695 research outputs found
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Acoustic localization of people in reverberant environments using deep learning techniques
La localización de las personas a partir de información acústica es cada vez más importante en aplicaciones del mundo real como la seguridad, la vigilancia y la interacción entre personas y robots. En muchos casos, es necesario localizar con precisión personas u objetos en función del sonido que generan, especialmente en entornos ruidosos y reverberantes en los que los métodos de localización tradicionales pueden fallar, o en escenarios en los que los métodos basados en análisis de vídeo no son factibles por no disponer de ese tipo de sensores o por la existencia de oclusiones relevantes. Por ejemplo, en seguridad y vigilancia, la capacidad de localizar con precisión una fuente de sonido puede ayudar a identificar posibles amenazas o intrusos. En entornos sanitarios, la localización acústica puede utilizarse para controlar los movimientos y actividades de los pacientes, especialmente los que tienen problemas de movilidad. En la interacción entre personas y robots, los robots equipados con capacidades de localización acústica pueden percibir y responder mejor a su entorno, lo que permite interacciones más naturales e intuitivas con los humanos. Por lo tanto, el desarrollo de sistemas de localización acústica precisos y robustos utilizando técnicas avanzadas como el aprendizaje profundo es de gran importancia práctica. Es por esto que en esta tesis doctoral se aborda dicho problema en tres líneas de investigación fundamentales: (i) El diseño de un sistema extremo a extremo (end-to-end) basado en redes neuronales capaz de mejorar las tasas de localización de sistemas ya existentes en el estado del arte. (ii) El diseño de un sistema capaz de localizar a uno o varios hablantes simultáneos en entornos con características y con geometrías de arrays de sensores diferentes sin necesidad de re-entrenar. (iii) El diseño de sistemas capaces de refinar los mapas de potencia acústica necesarios para localizar a las fuentes acústicas para conseguir una mejor localización posterior. A la hora de evaluar la consecución de dichos objetivos se han utilizado diversas bases de datos realistas con características diferentes, donde las personas involucradas en las escenas pueden actuar sin ningún tipo de restricción. Todos los sistemas propuestos han sido evaluados bajo las mismas condiciones consiguiendo superar en términos de error de localización a los sistemas actuales del estado del arte
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)
This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023
V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation
In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in
5G vehicular networks through a comprehensive end-to-end methodology
encompassing ray-tracing-based channel modeling, novel theoretical performance
bounds, high-resolution channel parameter estimation, and geometric positioning
using a round-trip-time (RTT) protocol. We first derive a novel, approximate
Cram\'er-Rao bound (CRB) on the connected road user (CRU) position, explicitly
taking into account multipath interference, path merging, and the RTT protocol.
Capitalizing on tensor decomposition and ESPRIT methods, we propose
high-resolution channel parameter estimation algorithms specifically tailored
to dense multipath V2X sidelink environments, designed to detect multipath
components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using
realistic ray-tracing data and antenna patterns, comprehensive simulations are
conducted to evaluate channel estimation and positioning performance,
indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the
proposed algorithms
The LISA Data Challenge Radler Analysis and Time-dependent Ultra-compact Binary Catalogues
Context. Galactic binaries account for the loudest combined continuous
gravitational wave signal in the Laser Interferometer Space Antenna (LISA)
band, which spans a frequency range of 0.1 mHz to 1 Hz.
Aims. A superposition of low frequency Galactic and extragalactic signals and
instrument noise comprise the LISA data stream. Resolving as many Galactic
binary signals as possible and characterising the unresolved Galactic
foreground noise after their subtraction from the data are a necessary step
towards a global fit solution to the LISA data. Methods. We analyse a simulated
gravitational wave time series of tens of millions of ultra-compact Galactic
binaries hundreds of thousands of years from merger. This data set is called
the Radler Galaxy and is part of the LISA Data challenges. We use a Markov
Chain Monte Carlo search pipeline specifically designed to perform a global fit
to the Galactic binaries and detector noise. Our analysis is performed for
increasingly larger observation times of 1.5, 3, 6 and 12 months.
Results. We show that after one year of observing, as many as ten thousand
ultra-compact binary signals are individually resolvable. Ultra-compact binary
catalogues corresponding to each observation time are presented. The Radler
Galaxy is a training data set, with binary parameters for every signal in the
data stream included. We compare our derived catalogues to the LISA Data
challenge Radler catalogue to quantify the detection efficiency of the search
pipeline. Included in the appendix is a more detailed analysis of two corner
cases that provide insight into future improvements to our search pipeline
Single Hydrophone Underwater Localization Approach in Sallow Waters
Applications of underwater signal processing are essential for environmental monitoring. Remote monitoring and passive sound source localization in an underwater environment can provide great insight into geological studies, environmental changes and marine lives monitoring. While various methods are available for Localization, they mostly employ arrays of hydrophones, requiring synchronization or prior knowledge of the source signals, which can prove costly, complicated, and hard to maintain. Remote monitoring applications require very high-range passive localization methods; and, given the frequency-selective nature of ambient noise and other channel parameters, current localization methods have short-distance range estimation or high localization error for long distances. The modal analysis makes it possible to study and localize sounds propagated over long distances using one passive hydrophone without a need for prior knowledge of the source or synchronization. This dissertation presents four new stand-alone multi/single hydrophone localization algorithms based on modal dispersion analysis to localize impulsive sound sources in a noisy shallow water environment. The first algorithm is named as selective multi-modal pair (SMP), enables utilizing modals with any wavenumbers as opposed to previously proposed methods based on only on the modes with sequential wavenumbers. The algorithm extracts the dispersion curves of the received signal to be compared against the dispersion curves computed using a custom channel. then chooses the most effective modes (that result in the lowest localization error) , estimated the range of a sound source. The resulting estimated range is the range that makes the best match between the selected modal dispersion curves and the estimated dispersion curves. Numerical results, using both simulated and actual recorded sounds of whale and underwater explosion show that the proposed algorithm can localize underwater sounds with high accuracy when the signal-to-noise ratio varies from 28dB to 45dB. The second Localization algorithm is named selective weighted genetic algorithm (SW-GA). This algorithm employs two weighting functions based on geolocation information of the source and a selective scaling function for the selection of the most noise resistive modal pairs. The proposed weighted localization scaling and selection functions are designed to ensure convergence towards the correct range estimation. We analyzed and compared this algorithm using the same signals and SNR scenarios as before and shows a better2D localization performance and noise resistivity compared with previously proposed methods. The third and fourth algorithms, named Weighted Multi-Modal (WMM)and Multi-Modal (MM) employs all available modal pairs instead of just a few or a sequential selection. They compute a modal contribution matrix based on all available modal pairs with common frequencies. Furthermore, the weighted version of the algorithm employs the contribution matrix for assigning weights based on contribution/noise resistivity to all modal pairs. Employing all available modes results in an algorithm capable of localizing signals with higher frequencies and the weighting function increase accuracy in low SNR environments. The noise performance analysis of both the WMM algorithm; and the non-weighted version yields considerable improvements in localization of sound sources in the presence of high level of ambient noise over other algorithms used in this work
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