DESIGN OPTIMIZATION OF EMBEDDED SIGNAL PROCESSING SYSTEMS FOR TARGET DETECTION

Sensor networks for automated detection of targets, such as pedestrians and vehicles, are highly relevant in defense and surveillance applications. For this purpose, a variety of target detection algorithms and systems using different types of sensors have been proposed in the literature. Among them, systems based on non-image sensors are of special interest in many practical deployment scenarios because of their power efficiency and low computational loads. In this thesis, we investigate low power sensor systems for detecting people and vehicles using non-image sensors such as acoustic and seismic sensors. Our investigation is focused on design optimization across trade-offs including real-time performance, energy efficiency, and target detection accuracy, which are key design evaluation metrics for this class of systems. Design and implementation of low power, embedded target detection systems can be decomposed into two major, inter-related subproblems: (a) algorithm development, which encompasses the development or selection of detection algorithms and optimization of their parameters, and (b) system development, which involves the mapping of the algorithms derived from (a) into real-time, energy efficient implementations on the targeted embedded platforms. In this thesis, we address both of these subproblems in an integrated manner. That is, we investigate novel algorithmic techniques for improvement of accuracy without excessive computational complexity, and we develop new design methodologies, tools, and implementations for efficient realization of target detection algorithms on embedded platforms. We focus specifically on target detection systems that employ acoustic and seismic sensing modalities. These selected modalities support the low power design objectives of our work. However, we envision that our developed algorithms and implementation techniques can be extended readily to other types or combinations of relevant sensing modalities. Throughout this research, we have developed prototypes of our new algorithms and design methods on embedded platforms, and we have experimented with these prototypes to demonstrate our findings, and iteratively improve upon the achieved implementation trade-offs. The main contributions of this thesis are summarized in the following. (1). Classification algorithm for acoustic and seismic signals. We have developed a new classification algorithm for discrimination among people, vehicles, and noise. The algorithm is based on a new fusion technique for acoustic and seismic signals. Our new fusion technique was evaluated through experiments using actual measured datasets, which were collected from different sensors installed in different locations and at different times of day. Our proposed classification algorithm was shown to achieve a significant reduction in the number of false alarms compared to a baseline fusion approach. (2). Joint target localization and classification framework using sensor networks. We designed a joint framework for target localization and classification using a single generalized model for non-imaging based multi- modal sensor data. For target localization, we exploited both sensor data and estimated dynamics within a local neighborhood. We validated the capabilities of our framework by using an actual multi-modal dataset, which includes ground truth GPS information (e.g., time and position) and data from co-located seismic and acoustic sensors. Experimental results showed that our framework achieves better classification accuracy compared to state of the art fusion algorithms using temporal accumulation and achieves more accurate target localizations than a baseline target localization approach. (3). Design and optimization of target detection systems on embedded platforms using dataflow methods. We developed a foundation for our system-level design research by introducing a new rapid prototyping methodology and associated software tool. Using this tool, we presented the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles related to our algorithmic contributions. We applied a strategically-configured suite of single- and dual-modality signal processing techniques together with dataflow-based design optimization for energy-efficient, real-time implementation. Through experiments using a Raspberry Pi platform, we demonstrated the capability of our target detection system to provide efficient operational trade-offs among detection accuracy, energy efficiency, and processing speed. (4). Software synthesis from dataflow schedule graphs on multicore platforms. We developed new software synthesis methods and tools for design and implementation of embedded signal processing systems using dataflow schedule graphs (DSGs). DSGs provide formal representations of dataflow schedules, which encapsulate information about the assignment of computational tasks (signal processing modules) to processing resources and the ordering of tasks that are assigned to the same resource. Building on fundamental DSG modeling concepts from the literature, we developed the first algorithms and supporting software synthesis tools for mapping DSG representations into efficient multi-threaded implementations. Our tools replace ad-hoc multicore signal processing system development processes with a structured process that is rooted in dataflow formalisms and supported with a high degree of automation. We evaluated our new DSG methods and tools through a demonstration involving multi-threaded implementation of our proposed classification algorithm and associated fusion technique for acoustic/seismic signals

Mapa de la recerca del Campus de Vilanova i la Geltrú

Postprint (author’s final draft

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Modelling, Simulation and Data Analysis in Acoustical Problems

Modelling and simulation in acoustics is currently gaining importance. In fact, with the development and improvement of innovative computational techniques and with the growing need for predictive models, an impressive boost has been observed in several research and application areas, such as noise control, indoor acoustics, and industrial applications. This led us to the proposal of a special issue about “Modelling, Simulation and Data Analysis in Acoustical Problems”, as we believe in the importance of these topics in modern acoustics’ studies. In total, 81 papers were submitted and 33 of them were published, with an acceptance rate of 37.5%. According to the number of papers submitted, it can be affirmed that this is a trending topic in the scientific and academic community and this special issue will try to provide a future reference for the research that will be developed in coming years

Abstracts of the 1st GeoDays, 14th–17th March 2023, Helsinki, Finland

Non peer reviewe

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry