An Enhanced MPR OLSR Protocol for Efficient Node Selection Process in Cognitive Radio Based VANET

Wireless Personal Communications - A VANET is an excellent instance of a wireless sensor network. The mobile vehicles are the nodes and communication happens between the vehicular nodes. This...     

Development of sensitive and specific multiplex PCR method for the detection of microcystin producing cyanobacteria in spirulina food supplements

Food Science and Biotechnology - Spirulina has emerged as the next-generation dietary supplement owing to its health benefits. Despite the advantages, there have been reports of contamination by...     

Multi response optimization of wire EDM operations using robust design of experiments

In this present study a multi response optimization method using Taguchi’s robust design approach is proposed for wire electrical discharge machining (WEDM) operations. Experimentation was planned as per Taguchi’s L16 orthogonal array. Each experiment has been performed under different cutting conditions of pulse on time, wire tension, delay time, wire feed speed, and ignition current intensity. Three responses namely material removal rate, surface roughness, and wire wear ratio have been considered for each experiment. The machining parameters are optimized with the multi response characteristics of the material removal rate, surface roughness, and wire wear ratio. Multi response S/N (MRSN) ratio was applied to measure the performance characteristics deviating from the actual value. Analysis of variance (ANOVA) is employed to identify the level of importance of the machining parameters on the multiple performance characteristics considered. Finally experimental confirmation was carried out to identify the effectiveness of this proposed method. A good improvement was obtained.     

Geospatial analysis of terrain through optimized feature extraction and regression model with preserved convex region

In this paper, cat optimization algorithm for feature extraction in satellite image has been proposed. In cat optimization, cost function computes the pixel in the satellite image to preserve the boundary shape and avoid non-convex part of the contour of the image. However, the existing feature extraction optimization algorithm measures the distinct data framework and thematic information to insight land cover such as waterbody, urban and vegetation. The land cover is obtained from different optimized feature extraction algorithms never provide proper boundary shape and land feature. Furthermore, the proposed cat optimized algorithm distinguishes the inner, outer and extended boundary along with the land cover. The cat-optimised algorithm for low and high-resolution satellite image shows the better result of 85%, with the preserved convex region when compared with the existing feature extraction algorithm such as fuzzy and Particle Swarm Optimization (PSO).     

Facial Recognition System for Suspect Identification Using a Surveillance Camera

Nowadays, finding and Tracking a person in the world of technology is becoming a necessary task for various security purposes. Since the advent of technology, the development in the field of Facial Recognition plays an important role and has been exponentially increasing in today’s world. In this, a model is proposed for facial recognition to identify and alert the system when a person in search has been found at a specific location under the surveillance of a CCTV camera. The CCTV cameras are connected to a centralized server to which the live streaming feed is uploaded by cameras at each location. The server contains a database of all persons to be found. Based on the video feed from each camera, if a particular person in search is found in a certain feed, then the location of that person will be tracked and also a signal is passed to the system responsible. This model is based on image processing concepts to match live images with the existing trained images of the person in search. Since this model recognizes a person based on the first and foremost primary unique feature of a human, that is, only the person’s face image is required and will be found to be stored in the database. Hence the task of finding a person reduces to the task of detecting human faces in the video feed and matching with the existing images from the database.     

A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

From geological and planetary exploration perspectives, automated sub-pixel classification of hyperspectral data is the most difficult task as it involves blind unmixing with library spectra of minerals. In this study, we demonstrate a procedure involving spectral transformation and linear unmixing to achieve the above task. For this purpose, infrared spectra of rocks from the spectral library, field, and remotely sensed hyperspectral image cube were used. Potential spectra of minerals for unmixing rock spectra were drawn from the library based on similarity of absorption features measured using Pearson correlation coefficient. Eight transformation techniques namely, first derivative, fast Fourier transform, discrete wavelet transform, Hilbert–Huang transform, crude low pass filter, S-transform, binary encoding, spectral effective peak matching, and two sparsity-based techniques (orthogonal matching pursuit, sparse unmixing via variable splitting, and augmented Lagrangian) were evaluated. Subsequently, minerals identified by above techniques were unmixed by linear mixture model (LMM) to decipher mineralogical composition and abundance. Results of LMM achieved using fully constrained least-square-estimation-based quadratic programming optimization approach were evaluated by conventional procedures such as X-ray diffraction and microscopy. In the case of image cube, endmembers derived using minimum noise fraction and pixel purity index were subjected to above procedure. It is evident that the discrete-wavelet-transformation-based approach produced excellent and meaningful results due to its flexibility in scaling the data and capability to handle noisy spectra. It is interesting to note that the adopted procedure could perform sub-pixel classification of image cube automatically and identify predominance of dolomite in limestone and sodium in alunite based on subtle differences in absorption positions.     

Electrochemical copolymerization and characterization of aniline and isoprene in aqueous p‐toluene sulfonic acid solution

Uniform and adherent copolymer coating of poly(aniline‐co‐isoprene) was successfully formed on low‐carbon‐steel electrodes by potentiostatic electropolymerization. Electropolymerization was performed by using aqueous p‐toluene sulfonic acid solution as electrolyte. Applied potential and feed ratios of monomers (aniline and isoprene) were systematically varied and the reactions were done under aqueous conditions. The copolymer coatings were characterized by infrared spectroscopy and the formation of copolymer was confirmed by the presence of aliphatic secondary amine, aromatic secondary amine, and aliphatic CH stretch groups. The electronic structure of the copolymers was further investigated by using UV/Vis absorption spectroscopy. The electrochemistry of the formation of copolymers was studied by using cyclic voltammetry. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 184–192, 2002; DOI 10.1002/app.10298     

Transporting compressed video over ATM networks with explicit-ratefeedback control

We propose a scheme for transmission of variable bit rate (VBR) compressed video for interactive applications using the explicit-rate congestion-control mechanisms proposed for the available bit rate (ABR) service in asynchronous transfer mode networks. Compressed video is inherently bursty, with rate fluctuations over both short and long time scales. This source behavior can be accommodated by the ABR service, since the explicit-rate scheme allows sources to request varying amounts of bandwidth over time. Moreover, when the bandwidth demand cannot be met, the network provides feedback indicating the bandwidth currently available to a connection. In our scheme, the video source rate is matched to the available bandwidth by modifying the quantization level used during compression. We use trace-driven simulations to examine how effective the enhanced explicit-rate scheme is in “rate matching” between the network and the source and the effect on end-to-end delay. We also look at the sensitivity of the proposed scheme to the estimates of the network round-trip times and to inaccuracies in the rate requests made by sources     

Mining very large databases

Established companies have had decades to accumulate masses of data about their customers, suppliers, products and services, and employees. Data mining, also known as knowledge discovery in databases, gives organizations the tools to sift through these vast data stores to find the trends, patterns, and correlations that can guide strategic decision making. Traditionally, algorithms for data analysis assume that the input data contains relatively few records. Current databases however, are much too large to be held in main memory. To be efficient, the data mining techniques applied to very large databases must be highly scalable. An algorithm is said to be scalable if (given a fixed amount of main memory), its runtime increases linearly with the number of records in the input database. Recent work has focused on scaling data mining algorithms to very large data sets. The authors describe a broad range of algorithms that address three classical data mining problems: market basket analysis, clustering, and classification