Cyclostationary error analysis and filter properties in a 3D wavelet coding framework

The reconstruction error due to quantization of wavelet subbands can be modeled as a cyclostationary process because of the linear periodically shift variant property of the inverse wavelet transform. For N-dimensional data, N-dimensional reconstruction error power cyclostationary patterns replicate on the data sample lattice. For audio and image coding applications this fact is of little practical interest since the decoded data is perceived in its wholeness, the error power oscillations on single data elements cannot be seen or heard and a global PSNR error measure is often used to represent the reconstruction quality. A different situation is the one of 3D data (static volumes or video sequences) coding, where decoded data are usually visualized by plane sections and the reconstruction error power is commonly measured by a PSNR[n] sequence, with n representing either a spatial slicing plane (for volumetric data) or the temporal reference frame (for video). In this case, the cyclostationary oscillations on single data elements lead to a global PSNR[n] oscillation and this effect may become a relevant concern. In this paper we study and describe the above phenomena and evaluate their relevance in concrete coding applications. Our analysis is entirely carried out in the original signal domain and can easily be extended to more than three dimensions. We associate the oscillation pattern with the wavelet filter properties in a polyphase framework and we show that a substantial reduction of the oscillation amplitudes can be achieved under a proper selection of the basis functions. Our quantitative model is initially made under high-resolution conditions and then qualitatively extended to all coding rates for the wide family of bit-plane quantization-based coding techniques. Finally, we experimentally validate the proposed models and we perform a subjective evaluation of the visual relevance of the PSNR[n] fluctuations in the cases of medical volumes and video coding

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Study of the cyclostationarity properties of various signals of opportunity

Global Navigation Satellite Systems (GNSS) offer precise position estimation and navigation services outdoor but they are rarely accessible in strong multipath environments, such as indoor environments. Fortunately, several Signals of Opportunity (SoO), (such as RFID, Wi-Fi, Bluetooth, digital TV signals, etc.) are readily available in these environments, creating an opportunity for seamless positioning. Performance evolution of positioning can be achieved through contextual exploitation of SoO. The detection and identification of available SoO signals or of the signals which are most relevant to localization and the signal selection in an optimum way, according to designer defined optimality criteria, are important stages to enter such contextual awareness domain. Man-made modulated signals have certain properties which vary periodically in time and this time-varying periodical characteristics trigger what is known as cyclostationarity. Cyclostationarity analysis can be used, among others, as a tool for signal detection. Detected signals through cyclostationary features can be exploited as SoO. The main purpose of this thesis is to study and analyze the cyclostationarity properties of various SoO. An additional goal is to investigate whether such cyclostationarity properties can be used to detect, identify and distinguish the signals which are present in a certain frequency band. The thesis is divided into two parts. In the literature review part, the physical layer study of several signals is given, by emphasizing the potential of SoO in positioning. In the implementation part, the possibility of signals detection through cyclostationary features is investigated through MATLAB simulations. Cyclostationary properties obtained through FFT accumulation Method (FAM) and statistical performance of detection are studied in the presence of stationary additive white Gaussian noise (AWGN). Besides that, the performance in signal detection using cyclostationary-based detector is also compared to the performance with the energy-based detectors, used as benchmarks. The simulated result suggest that cyclostationary features can certainly detect the presence of signals in noise, but simple cases, such as one type of signal only and AWGN noise, are better addressed via traditional energy-based detection. However, cyclostationary features can exhibit advantages in other types of noises and in the presence of signal mixtures which in fact may fulfil one of the preliminary requirements of cognitive positioning

PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach

PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell Transform (ST) with dual-resolution which has been proposed and validated in this work as part of spectrum sensing techniques. Afterwards, analysis of the state-of-the-art about learning dynamic models from observed features describes theoretical aspects of Machine Learning (ML). In particular, following the recent advances of ML, learning deep generative models with several layers of non-linear processing has been selected as AI method for the proposed spectrum abnormality detection in CR for a brain-inspired, data-driven SA. In the proposed approach, the features extracted from the ST representation of the wideband spectrum are organized in a high-dimensional generalized state vector and, then, a generative model is learned and employed to detect any deviation from normal situations in the analysed spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN), auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative models. A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency) with 800 MHz frequency range. Training of the deep generative model is performed on the generalized state vector representing the mmWave spectrum with normality pattern without any malicious activity. Testing is based on new and independent data samples corresponding to abnormality pattern where the moving signal follows a different behaviour which has not been observed during training. An abnormality indicator is measured and used for the binary classification (normality hypothesis otherwise abnormality hypothesis), while the performance of the generative models is evaluated and compared through ROC curves and accuracy metrics

Compression of 4D medical image and spatial segmentation using deformable models

Ph.DDOCTOR OF PHILOSOPH

Digital signal processing for sensing in software defined optical networks

Optical networks are moving from static point-to-point to dynamic configurations, where transmitter parameters are adaptively changing to meet traffic demands. Dynamic network reconfigurability is achievable through software-defined transceivers, capable of changing the data-rate, overhead, modulation format and reach. Additionally, flexibility in the spectral allocation of channels ensures that the available resources are efficiently distributed, as the increase in fibre capacity has reached a halt. The complexity of such highly reconfigurable systems and cost of their maintenance increase exponentially. Implemented as part of digital signal processing of coherent receivers, sensing is an enabling technology for future software defined optical networks, as it makes possible to both control and optimise transmission parameters, as well as to manage faulty links and mitigate channel impairments in a cost-effective manner. Symbol-rate is one of the parameters most likely to adaptively change according to existing fibre impairments, such as optical signal-to-noise ratio or chromatic dispersion. A single-channel symbol-rate estimation technique is demonstrated initially, yielding a sufficient accuracy to distinguish between different typical error-correction overheads, in the presence of dispersion and white Gaussian noise. Further increasing the capacity over fibre to 1 Tb/s and beyond means moving towards superchannel configurations that employ Nyquist pulse shaping to increase spectral efficiency. Novel sensing techniques applicable to such information dense configurations, that can jointly monitor the channel bandwidth, frequency offset, optical signal-to-noise ratio and chromatic dispersion are proposed and demonstrated herein. Based on time-domain and frequency-domain functions derived from the theory of cyclostationarity, the performance of this joint estimator is investigated with respect to a wide range of parameters. The required acquisition time of the receiver is approximately 6.55 μs, three orders of magnitude faster compared to the round-trip time in core networks. The pulse shaping at the transmitter limits the performance of this estimator, unless the excess bandwidth is 30% of the symbol-rate, or more

Audio-based signal extraction techniques for stamping tool condition monitoring

This thesis developed blind signal separation techniques to extract wear related information from the signal mixtures. Extracted signal analysis demonstrated that there is a significant qualitative association between the emitted audio and the wear progression of sheet metal stamping tools and this is the first study that identifies such correlation.<br /