A Novel Probability-based Data Clustering Application for Detecting Elongated Clusters with Application to the Line Detection Problem

Ένα σημαντικό πρόβλημα που απαντάται σε διάφορα πεδία εφαρμογών, όπως η ανάλυση γεωχωρικών δεδομένων (geospatial data analysis), η συμπίεση εικόνας (image compression) και η εξαγωγή δρόμων (road extraction), μεταξύ άλλων παραδειγμάτων, είναι αυτό της ανίχνευσης ευθείων γρμμών ή ευθυγράμμων τμημάτων σε μια δεδομένη εικόνα. Στη διατριβή αυτή, προτείνεται μια νέα προσέγγιση στο παραπάνω πρόβλημα, η οποία βασίζεται στην πιθανοτική ομαδοποίηση (probabilistic clustering). Πιο συγκεκριμένα, ορίζεται μια νέα κατανομή πυκνότητας πιθανότητας η οποία αποτελεί παραλλαγή της Γκαουσσιανής κατανομής πιθανοτήτων (Gaussian probability distribution), στην οποία το κέντρο της δεν είναι πλέον σημείο, αλλά ένα ευθύγραμμο τμήμα, με στόχο τη μοντελοποίηση ευθυγράμμων τμημάτων. Στη συνέχεια, το σύνολο των δεδομένων σημείων θεωρείται ότι προέρχεται από μία κατανομή που εκφράζεται ως ένα σταθμισμένο άθροισμα (μίξη)επιμέρους κατανομών και ο στόχος είναι ο προσδιορισμός αυτών των κατανομών, κάθε μία από τις οποίες μοντελοποιεί και μια (γραμμική) συστάδα (linear cluster). Προτείνεται ένας αγόριθμος, ο οποίος ονομάζεται Αγλόριθμος Πιθανοτικής Συσταδοποίησης Ευθυγράμμων Τμημάτων (Probabilistic Line Segment Clustering algorithm – PLSC) και ακολουθεί τη λογική της Αποδόμησης Μίξης (Mixture Decomposition). Η διαδικασία εύρεσης βέλτιστων τοποθετήσεων των ευθυγράμμων τμημάτων (κέντρων των κατανομών πιθανοτήτων) φέρεται εις πέρας μέσω μιας επαναληπτικής διαδικασίας παρόμοιας του αλγορίθμου Αναμενόμενης Τιμής Βελτιστοποίησης (Expectation Maximization), κατά την οποία, τα τμήματα μετακινούνται σταδιακά με σκοπό να ταιριάξουν στις γραμμικές ομάδες που σχηματίζονται από τα δεδομένα, βάσει ενός ευρετικού κανόνα (heuristic rule). Ο αλγόριθμος δεν απαιτεί εκ των προτέρων γνώση του αριθμού των συστάδων. Αντί αυτού, ξεκινά κάνοντας μiα υπερεκτίμηση του πλήθους τους και σταδιακά τις μειώνει μέσω κατάλληλων μηχανισμών απαλοιφής και συνένωσης. Με σκοπό την τεκμηρίωση της αξίας της προτεινώμενης μεθόδου, διεξήχθησαν αρκετά πειράματα, τα αποτελέσματα των οποίων δείχνουν ότι η τρέχουσα μέθοδος είναι ικανή να αναγνωρίσει σε πολύ ικανοποιητικό βαθμό συστάδες τόσο σε απλούστερες όσο και σε πολυπλοκότερες περιπτώσεις. Ο αλγόριθμος μπορεί να αποδώσει παρόμοια και, σε μερικές περιπτώσεις, καλύτερα απολέσματα συγκρινόμενος με ένα επιλεγμένο πλήθος σχετικών δημοσιευμένων μεθόδων.Line detection is the process of identifying straight lines or line segments in a given image. Potential applications are commonly found in a variety of fields, such as analysis of geospatial data, image compression and road extraction, to name a few. In this dissertation an approach to the above problem based on probabilistic clustering is explored. A variation of the Gaussian probability distribution centered around a line segment is defined accordingly for the two dimensional space in order to model the line segments in the image under study and an algorithm, called Probabilistic Line Segment Clustering (PLSC) that follows the Mixture Decomposition approach is proposed. The process of finding the optimal positioning of the line segments is carried out by an iterative ExpectationMaximizationlike procedure in which the segments are gradually moved in order to fit the actual edges of the image using a heuristic rule. In order to find the appropriate number of segments/clusters, the algorithm starts with an overestimation of it and progressively reduces it via appropriate elimination and unification mechanisms. Toward supporting the value of the proposed method, experimental results have been carried out and discussed in which it is shown that the current method is able to appropriately identify clusters in multiple scenarios. The algorithm can perform mostly comparably and in some cases, even favorably with regard to a selection of relevant published methods

Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas

User localization and tracking in the upcoming generation of wireless networks have the potential to be revolutionized by technologies such as the Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or require pilot transmission sequences whose length is comparable to the number of DMA elements, thus, leading to limited effectiveness and considerable measurement overheads in blocked LoS and dynamic multipath environments. In this paper, we present a two-stage machine-learning-based approach for user tracking, specifically designed for non-LoS multipath settings. A newly proposed attention-based Neural Network (NN) is first trained to map noisy channel responses to potential user positions, regardless of user mobility patterns. This architecture constitutes a modification of the prominent vision transformer, specifically modified for extracting information from high-dimensional frequency response signals. As a second stage, the NN's predictions for the past user positions are passed through a learnable autoregressive model to exploit the time-correlated channel information and obtain the final position predictions. The channel estimation procedure leverages a DMA receive architecture with partially-connected radio frequency chains, which results to reduced numbers of pilots. The numerical evaluation over an outdoor ray-tracing scenario illustrates that despite LoS blockage, this methodology is capable of achieving high position accuracy across various multipath settings.Comment: 5 pages, 3 figures, accepted for presentation by 2023 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2023

A Framework for Control Channels Applied to Reconfigurable Intelligent Surfaces

The research on Reconfigurable Intelligent Surfaces (RISs) has dominantly been focused on physical-layer aspects and analyses of the achievable adaptation of the propagation environment. Compared to that, the questions related to link/MAC protocol and system-level integration of RISs have received much less attention. This paper addresses the problem of designing and analyzing control/signaling procedures, which are necessary for the integration of RISs as a new type of network element within the overall wireless infrastructure. We build a general model for designing control channels along two dimensions: i) allocated bandwidth (in-band and out-of band) and ii) rate selection (multiplexing or diversity). Specifically, the second dimension results in two transmission schemes, one based on channel estimation and the subsequent adapted RIS configuration, while the other is based on sweeping through predefined RIS phase profiles. The paper analyzes the performance of the control channel in multiple communication setups, obtained as combinations of the aforementioned dimensions. While necessarily simplified, our analysis reveals the basic trade-offs in designing control channels and the associated communication algorithms. Perhaps the main value of this work is to serve as a framework for subsequent design and analysis of various system-level aspects related to the RIS technology.Comment: Submitted to IEEE TWC, the copyright may be transferred without further notic

The decline of vultures in India

The vulture decline was first noticed in 1999 (Prakash, 1999 as cited in Prakash,et al, 2003), but actions to reverse the situation could not be taken before the cause was identified. In the mean time, the number of vultures was decreasing at an annual rate of 43.9% for G. bengalensis and 16.1% for G. indicus and G. tenuistrosis (Gilbert, et al., 2002; Prakash, et al., 2003; Green, et al., 2004; Prakash, et al., 2007). Post-mortem analysis showed that vultures were dying because of the veterinary drug diclofenac (Green, et al., 2004; Oaks, et al., 2004; Shultz, et al., 2004), a non-steroidal anti- inflammatory (NSAID) drug administered to cattle. When vultures feed on cattle carcasses that have been recently treated with diclofenac, they suffer kidney failure and die (Green et al., 2004; Oaks et al., 2004; Shultz et al., 2004). Although the situation was already critical for the three vulture species, it wasn‟t until May 2006 that the Indian Government banned diclofenac (Taggart, et al., 2007). Unfortunately, recent research findings show that the drug is still widely available (O'Driscoll, 2008). In order to restore the populations of G. bengalensis, G. indicus and G. tenuirostris, three conservation breeding centers have been established, but more are required because of the low reproductive rate of Gyps and the continuing decline in their wild populations (Markandya, et al., 2008). A number of ecological, social and economic costs are associated with the decline of Indian vultures. The removal from the ecosystem of the most effective and highly specialized scavenger means that carcasses remain longer out in the open. Rotting carcasses are a major source of contamination for humans (Markandya, et al., 2008). Moreover, vultures are culturally important for Hindus, but mostly for Parsees, a small community of Zoroastrians that came to India around 1,000 AD. Parsees, in order to avoid contaminating the elements, rely on vultures to remove the flesh from the bodies of their dead (Boyce, 1979). Although vultures are a keystone species in nature, their catastrophic decline has received little attention both in India and internationally. Richard Cuthbert of the Royal Society for the Protection of Birds (RSPB) said: 1 ...Everyone interested in conservation, quite rightly knows about the plight of India's tigers, but in the race towards extinction the vultures will get there far sooner! (“RSPB”, 2009) The lack of media attention means that little money is directed towards vulture conservation. Unfortunately, the fact that they feed on decaying flesh might make them unpopular with some people so vultures are not an easy subject for animal campaigners. This „injustice‟ against a species, whose decline has serious health, cultural and economic implications in India, was the reason for Siddharth Nambiar and the author decide to make the documentary The Fall of Jataayu. The aim of the film is not only to inform the public about the catastrophic plight of vultures and the tremendous consequences on humans, but also to become a means of generating action leading to vulture conservation. In this thesis, I document the vulture decline in India and I argue that a film, like The Fall of Jatayuu, has the potential to bring positive action, and could become an invaluable tool for scientists and activists. I will provide evidence of how documentaries have been used throughout history to influence public attitudes and evaluate their impact

The decline of vultures in India

The vulture decline was first noticed in 1999 (Prakash, 1999 as cited in Prakash,et al, 2003), but actions to reverse the situation could not be taken before the cause was identified. In the mean time, the number of vultures was decreasing at an annual rate of 43.9% for G. bengalensis and 16.1% for G. indicus and G. tenuistrosis (Gilbert, et al., 2002; Prakash, et al., 2003; Green, et al., 2004; Prakash, et al., 2007). Post-mortem analysis showed that vultures were dying because of the veterinary drug diclofenac (Green, et al., 2004; Oaks, et al., 2004; Shultz, et al., 2004), a non-steroidal anti- inflammatory (NSAID) drug administered to cattle. When vultures feed on cattle carcasses that have been recently treated with diclofenac, they suffer kidney failure and die (Green et al., 2004; Oaks et al., 2004; Shultz et al., 2004). Although the situation was already critical for the three vulture species, it wasn‟t until May 2006 that the Indian Government banned diclofenac (Taggart, et al., 2007). Unfortunately, recent research findings show that the drug is still widely available (O'Driscoll, 2008). In order to restore the populations of G. bengalensis, G. indicus and G. tenuirostris, three conservation breeding centers have been established, but more are required because of the low reproductive rate of Gyps and the continuing decline in their wild populations (Markandya, et al., 2008). A number of ecological, social and economic costs are associated with the decline of Indian vultures. The removal from the ecosystem of the most effective and highly specialized scavenger means that carcasses remain longer out in the open. Rotting carcasses are a major source of contamination for humans (Markandya, et al., 2008). Moreover, vultures are culturally important for Hindus, but mostly for Parsees, a small community of Zoroastrians that came to India around 1,000 AD. Parsees, in order to avoid contaminating the elements, rely on vultures to remove the flesh from the bodies of their dead (Boyce, 1979). Although vultures are a keystone species in nature, their catastrophic decline has received little attention both in India and internationally. Richard Cuthbert of the Royal Society for the Protection of Birds (RSPB) said: 1 ...Everyone interested in conservation, quite rightly knows about the plight of India's tigers, but in the race towards extinction the vultures will get there far sooner! (“RSPB”, 2009) The lack of media attention means that little money is directed towards vulture conservation. Unfortunately, the fact that they feed on decaying flesh might make them unpopular with some people so vultures are not an easy subject for animal campaigners. This „injustice‟ against a species, whose decline has serious health, cultural and economic implications in India, was the reason for Siddharth Nambiar and the author decide to make the documentary The Fall of Jataayu. The aim of the film is not only to inform the public about the catastrophic plight of vultures and the tremendous consequences on humans, but also to become a means of generating action leading to vulture conservation. In this thesis, I document the vulture decline in India and I argue that a film, like The Fall of Jatayuu, has the potential to bring positive action, and could become an invaluable tool for scientists and activists. I will provide evidence of how documentaries have been used throughout history to influence public attitudes and evaluate their impact

A Probabilistic Clustering Approach for Detecting Linear Structures in Two-Dimensional Spaces

In this work, a novel probabilistic clustering algorithm suitable for the identification of linear elements in datasets containing linear-shaped clusters is proposed. The algorithm is an expectation-maximization-like procedure applied on a mixture of probability density functions, each one modeling a line segment. To that end, a suitable two-dimensional distribution is defined that models points that are spread around a line segment and is parameterized by the segment endpoints. An elaborate initialization process causes the algorithm to start with an overestimate of the number of the actual clusters (segments) formed by the data points. The clusters are gradually removed through the utilization of suitable merging and elimination mechanisms until the actual clusters are identified. The update of the parameters of the line segments at each iteration results from a least squares fitting procedure. The method is presented in the context of line segment detection problems in digital images whose pixels form straight lines or elongated objects, although it can be utilized in other relevant contexts. Experimental evaluation shows that the proposed approach compares equally well or outperforms relevant state-of-the-art clustering-based and traditional line detection approaches

Deep-Learning-Assisted Configuration of Reconfigurable Intelligent Surfaces in Dynamic Rich-Scattering Environments

International audienceThe integration of Reconfigurable Intelligent Surfaces (RISs) into wireless environments endows channels with programmability, and is expected to play a key role in future communication standards. To date, most RIS-related efforts focus on quasi-free-space, where wireless channels are typically modeled analytically. Many realistic communication scenarios occur, however, in rich-scattering environments which, moreover, evolve dynamically. These conditions present a tremendous challenge in identifying an RIS configuration that optimizes the achievable communication rate. In this paper, we make a first step toward tackling this challenge. Based on a simulator that is faithful to the underlying wave physics, we train a deep neural network as surrogate forward model to capture the stochastic dependence of wireless channels on the RIS configuration under dynamic rich-scattering conditions. Subsequently, we use this model in combination with a genetic algorithm to identify RIS configurations optimizing the communication rate. We numerically demonstrate the ability of the proposed approach to tune RISs to improve the achievable rate in rich-scattering setups

Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces

In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system. Building on the marriage between Lyapunov stochastic optimization and deep reinforcement learning (DRL), we devise a dynamic learning algorithm that jointly optimizes the data compression scheme, the allocation of radio resources (i.e., power, transmission precoding), the computation resources (i.e., CPU cycles), and the RIS reflectivity parameters (i.e., phase shifts), with the aim of performing energy-efficient edge classification with end-to-end (E2E) delay and inference accuracy constraints. The proposed strategy enables dynamic control of the system and of the wireless propagation environment, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. Numerical results assess the performance of the proposed RIS-empowered edge inference strategy in terms of trade-off between energy, delay, and accuracy of a classification task

Deep contextual bandits for orchestrating multi-user MISO systems with multiple RISs

Abstract The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from the field of Deep Reinforcement Learning (DRL) have recently gained popularity in maximizing the sum-rate performance in multi-user communication systems empowered by RISs. Such approaches are commonly based on Markov Decision Processes (MDPs). In this paper, we instead investigate the sum-rate design problem under the scope of the Multi-Armed Bandits (MAB) setting, which is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB formulation is more appropriate to the channel and system models under the assumptions typically made in the RIS literature. To this end, we propose a simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered multi-user Multiple-Input Single-Output (MISO) systems, which we numerically show to perform equally well with a state-of-the-art MDP-based approach, while being less demanding computationally