Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

Conductometric Sensing of H-2 by Chromium Niobate

Investigations on chromium niobate (CrNbO4) have shown that the material responds to a wide hydrogen concentration range of 0.01%-0.40% at 613 K. The conductivity of CrNbO4 is independent of oxygen partial pressure and offers to be a promising candidate for monitoring H-2 in radioactive spent fuel storage facilities. Reduction of metal ions to lower valence states during hydrogen sensing was evidenced by X-ray photoelectron spectroscopic studies.

Error exponent analysis of energy-based Bayesian decentralized spectrum sensing under fading

This paper considers decentralized spectrum sensing, i.e., detection of occupancy of the primary users' spectrum by a set of Cognitive Radio (CR) nodes, under a Bayesian set-up. The nodes use energy detection to make their individual decisions, which are combined at a Fusion Center (FC) using the K-out-of-N fusion rule. The channel from the primary transmitter to the CR nodes is assumed to undergo fading, while that from the nodes to the FC is assumed to be error-free. In this scenario, a novel concept termed as the Error Exponent with a Confidence Level (EECL) is introduced to evaluate and compare the performance of different detection schemes. Expressions for the EECL under general fading conditions are derived. As a special case, it is shown that the conventional error exponent both at individual sensors, and at the FC is zero. Further, closed-form lower bounds on the EECL are derived under Rayleigh fading and lognormal shadowing. As an example application, it answers the question of whether to use pilot-signal based narrowband sensing, where the signal undergoes Rayleigh fading, or to sense over the entire bandwidth of a wideband signal, where the signal undergoes lognormal shadowing. Theoretical results are validated using Monte Carlo simulations. (C) 2015 Elsevier B.V. All rights reserved.

Design and implementation of spectrum sensing for cognitive radios with a frequency-hopping primary system

In this work, spectrum sensing for cognitive radios is considered in the presence of multiple Primary Users (PU) using frequency-hopping communication over a set of frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses. (C) 2015 Elsevier B.V. All rights reserved.

Temperature-Dependent Photoluminescence of g-C3N4: Implication for Temperature Sensing

We report the temperature-dependent photoluminescence (PL) properties of polymeric graphite-like carbon nitride (g-C3N4) and a methodology for the determination of quantum efficiency along with the activation energy. The PL is shown to originate from three different pathways of transitions: sigma*-LP, pi*-LP, and pi*-pi, respectively. The overall activation energy is found to be similar to 73.58 meV which is much lower than the exciton binding energy reported theoretically but ideal for highly sensitive wide-range temperature sensing. The quantum yield derived from the PL data is 23.3%, whereas the absolute quantum yield is 5.3%. We propose that the temperature-dependent PL can be exploited for the evaluation of the temperature dependency of quantum yield as well as for temperature sensing. Our analysis further indicates that g-C3N4 is well-suited for wide-range temperature sensing.

A survey on compressed domain video analysis techniques

Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

Effect of RF power on the structural, optical and gas sensing properties of RF-sputtered Al doped ZnO thin films

The effect of Radio Frequency (RF) power on the properties of magnetron sputtered Al doped ZnO thin films and the related sensor properties are investigated. A series of 2 wt% Al doped ZnO; Zn0.98Al0.02O (AZO) thin films prepared with magnetron sputtering at different RF powers, are examined. The structural results reveal a good adhesive nature of thin films with quartz substrates as well as increasing thickness of the films with increasing RF power. Besides, the increasing RF power is found to improve the crystallinity and grain growth as confirmed by X-ray diffraction. On the other hand, the optical transmittance is significantly influenced by the RF power, where the transparency values achieved are higher than 82% for all the AZO thin films and the estimated optical band gap energy is found to decrease with RF power due to an increase in the crystallite size as well as the film thickness. In addition, the defect induced luminescence at low temperature (77 K) and room temperature (300 K) was studied through photoluminescence spectroscopy, it is found that the defect density of electronic states of the Al3+ ion increases with an increase of RF power due to the increase in the thickness of the film and the crystallite size. The gas sensing behavior of AZO films was studied for NO2 at 350 degrees C. The AZO film shows a good response towards NO2 gas and also a good relationship between the response and the NO2 concentration, which is modeled using an empirical formula. The sensing mechanism of NO2 is discussed.

The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems

Schemes that can be proven to be unconditionally stable in the linear context can yield unstable solutions when used to solve nonlinear dynamical problems. Hence, the formulation of numerical strategies for nonlinear dynamical problems can be particularly challenging. In this work, we show that time finite element methods because of their inherent energy momentum conserving property (in the case of linear and nonlinear elastodynamics), provide a robust time-stepping method for nonlinear dynamic equations (including chaotic systems). We also show that most of the existing schemes that are known to be robust for parabolic or hyperbolic problems can be derived within the time finite element framework; thus, the time finite element provides a unification of time-stepping schemes used in diverse disciplines. We demonstrate the robust performance of the time finite element method on several challenging examples from the literature where the solution behavior is known to be chaotic. (C) 2015 Elsevier Inc. All rights reserved.

The time finite element as a robust general scheme for solving nonlinear dynamic equations including chaotic systems

Schemes that can be proven to be unconditionally stable in the linear context can yield unstable solutions when used to solve nonlinear dynamical problems. Hence, the formulation of numerical strategies for nonlinear dynamical problems can be particularly challenging. In this work, we show that time finite element methods because of their inherent energy momentum conserving property (in the case of linear and nonlinear elastodynamics), provide a robust time-stepping method for nonlinear dynamic equations (including chaotic systems). We also show that most of the existing schemes that are known to be robust for parabolic or hyperbolic problems can be derived within the time finite element framework; thus, the time finite element provides a unification of time-stepping schemes used in diverse disciplines. We demonstrate the robust performance of the time finite element method on several challenging examples from the literature where the solution behavior is known to be chaotic. (C) 2015 Elsevier Inc. All rights reserved.

Effect of RF power on the structural, optical and gas sensing properties of RF-sputtered Al doped ZnO thin films

The effect of Radio Frequency (RF) power on the properties of magnetron sputtered Al doped ZnO thin films and the related sensor properties are investigated. A series of 2 wt% Al doped ZnO; Zn0.98Al0.02O (AZO) thin films prepared with magnetron sputtering at different RF powers, are examined. The structural results reveal a good adhesive nature of thin films with quartz substrates as well as increasing thickness of the films with increasing RF power. Besides, the increasing RF power is found to improve the crystallinity and grain growth as confirmed by X-ray diffraction. On the other hand, the optical transmittance is significantly influenced by the RF power, where the transparency values achieved are higher than 82% for all the AZO thin films and the estimated optical band gap energy is found to decrease with RF power due to an increase in the crystallite size as well as the film thickness. In addition, the defect induced luminescence at low temperature (77 K) and room temperature (300 K) was studied through photoluminescence spectroscopy, it is found that the defect density of electronic states of the Al3+ ion increases with an increase of RF power due to the increase in the thickness of the film and the crystallite size. The gas sensing behavior of AZO films was studied for NO2 at 350 degrees C. The AZO film shows a good response towards NO2 gas and also a good relationship between the response and the NO2 concentration, which is modeled using an empirical formula. The sensing mechanism of NO2 is discussed.

Studies on Carbon Mediated Paste Screen Printed Sensors for Blood Glucose Sensing Application

Ready-to-use screen printed glucose sensors are fabricated using Prussian Blue (PB) and Cobalt Phthalocyanine (CoPC) mediated carbon inks as working electrodes. The reference and counter electrodes are screen printed using silver/silver chloride and graphitic carbon paste respectively. The screen printed reference electrodes (internal reference electrode (IRE)) are found to be stable for more than 60 minutes when examined with saturated calomel electrode. Optimal operating voltage for PB and CoPC screen printed sensors are determined by hydrodynamic voltammetric technique. Glucose oxidase is immobilized on the working electrodes by cross-linking method. PB mediated glucose sensor exhibits a sensitivity of 5.60 mA cm(-2)/mM for the range, 10 to 1000 mu M. Sensitivity of CoPC mediated glucose sensor is found to be 5.224 mu A cm(-2)/mM and amperometeric response is linear for the range, 100 to 1500 mu M. Interference studies on the fabricated glucose sensors are conducted with species like uric acid and ascorbic acid. PB mediated sensors showed a completely interference-free behavior. The sensing characteristics of PB mediated glucose sensors are also studied in diluted human serum samples and the results are compared with the values obtained through standard clinical method. The co-efficient of variation is found to be less than 5%. (C) 2015 The Electrochemical Society. All rights reserved.

l(1)-K-SVD: A robust dictionary learning algorithm with simultaneous update

We develop a new dictionary learning algorithm called the l(1)-K-svp, by minimizing the l(1) distortion on the data term. The proposed formulation corresponds to maximum a posteriori estimation assuming a Laplacian prior on the coefficient matrix and additive noise, and is, in general, robust to non-Gaussian noise. The l(1) distortion is minimized by employing the iteratively reweighted least-squares algorithm. The dictionary atoms and the corresponding sparse coefficients are simultaneously estimated in the dictionary update step. Experimental results show that l(1)-K-SVD results in noise-robustness, faster convergence, and higher atom recovery rate than the method of optimal directions, K-SVD, and the robust dictionary learning algorithm (RDL), in Gaussian as well as non-Gaussian noise. For a fixed value of sparsity, number of dictionary atoms, and data dimension, l(1)-K-SVD outperforms K-SVD and RDL on small training sets. We also consider the generalized l(p), 0 < p < 1, data metric to tackle heavy-tailed/impulsive noise. In an image denoising application, l(1)-K-SVD was found to result in higher peak signal-to-noise ratio (PSNR) over K-SVD for Laplacian noise. The structural similarity index increases by 0.1 for low input PSNR, which is significant and demonstrates the efficacy of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.

Distributed Nonparametric Sequential Spectrum Sensing under Electromagnetic Interference

We propose a distributed sequential algorithm for quick detection of spectral holes in a Cognitive Radio set up. Two or more local nodes make decisions and inform the fusion centre (FC) over a reporting Multiple Access Channel (MAC), which then makes the final decision. The local nodes use energy detection and the FC uses mean detection in the presence of fading, heavy-tailed electromagnetic interference (EMI) and outliers. The statistics of the primary signal, channel gain and the EMI is not known. Different nonparametric sequential algorithms are compared to choose appropriate algorithms to be used at the local nodes and the Fe. Modification of a recently developed random walk test is selected for the local nodes for energy detection as well as at the fusion centre for mean detection. We show via simulations and analysis that the nonparametric distributed algorithm developed performs well in the presence of fading, EMI and outliers. The algorithm is iterative in nature making the computation and storage requirements minimal.

Design Optimization of Thin-Film Transistors Based on a Metal-Substrate-Semiconductor Architecture for High DC Voltage Sensing

We discuss the potential application of high dc voltage sensing using thin-film transistors (TFTs) on flexible substrates. High voltage sensing has potential applications for power transmission instrumentation. For this, we consider a gate metal-substrate-semiconductor architecture for TFTs. In this architecture, the flexible substrate not only provides mechanical support but also plays the role of the gate dielectric of the TFT. Hence, the thickness of the substrate needs to be optimized for maximizing transconductance, minimizing mechanical stress, and minimizing gate leakage currents. We discuss this optimization, and develop n-type and p-type organic TFTs using polyvinyldene fluoride as the substrate-gate insulator. Circuits are also realized to achieve level shifting, amplification, and high drain voltage operation.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.