A high gain aluminium electron multiplier

A twenty stage electron multiplier using aluminium as dynode material is described. When operated in DC mode, very stable gains approaching 106 were obtained with input currents of the order of 10-12 A, even after repeated exposures to the atmospheres.

Inductor realisation using a finite gain differential amplifier

A finite gain differential amplifier is used along with a few passive RC elements to simulate an inductor. Methods for obtaining low Q inductance and frequency dependent high QI inductance are described. Sensitivity analysis when the gain varies is also included.

Theoretical gain optimization studies in 10.6 µm CO2-N2 gasdynamic lasers. IV. Further results of parametric study

Based on a method proposed by Reddy and Shanmugasundaram, similar solutions have been obtained for the steady inviscid quasi-one-dimensional nonreacting flow in the supersonic nozzle of CO2-N2-H2O and CO2-N2-He gasdynamic laser systems. Instead of using the correlations of a nonsimilar function NS for pure N2 gas, as is done in previous publications, the NS correlations are computed here for the actual gas mixtures used in the gasdynamic lasers. Optimum small-signal optical gain and the corresponding optimum values of the operating parameters like reservoir pressure and temperature and nozzle area ratio are computed using these correlations. The present results are compared with the previous results and the main differences are discussed. Journal of Applied Physics is copyrighted by The American Institute of Physics.

Theoretical gain optimization in CO2-N2-H2 gasdynamic lasers with two-dimensional wedge nozzles

Attention is given to the results of optimization studies with a 16-micron CO2-N2-H2 GDL employing two-dimensional wedge nozzles. The optimum value of the achievable gain reaches 12.7 percent/cm on the P(15) line for a 30:50:20 percent respective apportionment of the aforementioned gases. The corresponding optimum values for reservoir pressure and area ratio are computed as functions of reservoir temperature, and presented graphically.

Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors

The subspace intersection method (SIM) provides unbiased bearing estimates of multiple acoustic sources in a range-independent shallow ocean using a one-dimensional search without prior knowledge of source ranges and depths. The original formulation of this method is based on deployment of a horizontal linear array of hydrophones which measure acoustic pressure. In this paper, we extend SIM to an array of acoustic vector sensors which measure pressure as well as all components of particle velocity. Use of vector sensors reduces the minimum number of sensors required by a factor of 4, and also eliminates the constraint that the intersensor spacing should not exceed half wavelength. The additional information provided by the vector sensors leads to performance enhancement in the form of lower estimation error and higher resolution.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Low ML-Decoding Complexity, Large Coding Gain, Full-Rate, Full-Diversity STBCs for 2 x 2 and 4 x 2 MIMO Systems

This paper deals with low maximum-likelihood (ML)-decoding complexity, full-rate and full-diversity space-time block codes (STBCs), which also offer large coding gain, for the 2 transmit antenna, 2 receive antenna (2 x 2) and the 4 transmit antenna, 2 receive antenna (4 x 2) MIMO systems. Presently, the best known STBC for the 2 2 system is the Golden code and that for the 4 x 2 system is the DjABBA code. Following the approach by Biglieri, Hong, and Viterbo, a new STBC is presented in this paper for the 2 x 2 system. This code matches the Golden code in performance and ML-decoding complexity for square QAM constellations while it has lower ML-decoding complexity with the same performance for non-rectangular QAM constellations. This code is also shown to be information-lossless and diversity-multiplexing gain (DMG) tradeoff optimal. This design procedure is then extended to the 4 x 2 system and a code, which outperforms the DjABBA code for QAM constellations with lower ML-decoding complexity, is presented. So far, the Golden code has been reported to have an ML-decoding complexity of the order of for square QAM of size. In this paper, a scheme that reduces its ML-decoding complexity to M-2 root M is presented.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Multilevel Dodecagonal Space Vector Generation for Open-end Winding Induction Motor Drive Using Conventional Three Level Inverters

A novel dodecagonal space vector structure for induction motor drive is presented in this paper. It consists of two dodecagons, with the radius of the outer one twice the inner one. Compared to existing dodecagonal space vector structures, to achieve the same PWM output voltage quality, the proposed topology lowers the switching frequency of the inverters and reduces the device ratings to half. At the same time, other benefits obtained from existing dodecagonal space vector structure are retained here. This includes the extension of the linear modulation range and elimination of all 6+/-1 harmonics (n=odd) from the phase voltage. The proposed structure is realized by feeding an open-end winding induction motor with two conventional three level inverters. A detailed calculation of the PWM timings for switching the space vector points is also presented. Simulation and experimental results indicate the possible application of the proposed idea for high power drives.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.