High voltage DC power supply topology for pulsed load applications with converter switching synchronized to load pulses

High voltage power supplies for radar applications are investigated, which are subjected to pulsed load (125 kHz and 10% duty cycle) with stringent specifications (<0.01% regulation, efficiency>85%, droop<0.5 V/micro-sec.). As good regulation and stable operation requires the converter to be switched at much higher frequency than the pulse load frequency, transformer poses serious problems of insulation failure and higher losses. This paper proposes a methodology to tackle the problems associated with this type of application. Synchronization of converter switching with load pulses enables the converter to switch at half the load switching frequency. Low switching frequency helps in ensuring safety of HV transformer insulation and reduction of losses due to skin and proximity effect. Phase-modulated series resonant converter with ZVS is used as the power converter.

Input voltage modulated high voltage DC power supply topology for pulsed load applications

High voltage power supplies for radar applications are investigated which are subjected to pulsed load with stringent specifications. In the proposed solution, power conversion is done in two stages. A low power-high frequency converter modulates the input voltage of a high power-low frequency converter. This method satisfies all the performance specifications and takes care of the critical aspects of HV transformer.

Decision tree design and applications in speech processing

The design and operation of the minimum cost classifier, where the total cost is the sum of the measurement cost and the classification cost, is computationally complex. Noting the difficulties associated with this approach, decision tree design directly from a set of labelled samples is proposed in this paper. The feature space is first partitioned to transform the problem to one of discrete features. The resulting problem is solved by a dynamic programming algorithm over an explicitly ordered state space of all outcomes of all feature subsets. The solution procedure is very general and is applicable to any minimum cost pattern classification problem in which each feature has a finite number of outcomes. These techniques are applied to (i) voiced, unvoiced, and silence classification of speech, and (ii) spoken vowel recognition. The resulting decision trees are operationally very efficient and yield attractive classification accuracies.

Microwave cavity resonators. Some perturbation effects and their applications

Lagrange's equation is utilized to show the analogy of a lossless microwave cavity resonator with the conventional LC network. A brief discussion on the resonant frequencies of a microwave cavity resonator and the two degenerate companion modes H01 and E11 appearing in a cavity is given. The first order perturbation theory of a small deformation of the wall of a cavity is discussed. The effects of perturbation, such as the change in the resonant frequency and the Q of a cavity, the change in the electromagnetic field configurations and hence mixing of modes are also discussed. An expression for the coupling coefficient between the two degenerate modes H01 and E11 is derived with the help of the field equations. Results indicate that in the absence of perturbation the above two degenerate modes can co-exist without losing their individual identities. Several applications of the perturbation theory, such as the measurement of the dielectric properties of matter, study of ferromagnetic resonance, etc., are described.

Immunomodulation using agonists and antagonists: potential clinical applications

Introduction: Extensive studies have gone into understanding the differential role of the innate and adaptive arms of the immune system in the context of various diseases. Receptor-ligand interactions are responsible for mediating cross-talk between the innate and adaptive arms of the immune system, so as to effectively counter the pathogenic challenge. While TLRs remain the best studied innate immune receptor, many other receptor families are now coming to the fore for their role in various pathologies. Research has focused on the discovery of novel agonists and antagonists for these receptors as potential therapeutics. Areas covered: In this review, we present an overview of the recent advances in the discovery of drugs targeting important receptors such as G-protein coupled receptors, TRAIL-R, IL-1 beta receptor, PPARs, etc. All these receptors play a critical role in the modulation of the immune response. We focus on the recent paradigms applied for the generation of specific and effective therapeutics for these receptors and their status in clinical trials. Expert opinion: Non-specific activation by antagonist/agonist is a difficult problem to dodge. This demands innovation in ligand designing with the use of strategies such as allosterism and dual-specific ligands. Rigorous preclinical and clinical studies are required in transforming a compound to a therapeutic.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Smooth DMS-FEM: A new approach to solving nearly incompressible nonlinear elasto-static problems

The DMS-FEM, which enables functional approximations with C(1) or still higher inter-element continuity within an FEM-based meshing of the domain, has recently been proposed by Sunilkumar and Roy [39,40]. Through numerical explorations on linear elasto-static problems, the method was found to have conspicuously superior convergence characteristics as well as higher numerical stability against locking. These observations motivate the present study, which aims at extending and exploring the DMS-FEM to (geometrically) nonlinear elasto-static problems of interest in solid mechanics and assessing its numerical performance vis-a-vis the FEM. In particular, the DMS-FEM is shown to vastly outperform the FEM (presently implemented through the commercial software ANSYS (R)) as the former requires fewer linearization and load steps to achieve convergence. In addition, in the context of nearly incompressible nonlinear systems prone to volumetric locking and with no special numerical artefacts (e.g. stabilized or mixed weak forms) employed to arrest locking, the DMS-FEM is shown to approach the incompressibility limit much more closely and with significantly fewer iterations than the FEM. The numerical findings are suggestive of the important role that higher order (uniform) continuity of the approximated field variables play in overcoming volumetric locking and the great promise that the method holds for a range of other numerically ill-conditioned problems of interest in computational structural mechanics. (C) 2011 Elsevier Ltd. All rights reserved.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Enhancing Speedup in Network Processing Applications by Exploiting Instruction Reuse with Flow Aggregation

Instruction reuse is a microarchitectural technique that improves the execution time of a program by removing redundant computations at run-time. Although this is the job of an optimizing compiler, they do not succeed many a time due to limited knowledge of run-time data. In this paper we examine instruction reuse of integer ALU and load instructions in network processing applications. Specifically, this paper attempts to answer the following questions: (1) How much of instruction reuse is inherent in network processing applications?, (2) Can reuse be improved by reducing interference in the reuse buffer?, (3) What characteristics of network applications can be exploited to improve reuse?, and (4) What is the effect of reuse on resource contention and memory accesses? We propose an aggregation scheme that combines the high-level concept of network traffic i.e. "flows" with a low level microarchitectural feature of programs i.e. repetition of instructions and data along with an architecture that exploits temporal locality in incoming packet data to improve reuse. We find that for the benchmarks considered, 1% to 50% of instructions are reused while the speedup achieved varies between 1% and 24%. As a side effect, instruction reuse reduces memory traffic and can therefore be considered as a scheme for low power.

Biocorrosion in industrial applications

Industrial situations afflicted by corrosion induced by microorganisms are illustrated with examples. The types and characteristics of microorganisms involved in biocorrosion processes are discussed. Possible mechanisms in biocorrosion as occurring under sub-soil, sea water and fresh water conditions are analyzed. Methods to combat biocorrosion are also outlined.

Session 6 grouting and deep mixing: New concept and applications

The theme of the session is the New Concept and Applications both for the grouting and deep mixing technologies. Nineteen papers were submitted to this session, and those covered a variety of topics; 1) New concepts and development, 2) Refinement of techniques, and 3) analysis and applications. Eight papers out of them were presented orally.

Non-Thermal Plasma Applications at Very Low Temperature

Application of non-thermal plasma for gas cleaning is gaining prominence in the recent years. Normally, the gas treatment was carried out at or above room temperature, by the dry type plasma reactor. However, this treatment is still inadequate in the removal of certain stable gases present in the flue gas mixture. We propose the non-thermal plasma process at very low temperature, and report here some interesting results of treatment of NO or N2O with pulsed plasma below — 100°C ambient temperature. Direct methanol synthesis from CH4 and CO2 at very low temperature is also reported. A comparative analysis of the various tests are presented together with a note on the energy consideration

Non-thermal plasma applications at Very Low Temperature

Application of non-thermal plasma for gas cleaning is gaining prominence in the recent years. Normally, the gas treatment was carried out at or above room temperature, by the dry type plasma reactor. However, this treatment is still inadequate in the removal of certain stable gases present in the flue gas mixture. We propose the non-thermal plasma process at very low temperature, and report here some interesting results of treatment of NO or N2O with pulsed plasma below — 100°C ambient temperature. Direct methanol synthesis from CH4 and CO2 at very low temperature is also reported. A comparative analysis of the various tests are presented together with a note on the energy consideration