Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Denoising sound signals in a bioinspired non-negative spectro-temporal domain

The representation of sound signals at the cochlea and auditory cortical level has been studied as an alternative to classical analysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non-negative sparse coding with a combined dictionary of atoms. These atoms represent the spectro-temporal receptive fields of the auditory cortical neurons, and are calculated from the auditory spectrograms of clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Experiments are presented using synthetic (chirps) and real data (speech), in the presence of additive noise. For the evaluation of the new method and its variants, we used two objective measures: the perceptual evaluation of speech quality and the segmental signal-to-noise ratio. Results show that the proposed method improves the quality of the signals, mainly under severe degradation.Fil: Martínez, César Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Goddard, J.. Universidad Autónoma Metropolitana; MéxicoFil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentin

Multistage adaptive noise cancellation and multi-dimensional signal processing for ultrasonic nondestructive evaluation

Ultrasonic signal processing presents several challenges with respect to both noise removal and interpretation. The interference of unwanted reflections from material grain structure can render the data extremely noisy and mask the detection of small flaws. It is therefore imperative to separate the flaw reflections from grain noise. The interpretation or classification of ultrasonic signals in general is relatively difficult due to the complexity of the physical process and similarity of signals from various classes of reflectors;Adaptive noise cancellation techniques are ideally suited for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. In this research, a multi-stage adaptive noise cancellation (MANC) scheme is proposed for reducing spatially varying grain noise and enhancing flaw detection in ultrasonic signals. The overall scheme is based on the use of an adaptive least mean square error (LMSE) filter with primary and reference signals derived from two adjacent positions of the transducers. Since grain noise is generally uncorrelated, in contrast to the correlated flaw echoes, adaptive filtering algorithms exploit the correlation properties of signals in a C-scan image to enhance the signal-to-noise ratio (SNR) of the output signal;A neural network-based signal classification system is proposed for the interpretation of ultrasonic signals obtained from inspection of welds, where signals have to be classified as resulting from porosity, slag, lack of fusion, or cracks in the weld region. Standard techniques rely on differences in individual A-scans to classify the signals. This thesis investigates the need for investigating signal features that incorporate the effects of beam spread and echo dynamics. Such effects call for data interpretation schemes that include a neighborhood of A-scans carrying information about a reflector. Several ultrasonic signal features based on the information in a two-dimensional array of ultrasonic waveforms, ranging from the estimation of statistical characteristics of signals to two and three-dimensional transform-based methods, are evaluated. A two-dimensional scan of ultrasonic testing is also represented in the form of images (B- and B\u27-scans). Multidimensional signal and image-processing algorithms are used to analyze the images. Two and three-dimensional Fourier transforms are applied to ultrasonic data that are inherently three-dimensional in nature (2 spatial and 1 time). A variety of transform-based features are then utilized for obtaining the final classification