Electrical and Electronic Engineering - Research Publications

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    Maximizing Sum-Rate via Relay Selection and Power Control in Dual-Hop Networks
    Dayarathna, S ; Senanayake, R ; Evans, J (IEEE, 2022-01-01)
    In this paper, we focus on the sum-rate optimization problem in a general dual-hop relay network by considering the joint relay selection and power control in the presence of interference. First, we propose a new relay selection algorithm which has better sum-rate performance than the existing relay selection techniques. Then we combine relay selection and power control to propose a novel iterative algorithm based on the tight lower bound approximation which maximizes the achievable sum-rate. We also prove that for the special case of two-user networks, binary power allocation is optimum for at least two transmitting nodes. Extensive numerical examples are used to compare the performance of the proposed algorithm and to illustrate the accuracy of the analysis.
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    SHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMBMOTION
    Datta, S ; Karmakar, C ; Rathore, P ; Palaniswami, M (IEEE, 2021-01-01)
    The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. Exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as Visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix for VAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.
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    Rapid Nonovershooting Control for Simultaneous Infusion of Anesthetics and Analgesics
    Wang, C ; Liu, Y ; Schmid, R (ELSEVIER, 2021-11-02)
    We propose a rapid nonovershooting tracking controller for the continuous infusion of anesthetics and analgesics to prevent overdosing and other harmful side effects on patients. The controller utilizes a state feedback control design methodology for multi-input multi-output systems to achieve a closed-loop eigenstructure that yields a nonovershooting transient response. The method is combined with a global optimization method to achieve a rapid nonovershooting response. The controller uses an extended Kalman filter to estimate system states from measurable outputs, and integral control is added to achieve robust tracking. The performance of the method is simulated on 20 patient models in two groups, and the results are compared against another recent study from the biomedical control literature.
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    Preface
    Hojjat, H ; Kafle, B (Open Publishing Association, 2021-09-13)
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    Moving Horizon Estimation for Linear Cascade Systems
    Guo, M ; Lang, A ; Cantoni, M (IEEE, 2018-01-01)
    A structured approach to the problem of state estimation for cascaded linear sub-systems is studied in terms of minimizing a measure of the error relative to a model over a moving horizon of past system input and output observations. A quadratic programming formulation of this optimization problem is considered and two approaches are explored. One approach involves solving the Karush-Kuhn-Tucker conditions directly, and the other is based on the alternating direction method of multipliers. In both cases, the problem structure can be exploited to yield distributed computations in the following sense: Construction of the estimate for each sub-system component of the state involves information pertaining to the two immediate neighbours only. Numerical simulations based on model data from an automated irrigation channel are used to investigate and compare the computational burden of the two approaches.
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    Structured moving horizon estimation for linear system chains
    Guo, M ; Lang, A ; Cantoni, M (IEEE, 2019-06-01)
    Computational aspects of moving horizon state estimation are studied for a class of chain networks with bidirectional coupling in the linear state dynamics, and measured outputs. Moving horizon estimation involves solving a quadratic program to minimize the estimation error relative to a model over a fixed window of past input-output observations. By exploiting the spatial structure of a chain, two algorithms for solving this quadratic program are considered. Both algorithms can be distributed in the sense that the computations associated with each sub-system component of the state depend only on information associated with the immediate neighbours. The algorithms differ in the way that the linear Karush-Kuhn-Tucker conditions for optimality are solved. Computational and information dependency overheads are analyzed. Numerical results are presented for a 1-D mass-spring-damper chain.
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    Direct Predictive Boundary Control of a First-order Quasilinear Hyperbolic PDE
    Strecker, T ; Aamo, OM ; Cantoni, M (IEEE, 2020-03-12)
    We present a method for the boundary control of a system governed by one hyperbolic PDE with a non-local coupling term by state feedback. The method is an extension of recently developed controllers for semilinear systems. The design consists of three steps: predicting the states up to the time when they are affected by the delayed input; virtually moving the input to the uncontrolled boundary (which makes characterizing stability trivial); and constructing the inputs by, starting with the desired boundary values at the uncontrolled boundary, solving an ODE governing the dynamics on the system's characteristic lines backwards in time. The controller steers the system to the origin in finite time. A discussion of potential extensions of the presented method is given.
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    Optimization based input preview filtering for dynamical systems
    Lang, A ; Cantoni, M (IEEE, 2020-03-12)
    This paper is about filtering uncertain forecast information to update a preview model of inputs to a linear dynamical system, as may be useful in predictive control schemes. A moving horizon optimization approach is proposed, with a view to smoothing abrupt changes in order based forecast information and to manage error, given observations of the dynamics. Numerical examples are used to illustrate a potential application of this approach within the context of processing demand profile requests in a water distribution system.
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    Anomalous Behavior Detection in Crowded Scenes Using Clustering and Spatio-Temporal Features
    Yang, M ; Rajasegarar, S ; Rao, AS ; Leckie, C ; Palaniswami, M ; Shi, Z ; Vadera, S ; Li, G (Springer, 2016)
    important problem in real-life applications. Detection of anomalous behaviors such as people standing statically and loitering around a place are the focus of this paper. In order to detect anomalous events and objects, ViBe was used for background modeling and object detection at first. Then, a Kalman filter and Hungarian cost algorithm were implemented for tracking and generating trajectories of people. Next, spatio-temporal features were extracted and represented. Finally, hyperspherical clustering was used for anomaly detection in an unsupervised manner. We investigate three different approaches to extracting and representing spatio-temporal features, and we demonstrate the effectiveness of our proposed feature representation on a standard benchmark dataset and a real-life video surveillance environment.
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    Non-Protruding Hazard Detection for the Aged Vision-Impaired
    Sridhara Rao, A ; Gubbi, J ; Palaniswami, M ; WONG, E (IEEE, 2016)
    Usage of the traditional white cane by the elderly with vision impairment is inefficient as many are also reliant on ambulatory aids such as wheelchairs and walking frames. The fall occurrence when using ambulatory aids is higher, contributed by non-protruding hazards such as potholes and drop-offs. Currently available technology for blind navigation, predominantly based on proximity sensing, is not designed to detect non protruding hazards. We address this critical need by developing a new optical laser system that combines innovative approaches in optical laser projection, vision-sensing, pattern recognition, and machine learning. Here, we present an overview of the system, including a new feature descriptor termed Histogram of Intersections, and results from our proof-of-concept demonstration.