Theory and design of uniform DFT, parallel, quadrature mirror filter banks

In this paper, the theory of uniform DFT, parallel, quadrature mirror filter (QMF) banks is developed. The QMF equations, i.e., equations that need to be satisfied for exact reconstruction of the input signal, are derived. The concept of decimated filters is introduced, and structures for both analysis and synthesis banks are derived using this concept. The QMF equations, as well as closed-form expressions for the synthesis filters needed for exact reconstruction of the input signalx(n), are also derived using this concept. In general, the reconstructed. signalhat{x}(n)suffers from three errors: aliasing, amplitude distortion, and phase distortion. Conditions for exact reconstruction (i.e., all three distortions are zero, andhat{x}(n)is equal to a delayed version ofx(n))of the input signal are derived in terms of the decimated filters. Aliasing distortion can always be completely canceled. Once aliasing is canceled, it is possible to completely eliminate amplitude distortion (if suitable IIR filters are employed) and completely eliminate phase distortion (if suitable FIR filters are employed). However, complete elimination of all three errors is possible only with some simple, pathalogical stable filter transfer functions. In general, once aliasing is canceled, the other distortions can be minimized rather than completely eliminated. Algorithms for this are presented. The properties of FIR filter banks are then investigated. Several aspects of IIR filter banks are also studied using the same framework

Measurement and modelling of head-related transfer function for spatial audio synthesis

There has been a growing interest in spatial sound generation arising from the development of new communications and media technologies. Binaural spatial sound systems are capable of encoding and rendering sound sources accurately in three dimensional space using only two recording/playback channels. This is based on the concept of the Head-Related Transfer Function (HRTF), which is a set of acoustic filters from the sound source to a listener's eardrums and contains all the listening cues used by the hearing mechanism for decoding spatial information encoded in binaural signals. The HRTF is usually obtained from acoustic measurements on different persons. In the case of discrete data and sets of measurements corresponding to different human subjects, it is desirable to have a continuous functional representation of the HRTF for efficiently rendering moving sounds in the virtual spatial audio systems; further this representation should be well-suited for customization to an individual listener. In this thesis, modal analysis is applied to examine the HRTF data structure, that is to employ the wave equation solutions to expand the HRTF with separable basis functions. This leads to a general representation of the HRTF into separated spatial and spectral components, where the spatial basis functions modes account for the HRTF spatial variations and the remaining HRTF spectral components provide a new means to examine the human body scattering behavior. The general model is further developed into the HRTF continuous functional representations. We use the normalized spatial modes to link near-field and far-field HRTFs directly, which provides a way to obtain the HRTFs at different ranges from measurements conducted at only a single range. The spatially invariant HRTF spectral components are represented continuously using an orthogonal series. Both spatial and spectral basis functions are well known functions, thus the developed analytical model can be used to easily examine the HRTF data feature-individualization. An important finding of this thesis is that the HRTF decomposition with the spatial basis functions can be well approximated by a finite number, which is defined as the HRTF spatial dimensionality. The dimensionality determines the least number of the HRTF measurements in space. We perform high resolution HRTF measurements on a KEMAR mannequin in a semi-anechoic acoustic chamber. Both signal processing aspects to extract HRTFs from the raw measurements and a practical high resolution spatial sampling scheme have been given in this thesis

Design, stability and applications of two dimensional recursive digital filters

Imperial Users onl

Channelization for Multi-Standard Software-Defined Radio Base Stations

As the number of radio standards increase and spectrum resources come under more pressure, it becomes ever less efficient to reserve bands of spectrum for exclusive use by a single radio standard. Therefore, this work focuses on channelization structures compatible with spectrum sharing among multiple wireless standards and dynamic spectrum allocation in particular. A channelizer extracts independent communication channels from a wideband signal, and is one of the most computationally expensive components in a communications receiver. This work specifically focuses on non-uniform channelizers suitable for multi-standard Software-Defined Radio (SDR) base stations in general and public mobile radio base stations in particular. A comprehensive evaluation of non-uniform channelizers (existing and developed during the course of this work) shows that parallel and recombined variants of the Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB) represent the best trade-off between computational load and flexibility for dynamic spectrum allocation. Nevertheless, for base station applications (with many channels) very high filter orders may be required, making the channelizers difficult to physically implement. To mitigate this problem, multi-stage filtering techniques are applied to the GDFT-FB. It is shown that these multi-stage designs can significantly reduce the filter orders and number of operations required by the GDFT-FB. An alternative approach, applying frequency response masking techniques to the GDFT-FB prototype filter design, leads to even bigger reductions in the number of coefficients, but computational load is only reduced for oversampled configurations and then not as much as for the multi-stage designs. Both techniques render the implementation of GDFT-FB based non-uniform channelizers more practical. Finally, channelization solutions for some real-world spectrum sharing use cases are developed before some final physical implementation issues are considered

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

Development of Advanced Mathematical Morphology Algorithms and their Application to the Detection of Disturbances in Power Systems

This thesis is concerned with the development of Mathematical morphology (MM)-based algorithms and their applications to signal processing in power systems, including typical power quality disturbances such as low frequency oscillations (LFO) and harmonics. Traditional morphological operators are extended to advanced ones in the thesis, including multi-resolution morphological gradient (MMG) algorithms, envelope extraction morphological filters (MF), LFO extraction MF and convolved morphological filters (CMF). These advanced morphological operators are applied to the detection and classification of power disturbances, detection of continuous and damped LFO, and the detection and removal of harmonics in power systems

Circuit paradigm in the 21

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