Large rapidity gaps survival probabilities at LHC

We calculate the probability of large rapidity gaps in high energy hadronic collisions using a model based on QCD mini-jets and soft gluon emission down into the infrared region. Comparing with other models we find a remarkable agreement among most predictions.

Network pricing for QoS: A 'Regulation' approach

V. S. Borkar’s work was supported in part by grant number III.5(157)/99-ET from the Department of Science and Technology, Government of India. D. Manjunath’s work was supported in part by grant number 1(1)/2004-E-Infra from the Ministry of Information Technology, Government of India.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Evaluation of Suitable Locations for Generation Expansion in Restructured Power Systems: A Novel Concept of T-index Thukaram

In developing countries, a high rate of growth in the demand for electric energy is felt, and so the addition of new generating units becomes inevitable. In deregulated power systems, private generating stations are encouraged to add new generations. Some of the factors considered while placing a new generating unit are: availability of esources, ease of transmitting power, distance from the load centre, etc. Finding the most appropriate locations for generation expansion can be done by running repeated power flows and carrying system studies like analyzing the voltage profile, voltage stability, loss analysis, etc. In this paper a new methodology is proposed which will mainly consider the existing network topology. A concept of T-index is introduced in this paper, which considers the electrical distances between generator and load nodes. This index is used for ranking the most significant new generation expansion locations and also indicates the amount of permissible generations that can be installed at these new locations. This concept facilitates for the medium and long term planning of power generation expansions within the available transmission corridors. Studies carried out on an EHV equivalent 10-bus system and IEEE 30 bus systems are presented for illustration purposes.

Virtually cool ternary content addressable memory

Fast content addressable data access mechanisms have compelling applications in today's systems. Many of these exploit the powerful wildcard matching capabilities provided by ternary content addressable memories. For example, TCAM based implementations of important algorithms in data mining been developed in recent years; these achieve an an order of magnitude speedup over prevalent techniques. However, large hardware TCAMs are still prohibitively expensive in terms of power consumption and cost per bit. This has been a barrier to extending their exploitation beyond niche and special purpose systems. We propose an approach to overcome this barrier by extending the traditional virtual memory hierarchy to scale up the user visible capacity of TCAMs while mitigating the power consumption overhead. By exploiting the notion of content locality (as opposed to spatial locality), we devise a novel combination of software and hardware techniques to provide an abstraction of a large virtual ternary content addressable space. In the long run, such abstractions enable applications to disassociate considerations of spatial locality and contiguity from the way data is referenced. If successful, ideas for making content addressability a first class abstraction in computing systems can open up a radical shift in the way applications are optimized for memory locality, just as storage class memories are soon expected to shift away from the way in which applications are typically optimized for disk access locality.

SpecNet: Spectrum Sensing Sans Frontieres

While the under-utilization of licensed spectrum based on measurement studies conducted in a few developed countries has spurred lots of interest in opportunistic spectrum access, there exists no infrastructure today for measuring real-time spectrum occupancy across vast geographical regions. In this paper, we present the design and implementation of SpecNet, a first-of-its-kind platform that allows spectrum analyzers around the world to be networked and efficiently used in a coordinated manner for spectrum measurement as well as implementa- tion and evaluation of distributed sensing applications. We demonstrate the value of SpecNet through three applications: 1) remote spectrum measurement, 2) primary transmitter coverage estimation and 3) Spectrum-Cop that quickly identifies and localizes transmitters in a frequency range and geographic region of interest.

A generalized Stein's estimation approach to speech enhancement based on perceptual criteria

We address the problem of speech enhancement using a risk- estimation approach. In particular, we propose the use the Stein’s unbiased risk estimator (SURE) for solving the problem. The need for a suitable finite-sample risk estimator arises because the actual risks invariably depend on the unknown ground truth. We consider the popular mean-squared error (MSE) criterion first, and then compare it against the perceptually-motivated Itakura-Saito (IS) distortion, by deriving unbiased estimators of the corresponding risks. We use a generalized SURE (GSURE) development, recently proposed by Eldar for MSE. We consider dependent observation models from the exponential family with an additive noise model,and derive an unbiased estimator for the risk corresponding to the IS distortion, which is non-quadratic. This serves to address the speech enhancement problem in a more general setting. Experimental results illustrate that the IS metric is efficient in suppressing musical noise, which affects the MSE-enhanced speech. However, in terms of global signal-to-noise ratio (SNR), the minimum MSE solution gives better results.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Timing Recovery Algorithms and Architectures for 2-D Magnetic Recording Systems

We investigate the problem of timing recovery for 2-D magnetic recording (TDMR) channels. We develop a timing error model for TDMR channel considering the phase and frequency offsets with noise. We propose a 2-D data-aided phase-locked loop (PLL) architecture for tracking variations in the position and movement of the read head in the down-track and cross-track directions and analyze the convergence of the algorithm under non-separable timing errors. We further develop a 2-D interpolation-based timing recovery scheme that works in conjunction with the 2-D PLL. We quantify the efficiency of our proposed algorithms by simulations over a 2-D magnetic recording channel with timing errors.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Análisis de riesgo de plagas de importancia fitosanitaria para Nicaragua en la importación de semilla de cebolla (Allium cepa L.) procecedente de Canadá

El presente Análisis de riesgo de Plagas (ARP) se realizó en el Centro Nacional de Diagnóstico Fitosanitario (CNDF) y Cuarentena del MAG-FOR en Managua, Nicaragua, e1 cual tuvo por objetivo evaluar el riesgo de introducción, establecimiento y dispersión de plagas de importancia fitosanitaria para Nicaragua en semilla de cebolla (Allium cepa L.) importada de Canadá, así como determinar las medidas adecuadas de protección fitosanitarias para evitar la introducción de las mismas a Nicaragua. De un listado inicial de 18 plagas, sólo 6 plagas son sujetas a evaluación y manejo del riesgo después de pasar por las tres etapas de evaluación de un ARP según la Norma Centroamericana del OIRSA, por considerarse que estas presentan capacidad biológica y de comportamiento, así como rangos óptimos de temperaturas que se asemejan a las condiciones de clima de las zonas productoras de Allium cepa L. del país. (Anexo1). El cuadro 1 contiene la lista de las plagas no sujetas a Evaluación del Riesgo Puccinia allii ( DC) Rudolphi y Urocistis Cepulae. Frost que causan la Roya y el carbón de la cebolla respectivamente son consideradas plagas específicas de los cultivos del género Allium P. allí es considerada plaga de bajo riesgo de introducción al país siendo la limitante para su establecimiento que su óptimo de temperatura ( 1 O" a 15" C), no se asemeja a las condiciones de temperatura de las zonas productoras del país( Anexo l ), mientras que U. cepulae es considerado como un hongo muy peligroso capaz de provocar pérdidas en la producción de hasta 80 por ciento tiene alta probabilidad de establecimiento v diseminación en las zonas productoras del país va que las condiciones de edafoclímaticas (16-22" C y pH 5-8) le favorecen( Anexo 1) Botryotinia squamosa. Viennot-Bourgín es un hongo que causa la pudrición blanda de la cebolla, se considera una plaga especifica de cultivos del género Allium con probabilidad alta de establecimiento v disemmac1ón en las zonas productoras de cebolla del país ya que el rango superior óptimo de temperatura (23" C) se asemeja a las condiciones de temperatura de las mismas (Anexo 11. Sclerotium cepivorum. Berkeley (1841 ). Es un hongo que causa la pudrición blanca de la cebolla y es considerado una plaga general de corto rango de hospedero quien además de atacar a cultivos del género Allium ataca también a lycopersicum esculentum (tomate) puede causar pérdidas en la producción de hasta un 70 por ciento (promedio) Y, con riesgo Alto de establecimiento en el país ya que temperaturas de 1 0"-20" C y baja humedad 40%. (pariona et. al) inclusive 30"- 5"C cuando hay humedad (Agrios 1985) y pH desde 1.4 a 8.8 le favorecen y las condiciones edafoclimáticas de las zonas productoras de cebolla del país se asemejan (Anexo ll). El nemátodo Ditylenchus dipsaci es un endoparásito destructor, y es considerado plaga general de cultivos del género Allium que ataca otros cultivos como cucurbitáceas. Zea mays (Maíz), Solanum tuberosum (Papa) y Nicotiana Tabacum (Tabaco). Es capaz de reducir la producción hasta un 75 por ciento (promedio) y la probabilidad de ingreso al país es media, a pesar de que su óptimo de temperatura (15º C) no se asemejan a la temperatura de las zonas productoras de cebolla del país. se considera de gran importancia sus características como: gran capacidad reproductiva es endoparásito y podemos encontrarlo dentro de cualquier parte de la planta inclusive dentro de la semilla. Aphelenchoides fragariae es un nematodo que tiene por hospedero secundario a Allium cepa. L puede ser capaz de ocasionar pérdidas en la producción de hasta 66.5% (promedio), es considerado plaga general de cultivos del género Allium con baja probabilidad de ingreso al país, va que su hospedero primario es la fresa y no se reportan daños en cebolla (no preferencia por Allium cepa L que es hospedero secundario) Todas estas plagas son consideradas de categoría A 1 1 para Nicaragua (no presentes en el país), lo que justifica plenamente la realización de este ARP a fin de tomar las medidas pertinentes que acompañen la importación de semilla y así evitar la introducción de nuevas plagas de difícil control.