Offline and real time noise reduction in speech signals using the discrete wavelet packet decomposition

This thesis describes the development of an offline and real time wavelet based speech enhancement system to process speech corrupted with various amounts of white Gaussian noise and other different noise types

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Methods of covert communication of speech signals based on a bio-inspired principle

This work presents two speech hiding methods based on a bio-inspired concept known as the ability of adaptation of speech signals. A cryptographic model uses the adaptation to transform a secret message to a non-sensitive target speech signal, and then, the scrambled speech signal is an intelligible signal. The residual intelligibility is extremely low and it is appropriate to transmit secure speech signals. On the other hand, in a steganographic model, the adapted speech signal is hidden into a host signal by using indirect substitution or direct substitution. In the first case, the scheme is known as Efficient Wavelet Masking (EWM), and in the second case, it is known as improved-EWM (iEWM). While EWM demonstrated to be highly statistical transparent, the second one, iEWM, demonstrated to be highly robust against signal manipulations. Finally, with the purpose to transmit secure speech signals in real-time operation, a hardware-based scheme is proposedEsta tesis presenta dos métodos de comunicación encubierta de señales de voz utilizando un concepto bio-inspirado, conocido como la “habilidad de adaptación de señales de voz”. El modelo de criptografía utiliza la adaptación para transformar un mensaje secreto a una señal de voz no confidencial, obteniendo una señal de voz encriptada legible. Este método es apropiado para transmitir señales de voz seguras porque en la señal encriptada no quedan rastros del mensaje secreto original. En el caso de esteganografía, la señal de voz adaptada se oculta en una señal de voz huésped, utilizando sustitución directa o indirecta. En el primer caso el esquema se denomina EWM y en el segundo caso iEWM. EWM demostró ser altamente transparente, mientras que iEWM demostró ser altamente robusto contra manipulaciones de señal. Finalmente, con el propósito de transmitir señales de voz seguras en tiempo real, se propone un esquema para dispositivos hardware

Efficient reconfigurable architectures for 3D medical image compression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.Ministry of Higher Education Malaysia (MOHE), Universiti Tun Hussein Onn Malaysia (UTHM) and the British Counci

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Information selection and fusion in vision systems

Handling the enormous amounts of data produced by data-intensive imaging systems, such as multi-camera surveillance systems and microscopes, is technically challenging. While image and video compression help to manage the data volumes, they do not address the basic problem of information overflow. In this PhD we tackle the problem in a more drastic way. We select information of interest to a specific vision task, and discard the rest. We also combine data from different sources into a single output product, which presents the information of interest to end users in a suitable, summarized format. We treat two types of vision systems. The first type is conventional light microscopes. During this PhD, we have exploited for the first time the potential of the curvelet transform for image fusion for depth-of-field extension, allowing us to combine the advantages of multi-resolution image analysis for image fusion with increased directional sensitivity. As a result, the proposed technique clearly outperforms state-of-the-art methods, both on real microscopy data and on artificially generated images. The second type is camera networks with overlapping fields of view. To enable joint processing in such networks, inter-camera communication is essential. Because of infrastructure costs, power consumption for wireless transmission, etc., transmitting high-bandwidth video streams between cameras should be avoided. Fortunately, recently designed 'smart cameras', which have on-board processing and communication hardware, allow distributing the required image processing over the cameras. This permits compactly representing useful information from each camera. We focus on representing information for people localization and observation, which are important tools for statistical analysis of room usage, quick localization of people in case of building fires, etc. To further save bandwidth, we select which cameras should be involved in a vision task and transmit observations only from the selected cameras. We provide an information-theoretically founded framework for general purpose camera selection based on the Dempster-Shafer theory of evidence. Applied to tracking, it allows tracking people using a dynamic selection of as little as three cameras with the same accuracy as when using up to ten cameras

Extracting ECG-based cardiac information from the upper arm

Cardiovascular disease (CVD) is the global number one cause of death. Therefore, there is an acute need for constantly monitoring cardiac conditions and/or cardiac monitoring for extended periods. The current clinical Electrocardiogram (ECG) recording systems require precise placement of electrodes on the patient’s body, often performed by trained medical professionals. These systems also have long wires that require repeated disinfection and can be easily tangled and interfered with clothing and garment. These limitations have severely restricted the possible application scenarios of ECG systems. To overcome these limitations, there is a need for wearable ECG devices with minimal wires to detect possible cardiac abnormalities with minimal intervention from healthcare professionals. Previous research on this topic has focused on extracting cardiac information from the body surface by investigating various electrode placements and developing ECG processing algorithms. Building on these studies, it is possible to develop devices and algorithms that can extract ECG-related information without the need for precise electrode placements on the body's surface. The present thesis aims to extract ECG-based cardiac information using signals recorded from the upper arm. Far-field ECG is prone to contamination by artifacts such as Electromyogram (EMG), which greatly reduces its clinical value. The current study examines how various state-of-the-art heartbeat detection algorithms perform in four levels of simulated EMG artifacts. The simulated EMG was added to Lead II from two different datasets: the MIT-BIH arrhythmia dataset (Dataset 1) and data we collected from 20 healthy participants (Dataset 2). Results show that Stationary Wavelet Transform (SWT) provided the most robust features against EMG intensity level increment among various algorithms. The next step involved recording bio-potential signals using a high-density bio-potential amplification system attached to the upper arm. The system used three high-density electrodes, each with 64 channels, in addition to the standard Lead II. Twenty participants, reported healthy, were asked to perform two tasks: Rest and Elbow Flexion (EF): holding three weights (C1: 1.2 kg, C2: 2.2 kg, and C3: 3.6 kg). The tasks were repeated 2 and 3 times, respectively. Firstly, I identified optimal electrode locations on the upper arm for each task. I then generated a synthesized ECG using the selected electrodes with generalized weights over subjects and trials. Considering the robustness of SWT to EMG intensity level increment, I next focused on optimizing SWT by addressing two of its drawbacks: introducing phase shift and the requirement of a pre-defined mother wavelet. Regarding the first drawback, zero-phase wavelet (Zephlet) was implemented to replace SWT filters with zero-phase filters for the matter of feature extraction from the synthesized ECG. Next, I incorporated the synchronized extracted features with a Multiagent Detection Scheme (MDS) for the means of heartbeat detection. The F1-score for the heartbeat detection was 0.94 ± 0.16, 0.86 ± 0.22, 0.79 ± 0.26, and 0.67 ± 0.31 for Rest and EF with three different levels of muscle contraction (C1 to C3), respectively. Changing the acceptable distance between the detected and actual heartbeats from 50 ms to 20 ms, the F1-score changed to 0.81 ± 0.20, 0.66 ± 0.26, 0.57 ± 0.26, and 0.44 ± 0.26 for Rest and C1 to C3, respectively. Regarding the second drawback, Lattice parametrization was used to optimize the mother wavelet for the means of PQRST delineation. The mother wavelet was generalized over subjects, trials, and tasks. The Pearson’s Correlation Coefficient (CC) between the averaged delineated PQRST from analyzing feature and the averaged PQRST from Lead II using this generalized mother wavelet was 0.88 ± 0.05, 0.85 ± 0.08, 0.83± 0.11, and 0.81 ± 0.12 for Rest and C1-C3, respectively. This thesis makes several contributions to the current literature. It introduces locations on the upper arm that can be used to place sensors in a wearable to capture cardiac activity with robustness across intra-subject, inter-subject and inter-contraction variabilities. It also identifies a robust method against noise increment for heartbeat detection. Zephlet was implemented for the first time that can replace SWT in many applications in which there is a need for synchrony with respect to the original signal or among components. And finally, this thesis introduces a generalized mother wavelet that can be used to extract PQRST and enhance SNR in many applications, such as ECG waveform extraction, arrhythmia detection, and denoising

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS

Ph.DDOCTOR OF PHILOSOPH