Interference‐aware clustering approach improving QoS for linear WSNs using a token‐based MAC protocol

Wireless sensor networks (WSNs) have received a lot of attention from both academia and industry due to the increasing need for ubiquitous computing for monitoring applications, the continuous advances in miniaturization of electronic devices, and the ultra‐low‐power wireless technologies. These innovations in technology have driven the curiosity to use sensor networks in a new kind of applications such as road track or railway monitoring, border monitoring, oil and gas, or even water pipeline monitoring. Due to the underlying linear topology of these applications, a new type of network, called a linear sensor network (LSN), has emerged. Because of the specific characteristics of this application and the resource constraints of sensors, some of the major challenges faced in LSNs are to reduce end‐to‐end delays, to maximize the packet delivery ratio to a sink, and an even distribution of the load between nodes. To achieve these objectives, it is necessary to control node‐to‐node packet traffic conditions and to manage radio interference created by simultaneously active nodes. This paper addresses these challenges and proposes a new method of clustering LSNs that reduces or controls radio interference risks in order to satisfy these objectives, application needs, and the resource limitations of sensor nodes in the best possible way. This method is applied for LSNs using a token‐passing mechanism to access the medium. The performance evaluation is conducted by using a realistic propagation model in the analytical evaluation and also a NS‐2 simulation process.     

Testing and Diagnosis of Digital Microfluidic Biochips using Multiple Droplets

Digital microfluidic biochip is a promising alternative to the traditional cumbersome laboratory equipment. Such automated biochips are used in many critical applications. Hence dependability is an essential attribute before the chip is in use. Due to mixed integration technologies, these chips have some unique failures. Hence robust offline and online tests are proposed to check the health of the biochips. When a chip undergoes a test in offline mode, then the entire biochip should be available for testing, whereas for the online mode test droplet might be stalled due to unavailability of the next cell in the routing path. However, in both the scenarios one or more droplets route across the chip and the arrival time is also recorded at the destination. So here we have proposed two test schemes to know the correctness of any biochip. Diagnosability is an important feature to find the exact position of the faulty electrode. Our proposed scheme reduces the overall testing and diagnosis time significantly. It also provides an alternative routing path in biochip for fault tolerance.

     

Single-Feed Dual-Polarized High Gain Microstrip Antenna

Wireless Personal Communications - In this work, a novel single-feed stacked microstrip antenna is proposed for high gain and dual polarization application. The designed antenna is useful at two...     

Statistical analysis of mammographic features and its classification using support vector machine

This study aims at designing a support vector machine (SVM)-based classifier for breast cancer detection with higher degree of accuracy. It introduces a best possible training scheme of the features extracted from the mammogram, by first selecting the kernel function and then choosing a suitable training-test partition. Prior to classification, detailed statistical analysis viz., test of significance, density estimation have been performed for identifying discriminating power of the features in between malignant and benign classes. A comparative study has been performed in respect to diagnostic measures viz., confusion matrix, sensitivity and specificity. Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification. Furthermore, the overall classification accuracy obtained by using the proposed classification strategy is 99.385% for dataset-I and 93.726% for dataset-II, respectively.     

Learning mixture models via component-wise parameter smoothing

The task of obtaining an optimal set of parameters to fit a mixture model has many applications in science and engineering domains and is a computationally challenging problem. A novel algorithm using a convolution based smoothing approach to construct a hierarchy (or family) of smoothed log-likelihood surfaces is proposed. This approach smooths the likelihood function and applies the EM algorithm to obtain a promising solution on the smoothed surface. Using the most promising solutions as initial guesses, the EM algorithm is applied again on the original likelihood. Though the results are demonstrated using only two levels, the method can potentially be applied to any number of levels in the hierarchy. A theoretical insight demonstrates that the smoothing approach indeed reduces the overall gradient of a modified version of the likelihood surface. This optimization procedure effectively eliminates extensive searching in non-promising regions of the parameter space. Results on some benchmark datasets demonstrate significant improvements of the proposed algorithm compared to other approaches. Empirical results on the reduction in the number of local maxima and improvements in the initialization procedures are provided.     

A new aluminum-based metal matrix composite reinforced with cobalt ferrite magnetic nanoparticle

A new composite with cobalt ferrite magnetic nanoparticle dispersed in an aluminum matrix has been prepared using the ball-milling technique followed by compaction and sintering. Our efforts were largely focused on investigating the contribution of cobalt ferrite to the enhancement of structural, mechanical and magnetic properties of aluminum. Incorporation of 1–10 weight (wt)% of nanosized cobalt ferrite into the aluminum matrix could affect remarkable change in mechanical properties. Enhancement of hardness value, elastic modulus, and compressive strength was observed in the case of cobalt ferrite-incorporated aluminum matrix as compared to the pure aluminum sample. Incorporation of cobalt ferrite could impart considerable improvement of magnetization value of the aluminum matrix with a saturation magnetization of 17.07 emu/g for the aluminum sample reinforced with 10 wt% of cobalt ferrite. A decrease in coercive force in the sample arising from the increase in surface effects and inter-particle interaction between the ferromagnetic cobalt ferrite and soft phases in the matrix was also observed.     

Study of mechanical,thermal, morphological,and process rheology of acrylonitrile styrene acrylate (ASA)/ Na+1 poly(ethylene‐co‐methacrylic acid) ionomer blend

Acrylonitrile–styrene–acrylic (ASA) terpolymer was blended with a sodium neutralized poly (ethylene‐co‐methacrylic acid) ionomer to develop a weather‐resistant ASA with good heat sealing and adhesion properties. Both tensile strengths and modulus were reduced by about 45% with an increase in Na‐ionomer concentration of 50%. The mechanical data were fitted with different models that predict mechanical behavior. The thermal stability was increased with the incorporation of ionomer. The temperature corresponding to 50% mass loss (T_m50) was increased from 400°C (for ASA) to 427°C for 50/50 ASA/Na‐ionomer blend. SEM and AFM micrographs reveal the cone‐shaped microstructure on the ASA matrix surface after a critical Na‐ionomer concentration of 30%. This blend system follows typical non‐Newtonian behavior. The heat sealing performance study with metal was also carried out to investigate the utilization of these blend systems as a protective layer for metal. POLYM. ENG. SCI., 55:1571–1579, 2015. © 2014 Society of Plastics Engineers     

Kruskal's Maximal Spanning Tree Algorithm for Optimizing Distribution Network Topology to Improve Voltage Stability

Abstract—Alteration of power network topology is often required to meet important objectives, such as restoring connectivity, minimizing power losses, maintaining stability, maximizing power transfer capability etc., and is achieved by switching of circuit breakers and other switching devices in the power network. Primary power distribution networks are often interconnected and meshed but should be transformed to radial topology to achieve various operational advantages. Distribution networks also need to be reconfigured after faults to restore power at all the load points. Reconfiguring a power network, however, is a complicated multi-constrained optimization problem, as there may exist many feasible switching combinations in a large power network. This article proposes a novel application of graph theory, supported by Kruskal's maximal spanning tree algorithm, to search for the optimal network topology and to optimally convert an interconnected meshed network into a radial system to achieve best operational characteristics, cost, and control. The proposed technique has been demonstrated on a 30-node primary distribution network originally having a mesh topology, and the results indicate significant performance improvement after transformation into optimal radial topology.     

Copper‐Catalyzed Enantioselective [2+2] Cycloadditions of 2‐Nitrosopyridine with Ketenes

Copper(I)‐catalyzed [2+2] cycloadditions of various ketenes with 2‐nitrosopyridine to afford synthetically highly valuable 1,2‐oxazetidine‐3‐ones are shown to occur with good enantioselectivities. The thermal uncatalyzed process furnishes the unstable regioisomeric oxazetidinone. Density function theory (DFT) calculations give evidence that the reaction occurs via a concerted [2+2] cycloaddtion pathway.