11 research outputs found

    Reliable Inference from Unreliable Agents

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    Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents LL and the sum rate RR tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the 1/R1/R behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

    On Distributed Estimation for Resource Constrained Wireless Sensor Networks

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    We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression, dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly: 1- Consider an inhomogeneous WSN where the sensors\u27 observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidth-constrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multi-bit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE. 2- Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multi-bit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closed-form. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian Cramer-Rao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound. As a dual problem we also answer the question: what is the minimum required bandwidth of the network to satisfy a predetermined target MSE? 3- Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as \u27enhancement mode\u27 of multiplicative noise. Through deriving different lower bounds, such as BCRLB,Weiss-Weinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. We also compare the performance of well-known estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes

    Sparse Signal Processing and Statistical Inference for Internet of Things

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    Data originating from many devices within the Internet of Things (IoT) framework can be modeled as sparse signals. Efficient compression techniques of such data are essential to reduce the memory storage, bandwidth, and transmission power. In this thesis, I develop some theory and propose practical schemes for IoT applications to exploit the signal sparsity for efficient data acquisition and compression under the frameworks of compressed sensing (CS) and transform coding. In the context of CS, the restricted isometry constant of finite Gaussian measurement matrices is investigated, based on the exact distributions of the extreme eigenvalues of Wishart matrices. The analysis determines how aggressively the signal can be sub-sampled and recovered from a small number of linear measurements. The signal reconstruction is guaranteed, with a predefined probability, via various recovery algorithms. Moreover, the measurement matrix design for simultaneously acquiring multiple signals is considered. This problem is important for IoT networks, where a huge number of nodes are involved. In this scenario, the presented analytical methods provide limits on the compression of joint sparse sources by analyzing the weak restricted isometry constant of Gaussian measurement matrices. Regarding transform coding, two efficient source encoders for noisy sparse sources are proposed, based on channel coding theory. The analytical performance is derived in terms of the operational rate-distortion and energy-distortion. Furthermore, a case study for the compression of real signals from a wireless sensor network using the proposed encoders is considered. These techniques can reduce the power consumption and increase the lifetime of IoT networks. Finally, a frame synchronization mechanism has been designed to achieve reliable radio links for IoT devices, where optimal and suboptimal metrics for noncoherent frame synchronization are derived. The proposed tests outperform the commonly used correlation detector, leading to accurate data extraction and reduced power consumption

    Adaptive data acquisition for communication networks

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    In an increasing number of communication systems, such as sensor networks or local area networks within medical, financial or military institutions, nodes communicate information sources (e.g., video, audio) over multiple hops. Moreover, nodes have, or can acquire, correlated information sources from the environment, e.g., from data bases or from measurements. Among the new design problems raised by the outlined scenarios, two key issues are addressed in this dissertation: 1) How to preserve the consistency of sensitive information across multiple hops; 2) How to incorporate the design of actuation in the form of data acquisition and network probing in the optimization of the communication network. These aspects are investigated by using information-theoretic (source and channel coding) models, obtaining fundamental insights that have been corroborated by various illustrative examples. To address point 1), the problem of cascade source coding with side information is investigated. The motivating observation is that, in this class of problems, the estimate of the source obtained at the decoder cannot be generally reproduced at the encoder if it depends directly on the side information. In some applications, such as the one mentioned above, this lack of consistency may be undesirable, and a so called Common Reconstruction (CR) requirement, whereby one imposes that the encoder be able to agree on the decoder’s estimate, may be instead in order. The rate-distortion region is here derived for some special cases of the cascade source coding problem and of the related Heegard-Berger (HB) problem under the CR constraint. As for point 2), the work is motivated by the fact that, in order to enable, or to facilitate, the exchange of information, nodes of a communication network routinely take various types of actions, such as data acquisition or network probing. For instance, sensor nodes schedule the operation of their sensing devices to measure given physical quantities of interest, and wireless nodes probe the state of the channel via training. The problem of optimal data acquisition is studied for a cascade source coding problem, a distributed source coding problem and a two-way source coding problem assuming that the side information sequences can be controlled via the selection of cost-constrained actions. It is shown that a joint design of the description of the source and of the control signals used to guide the selection of the actions at downstream nodes is generally necessary for an efficient use of the available communication links. Instead, the problem of optimal channel probing is studied for a broadcast channel and a point-to-point link in which the decoder is interested in estimating not only the message, but also the state sequence. Finally, the problem of embedding information on the actions is studied for both the source and the channel coding set-ups described above

    Design of large polyphase filters in the Quadratic Residue Number System

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    Temperature aware power optimization for multicore floating-point units

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    On unifying sparsity and geometry for image-based 3D scene representation

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    Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providing a satisfying, immersive experience. Camera networks offer a low cost solution to the acquisition of 3D visual information, by capturing multi-view images from different viewpoints. However, the camera's representation of the data is not ideal for common tasks such as data compression or 3D scene analysis, as it does not make the 3D scene geometry explicit. Image-based scene representations fundamentally require a multi-view image model that facilitates extraction of underlying geometrical relationships between the cameras and scene components. Developing new, efficient multi-view image models is thus one of the major challenges in image-based 3D scene representation methods. This dissertation focuses on defining and exploiting a new method for multi-view image representation, from which the 3D geometry information is easily extractable, and which is additionally highly compressible. The method is based on sparse image representation using an overcomplete dictionary of geometric features, where a single image is represented as a linear combination of few fundamental image structure features (edges for example). We construct the dictionary by applying a unitary operator to an analytic function, which introduces a composition of geometric transforms (translations, rotation and anisotropic scaling) to that function. The advantage of this approach is that the features across multiple views can be related with a single composition of transforms. We then establish a connection between image components and scene geometry by defining the transforms that satisfy the multi-view geometry constraint, and obtain a new geometric multi-view correlation model. We first address the construction of dictionaries for images acquired by omnidirectional cameras, which are particularly convenient for scene representation due to their wide field of view. Since most omnidirectional images can be uniquely mapped to spherical images, we form a dictionary by applying motions on the sphere, rotations, and anisotropic scaling to a function that lives on the sphere. We have used this dictionary and a sparse approximation algorithm, Matching Pursuit, for compression of omnidirectional images, and additionally for coding 3D objects represented as spherical signals. Both methods offer better rate-distortion performance than state of the art schemes at low bit rates. The novel multi-view representation method and the dictionary on the sphere are then exploited for the design of a distributed coding method for multi-view omnidirectional images. In a distributed scenario, cameras compress acquired images without communicating with each other. Using a reliable model of correlation between views, distributed coding can achieve higher compression ratios than independent compression of each image. However, the lack of a proper model has been an obstacle for distributed coding in camera networks for many years. We propose to use our geometric correlation model for distributed multi-view image coding with side information. The encoder employs a coset coding strategy, developed by dictionary partitioning based on atom shape similarity and multi-view geometry constraints. Our method results in significant rate savings compared to independent coding. An additional contribution of the proposed correlation model is that it gives information about the scene geometry, leading to a new camera pose estimation method using an extremely small amount of data from each camera. Finally, we develop a method for learning stereo visual dictionaries based on the new multi-view image model. Although dictionary learning for still images has received a lot of attention recently, dictionary learning for stereo images has been investigated only sparingly. Our method maximizes the likelihood that a set of natural stereo images is efficiently represented with selected stereo dictionaries, where the multi-view geometry constraint is included in the probabilistic modeling. Experimental results demonstrate that including the geometric constraints in learning leads to stereo dictionaries that give both better distributed stereo matching and approximation properties than randomly selected dictionaries. We show that learning dictionaries for optimal scene representation based on the novel correlation model improves the camera pose estimation and that it can be beneficial for distributed coding

    Sum-Rate Distortion Bound for Suboptimal Multiterminal Source Coding Applied in Medical Wireless Sensor Networks

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    College of Engineering

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    Cornell University Courses of Study Vol. 89 1997/9

    Renewable Energy

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    This book discusses renewable energy resources and systems as well as energy efficiency. It contains twenty-three chapters over six sections that address a multitude of renewable energy types, including solar and photovoltaic, biomass, hydroelectric, and geothermal. The information presented herein is a scientific contribution to energy and environmental regulations, quality and efficiency of energy services, energy supply security, energy market-based approaches, government interventions, and the spread of technological innovation
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