Firework inspired load balancing approach for wireless sensor networks

Wireless Networks - In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network’s lifetime is an area of constant study and innovation. The...     

Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Carbon-based TiO2-x heterostructure nanocomposites for enhanced photocatalytic degradation of dye molecules

Application of brown titanium dioxide (TiO_2-x) and its modified composite forms in the photocatalytic decomposition of organic pollutants in the environment is a promising way to provide solutions for environmental redemption. Herein, we report the synthesis of effective and stable TiO_2-x nanoparticles with g-C₃N₄, RGO, and multiwalled carbon nanotubes (CNTs) using a simple hydrothermal method. Among all the as-synthesized samples, excellent photocatalytic degradation activity was observed for RGO-TiO_2-x nanocomposite with high rate constants of 0.075 min^?1_, 0.083 min^?1 and 0.093 min^?1 for methylene blue, rhodamine-B, and rosebengal dyes under UV–Visible light irradiation, respectively. The altered bandgap (1.8 eV) and the large surface area of RGO-TiO_2-x nanocomposite impacts on both absorption of visible light and efficiency of photogenerated charge electron (e^?)/hole (h⁺) pair separation. This resulted in enhanced photocatalytic property of carbon-based TiO_2-x nanocomposites. A systematic study on the influence of different carbon nanostructures on the photocatalytic activity of brown TiO_2-x is carried out.     

Decay Rate Kinetics of Ozone Gas in Rice Grains

The present study was carried out to evaluate the effects of ozone on rice grains for the following three conditions: saturation time, decay rate, and half-life of ozone. Experiments were performed in different bed thicknesses (5 and 10 cm) and moisture content (11.4 and 14.2% wb) at atmospheric conditions. The lowest saturation time of ozone was 119 min, with the concentration of 516 ppm for rice grains ozonated at 5-cm bed thickness with 11.4% (wb) moisture content. The decay rate kinetics of ozone obtained were consistent with a first-order model. Regarding the half-life of ozone, the lowest value obtained was 6.78 min for rice grains ozonated at 10-cm bed thickness with 14.2% (wb) moisture content.     

A study of the energy consumption characteristics of cryptographic algorithms and security protocols

Security is becoming an everyday concern for a wide range of electronic systems that manipulate, communicate, and store sensitive data. An important and emerging category of such electronic systems are battery-powered mobile appliances, such as personal digital assistants (PDAs) and cell phones, which are severely constrained in the resources they possess, namely, processor, battery, and memory. This work focuses on one important constraint of such devices-battery life-and examines how it is impacted by the use of various security mechanisms. In this paper, we first present a comprehensive analysis of the energy requirements of a wide range of cryptographic algorithms that form the building blocks of security mechanisms such as security protocols. We then study the energy consumption requirements of the most popular transport-layer security protocol: Secure Sockets Layer (SSL). We investigate the impact of various parameters at the protocol level (such as cipher suites, authentication mechanisms, and transaction sizes, etc.) and the cryptographic algorithm level (cipher modes, strength) on the overall energy consumption for secure data transactions. To our knowledge, this is the first comprehensive analysis of the energy requirements of SSL. For our studies, we have developed a measurement-based experimental testbed that consists of an iPAQ PDA connected to a wireless local area network (LAN) and running Linux, a PC-based data acquisition system for real-time current measurement, the OpenSSL implementation of the SSL protocol, and parameterizable SSL client and server test programs. Based on our results, we also discuss various opportunities for realizing energy-efficient implementations of security protocols. We believe such investigations to be an important first step toward addressing the challenges of energy-efficient security for battery-constrained systems.     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.     

The effect of mischmetal addition on the structure and mechanical properties of a cast Al-7Si-0.3Mg alloy containing excess iron (up to 0.6 Pct)

The effect of Fe content (0.2 to 0.6 pct) on the microstructure and mechanical properties of a cast Al-7Si-0.3Mg (LM 25/356) alloy has been investigated. Further, 1 pct mischmetal (MM) additions (a mixture of rare-earth (RE) elements) were made to these alloys, and their mechanical properties at room and at elevated temperatures (up to 200 °C) were evaluated. A structure-property correlation on this alloy was attempted using optical microstructure analysis, fractographs, X-ray diffraction, energy-dispersive analysis of X-rays (EDX), and quantitative metallography by image analysis. An increase in Fe content increased the volume percentage of Fe-bearing intermetallic compounds (β and π phases), contributing to the loweryield strength (YS), ultimate tensile strength (UTS), percentage elongation, and higher hardness. An addition of 1 pct MM to the alloys containing 0.2 and 0.6 pct Fe was found to refine the microstructure; modify the eutectic silicon and La, Ce, and Nd present in the MM; form different intermetallic compounds with Al, Si, Fe, and Mg; and improve the mechanical properties of the alloys both at room and elevated temperatures.     

The hot blow forming of AZ31 Mg sheet: Formability assessment and application development

The hot blow forming of magnesium sheet offers significant opportunity for forming complex, lightweight parts for automotive applications. This paper characterizes the elevated-temperature formability of AZ31 magnesium sheet materials and the effect of processing conditions on the performance of these materials. In addition, magnesium sheet application development at General Motors Corporation is reviewed.     

A VLSI architecture for a half-edge-based corner detector

Corner detection is a low-level feature detection operator that is of great use in image processing applications, for example, optical flow and structure from motion by image correspondence. The detection of corners is a computationally intensive operation. Past implementations of corner detection techniques have been restricted to software. In this paper we propose an efficient very large-scale integration (VLSI) architecture for detection of corners in images. The corner detection technique is based on the half-edge concept and the first directional derivative of Gaussian. Apart from the location of the corner points, the algorithm also computes the corner orientation and the corner angle and outputs the edge map of the image. The symmetrical properties of the masks are utilized to reduce the number of convolutions effectively, from eight to two. Therefore, the number of multiplications required per pixel is reduced from 1800 to 392. Thus, the proposed architecture yields a speed-up factor of 4.6 over conventional convolution architectures. The architecture uses the principles of pipelining and parallelism and can be implemented in VLSI.