Ball Mill Synthesis of Bulk Quaternary Cu<Subscript>2</Subscript>ZnSnSe<Subscript>4</Subscript> and Thermoelectric Studies

In this work, quaternary chalcogenide Cu₂ZnSnSe₄ (CZTSe) was synthesized using a mechanochemical ball milling process and its thermoelectric properties were studied by electrical resistivity, Seebeck coefficient, and thermal conductivity measurements. The synthesis process comprises three steps viz., wet ball milling of the elemental precursors, vacuum annealing, and densification by hot pressing. The purpose of this is to evaluate the feasibility of introducing wet milling in place of vacuum melting in solid state synthesis for the reaction of starting elements. We report the structural characterization and thermoelectric studies conducted on samples that were milled at 300 rpm and 500 rpm. X-ray diffraction (XRD) analysis showed the existence of multiple phases in the as-milled samples, indicating the requirement for heat treatment. Therefore, the ball milled powders were cold pressed and vacuum annealed to eliminate the secondary phases. Annealed samples were hot pressed and made into dense pellets for further investigations. In addition to XRD, energy dispersive spectroscopy (EDS) studies were performed on hot pressed samples to study the composition. XRD and EDS studies confirm CZTSe phase formation along with ZnSe secondary phase. Electrical resistivity and Seebeck coefficient measurements were done on the hot pressed samples in the temperature range 340–670 K to understand the thermoelectric behaviour. Thermal conductivity was calculated from the specific heat capacity and thermal diffusivity values. The thermoelectric figure of merit zT values for samples milled at 300 rpm and 500 rpm are ～0.15 and ～0.16, respectively, at 630 K, which is in good agreement with the values reported for solid state synthesized compounds.     

Optical Properties of K<Subscript>2</Subscript>O-Li<Subscript>2</Subscript>O-WO<Subscript>3</Subscript>-B<Subscript>2</Subscript>O<Subscript>3</Subscript> Glasses: Evidence of Mixed Alkali Effect

Glass with compositions xK₂O-(30 ? x)Li₂O-10WO₃-60B₂O₃ for 0 ≤ x ≤ 30 mol.% have been prepared using the normal melt quenching technique. The optical reflection and absorption spectra were recorded at room temperature in the wavelength range 300–800 nm. From the absorption edge studies, the values of the optical band gap (E _opt) and Urbach energy (ΔE) have been evaluated. The values of E _opt and ΔE vary non-linearly with composition parameter, showing the mixed alkali effect. The dispersion of the refractive index is discussed in terms of the single oscillator Wemple Di-Domenico model.     

Spectroscopic and Photoluminescent Properties of Indium Zinc Oxide (IZO) and IZO/Ag/IZO Sandwiched Film

We report the ellipsometric and photoluminescence (PL) properties of Indium zinc oxide (IZO) films, which were grown by radio frequency sputtering under Ar and O₂ atmospheres. IZO films grown under an O₂ atmosphere (IZO (O₂)) showed enhanced PL properties when compared to the films grown under an Ar atmosphere (IZO (Ar)), particularly with respect to the band-edge emission. The enhancement of band-edge emission can be attributed to the reduction in the non-emissive defect states related to oxygen vacancies, which were repaired by sputtering under O₂ atmosphere, whereas the PL enhancement in green region is probably due to the formation of the different types of defects under the excess oxygen environment. This was also supported by the results of time-resolved PL measurements, where the band-edge emission of IZO (Ar) showed rapid decay with a 50 ps lifetime, which indicates the dominance of the relaxation pathway to underlying defect states. In contrast, the PL decay profiles of IZO (O₂) for band-edge and emissive defect states showed moderate decay with time-constants of 2.3 ns and 5.7 ns, respectively. The exciton relaxation dynamics were sensitive to the presence and its kinds of defect states, which were controlled by the growth conditions.     

A fast automatic optimal threshold selection technique for image segmentation

In this article, a fast context-sensitive threshold selection technique is presented to solve the image segmentation problems. In lieu of histogram, the proposed technique employs recently defined energy curve of the image. First, the initial thresholds are selected in the middle of two consecutive peaks on the energy curve. Then based on the cluster validity measure, the optimal number of potential thresholds and the bounds where the optimal value of each potential threshold may exist are determined. Finally, genetic algorithm (GA) is employed to detect the optimal value of each potential threshold from their respective defined bounds. The proposed technique incorporates spatial contextual information of the image in threshold selection process without loosing the benefits of histogram-based techniques. Computationally it is very efficient. Moreover, it is able to determine the optimal number of segments in the input image. To assess the effectiveness of the proposed technique, the results obtained are compared with four state-of-the-art methods cited in the literature. Experimental results on large number of images confirmed the effectiveness of the proposed technique.     

An efficient system for anomaly detection using deep learning classifier

In this paper, a deep learning-based anomaly detection (DLAD) system is proposed to improve the recognition problem in video processing. Our system achieves complete detection of abnormal events by involving the following significant proposed modules a Background Estimation (BE) Module, an Object Segmentation (OS) Module, a Feature Extraction (FE) Module, and an Activity Recognition (AR) Module. At first, we have presented a BE (Background Estimation) module that generated an accurate background in which two-phase model is generated to compute the background estimation. After a high-quality background is generated, the OS model is developed to extract the object from videos, and then, object tracking process is used to track the object through the overlapping detection scheme. From the tracked objects, the FE module is extracted for some useful features such as shape, wavelet, and histogram to the abnormal event detection. For the final step, the proposed AR module is classified as abnormal or normal event using the deep learning classifier. Experiments are performed on the USCD benchmark dataset of abnormal activities, and comparisons with the state-of-the-art methods validate the advantages of our algorithm. We can see that the proposed activity recognition system has outperformed by achieving better EER of 0.75 % when compared with the existing systems (20 %). Also, it shows that the proposed method achieves 85 % precision rate in the frame-level performance.     

A parametric intensity-based 3D image registration method for magnetic resonance imaging

Image registration (IR) aims to map one image to another of a same scene. With rapid progress in image acquisition technologies, 3D IR becomes an important problem in magnetic resonance imaging (MRI) and other applications. In the literature, however, most IR methods are for 2D images and there are only a limited number of 3D methods available. Because 3D images have much complicated structure than their 2D counterparts, 3D IR is not just a simple generalization of the 2D IR problem. In this paper, we develop a 3D IR method that can handle cases with affine geometric transformations well. By its definition, an affine transformation maps a line to a line, and it includes rotation, translation, and scaling as special cases. In practice, most geometric transformations involved in IR problems are affine transformations. Therefore, our proposed method can find many IR applications. It is shown that this method works well in various cases, including cases when the data size of a 3D image is reduced for different reasons. This latter property makes it attractive for many 3D IR applications, since 3D images are often big in data size and it is natural to reduce their size for fast computation.     

Smooth head tracking for virtual reality applications

In this work, we propose a new head-tracking solution for human–machine real-time interaction with virtual 3D environments. This solution leverages RGBD data to compute virtual camera pose according to the movements of the user’s head. The process starts with the extraction of a set of facial features from the images delivered by the sensor. Such features are matched against their respective counterparts in a reference image for the computation of the current head pose. Afterwards, a prediction approach is used to guess the most likely next head move (final pose). Pythagorean Hodograph interpolation is then adapted to determine the path and local frames taken between the two poses. The result is a smooth head trajectory that serves as an input to set the camera in virtual scenes according to the user’s gaze. The resulting motion model has the advantage of being: continuous in time, it adapts to any frame rate of rendering; it is ergonomic, as it frees the user from wearing tracking markers; it is smooth and free from rendering jerks; and it is also torsion and curvature minimising as it produces a path with minimum bending energy.     

Incremental augmented complex adaptive IIR algorithm for training widely linear ARMA model

In this paper, we propose a distributed adaptive learning algorithm to train the coefficients of a widely linear autoregressive moving average model by measurements collected by the nodes of a network. We assume that each node uses the augmented complex adaptive infinite impulse response (ACA-IIR) filter as the learning rule, and nodes interact with each other under an incremental mode of cooperation. To derive the proposed algorithm, called the incremental ACAIIR (IACA-IIR), we firstly formulate the distributed adaptive learning problem as an unconstrained minimization problem. Then, we apply stochastic gradient optimization argument to solve it and derive the proposed algorithm. We further find the step size range where the stability of the proposed algorithm is guaranteed. We also introduce a reduced-complexity version of the IACA-IIR algorithm. Since the proposed algorithm relies on the augmented complex statistics, it can be used to model both types of complex-valued signals (proper and improper signals). To evaluate the performance of the proposed algorithm, we use both synthetic and real-world complex signals in our simulations. The results exhibit superior performance of the proposed algorithm over the non-cooperative ACA-IIR algorithm.     

Retrospective analysis of time series for frame selection in surveillance video summarization

In recent days, we have witnessed a dramatical growth of videos in various real-life scenarios. In this paper, we address the problem of surveillance video summarization. We present a new method of key-frame selection for this task: By virtue of retrospective analysis of time series, temporal cuts are first detected by sequentially measuring dissimilarities on a given video with threshold-based decision making; then, with the detected cuts, the video is segmented into a number of consecutive clips containing similar video contents; key frames are last selected by performing a typical clustering procedure in each resulted clip for final video summary. We have conducted extensive experiments on the benchmarking ViSOR dataset and the publicly available IVY LAB dataset. Excellent performances outperforming state-of-the-art competitors were demonstrated on key-frame selection for surveillance video summarization, which suggests good potentials of the proposed method in real-world applications.     

Compressive sensing based high-resolution passive bistatic radar

Passive bistatic radar (PBR) systems using different communication signals can only offer low-resolution target detection due to their inherent low bandwidth. In this paper, compressive sensing (CS) is applied to multichannel FM and GSM PBR to achieve improved range-Doppler resolutions and avoid some limitations of classical multiband PBR processing. In CS context, block-structured time-domain dictionary which is formed from multichannel signals suffers from coherence when fine range resolution is employed. To overcome such a pitfall, this work first transforms the dictionary to spectral domain where only the most important spectral components are retained. Principle component analysis followed by a whitening method are then applied to this spectrally transformed data in order to reduce the dimensionality of problem, thereby reducing the dictionary size and most importantly fulfilling the required condition of dictionary incoherence for better CS-based recovery. Two different block-structured dictionary formations are tested. The performance of the recovery of spatially close targets, in both FM and GSM PBR setups, are reported.