High Diversity Gain MIMO-Antenna for UWB Application with WLAN Notch Band Characteristic Including Human Interface Devices

Wireless Personal Communications - In this paper, a UWB–MIMO antenna with the WLAN band-notch (5.1–5.85 GHz) characteristic is offered. This antenna consists of two radiated...     

Miniaturized Microstrip Patch Antenna Array for ISM Band Using Complementary Split Ring Resonator

Wireless Personal Communications - These days patch antenna arrays are widely used in various communication systems. In this research design of miniaturized microstrip patch antenna array is...     

A semi-supervised domain adaptation assembling approach for image classification

Automatic annotation of images is one of the fundamental problems in computer vision applications. With the increasing amount of freely available images, it is quite possible that the training data used to learn a classifier has different distribution from the data which is used for testing. This results in degradation of the classifier performance and highlights the problem known as domain adaptation. Framework for domain adaptation typically requires a classification model which can utilize several classifiers by combining their results to get the desired accuracy. This work proposes depth-based and iterative depth-based fusion methods which are basically rank-based fusion methods and utilize rank of the predicted labels from different classifiers. Two frameworks are also proposed for domain adaptation. The first framework uses traditional machine learning algorithms, while the other works with metric learning as well as transfer learning algorithm. Motivated from ImageCLEF’s 2014 domain adaptation task, these frameworks with the proposed fusion methods are validated and verified by conducting experiments on the images from five domains having varied distributions. Bing, Caltech, ImageNet, and PASCAL are used as source domains and the target domain is SUN. Twelve object categories are chosen from these domains. The experimental results show the performance improvement not only over the baseline system, but also over the winner of the ImageCLEF’s 2014 domain adaptation challenge.     

Mobile Network Operator and Mobile User Cooperation for Customized D2D Data Services

The explosive growth of smart devices has led to the evolution of multimedia data (mainly video) services in mobile networks. It attracted many mobile network operators (MNO) to deploy novel network architectures and develop effective economic policies. Mobile data offloading through smart devices (SDs) by exploring device-to-device (D2D) communications can significantly reduce network congestion and enhance quality of service at a lower cost, which is the key requirement of upcoming 5G networks. This reasonable cost solution is useful for attracting mobile users to participate in the offloading process by paying them proper incentives. It is beneficial for MNOs as well as mobile users. Moreover, D2D communications promise to be one of the prominent services for 5G networks. In this paper, we present a combinatorial optimal reverse auction (CORA) mechanism, which efficiently selects and utilizes available high-end SDs on the basis of available resources for offloading purposes. It also decides the optimal pricing policy for the selected SDs. The efficiency of CORA has been realized in terms of bandwidth and storage demand. Subsequently, we implement caching in SDs, eNodeB (eNB), and evolved packet core (EPC) with the help of our novel video dissemination cache update algorithm to solve the latency or delay issues in the offloading process. Due to high popularity, we specifically focus on video data. Simulation results show that the proposed SD caching scenario curtails the delay up to 75% and the combined cache (CC) scenario slashes the delay varying from 15 to 57%. We also scruitinize the video downloading time performance of various cache scenarios (i.e., CC, EPC cache, eNB cache, and SD cache scenarios).     

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

Software and Systems Modeling - Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the...     

Synthesis and properties of poly ether nitrile sulfone copolymers with pendant methyl groups

Poly ether nitrile and poly ether nitrile sulfone copolymers with pendant methyl groups were prepared by the nucleophilic substitution reaction of 2,6′‐dichlorobenzonitrile with methyl hydroquinone and with varying mole proportions of methyl hydroquinone and 4,4′dihydroxydiphenylsulfone using N‐methyl pyrrolidone as solvent in the presence of anhydrous K₂CO₃. The polymers were characterized by different physicochemical techniques. Copolymer composition was determined using FTIR technique. Thermogravimetric data reveals that all the polymers were stable up to 420°C with a char yield above 40% at 900°C in N₂ atmosphere. The glass transition temperature was found to increase and the activation energy and inherent viscosities were found to decrease with increase in concentration of the 4,4′‐dihydroxydiphenylsulfone units in the polymer. Trimerization reactions are found to be favorable with increase in concentration of methyl hydroquinone units in the polymer. Crystallinity of the polymer was also studied using wide angle X‐ray diffraction. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 1303–1309, 2006     

Comparative study of allelopathy as exhibited byProsopis Juliflora swartz andProsopis cineraria (L) druce

The allelopathic effects ofProsopis juliflora were studied both in the laboratory and in nature and compared with that ofProsopis cineraria to understand the chemical nature of allelochemics. Both species occupy the same habitats butP. cineraria does not appear to have any toxic effect on other plants under its canopy.P. juliflora is highly allelopathic and does not allow the growth of any other species. Leaf extracts and leaf leachates ofP. juliflora were inhibitory. Decaying leaves were also inhibitory at early stages of decomposition. Live roots were not found to be inhibitory in cogermination and interplanting of seeds. Chemical investigation of the extracts showed the allelopathic compounds to be phenolic in nature in both the species. Slow decomposition and heavy accumulation of leaf litter belowP. juliflora may possibly result in accumulation of toxic substances in soil layers, inhibiting growth of other species.     

A linear helicon plasma device with controllable magnetic field gradient

Current free double layers (CFDLs) are localized potential structures having spatial dimensions - Debye lengths and potential drops of more than local electron temperature across them. CFDLs do not need a current for them to be sustained and hence they differ from the current driven double layers. Helicon antenna produced plasmas in an expanded chamber along with an expanding magnetic field have shown the existence of CFDL near the expansion region. A helicon plasma device has been designed, fabricated, and installed in the Institute for Plasma Research, India to study the role of maximum magnetic field gradient as well as its location with respect to the geometrical expansion region of the chamber in CFDL formation. The special feature of this machine consisting of two chambers of different radii is its capability of producing different magnetic field gradients near the physical boundary between the two chambers either by changing current in one particular coil in the direction opposite to that in other coils and/or by varying the position of this particular coil. Although, the machine is primarily designed for CFDL experiments, it is also capable of carrying out many basic plasma physics experiments such as wave propagation, wave coupling, and plasma instabilities in a varying magnetic field topology. In this paper, we will present the details of the machine construction, its specialties, and some preliminary results about the production and characterization of helicon plasma in this machine.     

Tent-Shaped Surface Morphologies of Silicon: Texturization by Metal Induced Etching

Silicon - Nano-metal/semiconductor junction dependent porosification of silicon has been studied here. The silicon nanostructures (SiNSs) have been textured on n- and p- type silicon wafers using...     

Clustering‐based resource discovery on Internet‐of‐Things

Resource discovery on Internet‐of‐Things paradigm is an eminent challenge due to data‐specific activities with respect to foraging and sense‐making loops. The prerequisite to deal with the challenge is to process and analyze the data that require resources to be indexed, ranked, and stored in an efficient manner. A novel clustering technique is proposed to resolve the specified challenge. The technique, namely, iterative k‐means clustering algorithm, targets concrete cluster formation using similarity coefficients of vector space model and performs efficient search against matching criteria with respect to complexity. It is simulated on MATLAB, and the obtained results are compared with fuzzy k‐means and fuzzy c‐means clustering algorithm with similarity coefficients of vector space model against exponential increase in the number of resources.