Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

A reinforcement learning approach to automatic generation control

This paper formulates the automatic generation control (AGC) problem as a stochastic multistage decision problem. A strategy for solving this new AGC problem formulation is presented by using a reinforcement learning (RL) approach This method of obtaining an AGC controller does not depend on any knowledge of the system model and more importantly it admits considerable flexibility in defining the control objective. Two specific RL based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting area control error (ACE) excursions, where as, in the second algorithm, the controller can restore the load-generation balance by only monitoring deviation in tie line flows and system frequency and it does not need to know or estimate the composite ACE signal as is done by all current approaches. The effectiveness and versatility of the approaches has been demonstrated using a two area AGC model. (C) 2002 Elsevier Science B.V. All rights reserved.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

A coupled discriminative dictionary and transformation learning approach with applications to cross domain matching

Cross domain and cross-modal matching has many applications in the field of computer vision and pattern recognition. A few examples are heterogeneous face recognition, cross view action recognition, etc. This is a very challenging task since the data in two domains can differ significantly. In this work, we propose a coupled dictionary and transformation learning approach that models the relationship between the data in both domains. The approach learns a pair of transformation matrices that map the data in the two domains in such a manner that they share common sparse representations with respect to their own dictionaries in the transformed space. The dictionaries for the two domains are learnt in a coupled manner with an additional discriminative term to ensure improved recognition performance. The dictionaries and the transformation matrices are jointly updated in an iterative manner. The applicability of the proposed approach is illustrated by evaluating its performance on different challenging tasks: face recognition across pose, illumination and resolution, heterogeneous face recognition and cross view action recognition. Extensive experiments on five datasets namely, CMU-PIE, Multi-PIE, ChokePoint, HFB and IXMAS datasets and comparisons with several state-of-the-art approaches show the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

Signal subspace approach in localization of sound source in shallow water

A new algorithm based on signal subspace approach is proposed for localizing a sound source in shallow water. In the first instance we assumed an ideal channel with plane parallel boundaries and known reflection properties. The sound source is assumed to emit a broadband stationary stochastic signal. The algorithm takes into account the spatial distribution of all images and reflection characteristics of the sea bottom. It is shown that both range and depth of a source can be measured accurately with the help of a vertical array of sensors. For good results the number of sensors should be greater than the number of significant images; however, localization is possible even with a smaller array but at the cost of higher side lobes. Next, we allowed the channel to be stochastically perturbed; this resulted in random phase errors in the reflection coefficients. The most singular effect of the phase errors is to introduce into the spectral matrix an extra term which may be looked upon as a signal generated coloured noise. It is shown through computer simulations that the signal peak height is reduced considerably as a consequence of random phase errors.

Learning automata in feedforward connectionist systems

This paper analyses the behaviour of a general class of learning automata algorithms for feedforward connectionist systems in an associative reinforcement learning environment. The type of connectionist system considered is also fairly general. The associative reinforcement learning task is first posed as a constrained maximization problem. The algorithm is approximated hy an ordinary differential equation using weak convergence techniques. The equilibrium points of the ordinary differential equation are then compared with the solutions to the constrained maximization problem to show that the algorithm does behave as desired.

Flux Partitioning of Species Approach in Binary Interdiffusion Process

Validation of the flux partitioning of species model has been illustrated. Various combinations of inequality expression for the fluxes of species A and B in two successively grown hypothetical intermetallic phases in the interdiffusion zone have been considered within the constraints of this concept. Furthermore, ratio of intrinsic diffusivities of the species A and B in those two phases has been correlated in four different cases. Moreover, complete and or partial validation or invalidation of this model with respect to both the species, has been proven theoretically and also discussed with the Co-Si system as an example.

Nonthermal plasma approach in direct methanol synthesis from CH4

Direct methanol synthesis from CH4 and O2 has been experimentally studied using pulsed discharge plasma in concentric-cylinder-type reactors. The methanol production becomes efficient with an increase in the average electric field strength of the reactor. A combination of the pulsed discharge and catalysts was tested and was proved to be effective in increasing both the production and selectivity of methanol. In the present stage, about 2% of CH4 can be converted into other hydrocarbons, and a methanol yield of around 0.5% and selectivity of 38% can be obtained when a catalyst of V2O5+SiO2 is combined with the pulsed discharge plasma

Nonthermal plasma approach in direct methanol synthesis from CH4

Conversion of hydrocarbon fuels to methanol promoted their efficient utilization as methanol can easily be converted to hydrogen gas, which has higher available energy. In this regard, nonthermal plasma approach using electrical discharges is gaining significance to improve the conversion process of methanol. The efficiency of this nonthermal plasma chemical reaction is affected by various chemical and electrical parameters. This paper presents some important results of the parametric study carried out in methanol synthesis with the aim of reducing energy losses associated with the conventional method. The parameters include the concentration of the reactants, corona electrode configurations, gas mixtures, etc. Further, an attempt was made to study the combined effect of catalysts and electrical discharges on methanol synthesis. Main emphasis was laid on the electrical aspects like electric field, power transfer efficiency, etc. The gas analysis was carried out under carefully maintained laboratory conditions

Enhanced viability of probiotic Saccharomyces boulardii encapsulated by layer-by-layer approach in pH responsive chitosan-dextran sulfate polyelectrolytes

Saccharomyces boulardii was encapsulated by layer-by-layer technique (LbL) using oppositely charged polyelectrolytes, chitosan and dextran sulfate to protect from degradation during its gastrointestinal transit. The protective effect of the coating was evaluated by checking viability after subjecting the coated cells to lyophilisation and simulated gastrointestinal conditions. During lyophilization, coated S. boulardii was found to have an enhanced viability of 7.74 +/- 2.00 log CFU/100 mg (5.62 x 10(6) +/- 2.12 CFU/100 mg) and 5.53 +/- 1.85 log CFU/100 mg (3.46 x 10(5) 1.73 CFU/100 mg) for uncoated cells. On sequential treatment with simulated gastric and intestinal juice, the coated cells had a viability of 4.59 +/- 1.52 log CFU/100 mg (3.8 x 104 +/- 1.52 CFU/100 mg) while only 1.90 +/- 0.80 log CFU/100 mg (0.79 x 102 +/- 0.81 CFU/100 mg) of uncoated cells survived. Confocal studies displayed the selective permeability of the coated cells which plays a significant role in maintaining the integrity and viability of the yeast cells. This clearly indicates that LbL is an efficient protective encapsulation technique and it could be potentially used for improving therapeutic applications of yeast. (C) 2014 Elsevier Ltd. All rights reserved.