Minimization of errors in discrete wavelet filtering of signals during ultrasonic measurements and testing

Error minimizing methods for discrete wavelet filtering of ultrasonic meter signals are considered. For this purpose, special model signals containing various measuring pulses are generated. The psi function of the Daubechies 28 wavelet is used to generate the pulses. Noise is added to the generated pulses. A comparative analysis of the two filtering algorithms is performed. The first algorithm is to limit the amount of detail of the wavelet decomposition coefficients in relation to signal interference. The minimum value of the root mean square error of wavelet decomposition signal deviation which is restored at each level from the initial signal without noise is determined. The second algorithm uses a separate threshold for each level of wavelet decomposition to limit the magnitude of the detail coefficients that are proportional to the standard deviation. Like in the first algorithm, the task is to determine the level of wavelet decomposition at which the minimum standard error is achieved. A feature of both algorithms is an expanded base of discrete wavelets ‒ families of Biorthogonal, Coiflet, Daubechies, Discrete Meyer, Haar, Reverse Biorthogonal, Symlets (106 in total) and threshold functions garotte, garrote, greater, hard, less, soft (6 in total). The model function uses random variables in both algorithms, so the averaging base is used to obtain stable results. Given features of algorithm construction allowed to reveal efficiency of ultrasonic signal filtering on the first algorithm presented in the form of oscilloscopic images. The use of a separate threshold for limiting the number of detail coefficients for each level of discrete wavelet decomposition using the given wavelet base and threshold functions has reduced the filtering error.

Determination of Chewing Count from Video Recordings Using Discrete Wavelet Decomposition and Low Pass Filtration

Several studies have shown the importance of proper chewing and the effect of chewing speed on the human health in terms of caloric intake and even cognitive functions. This study aims at designing algorithms for determining the chew count from video recordings of subjects consuming food items. A novel algorithm based on image and signal processing techniques has been developed to continuously capture the area of interest from the video clips, determine facial landmarks, generate the chewing signal, and process the signal with two methods: low pass filter, and discrete wavelet decomposition. Peak detection was used to determine the chew count from the output of the processed chewing signal. The system was tested using recordings from 100 subjects at three different chewing speeds (i.e., slow, normal, and fast) without any constraints on gender, skin color, facial hair, or ambience. The low pass filter algorithm achieved the best mean absolute percentage error of 6.48%, 7.76%, and 8.38% for the slow, normal, and fast chewing speeds, respectively. The performance was also evaluated using the Bland-Altman plot, which showed that most of the points lie within the lines of agreement. However, the algorithm needs improvement for faster chewing, but it surpasses the performance of the relevant literature. This research provides a reliable and accurate method for determining the chew count. The proposed methods facilitate the study of the chewing behavior in natural settings without any cumbersome hardware that may affect the results. This work can facilitate research into chewing behavior while using smart devices.

Personal identity verification based ECG biometric using non-fiducial features

Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

An Approach to Detect Image Forgery by Discrete Wavelet Decomposition

Image forgery or manipulation by using the multimedia technology is becoming a challenging issue. The most common type of image forgery is copy-move forgery where some part of one image is copied and spliced in the other image. In this article, first the images in RGB color space is converted into YCbCr color space and the four-level discrete wavelet transform (DWT) is implemented to detect image forgery. The output of the DWT is further processed by using the image gradient technique for the edge detection of spliced objects. Morphological operation and Wiener filtering are applied for locating the tempered region in the forged image. Sensitivity, specificity and accuracy calculated for spliced images of CASIA datasets are obtained 89%, 86% and 88% respectively.