Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...     

QoS-Aware Optical Fog-Assisted Cyber-Physical System in the 5G Ready Heterogeneous Network

Wireless Personal Communications - In recent years, the emergence of Internet of things and cyber-physical system provide a proactive and efficacious solution to enable remote monitoring, machine...     

Spray dryer processed graphene oxide/reduced graphene oxide for high-performance supercapacitor

This study deals with the utility of mini spray dryer process to improve the dispersibility, of graphene oxide(GO) and its application for high-performance supercapacitor. Initially, the neutral solution of GO was obtained using the modified Hummer's method. After this, the prepared GO solution was processed by mini spray dryer to obtain a more purified, lighter, and dispersed form of GO which is named as spray dryer processed GO (SPGO). The SPGO thus obtained showed excellent dispersibility behavior with various solvents, which is not found in case of conventional oven drying. Furthermore, utility of SPGO and its reduced form (r-SPGO) for supercapacitor applications have been investigated. Results obtained from the cyclic voltammetry(CV) analysis, impedance, and charge-discharge behavior of supercapacitor fabricated using r-SPGO shows enhanced features. Therefore, the simple spray dried GO and its reduced form, that is, r-SPGO can be utilized as a potential candidate for the supercapacitor application. Herein, as synthesized SPGO exhibited the specific capacitance of 12.07 and 37.6 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively, at a scan rate of 5 mV/s. On the other hand, reduced form of SPGO, that is, r-SPGO showed the specific capacitance of 27.16 and 230 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively.     

Effects of chloroquine treatment on antioxidant enzymes in rat liver and kidney

The effect of chloroquine (CHQ) administration on antioxidant enzymes in rat liver and kidney was studied. Male Sprague-Dawley rats were administered 20 mg/kg CHQ once a week for 4 weeks (chronic treatment) or a single dose at 10 or 20 mg/kg (acute treatment). Antioxidant enzyme activities were determined in cytosolic fractions of liver and kidney, whereas reduced glutathione (GSH) and malondialdehyde (MDA) were determined in tissue samples. Results indicate minimal effects of acute CHQ treatment, whereas chronic treatment with CHQ differentially affected antioxidant enzymes in the two organs. Superoxide dismutase activity was increased nearly twofold, while activities of selenium glutathione peroxidase (GPX), catalase, and NAD (P) H: quinone oxidoreductase were decreased in livers of CHQ-treated rats compared to controls. No significant effects of CHQ on glutathione reductase, GSH, and MDA levels were seen in the liver. Fewer effects of CHQ were observed in the kidney where a decrease in GPX activity and an increase in MDA levels was seen. Lowering of antioxidant enzymes activities in the liver by CHQ could render the organ more susceptible to subsequent oxidative stress; while increased MDA production after CHQ treatment in the kidney indicate that the organ is being subjected to oxidative stress. This could have implications for prolonged chloroquine intake.     

Synthesis and characterization of poly(ethylene aspartate)-metal complexes

Poly(ethylene aspartate) [PEA] was synthesized by the melt condensation of D,L-aspartic acid and ethylene glycol. PEA containing pendent amino and carbonyl groups in its repeating chain was used as the polymeric ligand for complexation with transition metal ions, viz. Co(II), Ni(II), Cu(II), Mn(II), Zn(II), Cd(II), Ca(II), Mg(II), Pb(II) and Hg(II). Complexation was found to be most effective in DMSO. The resulting polyester-metal complexes were solid coloured materials which have been characterized by IR spectroscopy, elemental analysis and magnetic susceptibility measurements. The thermal stability of the polyester-metal complexes was investigated by thermogravimetric analysis (TGA). On the basis of the physico-chemical studies, an oxygen and nitrogen coordinated structure for the polyester-metal complexes is proposed.     

Prediction of thermal expansion coefficients of plain weave fabric composites

Three plain weave fabric composite analysis models are presented for the prediction of the on-axes thermal expansion coefficients. These are two-dimensional models in the sense that the actual strand cross-sectional geometry, strand undulation and the presence of a gap between the adjacent strands are taken into account. In the first two models, termed refined models, the representative unit cell is discretised into slices and elements and analysed. In the third method, a closed-form solution is presented. In this case, the representative unit cell is idealised as a crossply laminate and analysed. The relative merits and demerits of the models are also discussed. The predicted results are compared with the experimental values. A good correlation is observed.     

Test Generation for Crosstalk-Induced Faults: Framework and Computational Results

Due to technology scaling and increasing clock frequency, problems due to noise effects lead to an increase in design/debugging efforts and a decrease in circuit performance. This paper addresses the problem of efficiently and accurately generating two-vector tests for crosstalk induced effects, such as pulses, signal speedup and slowdown, in digital combinational circuits. These noise effects can propagate through a circuit and create a logic error in a latch or at a primary output. We have developed a mixed-signal test generator, called XGEN, that incorporates classical static values as well as dynamic signals such as transitions and pulses, and timing information such as signal arrival times, rise/fall times, and gate delay. In this paper we first discuss the general framework of the test generation algorithm followed by computational results. Comparison of results with SPICE simulations confirms the accuracy of this approach.     

Efficient computation of unique input/output sequences in finitestate machines

This paper makes two contributions toward computing unique input/output (UIO) sequences in finite-state machines. Our first contribution is to compute all UIO sequences of minimal lengths in a finite-state machine. Our second contribution is to present a generally efficient algorithm to compute a UIO sequence for each state, if it exists. We begin by defining a path vector, vector perturbation, and UIO tree. The perturbation process allows us to construct the complete UIO tree for a machine. Each sequence of input/output from the initial vector of a UIO tree to a singleton vector represents a UIO sequence. Next, we define the idea of an inference rule that allows us to infer UIO sequences of a number of states from the UIO sequence of some state. That is, for a large class of machines, it is possible to compute UIO sequences for all possible states from a small set of initial UIOs. We give a modified depth-first algorithm, called the hybrid approach, that computes a partial UIO tree, called an essential subtree, from which UIO sequences of all possible states can be inferred. Using the concept of projection machines, we show that sometimes it is unnecessary to construct even a partial subtree. We prove that if a machine remains strongly connected after deleting all the converging transitions, then all of the states have UIO sequences. To demonstrate the effectiveness of our approach, we develop a tool to perform experiments using both small and large machines     

Gradient Boundary Detection for Time Series Snapshot Construction in Sensor Networks

In many applications of sensor networks, the sink needs to keep track of the history of sensed data of a monitored region for scientific analysis or supporting historical queries. We call these historical data a time series of value distributions or snapshots. Obviously, to build the time series snapshots by requiring all of the sensors to transmit their data to the sink periodically is not energy efficient. In this paper, we introduce the idea of gradient boundary and propose the gradient boundary detection (GBD) algorithm to construct these time series snapshots of a monitored region. In GBD, a monitored region is partitioned into a set of subregions and all sensed data in one subregion are within a predefined value range, namely, the gradient interval. Sensors located on the boundaries of the subregions are required to transmit the data to the sink and, then, the sink recovers all subregions to construct snapshots of the monitored area. In this process, only the boundary sensors transmit their data and, therefore, energy consumption is greatly reduced. The simulation results show that GBD is able to build snapshots with a comparable accuracy and has up to 40 percent energy savings compared with the existing approaches for large gradient intervals.