Precoding by Pairing Subchannels to Increase MIMO Capacity With Discrete Input Alphabets

We consider Gaussian multiple-input multiple-output (MIMO) channels with discrete input alphabets. We propose a non-diagonal precoder based on the X-Codes in 1] to increase the mutual information. The MIMO channel is transformed into a set of parallel subchannels using singular value decomposition (SVD) and X-Codes are then used to pair the subchannels. X-Codes are fully characterized by the pairings and a 2 x 2 real rotation matrix for each pair (parameterized with a single angle). This precoding structure enables us to express the total mutual information as a sum of the mutual information of all the pairs. The problem of finding the optimal precoder with the above structure, which maximizes the total mutual information, is solved by: i) optimizing the rotation angle and the power allocation within each pair and ii) finding the optimal pairing and power allocation among the pairs. It is shown that the mutual information achieved with the proposed pairing scheme is very close to that achieved with the optimal precoder by Cruz et al., and is significantly better than Mercury/waterfilling strategy by Lozano et al. Our approach greatly simplifies both the precoder optimization and the detection complexity, making it suitable for practical applications.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Symmetry in Scalar Field Topology

Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights into the properties of the underlying phenomenon. Though geometric symmetry has been well studied within areas like shape processing, identifying symmetry in scalar fields has remained largely unexplored due to the high computational cost of the associated algorithms. We propose a computationally efficient algorithm for detecting symmetric patterns in a scalar field distribution by analysing the topology of level sets of the scalar field. Our algorithm computes the contour tree of a given scalar field and identifies subtrees that are similar. We define a robust similarity measure for comparing subtrees of the contour tree and use it to group similar subtrees together. Regions of the domain corresponding to subtrees that belong to a common group are extracted and reported to be symmetric. Identifying symmetry in scalar fields finds applications in visualization, data exploration, and feature detection. We describe two applications in detail: symmetry-aware transfer function design and symmetry-aware isosurface extraction.

Enabling node repair in any erasure code for distributed storage

Erasure codes are an efficient means of storing data across a network in comparison to data replication, as they tend to reduce the amount of data stored in the network and offer increased resilience in the presence of node failures. The codes perform poorly though, when repair of a failed node is called for, as they typically require the entire file to be downloaded to repair a failed node. A new class of erasure codes, termed as regenerating codes were recently introduced, that do much better in this respect. However, given the variety of efficient erasure codes available in the literature, there is considerable interest in the construction of coding schemes that would enable traditional erasure codes to be used, while retaining the feature that only a fraction of the data need be downloaded for node repair. In this paper, we present a simple, yet powerful, framework that does precisely this. Under this framework, the nodes are partitioned into two types and encoded using two codes in a manner that reduces the problem of node-repair to that of erasure-decoding of the constituent codes. Depending upon the choice of the two codes, the framework can be used to avail one or more of the following advantages: simultaneous minimization of storage space and repair-bandwidth, low complexity of operation, fewer disk reads at helper nodes during repair, and error detection and correction.

Enabling node repair in any erasure code for distributed storage

Erasure codes are an efficient means of storing data across a network in comparison to data replication, as they tend to reduce the amount of data stored in the network and offer increased resilience in the presence of node failures. The codes perform poorly though, when repair of a failed node is called for, as they typically require the entire file to be downloaded to repair a failed node. A new class of erasure codes, termed as regenerating codes were recently introduced, that do much better in this respect. However, given the variety of efficient erasure codes available in the literature, there is considerable interest in the construction of coding schemes that would enable traditional erasure codes to be used, while retaining the feature that only a fraction of the data need be downloaded for node repair. In this paper, we present a simple, yet powerful, framework that does precisely this. Under this framework, the nodes are partitioned into two types and encoded using two codes in a manner that reduces the problem of node-repair to that of erasure-decoding of the constituent codes. Depending upon the choice of the two codes, the framework can be used to avail one or more of the following advantages: simultaneous minimization of storage space and repair-bandwidth, low complexity of operation, fewer disk reads at helper nodes during repair, and error detection and correction.

OTCYMIST: Otsu-Canny minimal spanning tree for born-digital images

Text segmentation and localization algorithms are proposed for the born-digital image dataset. Binarization and edge detection are separately carried out on the three colour planes of the image. Connected components (CC's) obtained from the binarized image are thresholded based on their area and aspect ratio. CC's which contain sufficient edge pixels are retained. A novel approach is presented, where the text components are represented as nodes of a graph. Nodes correspond to the centroids of the individual CC's. Long edges are broken from the minimum spanning tree of the graph. Pair wise height ratio is also used to remove likely non-text components. A new minimum spanning tree is created from the remaining nodes. Horizontal grouping is performed on the CC's to generate bounding boxes of text strings. Overlapping bounding boxes are removed using an overlap area threshold. Non-overlapping and minimally overlapping bounding boxes are used for text segmentation. Vertical splitting is applied to generate bounding boxes at the word level. The proposed method is applied on all the images of the test dataset and values of precision, recall and H-mean are obtained using different approaches.

Nanocomposite based flexible ultrasensitive resistive gas sensor for chemical reactions studies

Room temperature operation, low detection limit and fast response time are highly desirable for a wide range of gas sensing applications. However, the available gas sensors suffer mainly from high temperature operation or external stimulation for response/recovery. Here, we report an ultrasensitive-flexible-silver-nanoparticle based nanocomposite resistive sensor for ammonia detection and established the sensing mechanism. We show that the nanocomposite can detect ammonia as low as 500 parts-per-trillion at room temperature in a minute time. Furthermore, the evolution of ammonia from different chemical reactions has been demonstrated using the nanocomposite sensor as an example. Our results demonstrate the proof-of-concept for the new detector to be used in several applications including homeland security, environmental pollution and leak detection in research laboratories and many others.

Non-Enzymatic Electroanalysis of Glucose on Electrodeposited Au-PEDOT Dendrites

Electrodeposition of Au on poly (3,4-ethylenedioxythiophene) (PEDOT) coated carbon paper electrode results in the formation of a stable 3-D urchin-like morphology. Au-PEDOT/C electrode exhibits higher surface area, greater catalytic activity, higher sensitivity and lower detection limit for glucose analysis in an alkaline medium than Au/C electrode. Au-PEDOT/C electrode exhibits a linear current response in glucose concentration ranging up to 10 mu M with sensitivity of 515 mu A cm(-2) mu M-1 (on the basis of geometric area) and a low detection limit of 0.03 mu M with signal to noise ratio of 3. Thus, the PEDOT under-layer improves the property of Au for glucose analysis. (c) 2013 The Electrochemical Society.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.

LHC constraints on light neutralino dark matter in the MSSM

Light neutralino dark matter can be achieved in the Minimal Supersymmetric Standard Model if staus are rather light, with mass around 100 GeV. We perform a detailed analysis of the relevant supersymmetric parameter space, including also the possibility of light selectons and smuons, and of light higgsino- or wino-like charginos. In addition to the latest limits from direct and indirect detection of dark matter, ATLAS and CMS constraints on electroweak-inos and on sleptons are taken into account using a ``simplified models'' framework. Measurements of the properties of the Higgs boson at 125 GeV, which constrain amongst others the invisible decay of the Higgs boson into a pair of neutralinos, are also implemented in the analysis. We show that viable neutralino dark matter can be achieved for masses as low as 15 GeV. In this case, light charginos close to the LEP bound are required in addition to light right-chiral staus. Significant deviations are observed in the couplings of the 125 GeV Higgs boson. These constitute a promising way to probe the light neutralino dark matter scenario in the next run of the LHC. (C) 2013 Elsevier B.V. All rights reserved.

A cationic cholesterol based nanocarrier for the delivery of p53-EGFP-C3 plasmid to cancer cells

The p53 protein mediated anti-tumor strategy is limited due to the lack of suitable delivery agent with insignificant immunogenic response, serum compatibility, and early and easy detection of the transfected cell population. To overcome these problems, we generated a p53-EGFP-C3 fusion construct which expressed easily detectable green fluorescence protein (GFP) and allowed an estimation of p53 mediated anti-tumor activity. A mixture of cationic cholesterol gemini (Choi-5L) with natural lipid, DOPE (molar ratio 1:4), acronymed as Chol-5LD, formed a nano-liposome as characterized by various physical methods. The prepared clone was evaluated for the expression of GFP and functional p53 in HeLa and two additional cell lines with varied p53 status namely, H1299 (p53(-/-)) and HEK293T (p53(+/+)). Transfected cells were screened using RT-PCR, Western blotting, FACS analysis, MTT, Trypan blue assay and visualized under a fluorescence microscope. The p53-EGFP-C3 fusion protein induced apoptosis in cancer cells as evident from DNA fragmentation, cell cycle analysis, Annexin-V staining and PARP cleavage assays. The transfection and apoptosis induction efficiency of Chol-5LD was significantly higher than commercial reagents Lipofectamine2000 and Effectene irrespective of the cell lines examined. Further it significantly decreases the xenograft tumor volume in nude mice tumors via apoptosis as observed in H&E staining. (C) 2013 Elsevier Ltd. All rights reserved.

Raman spectroscopy explores molecular structural signatures of hidden materials in depth: Universal Multiple Angle Raman Spectroscopy

Non-invasive 3D imaging in materials and medical research involves methodologies such as X-ray imaging, MRI, fluorescence and optical coherence tomography, NIR absorption imaging, etc., providing global morphological/density/absorption changes of the hidden components. However, molecular information of such buried materials has been elusive. In this article we demonstrate observation of molecular structural information of materials hidden/buried in depth using Raman scattering. Typically, Raman spectroscopic observations are made at fixed collection angles, such as, 906, 1356, and 1806, except in spatially offset Raman scattering (SORS) (only back scattering based collection of photons) and transmission techniques. Such specific collection angles restrict the observations of Raman signals either from or near the surface of the materials. Universal Multiple Angle Raman Spectroscopy (UMARS) presented here employs the principle of (a) penetration depth of photons and then diffuse propagation through non-absorbing media by multiple scattering and (b) detection of signals from all the observable angles.

pi-Electron rich small molecule sensors for the recognition of nitroaromatics

Selective and discriminative detection of -NO2 containing high energy organic compounds such as picric acid (PA), 2,4,6-trinitrotoluene (TNT) and dinitrotoluene (DNT) has become a challenging task due to concerns over national security, criminal investigations and environment protections. Among various known detection methods, fluorescence techniques have gained special attention in recent time. A wide variety of fluorescent chemosensors have been developed for nitroaromatic explosive detection. In this review article, we provide an overview of the recent developments made in small molecule-based turn-off fluorescent sensors for nitroaromatic explosives with special focus on organic and H-bonded supramolecular sensors. The fluorescent sensors discussed in this review are classified and organized according to their functionality and their recognition of nitroaromatics by fluorescence quenching.