Low-Complexity Near-MAP Decoding of Large Non-Orthogonal STBCs Using PDA

Non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) are attractive because they can simultaneously achieve both high spectral efficiencies (same spectral efficiency as in V-BLAST for a given number of transmit antennas) as well as full transmit diversity. Decoding of non-orthogonal STBCs with hundreds of dimensions has been a challenge. In this paper, we present a probabilistic data association (PDA) based algorithm for decoding non-orthogonal STBCs with large dimensions. Our simulation results show that the proposed PDA-based algorithm achieves near SISO AWGN uncoded BER as well as near-capacity coded BER (within 5 dB of the theoretical capacity) for large non-orthogonal STBCs from CDA. We study the effect of spatial correlation on the BER, and show that the performance loss due to spatial correlation can be alleviated by providing more receive spatial dimensions. We report good BER performance when a training-based iterative decoding/channel estimation is used (instead of assuming perfect channel knowledge) in channels with large coherence times. A comparison of the performances of the PDA algorithm and the likelihood ascent search (LAS) algorithm (reported in our recent work) is also presented.

Frechet distance based approach for searching online handwritten documents

We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.

Optical detection of submicromolar levels of nitro explosives by a submicron sized metal-organic phosphor material

Two isomorphous submicron sized metal-organic network compounds, Y-2(PDA)(3)(H2O)1]center dot 2H(2)O (PDA = 1,4-phenylenediacetate), 1 and Y1.8Tb0.2(PDA)(3)(H2O)1]center dot 2H(2)O, Tb@1 have been synthesized by employing solvent assisted liquid grinding followed by heating at 180 degrees C for 1' min and washing with water. Single crystal X-ray data of bulk 1 confirmed a three dimensional porous structure. The structure and morphology of 1 and Tb@1 were systematically characterized by PXRD, TGA, DSC, IR, SEM and EDX analysis. Dehydrated Tb@1 Tb@1'] shows a high intense visible green emission upon exposure to UV light. The green emission of Tb@1' was used for the detection of nitro explosives, such as 2,4,6-trinitrophenol (TNP), 1,3-dinitro benzene (DNB), 2,4-dinitro toluene (DNT), nitro benzene (NB), and 4-nitro toluene (NT) in acetonitrile. The results show that the emission intensity of dehydrated Tb@1' can be quenched by all the nitro analytes used in the present work. Remarkably, Tb@1' exhibited a high efficiency for TNP, DNB and DNT detection with K-SV K-SV = quenching constant based on linear Stern-Volmer plot] values of 70 920, 44 000 and 35 430 M-1, respectively, which are the highest values amongst known metal-organic materials. Using this material submicromolar level (equivalent to 0.18 ppm), a detection of nitro explosives has been achieved.