Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Video content analysis for automated detection and tracking of humans in CCTV surveillance applications

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented

Kodizajn arhitekture i algoritama za lokalizacijumobilnih robota i detekciju prepreka baziranih namodelu

This thesis proposes SoPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline on autonomous mobile robots. The obtained results are equivalent or better in comparison to state-ofthe- art. For localization, an efficient hardware architecture that supports EKF-SLAM's local map management with seven-dimensional landmarks in real time is developed. For obstacle detection a novel method of object recognition is proposed - detection by identification framework based on single detection window scale. This framework allows adequate algorithmic precision and execution speeds on embedded hardware platforms.Ova teza bavi se dizajnom SoPC (engl. System on a Programmable Chip) arhitektura i algoritama za efikasnu implementaciju zadataka lokalizacije i detekcije prepreka baziranih na viziji u kontekstu autonomne robotske navigacije. Za lokalizaciju, razvijena je efikasna računarska arhitektura za EKF-SLAM algoritam, koja podržava skladištenje i obradu sedmodimenzionalnih orijentira lokalne mape u realnom vremenu. Za detekciju prepreka je predložena nova metoda prepoznavanja objekata u slici putem prozora detekcije fiksne dimenzije, koja omogućava veću brzinu izvršavanja algoritma detekcije na namenskim računarskim platformama

Neural network analysis in Higgs search using ttH, H→bb and TAG database development for ATLAS

The Large Hadron Collider, LHC, at Conseil Europeen pour la Recherche Nucleaire, CERN, in Geneva, Switzerland, is an international physics project of unprecedented scale. First proton beams were circulated in the LHC in 2008. The ATLAS Collaboration, an international group of 2000 analysts, scientists, software developers and hardware experts, seeks to push the boundaries of our current understanding of the Universe, and our ability to undertake such studies. A central physics focus of the ATLAS experiment is study of a Higgs boson, a theoretically predicted particle, as yet unobserved in nature. In this thesis, a Neural Network is adopted and developed as an analysis method in a study of a Standard Model Higgs boson in the low mass Higgs range, using the physics channel ttH, H ?bb and Higgs mass mH = 120 GeV. The Neural Network analysis shows that a neural network method can give an improvement in sensitivity of the ttH, H → bb channel. A set of Event Characteristics, associated with a topology where the existence of a reconstructed Higgs boson is not required in each event are defined and it is demonstrated that these characteristics, when used in a neural network, can improve the sensitivity of the channel by improving separation of signal and background events. The neural network analysis uses a collection of Generic Event Characteristics, a neural network of layout 36 : 8 : 4 : 1, 1000 learning cycles and 734033 ttH, H → bb simulated signal and background events, for an integrated luminosity of 1fb-1, to give an output sensitivity of 4.74. We see that the neural network analysis method as described in this analysis improves the sensitivity of the channel from that of the Cuts-Based Analysis performed in previous studies. In the quest for new and multipurpose physics searches and studies, ATLAS will produce data of unprecedented volume and rate in Particle Physics. As analysts are internationally located, data must be accessible across worldwide collaborating institutions. A significant challenge for the ATLAS collaboration lies in developing the capacity in computing terms to manage an unprecedented data challenge in a fluid, sound and transparent way. The ATLAS Event Level Metadata System, TAG Database, is a central part of the ATLAS Computing system. The Event Level Metadata system captures information about ATLAS physics events on an event by event basis, and offers later access to the events for analysis. In this thesis, developments and implementation of the Event Level Metadata system are presented in terms of three studies, these are Feasibility, Scalability and Accessibility. Feasibility studies demonstrate that an Event Level Metadata system can operate within the larger ATLAS software system and gathered information on the implications for Event Level Metadata system development. Scalability studies present implementation and performance of a realistic terabyte scale relational TAG Database and demonstrate that an Event Level Metadata system at terabyte scale is achievable. Accessibility studies present the development of a web interface to the Event Level Metadata system. Studies in this thesis therefore demonstrate that an Event Level Metadata can be integrated with the ATLAS software system, develop solutions for integration, prove that an Event Level Metadata relational database can scale to ATLAS terabyte size, present performance results for a realistic ATLAS scale system and develop a user interface to the Event Level Metadata system

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs