Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

A novel two sources ultrasound modulated optical tomographic system for screening breast cancer through elasticity characterization

The study of non-invasive characterization of elastic properties of soft biological tissues has been a focus of active researches since recent years. Light is highly scattered by biological tissues and hence, sophisticated reconstruction algorithms are required to achieve good imaging depth and a reasonable resolution. Ultrasound (US), on the otherhand, is less scattered by soft tissues and it has been in use for imaging in biomedical ultrasound systems. Combination of the contrast sensitivity of light and good localization of ultrasound provides a challenging technique for characterization of thicker tissues deep inside the body non-invasively. The elasticity of the tissues is characterized by studying the response of tissues to mechanical excitation induced by an acoustic radiation force (remotely) using an optical laser. The US modulated optical signals which traverse the tissue are detected by using a CCD camera as detector array and the pixel map formed on the CCD is used to characterize the embedded inhomogeneities. The use of CCD camera improves the signal-noise-ratio (SNR) by averaging the signals from all of the CCD pixels.

Explicit codes minimizing repair bandwidth for distributed storage

We consider the problem of minimizing the bandwidth required to repair a failed node when data is stored across n nodes in a distributed manner, so as to facilitate reconstruction of the entire data by connecting to any k out of the n nodes. We provide explicit and optimal constructions which permit exact replication of a failed systematic node.

DMT of multi-hop cooperative networks

Some basic results that help in determining the Diversity-Multiplexing Tradeoff (DMT) of cooperative multihop networks are first identified. As examples, the maximum achievable diversity gain is shown to equal the min-cut between source and sink, whereas the maximal multiplexing gain is shown to equal the minimum rank of the matrix characterizing the MIMO channel appearing across a cut in the network. Two multi-hop generalizations of the two-hop network are then considered, namely layered networks as well as a class of networks introduced here and termed as K-parallel-path (KPP) networks. The DMT of KPP networks is characterized for K > 3. It is shown that a linear DMT between the maximum diversity dmax and the maximum multiplexing gain of 1 is achievable for fully-connected, layered networks. Explicit coding schemes achieving the DMT that make use of cyclic-division-algebra-based distributed space-time codes underlie the above results. Two key implications of the results in the paper are that the half-duplex constraint does not entail any rate loss for a large class of cooperative networks and that simple, amplify-and-forward protocols are often sufficient to attain the optimal DMT.

Single real-symbol decodable, high-rate, distributed space-time block codes

A scheme to apply the rate-1 real orthogonal designs (RODs) in relay networks with single real-symbol decodability of the symbols at the destination for any arbitrary number of relays is proposed. In the case where the relays do not have any information about the channel gains from the source to themselves, the best known distributed space time block codes (DSTBCs) for k relays with single real-symbol decodability offer an overall rate of complex symbols per channel use. The scheme proposed in this paper offers an overall rate of 2/2+k complex symbol per channel use, which is independent of the number of relays. Furthermore, in the scenario where the relays have partial channel information in the form of channel phase knowledge, the best known DSTBCs with single real-symbol decodability offer an overall rate of 1/3 complex symbols per channel use. In this paper, making use of RODs, a scheme which achieves the same overall rate of 1/3 complex symbols per channel use but with a decoding delay that is 50 percent of that of the best known DSTBCs, is presented. Simulation results of the symbol error rate performance for 10 relays, which show the superiority of the proposed scheme over the best known DSTBC for 10 relays with single real-symbol decodability, are provided.

Improved large-MIMO detection based on damped belief propagation

In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.

Modeling of growth and motion of equiaxed dendrites in a convecting melt

A numerical micro-scale model is developed to study the behavior of dendrite growth in presence of melt convection. In this method, an explicit, coupled enthalpy model is used to simulate the growth of an equiaxed dendrite, while a Volume of Fluid (VOF) method is used to track the movement of the dendrite in the convecting melt in a two-dimensional Eulerian framework. Numerical results demonstrate the effectiveness of the enthalpy model in simulating the dendritic growth involving complex shape, and the accuracy of VOF method in conserving mass and preserving the complex dendritic shape during motion. Simulations are performed in presence of uniform melt flow for both fixed and moving dendrites, and the difference in dendrite morphology is shown.

Translation Induction on Indian Language Corpora Using Translingual Themes from Other Languages

Identifying translations from comparable corpora is a well-known problem with several applications, e.g. dictionary creation in resource-scarce languages. Scarcity of high quality corpora, especially in Indian languages, makes this problem hard, e.g. state-of-the-art techniques achieve a mean reciprocal rank (MRR) of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. There exist comparable corpora in many Indian languages with other ``auxiliary'' languages. We observe that translations have many topically related words in common in the auxiliary language. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for translation induction. Extensive experiments on 35 comparable corpora using English and French as auxiliary languages show that this approach can yield dramatic improvements in performance (e.g. MRR improves by 124% to 0.419 for Telugu-Kannada). A user study on WikiTSu, a system for cross-lingual Wikipedia title suggestion that uses our approach, shows a 20% improvement in the quality of titles suggested.