300 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
Contributions to improve the technologies supporting unmanned aircraft operations
Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge.
Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential.
On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle.
This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies
the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir.
Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio.
Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav
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
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Adaptive Intelligent Systems for Extreme Environments
As embedded processors become powerful, a growing number of embedded systems equipped with artificial intelligence (AI) algorithms have been used in radiation environments to perform routine tasks to reduce radiation risk for human workers. On the one hand, because of the low price, commercial-off-the-shelf devices and components are becoming increasingly popular to make such tasks more affordable. Meanwhile, it also presents new challenges to improve radiation tolerance, the capability to conduct multiple AI tasks and deliver the power efficiency of the embedded systems in harsh environments. There are three aspects of research work that have been completed in this thesis: 1) a fast simulation method for analysis of single event effect (SEE) in integrated circuits, 2) a self-refresh scheme to detect and correct bit-flips in random access memory (RAM), and 3) a hardware AI system with dynamic hardware accelerators and AI models for increasing flexibility and efficiency.
The variances of the physical parameters in practical implementation, such as the nature of the particle, linear energy transfer and circuit characteristics, may have a large impact on the final simulation accuracy, which will significantly increase the complexity and cost in the workflow of the transistor level simulation for large-scale circuits. It makes it difficult to conduct SEE simulations for large-scale circuits. Therefore, in the first research work, a new SEE simulation scheme is proposed, to offer a fast and cost-efficient method to evaluate and compare the performance of large-scale circuits which subject to the effects of radiation particles. The advantages of transistor and hardware description language (HDL) simulations are combined here to produce accurate SEE digital error models for rapid error analysis in large-scale circuits. Under the proposed scheme, time-consuming back-end steps are skipped. The SEE analysis for large-scale circuits can be completed in just few hours.
In high-radiation environments, bit-flips in RAMs can not only occur but may also be accumulated. However, the typical error mitigation methods can not handle high error rates with low hardware costs. In the second work, an adaptive scheme combined with correcting codes and refreshing techniques is proposed, to correct errors and mitigate error accumulation in extreme radiation environments. This scheme is proposed to continuously refresh the data in RAMs so that errors can not be accumulated. Furthermore, because the proposed design can share the same ports with the user module without changing the timing sequence, it thus can be easily applied to the system where the hardware modules are designed with fixed reading and writing latency.
It is a challenge to implement intelligent systems with constrained hardware resources. In the third work, an adaptive hardware resource management system for multiple AI tasks in harsh environments was designed. Inspired by the “refreshing” concept in the second work, we utilise a key feature of FPGAs, partial reconfiguration, to improve the reliability and efficiency of the AI system. More
importantly, this feature provides the capability to manage the hardware resources for deep learning acceleration. In the proposed design, the on-chip hardware resources are dynamically managed to improve the flexibility, performance and power efficiency of deep learning inference systems. The deep learning units provided by Xilinx are used to perform multiple AI tasks simultaneously, and the experiments show significant improvements in power efficiency for a wide range of scenarios with different workloads. To further improve the performance of the system, the concept of reconfiguration was further extended. As a result, an adaptive DL software framework was designed. This framework can provide a significant level of adaptability support for various deep learning algorithms on an FPGA-based edge computing platform. To meet the specific accuracy and latency requirements derived from the running applications and operating environments, the platform may dynamically update hardware and software (e.g., processing pipelines) to achieve better cost, power, and processing efficiency compared to the static system
Radar-Based Multi-Target Classification Using Deep Learning
Real-time, radar-based human activity and target recognition has several applications in various fields. Examples include hand gesture recognition, border and home surveillance, pedestrian recognition for automotive safety and fall detection for assisted living. This dissertation sought to improve the speed and accuracy of a previously developed model classifying human activity and targets using radar data for outdoor surveillance purposes. An improvement in accuracy and speed of classification helps surveillance systems to provide reliable results on time. For example, the results can be used to intercept trespassers, poachers or smugglers. To achieve these objectives, radar data was collected using a C-band pulse-Doppler radar and converted to spectrograms using the Short-time Fourier transform (STFT) algorithm. Spectrograms of the following classes were utilised in classification: one human walking, two humans walking, one human running, moving vehicles, a swinging sphere and clutter/noise. A seven-layer residual network was proposed, which utilised batch normalisation (BN), global average pooling (GAP), and residual connections to achieve a classification accuracy of 92.90% and 87.72% on the validation and test data, respectively. Compared to the previously proposed model, this represented a 10% improvement in accuracy on the validation data and a 3% improvement on the test data. Applying model quantisation provided up to 3.8 times speedup in inference, with a less than 0.4% accuracy drop on both the validation and test data. The quantised model could support a range of up to 89.91 kilometres in real-time, allowing it to be used in radars that operate within this range
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