Advanced Statistical Signal Processing Methods in Sensing, Detection, and Estimation for Communication Applications

The applications of wireless communications and digital signal processing have dramatically changed the way we live, work, and learn over decades. The requirement of higher throughput and ubiquitous connectivity for wireless communication systems has become prevalent nowadays. Signal sensing, detection and estimation have been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal sensing, detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal sensing, detection and estimation schemes for wireless communications applications, such as spectrum sensing, symbol-detection/channel-estimation, and encoder identification. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Classification Based on Euclidean Distance Distribution for Blind Identification of Error Correcting Codes in Noncooperative Contexts

International audienc

Machine learning for outlier detection in medical imaging

Outlier detection is an important problem with diverse practical applications. In medical imaging, there are many diagnostic tasks that can be framed as outlier detection. Since pathologies can manifest in so many different ways, the goal is typically to learn from normal, healthy data and identify any deviations. Unfortunately, many outliers in the medical domain can be subtle and specific, making them difficult to detect without labelled examples. This thesis analyzes some of the nuances of medical data and the value of labels in this context. It goes on to propose several strategies for unsupervised learning. More specifically, these methods are designed to learn discriminative features from data of a single class. One approach uses divergent search to continually find different ways to partition the data and thereby accumulates a repertoire of features. The other proposed methods are based on a self-supervised task that distorts normal data to form a contrasting class. A network can then be trained to localize the irregularities and estimate the degree of foreign interference. This basic technique is further enhanced using advanced image editing to create more natural irregularities. Lastly, the same self-supervised task is repurposed for few-shot learning to create a framework for adaptive outlier detection. These proposed methods are able to outperform conventional strategies across a range of datasets including brain MRI, abdominal CT, chest X-ray, and fetal ultrasound data. In particular, these methods excel at detecting more subtle irregularities. This complements existing methods and aims to maximize benefit to clinicians by detecting fine-grained anomalies that can otherwise require intense scrutiny. Note that all approaches to outlier detection must accept some assumptions; these will affect which types of outliers can be detected. As such, these methods aim for broad generalization within the most medically relevant categories. Ultimately, the hope is to support clinicians and to focus their attention and efforts on the data that warrants further analysis.Open Acces

New Coding/Decoding Techniques for Wireless Communication Systems

Wireless communication encompasses cellular telephony systems (mobile communication), wireless sensor networks, satellite communication systems and many other applications. Studies relevant to wireless communication deal with maintaining reliable and efficient exchange of information between the transmitter and receiver over a wireless channel. The most practical approach to facilitate reliable communication is using channel coding. In this dissertation we propose novel coding and decoding approaches for practical wireless systems. These approaches include variable-rate convolutional encoder, modified turbo decoder for local content in Single-Frequency Networks, and blind encoder parameter estimation for turbo codes. On the other hand, energy efficiency is major performance issue in wireless sensor networks. In this dissertation, we propose a novel hexagonal-tessellation based clustering and cluster-head selection scheme to maximize the lifetime of a wireless sensor network. For each proposed approach, the system performance evaluation is also provided. In this dissertation the reliability performance is expressed in terms of bit-error-rate (BER), and the energy efficiency is expressed in terms of network lifetime

Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning

The paper introduces the application of information geometry to describe the ground states of Ising models by utilizing parity-check matrices of cyclic and quasi-cyclic codes on toric and spherical topologies. The approach establishes a connection between machine learning and error-correcting coding. This proposed approach has implications for the development of new embedding methods based on trapping sets. Statistical physics and number geometry applied for optimize error-correcting codes, leading to these embedding and sparse factorization methods. The paper establishes a direct connection between DNN architecture and error-correcting coding by demonstrating how state-of-the-art architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range arena can be equivalent to of block and convolutional LDPC codes (Cage-graph, Repeat Accumulate). QC codes correspond to certain types of chemical elements, with the carbon element being represented by the mixed automorphism Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix are elaborated upon in detail. The Quantum Approximate Optimization Algorithm (QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous to the back-propagation loss function landscape in training DNNs. This similarity creates a comparable problem with TS pseudo-codeword, resembling the belief propagation method. Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree. Overall, this work has the potential to advance multiple fields, from Information Theory, DNN architecture design (sparse and structured prior graph topology), efficient hardware design for Quantum and Classical DPU/TPU (graph, quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text overlap with arXiv:2109.08184 by other author