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

Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review

none5noNatural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.openEsposito M.; Palma L.; Belli A.; Sabbatini L.; Pierleoni P.Esposito, M.; Palma, L.; Belli, A.; Sabbatini, L.; Pierleoni, P

Adaptive tracking of people and vehicles using mobile platforms

Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost- and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.publishedVersionPeer reviewe

Intrusion Detection In Wireless Sensor Networks

There are several applications that use sensor motes and researchers continue to explore additional applications. For this particular application of detecting the movement of humans through the sensor field, a set of Berkley mica2 motes on TinyOS operating system is used. Different sensors such as pressure, light, and so on can be used to identify the presence of an intruder in the field. In our case, the light sensor is chosen for the detection. When an intruder crosses the monitored environment, the system detects the changes of the light values, and any significant change meaning that a change greater than a pre-defined threshold. This indicates the presence of an intruder. An integrated web cam is used to take snapshot of the intruder and transmit the picture through the network to a remote station. The basic motivation of this thesis is that a sensor web system can be used to monitor and detect any intruder in a specific area from a remote location

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

High Accuracy Distributed Target Detection and Classification in Sensor Networks Based on Mobile Agent Framework

High-accuracy distributed information exploitation plays an important role in sensor networks. This dissertation describes a mobile-agent-based framework for target detection and classification in sensor networks. Specifically, we tackle the challenging problems of multiple- target detection, high-fidelity target classification, and unknown-target identification. In this dissertation, we present a progressive multiple-target detection approach to estimate the number of targets sequentially and implement it using a mobile-agent framework. To further improve the performance, we present a cluster-based distributed approach where the estimated results from different clusters are fused. Experimental results show that the distributed scheme with the Bayesian fusion method have better performance in the sense that they have the highest detection probability and the most stable performance. In addition, the progressive intra-cluster estimation can reduce data transmission by 83.22% and conserve energy by 81.64% compared to the centralized scheme. For collaborative target classification, we develop a general purpose multi-modality, multi-sensor fusion hierarchy for information integration in sensor networks. The hierarchy is com- posed of four levels of enabling algorithms: local signal processing, temporal fusion, multi-modality fusion, and multi-sensor fusion using a mobile-agent-based framework. The fusion hierarchy ensures fault tolerance and thus generates robust results. In the meanwhile, it also takes into account energy efficiency. Experimental results based on two field demos show constant improvement of classification accuracy over different levels of the hierarchy. Unknown target identification in sensor networks corresponds to the capability of detecting targets without any a priori information, and of modifying the knowledge base dynamically. In this dissertation, we present a collaborative method to solve this problem among multiple sensors. When applied to the military vehicles data set collected in a field demo, about 80% unknown target samples can be recognized correctly, while the known target classification ac- curacy stays above 95%

Properties and Applications of Love Surface Waves in Seismology and Biosensors

Shear horizontal (SH) surface waves of the Love type are elastic surface waves propagating in layered waveguides, in which surface layer is “slower” than the substrate. Love surface waves are of primary importance in geophysics and seismology, since most structural damages in the wake of earthquakes are attributed to the devastating SH motion inherent to the Love surface waves. On the other hand, Love surface waves found benign applications in biosensors used in biology, medicine, and chemistry. In this chapter, we briefly sketch a mathematical model for Love surface waves and present examples of the resulting dispersion curves for phase and group velocities, attenuation as well as the amplitude distribution as a function of the depth. We illustrate damages due to Love surface waves generated by earthquakes on real-life examples. In the following of this chapter, we present a number of representative examples for Love wave biosensors, which have been already used to DNA characterization, bacteria and virus detection, measurements of toxic substances, etc. We hope that the reader, after studying this chapter, will have a clear idea that deadly earthquakes and a beneficiary biosensor technology share the same physical phenomenon, which is the basis of a fascinating interdisciplinary research