USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Track-oriented multiple hypothesis tracking based on Tabu search and Gibbs sampling

In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance

Impulsive Interference Avoidance in Dense Wireless Sensor Networks

ABSTRACT As with all wireless communication devices, wireless sensor networks (WSNs) are subject to interference from other users of the radio-frequency (RF) medium. Such interference is practically never random: originating in applications generally performing some practical and sensible activities, it naturally exhibits various regularities amounting to perceptible patterns, e.g., regularly-spaced short-duration impulses that correlate among multiple WSN nodes. If those nodes can recognize the interference pattern, they can benefit from steering their transmissions around it. This possibility has stirred some interest among researchers involved in cognitive radios, where special hardware has been postulated to circumvent non-random interference. Our goal is to explore ways of enhancing medium access control (MAC) schemes operating within the framework of traditional off-the-shelf RF modules applicable in low-cost WSN motes, such that they can detect interference patterns in the neighbourhood and creatively respond to them mitigating their negative impact on the packet reception rate. In this paper, we describe (a) a method for the post-deployment dynamic characterization of a channel aimed at identifying spiky interference patterns, (b) a way to incorporate interference models into an existing WSN emulator, and (c) the subsequent evaluation of a proof-of-concept MAC technique for circumventing the interference. We found that an interference-aware MAC can improve the packet delivery rates in these environments at the cost of increased latency. Notably, the latter is quite acceptable in the vast majority of WSN applications

Real-time online musical collaboration system for Indian percussion

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.Includes bibliographical references (p. 111-119).Thanks to the Internet, musicians located in different countries can now aspire to play with each other almost as if they were in the same room. However, the time delays due to the inherent latency in computer networks (up to several hundreds of milliseconds over long distances) are unsuitable for musical applications. Some musical collaboration systems address this issue by transmitting compressed audio streams (such as MP3) over low-latency and high-bandwidth networks (e.g. LANs or Internet2) to constrain time delays and optimize musician synchronization. Other systems, on the contrary, increase time delays to a musically-relevant value like one phrase, or one chord progression cycle, and then play it in a loop, thereby constraining the music being performed. In this thesis I propose TablaNet, a real-time online musical collaboration system for the tabla, a pair of North Indian hand drums. This system is based on a novel approach that combines machine listening and machine learning. Trained for a particular instrument, here the tabla, the system recognizes individual drum strokes played by the musician and sends them as symbols over the network. A computer at the receiving end identifies the musical structure from the incoming sequence of symbols by mapping them dynamically to known musical constructs. To deal with transmission delays, the receiver predicts the next events by analyzing previous patterns before receiving the original events, and synthesizes an audio output estimate with the appropriate timing. Although prediction approximations may result in a slightly different musical experience at both ends, we find that this system demonstrates a fair level of playability by tabla players of various levels, and functions well as an educational tool.by Mihir Sarkar.S.M

Developing models for the data-based mechanistic approach to systems analysis:Increasing objectivity and reducing assumptions

Stochastic State-Space Time-Varying Random Walk models have been developed, allowing the existing Stochastic State Space models to operate directly on irregularly sampled time-series. These TVRW models have been successfully applied to two different classes of models benefiting each class in different ways. The first class of models - State Dependent Parameter (SDP) models and used to investigate the dominant dynamic modes of nonlinear dynamic systems and the non-linearities in these models affected by arbitrary State Variables. In SDP locally linearised models it is assumed that the parameters that describe system’s behaviour changes are dependent upon some aspect of the system (it’s ‘state’). Each parameter can be dependent on one or more states. To estimate the parameters that are changing at a rate related to that of it’s states, the estimation procedure is conducted in the state-space along the potentially multivariate trajectory of the states which drive the parameters. The introduction of the newly developed TVRW models significantly improves parameter estimation, particularly in data rich neighbourhoods of the state-space when the parameter is dependent on more than one state, and the ends of the data-series when the parameter is dependent on one state with few data points. The second class of models are known as Dynamic Harmonic Regression (DHR) models and are used to identify the dominant cycles and trends of time-series. DHR models the assumption is that a signal (such as a time-series) can be broken down into four (unobserved) components occupying different parts of the spectrum: trend, seasonal cycle, other cycles, and a high frequency irregular component. DHR is confined to uniformly sampled time-series. The introduction of the TVRW models allows DHR to operate on irregularly sampled time-series, with the added benefit of forecasting origin no longer being confined to starting at the end of the time-series but can now begin at any point in the future. Additionally, the forecasting sampling rate is no longer limited to the sampling rate of the time-series. Importantly, both classes of model were designed to follow the Data-Based Mechanistic (DBM) approach to modelling environmental systems, where the model structure and parameters are to be determined by the data (Data-Based) and then the subsequent models are to be validated based on their physical interpretation (Mechanistic). The aim is to remove the researcher’s preconceptions from model development in order to eliminate any bias, and then use the researcher’s knowledge to validate the models presented to them. Both classes of model lacked model structure identification procedures and so model structure was determined by the researcher, against the DBM approach. Two different model structure identification procedures, one for SDP and the other for DHR, were developed to bring both classes of models back within the DBM framework. These developments have been presented and tested here on both simulated data and real environmental data, demonstrating their importance, benefits and role in environmental modelling and exploratory data analysis

Who Said That? Towards a Machine-Prediction-Based Approach to Tursiops Truncatus Whistle Localization and Attribution in a Reverberant Dolphinarium

Dolphin communication research is an active period of growth. Many researchers expect to find significant communicative capacity in dolphins given their known sociality and large and complex brains. Moreover, given dolphins’ known acoustic sensitivity, serving their well-studied echolocation ability, some researchers have speculated that dolphin communication is mediated in large part by a sophisticated “vocal” language. However, evidence supporting this belief is scarce. Among most dolphin species, a particular tonal class of call, termed the whistle, has been identified as socially important. In particular, for the common bottlenose dolphin, Tursiops truncatus – arguably the focal species of most dolphin cognitive and communication research – research has fixated on “signature whistles,” individuallydistinctive whistles that seem to convey an individual’s identity to conspecifics, can be mimicked, and can be modulated under certain circumstances in ways that may or may not be communicative. Apart from signature whistles, most studies of dolphin calls concern group-based repertoires of whistles and other, pulse-form call types. However, studies of individual repertoires of non-signature whistles, and the phenomenon of combined signature and non-signature vocal exchanges among dolphins, are conspicuously rare in the literature, tending to be limited by either extreme subject confinement or sparse attributions of vocalizer identity. Nevertheless, such studies constitute a logical prerequisite to an understanding of the communicative potential of whistles. This absence can be explained by a methodological limitation in the way in which dolphin sounds are recorded. In particular, no established method exists for recording the whistles of an entire social group of dolphins so as to reliably attribute them to their vocalizers. This thesis proposes a dolphinarium-based system for achieving audio recording with whistle attribution, as well as visual behavioral tracking. Towards achieving the proposed system, I present foundational work involving the installation of permanent hydrophone arrays and cameras in a dolphinarium that enforces strict animal safety regulations. Attributing tonal sounds via the process of sound localization – estimation of a sound’s point of origin based on the physical properties of its propagation – in a highly reverberant environment is a notoriously difficult problem, resistant to many conventional signal processing techniques. This thesis will provide evidence of this difficulty, and also a demonstration of a highly e↵ective machine-learning-based solution to the problem. This thesis also provides miscellaneous hardware and the pieces of a computational pipeline towards completion of the full proposed, automated system. Once completed, the proposed system will provide an enormous data stream that will lend itself to large-scale studies of individual repertoires of non-signature whistles and combined signature and non-signature vocal exchanges among an invariant group of socializing dolphins, representing a unique and necessary achievement in dolphin communication research

Optimization of the Geometry of Communication for Autonomous Missions of Underwater Vehicles

The potential of Autonomous Underwater Vehicles (AUVs) working as a team in sampling, monitoring and surveillance of the marine environment has been realized since quite a long time. One of the most relevant obstacle to their operational implementation resides in the limitations of the acoustic channel for inter-vehicle communications. Underwater acoustic modeling and simulation plays an important role in predicting possible losses and transmission failures between them, and underwater sound propagation can be precisely measured or estimated. In this thesis, sound speed data from a real experiment (CommsNet13) were used to simulate environmental conditions and analyze acoustic communication between an USBL-vehicle on the sea surface and an acoustic modem on the sea bottom, in order achieve an effective geometry of transmission for future trials

Multimodal Sensing for Robust and Energy-Efficient Context Detection with Smart Mobile Devices

Adoption of smart mobile devices (smartphones, wearables, etc.) is rapidly growing. There are already over 2 billion smartphone users worldwide [1] and the percentage of smartphone users is expected to be over 50% in the next five years [2]. These devices feature rich sensing capabilities which allow inferences about mobile device user’s surroundings and behavior. Multiple and diverse sensors common on such mobile devices facilitate observing the environment from different perspectives, which helps to increase robustness of inferences and enables more complex context detection tasks. Though a larger number of sensing modalities can be beneficial for more accurate and wider mobile context detection, integrating these sensor streams is non-trivial. This thesis presents how multimodal sensor data can be integrated to facilitate ro- bust and energy efficient mobile context detection, considering three important and challenging detection tasks: indoor localization, indoor-outdoor detection and human activity recognition. This thesis presents three methods for multimodal sensor inte- gration, each applied for a different type of context detection task considered in this thesis. These are gradually decreasing in design complexity, starting with a solution based on an engineering approach decomposing context detection to simpler tasks and integrating these with a particle filter for indoor localization. This is followed by man- ual extraction of features from different sensors and using an adaptive machine learn- ing technique called semi-supervised learning for indoor-outdoor detection. Finally, a method using deep neural networks capable of extracting non-intuitive features di- rectly from raw sensor data is used for human activity recognition; this method also provides higher degree of generalization to other context detection tasks. Energy efficiency is an important consideration in general for battery powered mo- bile devices and context detection is no exception. In the various context detection tasks and solutions presented in this thesis, particular attention is paid to this issue by relying largely on sensors that consume low energy and on lightweight computations. Overall, the solutions presented improve on the state of the art in terms of accuracy and robustness while keeping the energy consumption low, making them practical for use on mobile devices