A high performance scheme for EEG compression using a multichannel model

The amount of data contained in electroencephalogram (EEG) recordings is quite massive and this places constraints on bandwidth and storage. The requirement of online transmission of data needs a scheme that allows higher performance with lower computation. Single channel algorithms, when applied on multichannel EEG data fail to meet this requirement. While there have been many methods proposed for multichannel ECG compression, not much work appears to have been done in the area of multichannel EEG. compression. In this paper, we present an EEG compression algorithm based on a multichannel model, which gives higher performance compared to other algorithms. Simulations have been performed on both normal and pathological EEG data and it is observed that a high compression ratio with very large SNR is obtained in both cases. The reconstructed signals are found to match the original signals very closely, thus confirming that diagnostic information is being preserved during transmission.

Effect of strain rate on tensile and compression behaviour of Al-Si/graphite composite

Effects of strain rate (10(-4)-10(-2) s(-1)) on tensile and compressive strength of the Al-Si alloy and Al-Si/graphite composite are investigated. The strain hardening exponent value of the composite was more than that of the alloy for all strain rates during tensile and compressive loading. The yield stress of the composite was more than that of the ultimate tensile strength of the alloy for all strain rates. Tensile and compressive properties of the alloy and composite are dependent on strain rates. The negative strain rate sensitivity was observed for the composite and alloy at lower strain rates during the compression and tension loading respectively. (C) 2011 Elsevier B.V. All rights reserved.

Efficient statistical modeling for the compression of tree structured intermediate code

We propose a scheme for the compression of tree structured intermediate code consisting of a sequence of trees specified by a regular tree grammar. The scheme is based on arithmetic coding, and the model that works in conjunction with the coder is automatically generated from the syntactical specification of the tree language. Experiments on data sets consisting of intermediate code trees yield compression ratios ranging from 2.5 to 8, for file sizes ranging from 167 bytes to 1 megabyte.

Exergetic analysis of carbon dioxide vapour compression refrigeration cycle using the new fundamental equation of state

The purpose of this paper is to present exergy charts for carbon dioxide (CO2) based on the new fundamental equation of state and the results of a thermodynamic analysis of conventional and trans-critical vapour compression refrigeration cycles using the data thereof. The calculation scheme is anchored on the Mathematica platform. There exist upper and lower bounds for the high cycle pressure for a given set of evaporating and pre-throttling temperatures. The maximum possible exergetic efficiency for each case was determined. Empirical correlations for exergetic efficiency and COP, valid in the range of temperatures studied here, are obtained. The exergy losses have been quantified. (C) 2003 Elsevier Ltd. All rights reserved.

Guessing and compression subject to distortion

The problem of guessing a random string is revisited. The relation-ship between guessing without distortion and compression is extended to the case when source alphabet size is countably in¯nite. Further, similar relationship is established for the case when distortion allowed by establishing a tight relationship between rate distortion codes and guessing strategies.

VLSI Implementation of Spatial prediction Based Image Compression Scheme

We propose the design and implementation of hardware architecture for spatial prediction based image compression scheme, which consists of prediction phase and quantization phase. In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates an error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. The software model is tested for its performance in terms of entropy, standard deviation. The memory and silicon area constraints play a vital role in the realization of the hardware for hand-held devices. The hardware architecture is constructed for the proposed scheme, which involves the aspects of parallelism in instructions and data. The processor consists of pipelined functional units to obtain the maximum throughput and higher speed of operation. The hardware model is analyzed for performance in terms throughput, speed and power. The results of hardware model indicate that the proposed architecture is suitable for power constrained implementations with higher data rate

Measurement of Stress-Strain Response of A Rammed Earth Prism In Compression Using Fiber Bragg Grating Sensors

A comparative study of strain response and mechanical properties of rammed earth prisms, has been made using Fiber Bragg Grating (FBG) sensors (optical) and clip-on extensometer (electro-mechanical). The aim of this study is to address the merits and demerits of traditional extensometer vis-à-vis FBG sensor; a uni-axial compression test has been performed on a rammed earth prism to validate its structural properties from the stress - strain curves obtained by two different methods of measurement. An array of FBG sensors on a single fiber with varying Bragg wavelengths (..B), has been used to spatially resolve the strains along the height of the specimen. It is interesting to note from the obtained stress-strain curves that the initial tangent modulus obtained using the FBG sensor is lower compared to that obtained using clip-on extensometer. The results also indicate that the strains measured by both FBG and extensometer sensor follow the same trend and both the sensors register the maximum strain value at the same time.

High-Rate Vector Quantization for Noisy Channels With Applications to Wideband Speech Spectrum Compression

This paper considers the high-rate performance of source coding for noisy discrete symmetric channels with random index assignment (IA). Accurate analytical models are developed to characterize the expected distortion performance of vector quantization (VQ) for a large class of distortion measures. It is shown that when the point density is continuous, the distortion can be approximated as the sum of the source quantization distortion and the channel-error induced distortion. Expressions are also derived for the continuous point density that minimizes the expected distortion. Next, for the case of mean squared error distortion, a more accurate analytical model for the distortion is derived by allowing the point density to have a singular component. The extent of the singularity is also characterized. These results provide analytical models for the expected distortion performance of both conventional VQ as well as for channel-optimized VQ. As a practical example, compression of the linear predictive coding parameters in the wideband speech spectrum is considered, with the log spectral distortion as performance metric. The theory is able to correctly predict the channel error rate that is permissible for operation at a particular level of distortion.

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.

Softening and dynamic recrystallization in magnesium single crystals during c-axis compression

Commercial purity (99.8%) magnesium single crystals were subjected to plane strain compression (PSC) along the c-axis at 200 and 370 degrees C and a constant strain rate of 10(-3) s(-1). Extension was confined to the < 1 1 (2) over bar 0 > direction and the specimens were strained up to a logarithmic true strain of -1. The initial rapid increase in flow stress was followed by significant work softening at different stresses and comparable strains of about -0.05 related to macroscopic twinning events. The microstructure of the specimen after PSC at 200 degrees C was characterized by a high density of {1 0 (1) over bar 1} and {1 0 (1) over bar 3} compression twins, some of which were recrystallized. After PSC at 370 degrees C, completely recrystallized twin bands were the major feature of the observed microstructure. All new grains in these bands retained the same c-axis orientation of their compression twin hosts. The basal plane in these grains was randomly rotated around the c-axis, forming a fiber texture component. The obtained results are discussed with respect to the mechanism of recrystallization, the specific character of the boundaries between new grains and the initial matrix, and the importance of the dynamically recrystallized bands for strain accommodation in these deformed magnesium single crystals. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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.

Evaluation of physico-mechanical properties and in vitro biocompatibility of compression molded HDPE based biocomposites with HA/Al2O3 ceramic fillers and titanate coupling agents

The present article demonstrates how the stiffness, hardness as well as the cellular response of bioinert high-density polyethylene (HDPE) can be significantly improved with combined addition of both bioinert and bioactive ceramic fillers. For this purpose, different amounts of hydroxyapatite and alumina, limited to a total of 40 wt %, have been incorporated in HDPE matrix. An important step in composite fabrication was to select appropriate solvent and optimal addition of coupling agent (CA). In case of chemically coupled composites, 2% Titanium IV, 2-propanolato, tris iso-octadecanoato-O was used as a CA. All the hybrid composites, except monolithic HDPE, were fabricated under optimized compression molding condition (140 degrees C, 0.75 h, 10 MPa pressure). The compression molded composites were characterized, using X-ray diffraction, Fourier transformed infrared spectroscopy, and scanning electron microscopy. Importantly, in vitro cell culture and cell viability study (MTT) using L929 fibroblast and SaOS2 osteoblast-like cells confirmed good cytocompatibility properties of the developed hybrid composites. (C) 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

Determining optimum subband edges for signal compression

The coding gain in subband coding, a popular technique for achieving signal compression, depends on how the input signal spectrum is decomposed into subbands. The optimality of such decomposition is conventionally addressed by designing appropriate filter banks. The issue of optimal decomposition of the input spectrum is addressed by choosing the set of band that, for a given number of bands, will achieve maximum coding gain. A set of necessary conditions for such optimality is derived, and an algorithm to determine the optimal band edges is then proposed. These band edges along with ideal filters, achieve the upper bound of coding gain for a given number of bands. It is shown that with ideal filters, as well as with realizable filters for some given effective length, such a decomposition system performs better than the conventional nonuniform binary tree-structured decomposition in some cases for AR sources as well as images