Spectral entropy and spectral shape based pre-quantization for real time speaker identification system

Pre-processing is one of the vital steps for developing robust and efficient recognition system. Better pre-processing not only aid in better data selection but also in significant reduction of computational complexity. Further an efficient frame selection technique can improve the overall performance of the system. Pre-quantization (PQ) is the technique of selecting less number of frames in the pre-processing stage to reduce the computational burden in the post processing stages of speaker identification (SI). In this paper, we develop PQ techniques based on spectral entropy and spectral shape to pick suitable frames containing speaker specific information that varies from frame to frame depending on spoken text and environmental conditions. The attempt is to exploit the statistical properties of distributions of speech frames at the pre-processing stage of speaker recognition. Our aim is not only to reduce the frame rate but also to maintain identification accuracy reasonably high. Further we have also analyzed the robustness of our proposed techniques on noisy utterances. To establish the efficacy of our proposed methods, we used two different databases, POLYCOST (telephone speech) and YOHO (microphone speech).     

Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach

In this paper, the automatic segmentation of a multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. The automatic clustering problem is thereafter modelled as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. A multiobjective clustering technique, named MCMOClust, is used to solve this problem. MCMOClust utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously to automatically evolve the appropriate number of clusters present in MR brain images. A semi-supervised method is used to select a single solution from the final Pareto optimal front of MCMOClust. The present method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the present method over Fuzzy C-means, Expectation Maximization clustering algorithms and a newly developed symmetry based fuzzy genetic clustering technique (Fuzzy-VGAPS), are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCMOClust) is also compared with the available ground truth information.     

Differential regulation of redox proteins and chaperones in HbEβ-thalassemia erythrocyte proteome

Purpose: In (hemoglobin, Hb) HbEβ‐thalassemia, HbE (β‐26 Glu→Lys) interacts with β‐thalassemia to produce clinical manifestation of varying severity. This is the first proteomic effort to study changes in protein levels of erythrocytes isolated from HbEβ‐thalassemic patients compared to normal. Experimental design: We have used 2‐DE and MALDI‐MS/MS‐based techniques to investigate the differential proteome profiling of membrane and Hb‐depleted fraction of cytosolic proteins of erythrocytes isolated from the peripheral blood samples of HbEβ‐thalassemia patients and normal volunteers. Results: Our study showed that redox regulators such as peroxiredoxin 2, Cu‐Zn superoxide dismutase and thioredoxin and chaperones such as α‐hemoglobin stabilizing protein and HSP‐70 were upregulated in HbEβ‐thalassemia. We have also observed larger amounts of membrane associated globin chains and indications of disruption of spectrin‐based junctional complex in the membrane skeleton of HbEβ‐thalassemic erythrocytes upon detection of low molecular weight fragments of β‐spectrin and decrease in β‐actin and dematin content. Conclusion and clinical relevance: We have observed interesting changes in the proteomic levels of redox regulators and chaperons in the thalassemic hemolysates and have observed strong correlation or association of the extent of such proteomic changes with HbE levels. This could be important in understanding the role of HbE in disease progression and pathophysiology.     

A dynamic model for short-term prediction of stream attributes

Multiple data streams coming out of a complex system form the observable state of the system. The streams may correspond to various sensors attached with the system or outcome of internal processes. Such stream data may consist of multiple attributes and may differ in terms of their frequency of generation and observation. The streams may have dependency among themselves. One will have to rely on such data streams for monitoring the health of the system or to take any corrective measure. Predicting the value of certain stream data is an important task that can help one to take decision and act accordingly. In this work, a simple but generic visualization of a complex system is presented and thereafter a linear regression-based dynamic model for short-term prediction is proposed. The model is based on the past history of the attributes of multiple streams as suggested by the domain experts. But, it automatically determines the meaningful attributes and reformulates the model. The model is also re-computed if the prediction error exceeds the allowable tolerance. All these make the model dynamic. Experiment is carried out with stock market data streams to predict the close value well in advance. It is observed that in terms of quality of prediction and performance metric, the proposed model is quite effective.     

Optimum image thresholding via class uncertainty and regionhomogeneity

Thresholding is a popular image segmentation method that converts a gray-level image into a binary image. The selection of optimum thresholds has remained a challenge over decades. Besides being a segmentation tool on its own, often it is also a step in many advanced image segmentation techniques in spaces other than the image space. We introduce a thresholding method that accounts for both intensity-based class uncertainty-a histogram-based property-and region homogeneity-an image morphology-based property. A scale-based formulation is used for region homogeneity computation. At any threshold, intensity-based class uncertainty is computed by fitting a Gaussian to the intensity distribution of each of the two regions segmented at that threshold. The theory of the optimum thresholding method is based on the postulate that objects manifest themselves with fuzzy boundaries in any digital image acquired by an imaging device. The main idea here is to select that threshold at which pixels with high class uncertainty accumulate mostly around object boundaries. To achieve this, a threshold energy criterion is formulated using class-uncertainty and region homogeneity such that, at any image location, a high energy is created when both class uncertainty and region homogeneity are high or both are low. Finally, the method selects that threshold which corresponds to the minimum overall energy. The method has been compared to a maximum segmented image information method. Superiority of the proposed method was observed both qualitatively on clinical medical images as well as quantitatively on 250 realistic phantom images generated by adding different degrees of blurring, noise, and background variation to real objects segmented from clinical images     

A stability-based distributed routing mechanism to support unicast and multicast routing in ad hoc wireless network

An ad hoc network can be envisioned as a collection of mobile routers, each equipped with a wireless transceiver, which are free to move about arbitrarily. In ad hoc wireless networks, even if two nodes are outside the wireless transmission range of each other, they may still be able to communicate in multiple hops using other intermediate nodes. However, the dynamics of these networks, as a consequence of mobility and disconnection of mobile hosts, pose a number of problems in designing routing schemes for effective communication between any pair of source and destination. In this paper, a stability-based unicast routing mechanism, that considers both link affinity and path stability in order to find out a stable route from source to destination, is proposed. It is then extended to support multicast routing as well where only local state information (at source) is utilized for constructing a multicast tree. The performance of the proposed scheme is evaluated on a simulated environment to show that the stability-based scheme provides a unified framework for both unicast and multicast routing and reduces the probability of route error drastically in both the cases.     

Polyaniline and polypyrrole modified conductive nanocomposites of polyfuran and polythiophene with montmorillonite clay

Nanocomposites of polyfuran (PF) and polythiophene (PTP) with montmorillonite clay (MMT) were prepared and modified by loading of polyaniline (PANI) and polypyrrole (PPY) moieties via polymerization of aniline (ANI) and pyrrole (PY) in aqueous dispersions of PF-MMT and PTP-MMT nanocomposites. Formation of PANI and PPY and their subsequent incorporation in the PF-MMT and PTP-MMT composites was confirmed by FTIR absorption studies. X-ray diffraction (XRD) patterns of PANI and PPY modified PF-MMT and PTP-MMT composites showed that PF-MMT and PTP-MMT intercalates were still present in the modified composites. Scanning electron microscopic analysis revealed distinctive morphological patterns of the various composite particles. The dc conductivity values of PANI and PPY modified PF-MMT and PTP-MMT composites were in the order of 10⁻² S/cm in either system – a value much improved compared to the same for both of the unmodified PF-MMT (10⁻⁷ S/cm) and PTP-MMT (10⁻⁵ S/cm) nanocomposites respectively.     

The Corrosion and Wear Performance of Microcrystalline WC-10Co-4Cr and Near-Nanocrystalline WC-17Co High Velocity Oxy-Fuel Sprayed Coatings on Steel Substrate

The study of near-nanocrystalline cermet composite coating was performed by depositing near-nanocrystalline WC-17Co powder using the high velocity oxy-fuel spraying technique. The WC-17Co powder consists of a core with an engineered near-nano-scale WC dispersion with a mean grain size 427 nm. The powder particle contains 6 wt pct of the ductile phase Co matrix mixed into the core to ensure that the reinforcing ceramic phase WC material is discontinuous to limit debridement during wear, while the remainder of the binding phase (11 wt pct) is applied as a coating on the powder particle to improve the ductility. The tribological properties of the coating, in terms of corrosion resistance, microhardness, and sliding abrasive wear, were studied and compared with those of an industrially standard microcrystalline WC-10Co-4Cr coating with a WC mean grain size 3 μm. Results indicated that the WC-17Co coating had superior wear and corrosion resistance compared to the WC-10Co-4Cr coating. The engineered WC-17Co powder with a duplex Co layer had prevented significant decarburization of the WC dispersion in the coating, thereby reducing the intersplat microporosity necessary for initiating microgalvanic cells. The improved wear resistance was attributed to the higher hardness value of the near-nanocrystalline WC-17Co coating.     

Study on adsorption refrigeration cycle utilizing activated carbon fibers. Part 2. Cycle performance evaluation

Thermal heat driven adsorption systems have been gained considerable attention on the recent energy utilization trend. However, the drawbacks of these adsorption systems are their poor performance. It is urgently necessary to improve the system performance of the adsorption cycles. There are two major ways for the system performance improvement. One is to develop new adsorbent material well suited to low temperature heat regeneration. The other is to enhance heat and mass transfer in the adsorber/desorber heat exchanger. The objective of the paper is to investigate the system performance of an adsorption cycle. The cycle utilizes activated carbon fiber (ACF)/methanol as adsorbent/refrigerant pair. In this paper, specific cooling effect SCE and COP of the system are numerically evaluated from the adsorption equilibrium theory with different hot, cooling and chilled fluid inlet temperatures. It is confirmed that the influences of hot, cooling and chilled fluid inlet temperatures on the system performance are qualitatively similar to those of silica gel/water pair. Even though, the driving temperature levels of ACF/methanol and silica gel/water are different. There is an optimum condition for COP to reach at maximum for ACF/methanol pair. Particularly, the ACF/methanol system shows better performance with lower chilled fluid inlet temperature between −20 and 20 °C.     

Analytical study of nuclear-coupled density-wave instability in a natural circulation pressure tube type boiling water reactor

An analytical model has been developed to study the nuclear-coupled density-wave instability in the Indian advanced heavy water reactor (AHWR) which is a natural circulation pressure tube type boiling water reactor. The model considers a point kinetics model for the neutron dynamics and a lumped parameter model for the fuel thermal dynamics along with the conservation equations of mass, momentum and energy and equation of state for the coolant. In addition, to study the effect of neutron interactions between different parts of the core, the model considers a coupled multipoint kinetics equation in place of simple point kinetics equation. Linear stability theory was applied to reveal the instability of in-phase and out-of-phase modes in the boiling channels of the AHWR. The results indicate that the stability behavior of the reactor is greatly influenced by the void reactivity coefficient, fuel time constant, radial power distribution and channel inlet orificing. The delayed neutrons were found to have a strong influence on the Type I and Type II instabilities observed at low and high channel powers, respectively. Also, it was found that the coupled multipoint kinetics model and the modal point kinetics model predict the same threshold power for out-of-phase instability if the coupling coefficient in the former model is half the eigen value separation between the fundamental and the first harmonic mode in the latter model. Decay ratio maps were predicted considering various operating parameters of the reactor, which are useful for its design.