Scalable and perceptual audio compression

This thesis deals with scalable perceptual audio compression. Two scalable perceptual solutions as well as a scalable to lossless solution are proposed and investigated. One of the scalable perceptual solutions is built around sinusoidal modelling of the audio signal whilst the other is built on a transform coding paradigm. The scalable coders are shown to scale both in a waveform matching manner as well as a psychoacoustic manner. In order to measure the psychoacoustic scalability of the systems investigated in this thesis, the similarity between the original signal\u27s psychoacoustic parameters and that of the synthesized signal are compared. The psychoacoustic parameters used are loudness, sharpness, tonahty and roughness. This analysis technique is a novel method used in this thesis and it allows an insight into the perceptual distortion that has been introduced by any coder analyzed in this manner

Yield Detection for Non-Destructive Testing using Ultrasonic Signal Processing

Structural health monitoring (SHM) is a very important field for many engineering disciplines. SHM deals with the monitoring of material structures periodically for assessing the lifetimes of the structures. There are various techniques for SHM. Non-destructive testing (NDT) is one of the most popular SHM tools to monitor structures. It demonstrates the indispensable advantage of providing structural health assessment without the need of intrusion. In this thesis, a new NDT tool for yield detection using ultrasonic signal processing is investigated. In this work, for the study of yield detection, steel specimen samples have been acquired, which were obtained from the laboratory of Department of Civil and Environmental engineering at Louisiana State University (LSU). An ultrasonic transducer then collected the signal data when these samples were tested. The data were preprocessed and segmented. For each acquired ultrasonic signal waveform, a total of three dominant echoes were extracted for the yield detection. A total of nine different signal features were extracted from these echoes for each ultrasonic signal. These nine features include time-domain features (signal amplitude, signal energy) and transform-domain features (wavelets, discrete Fourier transform, chirp Z-transform, discrete cosine transform, and discrete sine transform). Based on these aforementioned features, the linear discriminant analysis (LDA) technique is proposed to classify two situations (no-yield and yield). The proposed LDA-based classifier is compared with the conventional classifiers using individual features. The classifiers’ performances are evaluated using the receiver operating characteristics (ROC) plots. According to our experiments, it is discovered that the LDA-based classifier for yield detection is superior to all conventional classifiers using individual features, in terms of high detection rates subject to the fixed false detection rates

Orthonormal-Basis Partitioning And Time-Frequency Representation of Non-Stationary Signals

Spectral analysis is important in many fields, such as speech, radar and biomedicine. Many signals encountered in these areas possess time-varying spectral characteristics. The power spectrum indicates what frequencies exist in the signal but it does not show when those frequencies occur. Time-frequency analysisprovides this missing information. A time-frequency representation of the signal shows the intensities of the frequencies in the signal at the times they occur, and thus reveals if and how the frequencies of a signal are changing over time.Time-dependent spectral analysis of beat-to-beat variations of cardiac rhythm, or heart rate variability (HRV), represents a major challenge due to the structure of the signal. A number oftime-frequency representations have been proposed for the estimation of the time-dependent spectra. However, time-frequency analysis of multicomponent physiological signals such as cardiac rhythm is complicated by the presence of numerous, ill-structured frequency elements. We sought to develop a simple method for 1)detecting changes in the structure of the HRV signal, 2)segmenting the signal into pseudo-stationary portions, and 3)exposing characteristic patterns of the changes in thetime-frequency plane. The method, referred to as Orthonormal-Basis Partitioning and Time-Frequency Representation (OPTR), is validated on simulated signals and HRV data. Unlike the traditional time-frequency HRV representations, which are usuallyapplied to short segments of signals recorded in controlled conditions, OPTR can be applied to long and "content-rich" ambulatory signals to obtain the signal representation along withits time-varying spectrum. Thus, the proposed approach extends the scope of applications of the time-frequency analysis to all types of HRV signals and to other physiological data

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

Unsupervised clustering of IoT signals through feature extraction and self organizing maps

This thesis scope is to build a clustering model to inspect the structural properties of a dataset composed of IoT signals and to classify these through unsupervised clustering algorithms. To this end, a feature-based representation of the signals is used. Different feature selection algorithms are then used to obtain reduced feature spaces, so as to decrease the computational cost and the memory demand. Thus, the IoT signals are clustered using Self-Organizing Maps (SOM) and then evaluatedope