Neutrosophic classifier: An extension of fuzzy classifer

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.     

CONTOUR: an efficient algorithm for discovering discriminating subsequences

In recent years we have witnessed several applications of frequent sequence mining, such as feature selection for protein sequence classification and mining block correlations in storage systems. In typical applications such as clustering, it is not the complete set but only a subset of discriminating frequent subsequences which is of interest. One approach to discovering the subset of useful frequent subsequences is to apply any existing frequent sequence mining algorithm to find the complete set of frequent subsequences. Then, a subset of interesting subsequences can be further identified. Unfortunately, it is very time consuming to mine the complete set of frequent subsequences for large sequence databases. In this paper, we propose a new algorithm, CONTOUR, which efficiently mines a subset of high-quality subsequences directly in order to cluster the input sequences. We mainly focus on how to design some effective search space pruning methods to accelerate the mining process and discuss how to construct an accurate clustering algorithm based on the result of CONTOUR. We conducted an extensive performance study to evaluate the efficiency and scalability of CONTOUR, and the accuracy of the frequent subsequence-based clustering algorithm.     

Detection of low levels of trichloroethylene vapor with Raman spectrometry

The response of a novel fiber-optic Raman probe to low levels of trichloroethylene (TCE) vapors is characterized. The detection limit of the current probe for TCE vapor is 34 mg/L, and the probe exhibits a fully reversible response. The probe uses an organic-polymer, low-density polyethylene to concentrate TCE vapors in the optical path of the fiber-optic Raman spectrometer. The relative standard deviation for measurement of 677 mg/L of TCE in the vapor is 0.3%.     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

     

A multisource energy harvesting utilizing highly efficient ferroelectric PMN-PT single crystal

This paper demonstrates a multi-source energy harvester that is able to utilize simultaneously both piezoelectric and pyroelectric effects in lead magnesium niobate-lead titanate (PMN-PT) single crystal. The paper presents a study of PMN-PT single crystal with a (67:33) composition grown in our laboratory via a vertical gradient freeze method without any flux. The performance of the piezoelectric and pyroelectric energy harvester using unimorph device structure was evaluated via modeling and experiment. The theoretical study was implemented based on a distributed parameter electromechanical model and the modelling procedure was approximated using finite element analysis to predict the electromechanical behavior of the harvester. The maximum power density at a resonance frequency of 50 Hz and optimum resistance of 2 MΩ was 16.7 nW/(g² cm³) under a 1 g acceleration of vibration. The measured values of electrical output parameters were in good agreement with theoretical and modelling results using MATLAB and COMSOL Multiphysics, respectively. By using the pyroelectric effect along with the piezoelectric effect, the output voltage of the energy harvester was found to be enhanced at the optimum resistance and specific frequency values. It was noticed that the output voltage was increased monotonically with temperature-difference (ΔT) and reaches up to 180 % of its original value under temperature difference of 1.7 °C at a frequency value of 49 Hz.     

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.     

Linguistic Discriminative Aggregation in Multicriteria Decision Making

Several new aggregation operators are proposed in the context of multicriteria decision making (MCDM) in the linguistic domain. The proposed operators first infer the discrimination index, based on the extent of variability in the various linguistic evaluations against a criterion. This value is then utilized in the actual aggregation step to discriminate among the alternatives. Besides, the proposed operators also take into account the a priori weights associated with the criteria. The proposed concepts are illustrated through an example in group MCDM.     

Experimental characterization of piezoelectrically actuated micromachined silicon valveless micropump

In this paper, performance of piezoelectrically actuated pyramidal valveless micropumps is studied experimentally in detail. Valveless micropumps based on silicon and glass substrate are fabricated using MEMS technology. Two different sizes of micropumps having overall dimensions of 5 mm × 5 mm × 1 mm and 10 mm × 10 mm × 1 mm are fabricated and characterized. In the fabricated micropumps, the thickness of silicon diaphragm is <20 µm which gives the advantage of operating pump at low voltage with excellent stability and consistency. The performance of micropumps in terms of flowrate and backpressure is evaluated for a wide range of driving frequency and actuating voltages. The maximum flowrate of water in the 10-mm micropump is 355 µl/min and backpressure of 3.1 kPa at zero flowrate for an applied voltage of 80 V at frequency 1.05 kHz. The reported micropumps have low footprint, high flowrate and backpressure. Thus, these micropumps are especially suited for biological applications as these can withstand adequate amount of backpressure. Comparative study of the performance of these micropumps with those available in the literature brings out the efficacy of these micropumps.     

MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences

In this paper, we present a method called MODEEP (Motion-based Object DEtection and Estimation of Pose) to detect independently moving objects (IMOs) in forward-looking infrared (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Thereafter, areas with residual motion indicate potential object activity. These areas are detected, refined and selected using a Bayesian classifier. The resulting regions are clustered into pairs such that each pair represents one object's front and rear end. Using motion and scene knowledge, we estimate object pose and establish a region of interest (ROI) for each pair. Edge elements within each ROI are used to segment the convex cover containing the IMO. We show detailed results on real, complex, cluttered and noisy sequences. Moreover, we outline the integration of our fast and robust system into a comprehensive automatic target recognition (ATR) and action classification system.     

Matching three-dimensional objects using silhouettes

A method for matching three-dimensional objects against a library of models from an observed sequence of silhouettes is presented in this correspondence. Based upon the observed silhouettes, the three-dimensional structure of the object is constructed and refined. The principal moments and three primary silhouettes are computed for the constructed three-dimensional objects to represent the aggregate and detailed structure parameters. The adaptive matching technique requires that sufficient silhouettes be added to modify the structure of the unknown object until consistent and steady matching results are obtained. The library for matching is based on three primary silhouettes of the model objects. Experiments conducted show a fast convergence to a consistent result may be achieved provided that a reasonable choice of silhouettes is made.