Joint Approximately Sparse Channel Estimation and Data Detection in OFDM Systems Using Sparse Bayesian Learning

It is well known that the impulse response of a wide-band wireless channel is approximately sparse, in the sense that it has a small number of significant components relative to the channel delay spread. In this paper, we consider the estimation of the unknown channel coefficients and its support in OFDM systems using a sparse Bayesian learning (SBL) framework for exact inference. In a quasi-static, block-fading scenario, we employ the SBL algorithm for channel estimation and propose a joint SBL (J-SBL) and a low-complexity recursive J-SBL algorithm for joint channel estimation and data detection. In a time-varying scenario, we use a first-order autoregressive model for the wireless channel and propose a novel, recursive, low-complexity Kalman filtering-based SBL (KSBL) algorithm for channel estimation. We generalize the KSBL algorithm to obtain the recursive joint KSBL algorithm that performs joint channel estimation and data detection. Our algorithms can efficiently recover a group of approximately sparse vectors even when the measurement matrix is partially unknown due to the presence of unknown data symbols. Moreover, the algorithms can fully exploit the correlation structure in the multiple measurements. Monte Carlo simulations illustrate the efficacy of the proposed techniques in terms of the mean-square error and bit error rate performance.

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Stable isotope evidence for ca. 2.7-Ga-old Archean cap carbonates from the Dharwar Supergroup, southern India

Carbon isotope compositions of carbonate rocks from similar to 2.7-Ga-old Neoarchean Vanivilas Formation of the Dharwar Supergroup presented earlier by us are re-evaluated in this study, besides oxygen isotope compositions of a few silica dolomite pairs. The purpose of such a revisit assumes significance in view of recent field evidences that suggest a glaciomarine origin for the matrix-supported conglomerate member, the Talya conglomerate, which underlies the carbonate rocks of the Vanivilas Formation. An in-depth analysis of carbon isotope data reveals preservation of their pristine character despite the rocks having been subjected to metamorphism to different degrees (from lower greenschist to lower amphibolite facies). The dolomitic member of Vanivilas Formation of Marikanive area is characterized by highly depleted delta C-13 value (up to -5 parts per thousand VPDB) and merits as the Indian example of ca. 2.7-Ga-old cap carbonate. This inference is further supported by estimated low temperature of equilibration documented by a few silica dolomite pairs from the Vanivilas Formation collected near Kalche area. These pairs show evidence for oxygen isotopic equilibrium at low temperatures (similar to 0-20 degrees C) with depleted water (delta O-18 = -21 parts per thousand to -15 parts per thousand VSMOW) of glacial origin. We propose that the mineral pairs were deposited during the deglaciation period when the ocean temperature was in its gradual restoration phase. The dolomite of Marikanive area is the first record of cap carbonates from the Indian subcontinent with Neoarchean antiquity.

Minimization Problems Based on Relative alpha-Entropy I: Forward Projection

Minimization problems with respect to a one-parameter family of generalized relative entropies are studied. These relative entropies, which we term relative alpha-entropies (denoted I-alpha), arise as redundancies under mismatched compression when cumulants of compressed lengths are considered instead of expected compressed lengths. These parametric relative entropies are a generalization of the usual relative entropy (Kullback-Leibler divergence). Just like relative entropy, these relative alpha-entropies behave like squared Euclidean distance and satisfy the Pythagorean property. Minimizers of these relative alpha-entropies on closed and convex sets are shown to exist. Such minimizations generalize the maximum Renyi or Tsallis entropy principle. The minimizing probability distribution (termed forward I-alpha-projection) for a linear family is shown to obey a power-law. Other results in connection with statistical inference, namely subspace transitivity and iterated projections, are also established. In a companion paper, a related minimization problem of interest in robust statistics that leads to a reverse I-alpha-projection is studied.

A Simple Label Switching Algorithm for Semisupervised Structural SVMs

In structured output learning, obtaining labeled data for real-world applications is usually costly, while unlabeled examples are available in abundance. Semisupervised structured classification deals with a small number of labeled examples and a large number of unlabeled structured data. In this work, we consider semisupervised structural support vector machines with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labeled and unlabeled examples, along with the domain constraints. We propose a simple optimization approach that alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective label switching method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching and avoiding poor local minima, which are not very useful. The algorithm is simple and easy to implement. Further, it is suitable for any structured output learning problem where exact inference is available. Experiments on benchmark sequence labeling data sets and a natural language parsing data set show that the proposed approach, though simple, achieves comparable generalization performance.

Triarylborane conjugated dicyanovinyl chromophores: intriguing optical properties and colorimetric anion discrimination

Three new triarylborane conjugated dicyanovinyl chromophores (Mes(2)B-pi-donor-DCV); donor: N-methyldiphenylamine (1) and triphenylamine (2 and 3 with two BMes(2) substitutions]) of type A-D-A (acceptor-donor- acceptor) are reported. Compounds 1-3 exhibit intense charge transfer (CT) absorption bands in the visible region. These absorption peaks are combination CT bands of the amine donor to both the BMes(2) and DCV units. This inference was supported by theoretical studies. Compound 1 shows weak fluorescence compared to 2 and 3. The discrimination of fluoride and cyanide ions is essential in the case of triarylborane (TAB) based anion sensors as a similar response is given towards both the anions. Anion binding studies of 1, 2 and 3 showed that fluoride ions bind selectively to the boron centre and block the corresponding CT transition (donor to BMes(2)) leaving the other CT transition to be red shifted. On the other hand, cyanide ions bind with both the receptor sites and stop both the CT transition processes and hence a different colorimetric response was noted. The binding of F-/CN- induces colour changes in the visible region of the electronic spectra of 2 and 3, which allows for the naked-eye detection of F- and CN- ions. The anion binding mechanisms are established using NMR titration experiments.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.