Linear Filtering in MDCT domain

In this paper, expressions for convolution multiplication properties of MDCT are derived starting from the equivalent DFT representations. Using these expressions, methods for implementing linear filtering through block convolution in the MDCT domain are presented. The implementation is exact for symmetric filters and approximate for non-symmetric filters in the case of rectangular window based MDCT. For a general MDCT window function, the filtering is done on the windowed segments and hence the convolution is approximate for symmetric as well as non-symmetric filters. This approximation error is shown to be perceptually insignificant for symmetric impulse response filters. Moreover, the inherent $50 \%$ overlap between adjacent frames used in MDCT computation does reduce this approximation error similar to smoothing of other block processing errors. The presented techniques are useful for compressed domain processing of audio signals.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Analysis of LDPC Codes for Compression of Nonuniform Sources with Side Information Using Density Evolution

In this paper, we explore the use of LDPC codes for nonuniform sources under distributed source coding paradigm. Our analysis reveals that several capacity approaching LDPC codes indeed do approach the Slepian-Wolf bound for nonuniform sources as well. The Monte Carlo simulation results show that highly biased sources can be compressed to 0.049 bits/sample away from Slepian-Wolf bound for moderate block lengths.

Distributed Function Computation over Fields and Rings via Linear Compression of Sources

The setting considered in this paper is one of distributed function computation. More specifically, there is a collection of N sources possessing correlated information and a destination that would like to acquire a specific linear combination of the N sources. We address both the case when the common alphabet of the sources is a finite field and the case when it is a finite, commutative principal ideal ring with identity. The goal is to minimize the total amount of information needed to be transmitted by the N sources while enabling reliable recovery at the destination of the linear combination sought. One means of achieving this goal is for each of the sources to compress all the information it possesses and transmit this to the receiver. The Slepian-Wolf theorem of information theory governs the minimum rate at which each source must transmit while enabling all data to be reliably recovered at the receiver. However, recovering all the data at the destination is often wasteful of resources since the destination is only interested in computing a specific linear combination. An alternative explored here is one in which each source is compressed using a common linear mapping and then transmitted to the destination which then proceeds to use linearity to directly recover the needed linear combination. The article is part review and presents in part, new results. The portion of the paper that deals with finite fields is previously known material, while that dealing with rings is mostly new.Attempting to find the best linear map that will enable function computation forces us to consider the linear compression of source. While in the finite field case, it is known that a source can be linearly compressed down to its entropy, it turns out that the same does not hold in the case of rings. An explanation for this curious interplay between algebra and information theory is also provided in this paper.

Hydrogen generation fromammonia borane using nanocatalysts

The rapidly depleting petroleum feed stocks and increasing green house gas emissions around the world has necessitated a search for alternative renewable energy sources. Hydrogen with molecular weight of 2.016 g/mol and high chemical energy per mass equal to 142 MJ/kg has clearly emerged as an alternative to hydrocarbon fuels. Means for safe and cost effective storage are needed for widespread usage of hydrogen as a fuel.Chemical storage is the one of the safer ways to store hydrogen compared to compressed and liquefied hydrogen. It involves storing hydrogen in chemical bonds in molecules and materials where an on-board reaction is used to release hydrogen. Ammonia–borane, (AB,H3N·BH3) with a potential capacity of 19.6 wt% is considered a very promising solid state hydrogen storage material. It is thermally stable at ambient temperatures. There are two major routes for the generation of H2 from AB: catalytic hydrolysis/alcoholysis and catalytic thermal decomposition. There has been a flurry of research activity on the generation of H2 from AB recently. The present review deals with an overview of our efforts in developing cost-effective nanocatalysts for hydrogen generation from ammonia borane in protic solvents.

Optimal sparsifying bases for frequency-domain optical-coherence tomography

We address the reconstruction problem in frequency-domain optical-coherence tomography (FDOCT) from under-sampled measurements within the framework of compressed sensing (CS). Specifically, we propose optimal sparsifying bases for accurate reconstruction by analyzing the backscattered signal model. Although one might expect Fourier bases to be optimal for the FDOCT reconstruction problem, it turns out that the optimal sparsifying bases are windowed cosine functions where the window is the magnitude spectrum of the laser source. Further, the windowed cosine bases can be phase locked, which allows one to obtain higher accuracy in reconstruction. We present experimental validations on real data. The findings reported in this Letter are useful for optimal dictionary design within the framework of CS-FDOCT. (C) 2012 Optical Society of America

Improving query focused summarization using look-ahead strategy

Query focused summarization is the task of producing a compressed text of original set of documents based on a query. Documents can be viewed as graph with sentences as nodes and edges can be added based on sentence similarity. Graph based ranking algorithms which use 'Biased random surfer model' like topic-sensitive LexRank have been successfully applied to query focused summarization. In these algorithms, random walk will be biased towards the sentences which contain query relevant words. Specifically, it is assumed that random surfer knows the query relevance score of the sentence to where he jumps. However, neighbourhood information of the sentence to where he jumps is completely ignored. In this paper, we propose look-ahead version of topic-sensitive LexRank. We assume that random surfer not only knows the query relevance of the sentence to where he jumps but he can also look N-step ahead from that sentence to find query relevance scores of future set of sentences. Using this look ahead information, we figure out the sentences which are indirectly related to the query by looking at number of hops to reach a sentence which has query relevant words. Then we make the random walk biased towards even to the indirect query relevant sentences along with the sentences which have query relevant words. Experimental results show 20.2% increase in ROUGE-2 score compared to topic-sensitive LexRank on DUC 2007 data set. Further, our system outperforms best systems in DUC 2006 and results are comparable to state of the art systems.

Manually operated piston-driven shock tube

A simple hand-operated shock tube capable of producing Mach 2 shock waves is described. Performance of this miniature shock tube using compressed high pressure air created by a manually operated piston in the driver section of the shock tube as driver gas with air at 1 atm pressure as the test gas in the driven tube is presented. The performance of the shock tube is found to match well with the theoretically estimated values using normal shock relations. Applications of this shock tube named Reddy tube, include study of blast-induced traumatic brain injuries and high temperature chemical kinetics.

Further results on geometric properties of a family of relative entropies

This paper extends some geometric properties of a one-parameter family of relative entropies. These arise as redundancies when cumulants of compressed lengths are considered instead of expected compressed lengths. These parametric relative entropies are a generalization of the Kullback-Leibler divergence. They satisfy the Pythagorean property and behave like squared distances. This property, which was known for finite alphabet spaces, is now extended for general measure spaces. Existence of projections onto convex and certain closed sets is also established. Our results may have applications in the Rényi entropy maximization rule of statistical physics.

On Selection of Search Space Dimension in Compressive Sampling Matching Pursuit

Compressive Sampling Matching Pursuit (CoSaMP) is one of the popular greedy methods in the emerging field of Compressed Sensing (CS). In addition to the appealing empirical performance, CoSaMP has also splendid theoretical guarantees for convergence. In this paper, we propose a modification in CoSaMP to adaptively choose the dimension of search space in each iteration, using a threshold based approach. Using Monte Carlo simulations, we show that this modification improves the reconstruction capability of the CoSaMP algorithm in clean as well as noisy measurement cases. From empirical observations, we also propose an optimum value for the threshold to use in applications.

Microscopic dynamics of nanoparticle monolayers at air-water interface

We present results of surface mechanical and particle tracking measurements of nanoparticles trapped at the air-water interface as a function of their areal density. We monitor both the surface pressure (II) and isothermal compression modulus (epsilon) as well as the dynamics of nanoparticle clusters, using fluorescence confocal microscopy while they are compressed to very high density near the two dimensional close packing density Phi similar to 0.82. We observe non-monotonic variation in both epsilon and the dynamic heterogeneity, characterized by the dynamical susceptibility chi(4) with Phi, in such high density monolayers. We provide insight into the underlying nature of such transitions in close packed high density nanoparticle monolayers in terms of the morphology and flexibility of these soft colloidal particles.. We discuss the significance our results in the context of related studies on two dimensional granular or colloidal systems. (C) 2013 Elsevier Inc. All rights reserved.

Low order anti-aliasing filters for sparse signals in embedded applications

Major emphasis, in compressed sensing (CS) research, has been on the acquisition of sub-Nyquist number of samples of a signal that has a sparse representation on some tight frame or an orthogonal basis, and subsequent reconstruction of the original signal using a plethora of recovery algorithms. In this paper, we present compressed sensing data acquisition from a different perspective, wherein a set of signals are reconstructed at a sampling rate which is a multiple of the sampling rate of the ADCs that are used to measure the signals. We illustrate how this can facilitate usage of anti-aliasing filters with relaxed frequency specifications and, consequently, of lower order.

Low complexity joint estimation of synchronization impairments in sparse channel for MIMO-OFDM system

Low complexity joint estimation of synchronization impairments and channel in a single-user MIMO-OFDM system is presented in this paper. Based on a system model that takes into account the effects of synchronization impairments such as carrier frequency offset, sampling frequency offset, and symbol timing error, and channel, a Maximum Likelihood (ML) algorithm for the joint estimation is proposed. To reduce the complexity of ML grid search, the number of received signal samples used for estimation need to be reduced. The conventional channel estimation techniques using Least-Squares (LS) or Maximum a posteriori (MAP) methods fail for the reduced sample under-determined system, which results in poor performance of the joint estimator. The proposed ML algorithm uses Compressed Sensing (CS) based channel estimation method in a sparse fading scenario, where the received samples used for estimation are less than that required for an LS or MAP based estimation. The performance of the estimation method is studied through numerical simulations, and it is observed that CS based joint estimator performs better than LS and MAP based joint estimator. (C) 2013 Elsevier GmbH. All rights reserved.

Two-step Synthesis of MoS2 Nanotubes using Shock Waves with Lead as Growth Promoter

A new two-step procedure for the synthesis of MoS2 nanotubes using lead as a growth promoter is reported. In the first step, molybdenum suboxide nanowhiskers containing a small amount of lead atoms were created by exposing a pressed MoS2+Pb mixture to highly compressed shock-heated argon gas, with estimated temperatures exceeding 9900 K. In the second step, these molybdenum suboxide nanowhiskers served as templates for the sulfidization of the oxide into MoS2 nanotubes (by using H2S gas in a reducing atmosphere at 820 degrees C). Unlike the case of WS2 nanotubes, the synthesis of a pure phase of MoS2 nanotubes from molybdenum oxide has proven challenging, due mostly to the volatile nature of the latter at the high requisite reaction temperatures (>800 degrees C). In contrast, the nature and apparent reaction mechanism of the method reported herein are amenable to future scale-up. The high-temperature shockwave system should also facilitate the synthesis of new nanostructures from other layered materials.

Effect of port gas injection on in-cylinder stratification in a CNG fuelled SI engine

The concept of barrel stratification of air-fuel mixture is evaluated for a port gas injection (PGI) single cylinder spark ignition (SI) internal combustion (IC) engine using a transient three-dimensional computational fluid dynamic (CFD) model. The gaseous fuel used in the study is compressed natural gas (CNG). It is observed that compared to the premixed gas carburettor case, a substantial amount of in-cylinder stratification can be achieved with port gas injection system. A detailed parametric study is reported to understand the effect of the various injection parameters such as injection location, injection orientation, start of injection (SOT) and its duration, and injection rate. Furthermore, the best injection timing is evaluated for various load and speed cases. It is observed that the best stratification pattern can be achieved at 50% engine load. The injection location is observed to have a profound effect on the in-cylinder stratification pattern, and injection towards the side of the spark plug is observed to give a rich fuel-air mixture near the spark plug. It is also shown that there exists an optimal injection pressure.