off where proposed demonstrates 19.75%, 31.02%, 12.65%, 44.65% and 29.91% respective reduction in comparison of previous design. Error analysis was done using MATLAB for proposed conversion method and reported methods. Suggested antilogarithmic converters have 1.559% error only in comparison of 1.7327% error reported by Kuo et al. On behalf of hardware complexity and error analysis results, it can say that the proposed converters could perform better in comparisons of all aspects of reported design.