Performance analysis of subspace based method for source localization in shallow water

The present paper aims at studying the performance characteristics of a subspace based algorithm for source localization in shallow water such as coastal water. Specifically, we study the performance of Multi Image Subspace Algorithm (MISA). Through first-order perturbation analysis and computer simulation it is shown that MISA is unbiased and statistically efficient. Further, we bring out the role of multipaths (or images) in reducing the error in the localization. It is shown that the presence of multipaths is found to improve the range and depth estimates. This may be attributed to the increased curvature of the wavefront caused by interference from many coherent multipaths.

Multi-installment load distribution in tree networks with delays

This paper presents a new strategy for load distribution in a single-level tree network equipped with or without front-ends. The load is distributed in more than one installment in an optimal manner to minimize the processing time. This is a deviation and an improvement over earlier studies in which the load distribution is done in only one installment. Recursive equations for the general case, and their closed form solutions for a special case in which the network has identical processors and identical links, are derived. An asymptotic analysis of the network performance with respect to the number of processors and the number of installments is carried out. Discussions of the results in terms of some practical issues like the tradeoff relationship between the number of processors and the number of installments are also presented.

Strip based coding for large images using wavelets

Thanks to advances in sensor technology, today we have many applications (space-borne imaging, medical imaging, etc.) where images of large sizes are generated. Straightforward application of wavelet techniques for above images involves certain difficulties. Embedded coders such as EZW and SPIHT require that the wavelet transform of the full image be buffered for coding. Since the transform coefficients also require storing in high precision, buffering requirements for large images become prohibitively high. In this paper, we first devise a technique for embedded coding of large images using zero trees with reduced memory requirements. A 'strip buffer' capable of holding few lines of wavelet coefficients from all the subbands belonging to the same spatial location is employed. A pipeline architecure for a line implementation of above technique is then proposed. Further, an efficient algorithm to extract an encoded bitstream corresponding to a region of interest in the image has also been developed. Finally, the paper describes a strip based non-embedded coding which uses a single pass algorithm. This is to handle high-input data rates. (C) 2002 Elsevier Science B.V. All rights reserved.

Believe it or Not! Multicore CPUs can Match GPUs for FLOP-intensive Applications

In this work, we evaluate performance of a real-world image processing application that uses a cross-correlation algorithm to compare a given image with a reference one. The algorithm processes individual images represented as 2-dimensional matrices of single-precision floating-point values using O(n4) operations involving dot-products and additions. We implement this algorithm on a nVidia GTX 285 GPU using CUDA, and also parallelize it for the Intel Xeon (Nehalem) and IBM Power7 processors, using both manual and automatic techniques. Pthreads and OpenMP with SSE and VSX vector intrinsics are used for the manually parallelized version, while a state-of-the-art optimization framework based on the polyhedral model is used for automatic compiler parallelization and optimization. The performance of this algorithm on the nVidia GPU suffers from: (1) a smaller shared memory, (2) unaligned device memory access patterns, (3) expensive atomic operations, and (4) weaker single-thread performance. On commodity multi-core processors, the application dataset is small enough to fit in caches, and when parallelized using a combination of task and short-vector data parallelism (via SSE/VSX) or through fully automatic optimization from the compiler, the application matches or beats the performance of the GPU version. The primary reasons for better multi-core performance include larger and faster caches, higher clock frequency, higher on-chip memory bandwidth, and better compiler optimization and support for parallelization. The best performing versions on the Power7, Nehalem, and GTX 285 run in 1.02s, 1.82s, and 1.75s, respectively. These results conclusively demonstrate that, under certain conditions, it is possible for a FLOP-intensive structured application running on a multi-core processor to match or even beat the performance of an equivalent GPU version.

Fusion of multi resolution remote sensing data for urban sprawl analysis

Urban population is growing at around 2.3 percent per annum in India. This is leading to urbanisation and often fuelling the dispersed development in the outskirts of urban and village centres with impacts such as loss of agricultural land, open space, and ecologically sensitive habitats. This type of upsurge is very much prevalent and persistent in most places, often inferred as sprawl. The direct implication of such urban sprawl is the change in land use and land cover of the region and lack of basic amenities, since planners are unable to visualise this type of growth patterns. This growth is normally left out in all government surveys (even in national population census), as this cannot be grouped under either urban or rural centre. The investigation of patterns of growth is very crucial from regional planning point of view to provide basic amenities in the region. The growth patterns of urban sprawl can be analysed and understood with the availability of temporal multi-sensor, multi-resolution spatial data. In order to optimise these spectral and spatial resolutions, image fusion techniques are required. This aids in integrating a lower spatial resolution multispectral (MSS) image (for example, IKONOS MSS bands of 4m spatial resolution) with a higher spatial resolution panchromatic (PAN) image (IKONOS PAN band of 1m spatial resolution) based on a simple spectral preservation fusion technique - the Smoothing Filter-based Intensity Modulation (SFIM). Spatial details are modulated to a co-registered lower resolution MSS image without altering its spectral properties and contrast by using a ratio between a higher resolution image and its low pass filtered (smoothing filter) image. The visual evaluation and statistical analysis confirms that SFIM is a superior fusion technique for improving spatial detail of MSS images with the preservation of spectral properties.

Optimality of orthogonal sequences with multi-dimensional signaling

The sum capacity on a symbol-synchronous CDMA system having processing gain N and supporting K power constrained users is achieved by employing any set of N orthogonal sequences if a few users are allowed to signal along multiple dimensions. Analogously, the minimum received power (energy-per-chip) on the symbolsynchronous CDMA system supporting K users that demand specified data rates is attained by employing any set of N orthogonal sequences. At most (N - 1) users need to be split and if there are no oversized users, these split users need to signal only in two dimensions each. These results show that sum capacity or minimum sum power can be achieved with minimal downlink signaling.

A DCT Based Filtering of Biomedical Images

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.

Evolution of sub-micron grain size and weak texture in magnesium alloy Mg-3Al-0.4Mn by a modified multi-axial forging process

This is the first successful attempt to produce simultaneously ultrafine grain size and weak texture in a single-phase magnesium alloy Mg-3Al-0.4Mn through an optimal choice of processing parameters in a modified multi-axial forging (MAF) process. An average grain size of similar to 0.4 mu m and a weak texture could be achieved. This has led to an increase in the strength as well as room-temperature ductility (55%). The plot of the yield loci shows a decrease in anisotropy after MAF. (C) 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

High emission currents and low threshold fields in multi-wall carbon nanotube-polymer composites in the vertical configuration

In this work, we present field emission characteristics of multi-wall carbon nanotube (MWCNT)-polystyrene composites at various weight fractions along the cross-section of sample. Scanning electron microscope images in cross-sectional view reveal that MWCNTs are homogeneously distributed across the thickness and the density of protruding tubes can be scaled with weight fraction of the composite film. Field emission from composites has been observed to vary considerably with density of MWCNTs in the polymer matrix. High current density of 100 mA/cm(2) was achieved at a field of 2.2 V/lm for 0.15 weight fraction. The field emission is observed to follow the Fowler-Nordheim tunneling mechanism, however, electrostatic screening is observed to play a role in limiting the current density at higher weight fractions. (C) 2012 American Institute of Physics. [doi:10.1063/1.3685754]

Charge transport in functionalized multi-wall carbon nanotube-Nafion composite

The charge transport in sulfonated multi-wall carbon nanotube (sMWNT)-Nafion composite is reported. The scanning electron microscope images of the composite, at 1 and 10 wt % of sMWNT, show that the nanotubes are well dispersed in polymer matrix, with conductivity values of 0.005 and 3.2 S/cm, respectively; and the percolation threshold is nearly 0.42 wt. %. The exponent (∼0.25) of the temperature dependence of conductivity in both samples indicates Mott's variable range hopping (VRH) transport. The conductance in 1 wt. % sample increases by three orders of magnitude at high electric-fields, consistent with VRH model. The negative magnetoresistance in 10 wt. % sample is attributed to the forward interference scattering mechanism in VRH transport. The ac conductance in 1 wt. % sample is expressed by σ(ω)∝ωs, and the temperature dependence of s follows the correlated barrier hopping model.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Power-law transformation for enhanced recognition of born-digital word images

In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm with fixed stroke width threshold. We have exhaustively experimented our algorithm by varying the gamma and stroke width threshold value. By varying the gamma value, we found that our algorithm performed better than the results reported in the literature. On the ICDAR Robust Reading Systems Challenge-1: Word Recognition Task on born digital dataset, as compared to the recognition rate of 61.5% achieved by TH-OCR after suitable pre-processing by Yang et. al. and 63.4% by ABBYY Fine Reader (used as baseline by the competition organizers without any preprocessing), we achieved 82.9% using Omnipage OCR applied on the images after being processed by our algorithm.

Boosting-based transfer learning for multi-view head-pose classification from surveillance videos

This work proposes a boosting-based transfer learning approach for head-pose classification from multiple, low-resolution views. Head-pose classification performance is adversely affected when the source (training) and target (test) data arise from different distributions (due to change in face appearance, lighting, etc). Under such conditions, we employ Xferboost, a Logitboost-based transfer learning framework that integrates knowledge from a few labeled target samples with the source model to effectively minimize misclassifications on the target data. Experiments confirm that the Xferboost framework can improve classification performance by up to 6%, when knowledge is transferred between the CLEAR and FBK four-view headpose datasets.