Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection

This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment

Vision-Based Eye Image Classification for Ophthalmic Measurement Systems

: The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light Reflex measurement. To test the implemented solutions, we collected and publicly released PopEYE as one of the first datasets consisting of 15 k eye images belonging to 22 different subjects acquired through the aforementioned specialized ophthalmic device. Finally, we discuss the experimental results in terms of classification accuracy of the eye status, as well as computational load analysis, since the proposed solution is designed to be implemented in embedded boards, which have limited hardware resources in computational power and memory size

Performance analysis of sphere packed aided differential space-time spreading with iterative source-channel detection

The introduction of 5G with excessively high speeds and ever-advancing cellular device capabilities has increased the demand for high data rate wireless multimedia communication. Data compression, transmission robustness and error resilience are introduced to meet the increased demands of high data rates of today. An innovative approach is to come up with a unique setup of source bit codes (SBCs) that ensure the convergence and joint source-channel coding (JSCC) correspondingly results in lower bit error ratio (BER). The soft-bit assisted source and channel codes are optimized jointly for optimum convergence. Source bit codes assisted by iterative detection are used with a rate-1 precoder for performance evaluation of the above mentioned scheme of transmitting sata-partitioned (DP) H.264/AVC frames from source through a narrowband correlated Rayleigh fading channel. A novel approach of using sphere packing (SP) modulation aided differential space time spreading (DSTS) in combination with SBC is designed for the video transmission to cope with channel fading. Furthermore, the effects of SBC with different hamming distances d(H,min) but similar coding rates is explored on objective video quality such as peak signal to noise ratio (PSNR) and also the overall bit error ratio (BER). EXtrinsic Information Transfer Charts (EXIT) are used for analysis of the convergence behavior of SBC and its iterative scheme. Specifically, the experiments exhibit that the proposed scheme of error protection of SBC d(H,min) = 6 outperforms the SBCs having same code rate, but with d(H,min) = 3 by 3 dB with PSNR degradation of 1 dB. Furthermore, simulation results show that a gain of 27 dB Eb/N0 is achieved with SBC having code rate 1/3 compared to the benchmark Rate-1 SBC codes.Web of Science2116art. no. 546

Reconstructing Biological and Digital Phylogenetic Trees in Parallel

In this paper, we study the parallel query complexity of reconstructing biological and digital phylogenetic trees from simple queries involving their nodes. This is motivated from computational biology, data protection, and computer security settings, which can be abstracted in terms of two parties, a responder, Alice, who must correctly answer queries of a given type regarding a degree-d tree, T, and a querier, Bob, who issues batches of queries, with each query in a batch being independent of the others, so as to eventually infer the structure of T. We show that a querier can efficiently reconstruct an n-node degree-d tree, T, with a logarithmic number of rounds and quasilinear number of queries, with high probability, for various types of queries, including relative-distance queries and path queries. Our results are all asymptotically optimal and improve the asymptotic (sequential) query complexity for one of the problems we study. Moreover, through an experimental analysis using both real-world and synthetic data, we provide empirical evidence that our algorithms provide significant parallel speedups while also improving the total query complexities for the problems we study

A Hardware-Based Configurable Algorithm for Eye Blink Signal Detection Using a Single-Channel BCI Headset

Eye blink artifacts in electroencephalographic (EEG) signals have been used in multiple applications as an effective method for human-computer interaction. Hence, an effective and low-cost blinking detection method would be an invaluable aid for the development of this technology. A configurable hardware algorithm, described using hardware description language, for eye blink detection based on EEG signals from a one-channel brain-computer interface (BCI) headset was developed and implemented, showing better performance in terms of effectiveness and detection time than manufacturer-provided software

PRIVIC: A privacy-preserving method for incremental collection of location data

With recent advancements in technology, the threats of privacy violations of individuals' sensitive data are surging. Location data, in particular, have been shown to carry a substantial amount of sensitive information. A standard method to mitigate the privacy risks for location data consists in adding noise to the true values to achieve geo-indistinguishability. However, we argue that geo-indistinguishability alone is not sufficient to cover all privacy concerns. In particular, isolated locations are not sufficiently protected by the state-of-the-art Laplace mechanism (LAP) for geoindistinguishability. In this paper, we focus on a mechanism that can be generated by using the Blahut-Arimoto algorithm (BA) from rate-distortion theory. We show that the BA mechanism, in addition to providing geo-indistinguishability, enforces an elastic metric that mitigates the problem of isolation. We then proceed to study the utility of BA in terms of the precision of the statistics that can be derived from the reported data, focusing on the inference of the original distribution. To this purpose, we apply the iterative Bayesian update (IBU), an instance of the famous expectation-maximization method from statistics, that produces the most likely distribution for any obfuscation mechanism. We show that BA harbours a better statistical utility than LAP for high levels of privacy, and becomes comparable as the level of privacy decreases. Remarkably, we point out a surprising connection, namely that BA and IBU, two apparently unrelated methods that were developed for completely different purposes, are dual to each other. Exploiting this duality and the privacy-preserving properties of BA, we propose an iterative method, PRIVIC, for a privacy-friendly incremental collection of location data from users by service providers. In addition to extending the privacy guarantees of geo-indistinguishability and retaining a better statistical utility than LAP, PRIVIC also provides an optimal trade-off between information leakage and quality of service. We illustrate the soundness and functionality of our method both analytically and with experiments

Secure and Privacy-Preserving Cyber-Physical Systems

RÉSUMÉ Dans cette thèse de doctorat, nous étudions le problème de conception d’estimateur et de commande préservant la confidentialité de données dans un système multi-algent composé de systèmes individuels linéaires incertains ainsi que le problème de conception d’attaques furtives et d’estimateurs résilients aux attaques dans les système cyber-physiques. Les systèmes de surveillance et de commande à grande échelle permettant une infrastructure de plus en plus intelligente s’appuient de plus en plus sur des données sensibles obtenues auprès d’agents privés. Par exemple, ces systèmes collectent des données de localisation d’utilisateurs d’un système de transport intelligent ou des données médicales de patients pour une détection intelligente d’épidémie. Cependant, les considérations de confidentialité peuvent rendre les agents réticents à partager les informations nécessaires pour améliorer les performances d’une infrastructure intelligente. Dans le but d’encourager la participation de ces agents, il s’avère important de concevoir des algorithmes qui traitent les données d’une manière qui preserve leur confidentialité. Durant la première partie de cette thèse, nous considérons des scénarios dans lesquels les systèmes individuels sont indépendants et sont des systèmes linéaires gaussiens. Nous revisitons les problèmes de filtrage de Kalman et de commande linéaire quadratique gaussienne (LQG), sous contraintes de preservation de la confidentialité. Nous aimerions garantir la confidentialité differentielle, une définition formelle et à la pointe de la technologie concernant la confidentialité, et qui garantit que la sortie d’un algorithme ne soit pas trop sensible aux données collectées auprès d’un seul agent. Nous proposons une architecture en deux étapes, qui agrège et combine d’abord les signaux des agents individuels avant d’ajouter du bruit préservant la confidentialité et post-filtrer le résultat à publier. Nous montrons qu’une amélioration significative des performances est offerte par cette architecture par rapport aux architectures standards de perturbations d’entrée à mesure que le nombre de signaux d’entrée augmente. Nous prouvons qu’un pré-filtre optimal d’agrégation statique peut être conçu en résolvant un programme semi-défini. L’architecture en deux étapes, que nous développons d’abord pour le filtrage de Kalman, est ensuite adaptée au problème de commande LQG en exploitant le principe de séparation. A travers des simulations numériques, nous illustrons les améliorations de performance de notre architecture par rapport aux algorithmes de confidentialité différentielle qui n’utilisent pas d’agrégation de signal.----------ABSTRACT This thesis studies the problem of privacy-preserving estimator and control design in a multiagent system composed of uncertain individual linear systems and the problem of design of undetectable attacks and attack-resilient estimators for cyber-physical systems. Largescale monitoring and control systems enabling a more intelligent infrastructure increasingly rely on sensitive data obtained from private agents, e.g., location traces collected from the users of an intelligent transportation system or medical records collected from patients for intelligent health monitoring. Nevertheless, privacy considerations can make agents reluctant to share the information necessary to improve the performance of an intelligent infrastructure. In order to encourage the participation of these agents, it becomes then critical to design algorithms that process information in a privacy-preserving way. The first part of this thesis consider scenarios in which the individual agent systems are linear Gaussian systems and are independent. We revisit the Kalman filtering and Linear Quadratic Gaussian (LQG) control problems, subject to privacy constraints. We aim to enforce differential privacy, a formal, state-of-the-art definition of privacy ensuring that the output of an algorithm is not too sensitive to the data collected from any single participating agent. We propose a twostage architecture, which first aggregates and combines the individual agent signals before adding privacy-preserving noise and post-filtering the result to be published. We show a significant performance improvement offered by this architecture over input perturbation schemes as the number of input signals increases and that an optimal static aggregation stage can be computed by solving a semidefinite program. The two-stage architecture, which we develop first for Kalman filtering, is then adapted to the LQG control problem by leveraging the separation principle. We provide numerical simulations that illustrate the performance improvements over differentially private algorithms without first-stage signal aggregation. The second part of this thesis considers the problem of privacy-preserving estimator design for a multi-agent system composed of individual linear time-invariant systems affected by uncertainties whose statistical properties are not available. Only bounds are given a priori for these uncertainties. We propose a privacy-preserving interval estimator architecture, which releases publicly estimates of lower and upper bounds for an aggregate of the states of the individual systems. Particularly, we add a bounded privacy-preserving noise to each participant’s data before sending it to the estimator. The estimates published by the observer guarantee differential privacy for the agents’ data. We provide a numerical simulation that illustrates the behavior of the proposed architecture

Fuzzy-Based Distributed Cooperative Secondary Control with Stability Analysis for Microgrids

This research suggests a novel distributed cooperative control methodology for a secondary controller in islanded microgrids (MGs). The proposed control technique not only brings back the frequency/voltage to its reference values, but also maintains precise active and reactive power-sharing among distributed generation (DG) units by means of a sparse communication system. Due to the dynamic behaviour of distributed secondary control (DSC), stability issues are a great concern for a networked MG. To address this issue, the stability analysis is undertaken systematically, utilizing the small-signal state-space linearized model of considering DSC loops and parameters. As the dynamic behaviour of DSC creates new oscillatory modes, an intelligent fuzzy logic-based parameter-tuner is proposed for enhancing the system stability. Accurate tuning of the DSC parameters can develop the functioning of the control system, which increases MG stability to a greater extent. Moreover, the performance of the offered control method is proved by conducting a widespread simulation considering several case scenarios in MATLAB/Simscape platform. The proposed control method addresses the dynamic nature of the MG by supporting the plug-and-play functionality, and working even in fault conditions. Finally, the convergence and comparison study of the offered control system is shown