Locating Two Transfer Points on a Network with a Trip Covering Criterion and Mixed Distances

In this paper we consider a set of origin-destination pairs in a mixed model in which a network embedded in the plane represents an alternative high-speed transportation system, and study a trip covering problem which consists on locating two points in the network which maximize the number of covered pairs, that is, the number of pairs which use the network by acceding and exiting through such points. To deal with the absence of convexity of this mixed distance function we propose a decomposition method based on formulating a collection of subproblems and solving each of them via discretization of the solution set.Ministerio de Educación, Ciencia e Innovación MTM2009-14243Ministerio de Economía y Competitividad MTM2012-37048Junta de Andalucía P09-TEP-5022Junta de Andalucía P10-FQM-584

A general approach for the location of transfer points on a network with a trip covering criterion and mixed distances

In this paper we consider a trip covering location model in a mixed planar-network space. An embed- ded network in the plane represents an alternative transportation system in which traveling is fasterthan traveling within the plane. We assume that the demand to be covered is given by a set of origin- destination pairs in the plane, with some traffic between them. An origin-destination pair is covered bytwo facility points on the network (or transfer points), if the travel time from the origin to destinationby using the network through such points is not higher than a given acceptance level related to the traveltime without using the network. The facility location problems studied in this work consist of locatingone or two transfer points on the network such that, under several objective functions, the traffic throughthe network is maximized. Due to the continuous nature of these problems, a general approach is pro- posed for discretizing them. Since the non-convexity of the distance function on cyclic networks alsoimplies the absence of convexity of the mixed distance function, such an approach is based on a decom- position process which leads to a collection of subproblems whose solution set can be found by adaptingthe general strategy to each problem considered.Ministerio de Economía y Competitividad MTM2012-37048Ministerio de Economía y Competitividad MTM2015-67706-PJunta de Andalucía P10-FQM-584

Key Generation in Wireless Sensor Networks Based on Frequency-selective Channels - Design, Implementation, and Analysis

Key management in wireless sensor networks faces several new challenges. The scale, resource limitations, and new threats such as node capture necessitate the use of an on-line key generation by the nodes themselves. However, the cost of such schemes is high since their secrecy is based on computational complexity. Recently, several research contributions justified that the wireless channel itself can be used to generate information-theoretic secure keys. By exchanging sampling messages during movement, a bit string can be derived that is only known to the involved entities. Yet, movement is not the only possibility to generate randomness. The channel response is also strongly dependent on the frequency of the transmitted signal. In our work, we introduce a protocol for key generation based on the frequency-selectivity of channel fading. The practical advantage of this approach is that we do not require node movement. Thus, the frequent case of a sensor network with static motes is supported. Furthermore, the error correction property of the protocol mitigates the effects of measurement errors and other temporal effects, giving rise to an agreement rate of over 97%. We show the applicability of our protocol by implementing it on MICAz motes, and evaluate its robustness and secrecy through experiments and analysis.Comment: Submitted to IEEE Transactions on Dependable and Secure Computin

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Recognition of Radar-Based Deaf Sign Language Using Convolution Neural Network

The difficulties in the communication between the deaf and normal people through sign language can be overcome by implementing deep learning in the gestures signal recognition. The use of the Convolution Neural Network (CNN) in distinguishing radar-based gesture signals of deaf sign language has not been investigated. This paper describes the recognition of gestures of deaf sign language using radar and CNN. Six gestures of deaf sign language were acquired from normal subjects using a radar system and processed. Short-time Fourier Transform was performed to extract the gestures features and the classification was performed using CNN. The performance of CNN was examined using two types of inputs; segmented and non-segmented spectrograms. The accuracy of recognising the gestures is higher (92.31%) using the non-segmented spectrograms compared to the segmented spectrogram. The radar-based deaf sign language could be recognised accurately using CNN without segmentation

Compiler Optimization Techniques for Scheduling and Reducing Overhead

Exploiting parallelism in loops in programs is an important factor in realizing the potential performance of processors today. This dissertation develops and evaluates several compiler optimizations aimed at improving the performance of loops on processors. An important feature of a class of scientific computing problems is the regularity exhibited by their access patterns. Chapter 2 presents an approach of optimizing the address generation of these problems that results in the following: (i) elimination of redundant arithmetic computation by recognizing and exploiting the presence of common sub-expressions across different iterations in stencil codes; and (ii) conversion of as many array references to scalar accesses as possible, which leads to reduced execution time, decrease in address arithmetic overhead, access to data in registers as opposed to caches, etc. With the advent of VLIW processors, the exploitation of fine-grain instruction-level parallelism has become a major challenge to optimizing compilers. Fine-grain scheduling of inner loops has received a lot of attention, little work has been done in the area of applying it to nested loops. Chapter 3 presents an approach to fine-grain scheduling of nested loops by formulating the problem of finding theminimum iteration initiation interval as one of finding a rational affine schedule for each statement in the body of a perfectly nested loop which is then solved using linear programming. Frequent synchronization on multiprocessors is expensive due to its high cost. Chapter 4 presents a method for eliminating redundant synchronization for nested loops. In nested loops, a dependence may be redundant in only a portion of the iteration space. A characterization of the non-uniformity of the redundancy of a dependence is developed in terms of the relation between the dependences and the shape and size of the iteration space. Exploiting locality is critical for achieving high level of performance on a parallel machine. Chapter 5 presents an approach using the concept of affinity regions to find transformations such that a suitable iteration-to-processor mapping can be found for a sequence of loop nests accessing shared arrays. This not only improves the data locality but significantly reduces communication overhead

Projection-based curve clustering

This paper focuses on unsupervised curve classification in the context of nuclear industry. At the Commissariat à l'Energie Atomique (CEA), Cadarache (France), the thermal-hydraulic computer code CATHARE is used to study the reliability of reactor vessels. The code inputs are physical parameters and the outputs are time evolution curves of a few other physical quantities. As the CATHARE code is quite complex and CPU-time consuming, it has to be approximated by a regression model. This regression process involves a clustering step. In the present paper, CATHARE output curves are clustered using a k-means scheme, with a projection onto a lower dimensional space. We study the properties of the empirically optimal cluster centers found by the clustering method based on projections, compared to the “true” ones. The choice of the projection basis is discussed, and an algorithm is implemented to select the best projection basis among a library of orthonormal bases. The approach is illustrated on a simulated example and then applied to the industrial problem