HD resolution intra prediction architecture for H.264 decoder

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution. The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

Compressed acquisition of correlated signals

Distributed compressed sensing exploits information redundancy, inbuilt in multi-signal ensembles with interas well as intra-signal correlations, to reconstruct undersampled signals. In this paper we revisit this problem, albeit from a different perspective, of taking streaming data, from several correlated sources, as input to a real time system which, without any a priori information, incrementally learns and admits each source into the system.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Natural motion of one-dimensional flexible objects using minimization approaches

For one-dimensional flexible objects such as ropes, chains, hair, the assumption of constant length is realistic for large-scale 3D motion. Moreover, when the motion or disturbance at one end gradually dies down along the curve defining the one-dimensional flexible objects, the motion appears ``natural''. This paper presents a purely geometric and kinematic approach for deriving more natural and length-preserving transformations of planar and spatial curves. Techniques from variational calculus are used to determine analytical conditions and it is shown that the velocity at any point on the curve must be along the tangent at that point for preserving the length and to yield the feature of diminishing motion. It is shown that for the special case of a straight line, the analytical conditions lead to the classical tractrix curve solution. Since analytical solutions exist for a tractrix curve, the motion of a piecewise linear curve can be solved in closed-form and thus can be applied for the resolution of redundancy in hyper-redundant robots. Simulation results for several planar and spatial curves and various input motions of one end are used to illustrate the features of motion damping and eventual alignment with the perturbation vector.

Network, nodes and nexus: systems approach to multitarget therapeutics

Systems biology is revealing multiple layers of regulatory networks that manifest spatiotemporal variations. Since genes and environment also influence the emergent property of a cell, the biological output requires dynamic understanding of various molecular circuitries. The metabolic networks continually adapt and evolve to cope with the changing milieu of the system, which could also include infection by another organism. Such perturbations of the functional networks can result in disease phenotypes, for instance tuberculosis and cancer. In order to develop effective therapeutics, it is important to determine the disease progression profiles of complex disorders that can reveal dynamic aspects and to develop mutitarget systemic therapies that can help overcome pathway adaptations and redundancy.

On Averaging Multiview Relations for 3D Scan Registration

In this paper, we present an extension of the iterative closest point (ICP) algorithm that simultaneously registers multiple 3D scans. While ICP fails to utilize the multiview constraints available, our method exploits the information redundancy in a set of 3D scans by using the averaging of relative motions. This averaging method utilizes the Lie group structure of motions, resulting in a 3D registration method that is both efficient and accurate. In addition, we present two variants of our approach, i.e., a method that solves for multiview 3D registration while obeying causality and a transitive correspondence variant that efficiently solves the correspondence problem across multiple scans. We present experimental results to characterize our method and explain its behavior as well as those of some other multiview registration methods in the literature. We establish the superior accuracy of our method in comparison to these multiview methods with registration results on a set of well-known real datasets of 3D scans.

CONFORMATIONAL FEATURES OF SIGMA FACTOR/ANTI-SIGMA COMPLEXES

The association of a factors with the RNA polymerase dictates the expression profile of a bacterial cell. Major changes to the transcription profile are achieved by the use of multiple sigma factors that confer distinct promoter selectivity to the holoenzyme. The cellular concentration of a sigma factor is regulated by diverse mechanisms involving transcription, translation and post-translational events. The number of sigma factors varies substantially across bacteria. The diversity in the interactions between sigma factors also vary-ranging from collaboration, competition or partial redundancy in some cellular or environmental contexts. These interactions can be rationalized by a mechanistic model referred to as the partitioning of a space model of bacterial transcription. The structural similarity between different sigma/anti-sigma complexes despite poor sequence conservation and cellular localization reveals an elegant route to incorporate diverse regulatory mechanisms within a structurally conserved scaffold. These features are described here with a focus on sigma/anti-sigma complexes from Mycobacterium tuberculosis. In particular, we discuss recent data on the conditional regulation of sigma/anti-sigma factor interactions. Specific stages of M. tuberculosis infection, such as the latent phase, as well as the remarkable adaptability of this pathogen to diverse environmental conditions can be rationalized by the synchronized action of different a factors.

Plasmodium berghei glycine cleavage system T-protein is non-essential for parasite survival in vertebrate and invertebrate hosts

T-protein, an aminomethyltransferase, represents one of the four components of glycine cleavage system (GCS) and catalyzes the transfer of methylene group from H-protein intermediate to tetrahydrofolate (THF) forming N-5, N-10-methylene THF (CH2-THF) with the release of ammonia. The malaria parasite genome encodes T-, H- and L-proteins, but not P-protein which is a glycine decarboxylase generating the aminomethylene group. A putative GCS has been considered to be functional in the parasite mitochondrion despite the absence of a detectable P-protein homologue. In the present study, the mitochondrial localization of T-protein in the malaria parasite was confirmed by immunofluorescence and its essentiality in the entire parasite life cycle was studied by targeting the T-protein locus in Plasmodium berghei (Pb). PbT knock out parasites did not show any growth defect in asexual, sexual and liver stages indicating that the T-protein is dispensable for parasite survival in vertebrate and invertebrate hosts. The absence of P-protein homologue and the non-essentiality of T protein suggest the possible redundancy of GCS activity in the malaria parasite. Nevertheless, the H- and L-proteins of GCS could be essential for malaria parasite because of their involvement in alpha-lcetoacid dehydrogenase reactions. (C) 2014 Elsevier B.V. All rights reserved.

Boundary Tracking using a Hybrid Cyclic Pursuit Scheme

There has been a lot of work in the literature, related to the mapping of boundaries of regions, using multiple agents. Most of these are based on optimization techniques or rely on potential fields to drive the agents towards the boundary and then retain them there while they space out evenly along the perimeter or surface (in two-dimensional and three-dimensional cases, respectively). In this paper an algorithm to track the boundary of a region in space is provided based on the cyclic pursuit scheme. This enables the agents to constantly move along the perimeter in a cluster, thereby tracking a dynamically changing boundary. The trajectories of the agents provide a sketch of the boundary. The use of multiple agents may facilitate minimization of tracking error by providing accurate estimates of points on the boundary, besides providing redundancy. Simulation results are provided to highlight the performance of the proposed scheme.