Subspace Pursuit Embedded in Orthogonal Matching Pursuit

Orthogonal Matching Pursuit (OMP) is a popular greedy pursuit algorithm widely used for sparse signal recovery from an undersampled measurement system. However, one of the main shortcomings of OMP is its irreversible selection procedure of columns of measurement matrix. i.e., OMP does not allow removal of the columns wrongly estimated in any of the previous iterations. In this paper, we propose a modification in OMP, using the well known Subspace Pursuit (SP), to refine the subspace estimated by OMP at any iteration and hence boost the sparse signal recovery performance of OMP. Using simulations we show that the proposed scheme improves the performance of OMP in clean and noisy measurement cases.

Projection-based atom selection in orthogonal matching pursuit for compressive sensing

For compressive sensing, we endeavor to improve the atom selection strategy of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a least squares solution based atom selection method. From a set of promising atoms, the choice of an atom is performed through a new method that uses orthogonal projection along-with a standard matched filter. Through experimental evaluations, the effect of projection based atom selection strategy is shown to provide a significant improvement for the support set recovery performance, in turn, the compressive sensing recovery.

Progressive fusion of reconstruction algorithms for low latency applications in compressed sensing

Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data. (C) 2013 Elsevier B.V. All rights reserved.

Event-triggered Sampling and Reconstruction of Sparse Real-valued Trigonometric Polynomials

We propose data acquisition from continuous-time signals belonging to the class of real-valued trigonometric polynomials using an event-triggered sampling paradigm. The sampling schemes proposed are: level crossing (LC), close to extrema LC, and extrema sampling. Analysis of robustness of these schemes to jitter, and bandpass additive gaussian noise is presented. In general these sampling schemes will result in non-uniformly spaced sample instants. We address the issue of signal reconstruction from the acquired data-set by imposing structure of sparsity on the signal model to circumvent the problem of gap and density constraints. The recovery performance is contrasted amongst the various schemes and with random sampling scheme. In the proposed approach, both sampling and reconstruction are non-linear operations, and in contrast to random sampling methodologies proposed in compressive sensing these techniques may be implemented in practice with low-power circuitry.

Use of a volatile buffer at ambient temperature *1: Versatile approach to the purification of self-complementary synthetic deoxyoligonucleotides by reversed-phase high-performance liquid chromatography

Abstract is not available.

A self-supported 40W direct methanol fuel cell system

A self-supported 40W Direct Methanol Fuel Cell (DMFC) system has been developed and performance tested. The auxiliaries in the DMFC system comprise a methanol sensor, a liquid-level indicator, and fuel and air pumps that consume a total power of about 5W. The system has a 15-cell DMFC stack with active electrode-area of 45 cm(2). The self-supported DMFC system addresses issues related to water recovery from the cathode exhaust, and maintains a constant methanol-feed concentration with thermal management in the system. Pure methanol and water from cathode exhaust are pumped to the methanol-mixing tank where the liquid level is monitored and controlled with the help of a liquid-level indicator. During the operation, methanol concentration in the feed solution at the stack outlet is monitored using a methanol sensor, and pure methanol is added to restore the desired methanol concentration in the feed tank by adding the product water from the cathode exhaust. The feed-rate requirements of fuel and oxidant are designed for the stack capacity of 40W. The self-supported DMFC system is ideally suited for various defense and civil applications and, in particular, for charging the storage batteries.

Kinematic and Mechanical Profile of the Self-Actuation of Thermosalient Crystal Twins of 1,2,4,5-Tetrabromobenzene: A Molecular Crystalline Analogue of a Bimetallic Strip

A paradigm shift from hard to flexible, organic-based optoelectronics requires fast and reversible mechanical response from actuating materials that are used for conversion of heat or light into mechanical motion. As the limits in the response times of polymer-based actuating materials are reached, which are inherent to the less-than-optimal coupling between the light/heat and mechanical energy in them, 1 a conceptually new approach to mechanical actuation is required to leapfrog the performance of organic actuators. Herein, we explore single crystals of 1,2,4,5-tetrabromobenzene (TBB) as actuating elements and establish relations between their kinematic profile and mechanical properties. Centimeter-size acicular crystals of TBB are the only naturally twinned crystals out of about a dozen known materials that exhibit the thermosalient effect-an extremely rare and visually impressive crystal locomotion. When taken over a phase transition, crystals of this material store mechanical strain and are rapidly self-actuated to sudden jumps to release the internal strain, leaping up to several centimeters. To establish the structural basis for this colossal crystal motility, we investigated the mechanical profile of the crystals from macroscale, in response to externally induced deformation under microscope, to nanoscale, by using nanoindentation. Kinematic analysis based on high-speed recordings of over 200 twinned TBB crystals exposed to directional or nondirectional heating unraveled that the crystal locomotion is a kinematically complex phenomenon that includes at least six kinematic effects. The nanoscale tests confirm the highly elastic nature, with an elastic deformation recovery (60%) that is far superior to those of molecular crystals reported earlier. This property appears to be critical for accumulation of stress required for crystal jumping. Twinned crystals of TBB exposed to moderate directional heating behave as all-organic analogue of a bimetallic `strip, where the lattice misfit between the two crystal components drives reveriible deformation of the crystal.

Self-tuning control of a class of multivariable non-linear systems

Self-tuning is applied to the minimum variance control of non-linear multivariable systems which can be characterized by a ' multivariable Hammerstein model '. It is also shown that such systems are not amenable to self-tuning control if control costing is to be included in the performance criterion.

High lithium storage in micrometre sized mesoporous spherical self-assembly of anatase titania nanospheres and carbon

Composite of anatase titania (TiO2) nanospheres and carbon grown and self-assembled into micron-sized mesoporous spheres via a solvothermal synthesis route are discussed here in the context of rechargeable lithium-ion battery. The morphology and carbon content and hence the electrochemical performance are observed to be significantly influenced by the synthesis parameters. Synthesis conditions resulting in a mesoporous arrangement of an optimized amount carbon and TiO2 exhibited the best lithium battery performance. The first discharge cycle capacity of carbon-titania mesoporous spheres (solvothermal reaction at 150 degrees C at 6 h, calcination at 500 degrees C under air, BET surface area 80 m(2)g(-1)) was 334 mAhg(-1) (approximately 1 Li) at current rate of 0.066 Ag-1. High storage capacity and good cyclability is attributed to the nanostructuring of TiO2 (mesoporosity) as well as due to formation of a percolation network of carbon around the TiO2 nanoparticles. The micron-sized mesoporous spheres of carbon-titania composite nanoparticles also show good rate cyclability in the range (0.066-6.67) Ag-1.

Performance Analysis of Reconstruction Techniques for Frequency-Domain Optical-Coherence Tomography

We address the issue of noise robustness of reconstruction techniques for frequency-domain optical-coherence tomography (FDOCT). We consider three reconstruction techniques: Fourier, iterative phase recovery, and cepstral techniques. We characterize the reconstructions in terms of their statistical bias and variance and obtain approximate analytical expressions under the assumption of small noise. We also perform Monte Carlo analyses and show that the experimental results are in agreement with the theoretical predictions. It turns out that the iterative and cepstral techniques yield reconstructions with a smaller bias than the Fourier method. The three techniques, however, have identical variance profiles, and their consistency increases linearly as a function of the signal-to-noise ratio.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).