Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity) Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context Through this survey, it is clear that context-aware research is an old but rich area for research The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality

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A Survey of Context-Aware Mobile Computing Research

The Transmission Control Protocol.

Recent developments and technological advancements in wireless communication, MicroElectroMechanical Systems (MEMS) technology and integrated circuits has enabled low-power, intelligent, miniaturized, invasive/non-invasive micro and nano-technology sensor nodes strategically placed in or around the human body to be used in various applications, such as personal health monitoring. This exciting new area of research is called Wireless Body Area Networks (WBANs) and leverages the emerging IEEE 802.15.6 and IEEE 802.15.4j standards, specifically standardized for medical WBANs. The aim of WBANs is to simplify and improve speed, accuracy, and reliability of communication of sensors/actuators within, on, and in the immediate proximity of a human body. The vast scope of challenges associated with WBANs has led to numerous publications. In this paper, we survey the current state-of-art of WBANs based on the latest standards and publications. Open issues and challenges within each area are also explored as a source of inspiration towards future developments in WBANs.

Wireless Body Area Networks: A Survey

Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.

Body Area Networks: A Survey

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.

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Growing an organic indoor location system

Many indoor localization methods are based on the association of 802.11 wireless RF signals from wireless access points (WAPs) with location labels. An “organic” RF positioning system relies on regular users, not dedicated surveyors, to build the map of RF fingerprints to location labels. However, signal variation due to device heterogeneity may degrade localization performance. We analyze the diversity of those signal characteristics pertinent to indoor localization — signal strength and AP detection — as measured by a variety of 802.11 devices. We first analyze signal strength diversity, and show that pairwise linear transformation alone does not solve the problem. We propose kernel estimation with a wide kernel width to reduce the difference in probability estimates. We also investigate diversity in access point detection. We demonstrate that localization performance may degrade significantly when AP detection rate is used as a feature for localization, and correlate the loss of performance to a device dissimilarity measure captured by Kullback-Leibler divergence. Based on this analysis, we show that using only signal strength, without incorporating negative evidence, achieves good localization performance when devices are heterogeneous.

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Implications of device diversity for organic localization

Argus is a programming language and system developed to support the construction and execution of distributed programs. This paper describes the implementation of Argus, with particular emphasis on the way we implement atomic actions, because this is where Argus differs most from other implemented systems. The paper also discusses the performance of Argus. The cost of actions is quite reasonable, indicating that action systems like Argus are practical.

Implementation of Argus

The present invention provides systems and methods for performing automated auscultation and diagnosis of conditions of the cardiovascular system. The invention acquires an acoustic signal emanating from the cardiovascular system via an sensor. In addition, in certain embodiments of the invention an electrical signal, e.g., an electrocardiogram (EKG) is simultaneously acquired. The signals are digitized, and, optionally, filtered to remove noise. The invention then processes and analyses the signal(s) so as to provide a clinically relevant conclusion or recommendation such as a 10 diagnosis or suggested additional tests or therapy. In preferred embodiments the system comprises: (1) a beat selection component that selects a plurality of beats for analysis, wherein each beat comprises an acoustic signal emanating from the cardiovascular system; (2) a time-frequency analysis component that performs a time­frequency decomposition of beats selected for analysis so as to identify or extract physiologically relevant features; and (3) a processing component that processes the information so at to provide a clinically relevant conclusion or recommendation. In preferred embodiments of the invention the system further includes an aggregation component that combines information obtained from a plurality of the beats selected for analysis. In one embodiment, the system diagnoses mitral valve prolapse.

Automated auscultation system

We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

/pdf/online-pose-classification-and-walking-speed-estimation-6bykbu8fh8.pdf

Dorothy Curtis

Papers

Growing an organic indoor location system

Implications of device diversity for organic localization

Implementation of Argus

Automated auscultation system

Online pose classification and walking speed estimation using handheld devices