Jump Particle Filtering Framework for Joint Target Tracking and Intent Recognition

This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems (T-AES

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine

Fast human behavior analysis for scene understanding

Human behavior analysis has become an active topic of great interest and relevance for a number of applications and areas of research. The research in recent years has been considerably driven by the growing level of criminal behavior in large urban areas and increase of terroristic actions. Also, accurate behavior studies have been applied to sports analysis systems and are emerging in healthcare. When compared to conventional action recognition used in security applications, human behavior analysis techniques designed for embedded applications should satisfy the following technical requirements: (1) Behavior analysis should provide scalable and robust results; (2) High-processing efficiency to achieve (near) real-time operation with low-cost hardware; (3) Extensibility for multiple-camera setup including 3-D modeling to facilitate human behavior understanding and description in various events. The key to our problem statement is that we intend to improve behavior analysis performance while preserving the efficiency of the designed techniques, to allow implementation in embedded environments. More specifically, we look into (1) fast multi-level algorithms incorporating specific domain knowledge, and (2) 3-D configuration techniques for overall enhanced performance. If possible, we explore the performance of the current behavior-analysis techniques for improving accuracy and scalability. To fulfill the above technical requirements and tackle the research problems, we propose a flexible behavior-analysis framework consisting of three processing-layers: (1) pixel-based processing (background modeling with pixel labeling), (2) object-based modeling (human detection, tracking and posture analysis), and (3) event-based analysis (semantic event understanding). In Chapter 3, we specifically contribute to the analysis of individual human behavior. A novel body representation is proposed for posture classification based on a silhouette feature. Only pure binary-shape information is used for posture classification without texture/color or any explicit body models. To this end, we have studied an efficient HV-PCA shape-based descriptor with temporal modeling, which achieves a posture-recognition accuracy rate of about 86% and outperforms other existing proposals. As our human motion scheme is efficient and achieves a fast performance (6-8 frames/second), it enables a fast surveillance system or further analysis of human behavior. In addition, a body-part detection approach is presented. The color and body ratio are combined to provide clues for human body detection and classification. The conventional assumption of up-right body posture is not required. Afterwards, we design and construct a specific framework for fast algorithms and apply them in two applications: tennis sports analysis and surveillance. Chapter 4 deals with tennis sports analysis and presents an automatic real-time system for multi-level analysis of tennis video sequences. First, we employ a 3-D camera model to bridge the pixel-level, object-level and scene-level of tennis sports analysis. Second, a weighted linear model combining the visual cues in the real-world domain is proposed to identify various events. The experimentally found event extraction rate of the system is about 90%. Also, audio signals are combined to enhance the scene analysis performance. The complete proposed application is efficient enough to obtain a real-time or near real-time performance (2-3 frames/second for 720×576 resolution, and 5-7 frames/second for 320×240 resolution, with a P-IV PC running at 3GHz). Chapter 5 addresses surveillance and presents a full real-time behavior-analysis framework, featuring layers at pixel, object, event and visualization level. More specifically, this framework captures the human motion, classifies its posture, infers the semantic event exploiting interaction modeling, and performs the 3-D scene reconstruction. We have introduced our system design based on a specific software architecture, by employing the well-known "4+1" view model. In addition, human behavior analysis algorithms are directly designed for real-time operation and embedded in an experimental runtime AV content-analysis architecture. This executable system is designed to be generic for multiple streaming applications with component-based architectures. To evaluate the performance, we have applied this networked system in a single-camera setup. The experimental platform operates with two Pentium Quadcore engines (2.33 GHz) and 4-GB memory. Performance evaluations have shown that this networked framework is efficient and achieves a fast performance (13-15 frames/second) for monocular video sequences. Moreover, a dual-camera setup is tested within the behavior-analysis framework. After automatic camera calibration is conducted, the 3-D reconstruction and communication among different cameras are achieved. The extra view in the multi-camera setup improves the human tracking and event detection in case of occlusion. This extension of multiple-view fusion improves the event-based semantic analysis by 8.3-16.7% in accuracy rate. The detailed studies of two experimental intelligent applications, i.e., tennis sports analysis and surveillance, have proven their value in several extensive tests in the framework of the European Candela and Cantata ITEA research programs, where our proposed system has demonstrated competitive performance with respect to accuracy and efficiency

Wave-by-wave Forecasting of Sea Surface Elevation for WEC Applications Utilizing NARX Neural Networks

Forecasting of ocean waves over a short duration on the order of tens of seconds was approached with the optimization of wave energy conversion in mind. This study outlines the development of an artificial neural network model, specifically the Nonlinear Autoregressive Network with Exogenous Input (NARX), to predict a wave-by-wave surface elevation time series based entirely on previous observations at the site of interest. Such a model would be computationally less intensive than competing deterministic techniques rooted in wave theory. Furthermore, it could potentially fit irregular patterns without some predetermined function as would be necessary for many other stochastic approaches. In principle, neural networks can be trained to learn previous patterns based on a recent wave record and then utilized in a feedback mode to yield multistep predictions for perhaps up to three wave periods (~45 seconds). The challenge of this approach is error accumulation in the intermediary steps that can lead to poor performance for longer prediction horizons. It was hypothesized that filtering the wave record input via wavelet or Fourier transformations could enhance model performance and hence, explorations of these types of input preprocessing were an integral part of the study. The NARX architecture allowed for an exogenous input time series that could conceivably mitigate error accumulation by providing an additional degree of signal correlation. Accordingly, the investigation also included potential exogenous inputs that could be derived from the original wave record. The work culminated with a band-pass exogenous input delivering a significant forecasting advantage. Yet, providing the zero-phase narrow-band signal posed a challenge when applied in real- time, without the use of future data. A two-prong tactical approach was undertaken to address this challenge but ultimately proved insufficient. Consequently, the success of the NARX wave-by-wave forecasting method under the conditions of a real-world application will depend upon a better solution to the zero-phase filtering challenge

Robust and efficient meshfree solid thermo-mechanics simulation of friction stir welding

Friction stir welding, FSW, is a solid-state joining method that is ideally suited for welding aluminum alloys. Welding of the aluminum is accomplished by way of a hardened steel tool that rotates and is pushed with great force into the work pieces. Friction between the tool and the aluminum causes heat to be generated, which softens the aluminum, rendering it easy to deform plastically. In recent years, the FSW process has steadily gained interest in various fabrication industries. However, wide spread acceptance has not yet been attained. Some of the main reasons for this are due to the complexity of the process and the capital cost to procure the required welding equipment and infrastructure. To date, little attention has been paid towards finding optimal process parameters that will increase the economic viability of the FSW process, thus offsetting the high initial investment most. In this research project, a robust and efficient numerical simulation code called SPHriction-3D is developed that can be used to find optimal FSW process parameters. The numerical method is meshfree, allowing for all of the phases of the FSW process to be simulated with a phenomenological approach. The dissertation starts with a focus on the current state of art. Next an in-depth development of the proposed meshfree formulation is presented. Then, the emphasis turns towards the presentation of various test cases along with experimental validation (the focus is on temperature, defects, and tool forces). The remainder of the thesis is dedicated to the development of a robust approach to find the optimal weld quality, and the associated tool rpm and advancing speed. The presented results are of engineering precision and are obtained with low calculation times (hours as opposed to days or weeks). This is possible, since the meshfree code is developed to run in parallel entirely on the GPU. The overall outcome is a cutting edge simulation approach for the entire FSW process. Le soudage par friction malaxage, SFM, est une méthode idéale pour relier ensemble des pièces en aluminium. Lors du procédé, un outil en acier très dur tourne à haute vitesse et est presser dans les plaques avec beaucoup de force. L’outil frotte sur les plaques et génère la chaleur, ce qui ramollie l’aluminium, ceci le rendant plus facile à déformé mécaniquement. Récemment, le SFM a connu une croissance de reconnaissance important, par contre, l’industrie ne l’as pas encore adopté unilatéralement. Il existe encore beaucoup de terrain à défricher avant de bien comprendre comment les paramètres du procédé font effet sur la qualité de la soudure. Dans ce travail, on présente une approche de simulation numérique sans maillage pour le SFM. Le code développé est capable de prendre en considération des grandes déformations plastiques, le ramollissement de l’aluminium avec la température, et la condition de frottement complexe. Cette méthode permet de simulé tous les phases du procédé SFM dans une seule modèle. La thèse commence avec un mis en contexte de l’état actuel de la simulation numérique du SFM. Une fois la méthodologie de simulation sans maillage présenté, la thèse concentre sur différents cas de vérification et validation. Finalement, un travail d’optimisation des paramètres du procédé est réalisé avec le code numérique. La méthode de simulation présentée s’agit d’une approche efficace et robuste, ce qui le rend un outil de conception valable pour les ingénieurs qui travaille dans le domaine de SFM

Image analysis of sports events

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major de Automação). Faculdade de Engenharia. Universidade do Porto. 200

High-Energy Gamma-Ray Astronomy

This volume celebrates the 30th anniversary of the first very-high energy (VHE) gamma-ray Source detection: the Crab Nebula, observed by the pioneering ground-based Cherenkov telescope Whipple, at teraelectronvolts (TeV) energies, in 1989. As we entered a new era in TeV astronomy, with the imminent start of operations of the Cherenkov Telescope Array (CTA) and new facilities such as LHAASO and the proposed Southern Wide-Field Gamma-ray Observatory (SWGO), we conceived of this volume as a broad reflection on how far we have evolved in the astrophysics topics that dominated the field of TeV astronomy for much of recent history.In the past two decades, H.E.S.S., MAGIC and VERITAS pushed the field of TeV astronomy, consolidating the field of TeV astrophysics, from few to hundreds of TeV emitters. Today, this is a mature field, covering almost every topic of modern astrophysics. TeV astrophysics is also at the center of the multi-messenger astrophysics revolution, as the extreme photon energies involved provide an effective probe in cosmic-ray acceleration, propagation and interaction, in dark matter and exotic physics searches. The improvement that CTA will carry forward and the fact that CTA will operate as the first open observatory in the field, mean that gamma-ray astronomy is about to enter a new precision and productive era.This book aims to serve as an introduction to the field and its state of the art, presenting a series of authoritative reviews on a broad range of topics in which TeV astronomy provided essential contributions, and where some of the most relevant questions for future research lie