Source-channel coding for Gaussian sources over a Gaussian multiple access channel

We consider the problem of distributed joint source-channel coding of correlated Gaussian sources over a Gaussian Multiple Access Channel (MAC). There may be side information at the encoders and/or at the decoder. First we specialize a general result in [16] to obtain sufficient conditions for reliable transmission over a Gaussian MAC. This system does not satisfy the source channel separation. Thus, next we study and compare three joint source channel coding schemes available in literature.

A Unified Approach to Encoding and Classification Using Bimodal Projection-Based Features

In general the objective of accurately encoding the input data and the objective of extracting good features to facilitate classification are not consistent with each other. As a result, good encoding methods may not be effective mechanisms for classification. In this paper, an earlier proposed unsupervised feature extraction mechanism for pattern classification has been extended to obtain an invertible map. The method of bimodal projection-based features was inspired by the general class of methods called projection pursuit. The principle of projection pursuit concentrates on projections that discriminate between clusters and not faithful representations. The basic feature map obtained by the method of bimodal projections has been extended to overcome this. The extended feature map is an embedding of the input space in the feature space. As a result, the inverse map exists and hence the representation of the input space in the feature space is exact. This map can be naturally expressed as a feedforward neural network.

Structural Alignment based Kernels for Protein Structure Classification

Structural alignments are the most widely used tools for comparing proteins with low sequence similarity. The main contribution of this paper is to derive various kernels on proteins from structural alignments, which do not use sequence information. Central to the kernels is a novel alignment algorithm which matches substructures of fixed size using spectral graph matching techniques. We derive positive semi-definite kernels which capture the notion of similarity between substructures. Using these as base more sophisticated kernels on protein structures are proposed. To empirically evaluate the kernels we used a 40% sequence non-redundant structures from 15 different SCOP superfamilies. The kernels when used with SVMs show competitive performance with CE, a state of the art structure comparison program.

An Empirical Study of Synthesis of Multiple State Devices by Engineering Designers

Automated synthesis of mechanical designs is an important step towards the development of an intelligent CAD system. Research into methods for supporting conceptual design using automated synthesis has attracted much attention in the past decades. The research work presented here is based on an empirical study of the process of synthesis of multiple state mechanical devices. As a background to the work, the paper explores concepts of what mechanical device, state, single state and multiple state are, and in the context of the above observational studies, attempts to identify the outstanding issues for supporting multiple state synthesis of mechanical devices.

Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

Computational Analysis of Flow through a Multiple Nozzle Driven Laser Cavity and Diffuser

Computations have been carried out for simulating supersonic flow through a set of converging-diverging nozzles with their expanding jets forming a laser cavity and flow patterns through diffusers, past the cavity. A thorough numerical investigation with 3-D RANS code is carried out to capture the flow distribution which comprises of shock patterns and multiple supersonic jet interactions. The analysis of pressure recovery characteristics during the flow through the diffusers is an important parameter of the simulation and is critical for the performance of the laser device. The results of the computation have shown a close agreement with the experimentally measured parameters as well as other established results indicating that the flow analysis done is found to be satisfactory.

Investigating Diproline Segments in Proteins: Occurrences, Conformation and Classification

The covalent linkage between the side-chain and the backbone nitrogen atom of proline leads to the formation of the five-membered pyrrolidine ring and hence restriction of the backbone torsional angle phi to values of -60 degrees +/- 30 degrees for the L-proline. Diproline segments constitute a chain fragment with considerably reduced conformational choices. In the current study, the conformational states for the diproline segment ((L)Pro-(L)Pro) found in proteins has been investigated with an emphasis on the cis and trans states for the Pro-Pro peptide bond. The occurrence of diproline segments in turns and other secondary structures has been studied and compared to that of Xaa-Pro-Yaa segments in proteins which gives us a better understanding on the restriction imposed on other residues by the diproline segment and the single proline residue. The study indicates that P(II)-P(II) and P(II)-alpha are the most favorable conformational states for the diproline segment. The analysis on Xaa-Pro-Yaa sequences reveals that the XaaPro peptide bond exists preferably as the trans conformer rather than the cis conformer. The present study may lead to a better understanding of the behavior of proline occurring in diproline segments which can facilitate various designed diproline-based synthetic templates for biological and structural studies. (C) 2011 Wiley Periodicals, Inc. Biopolymers 97: 54-64, 2012.

Modelling and classification of respiratory volume wavefourms

A technique is proposed for classifying respiratory volume waveforms(RVW) into normal and abnormal categories of respiratory pathways. The proposed method transforms the temporal sequence into frequency domain by using an orthogonal transform, namely discrete cosine transform (DCT) and the transformed signal is pole-zero modelled. A Bayes classifier using model pole angles as the feature vector performed satisfactorily when a limited number of RVWs recorded under deep and rapid (DR) manoeuvre are classified.

A generalized network alignment for three-source three-destination multiple unicast networks with delays

The concept of interference alignment when extended to three-source three-destination instantaneous multiple unicast network for the case where, each source-destination pair has a min-cut of 1 and zero-interference conditions are not satisfied, is known to achieve a rate of half for every source-destination pair under certain conditions [6]. This was called network alignment. We generalize this concept of network alignment to three-source three-destination multiple unicast (3S-3D-MU) networks with delays, without making use of memory at the intermediate nodes (i.e., nodes other than the sources and destinations) and using time varying Local Encoding Kernels (LEKs). This achieves half the rate corresponding to the individual source-destination min-cut for some classes of 3S-3D-MU network with delays which do not satisfy the zero-interference conditions.