NOPR Collection:

Development of Inexpensive Adsorbent from Agro-Waste for Phenol Adsorption

Fri, 01 Jul 2016 00:00:00 GMT

Title: Development of Inexpensive Adsorbent from Agro-Waste for Phenol Adsorption Authors: Singh, K; Chandra, B; Gautam, M Abstract: Potential adsorption of phenols- major pollutants present in various industrial wastewaters onto adsorbent powdered activated mustard cake (PAMC) derived from (mustard cake MC) agriculture waste materials was evaluated. The PAMC had meso porous pore size distribution with surface area (27 m²g^-1). The adsorption characteristics of the PAMC primarily attributed to the presence of silica or mixed oxides of Ca, Mg, Al and Fe not by the carbon. The Langmuir, Freundlich, Redlich-Peterson and Temkin models were applied to evaluate the adsorption parameters. The adsorption decreased in the order of p-nitrophenol > p-chlorophenol > phenol. Adsorption kinetics was examined using different kinetics model (Lagergren first order, pseudo second order and intra particle diffusion model). The best results were achieved with the Langmuir isotherm equilibrium model and followed pseudo second order kinetics (R² = 0.99). The possible adsorption interactions were discussed. The adsorption mechanism is verified by FT-IR, PZC, effects of pH, urea, ionic strength and comparison between adsorption of phenols derivatives on PAMC. The adsorption mechanism is governed by complex interplay of electrostatic, π–π dispersive as well as by a charge transfer interaction. In cost analysis, it was found that the cost of PAMC ($ 9.5/- only) have very little compared to commercial activated carbon. The PAMC was found to be very effective for removal of phenols from their aqueous solutions. Page(s): 444-451

A Low Power and Low Latency Inter Carrier Interference Cancellation Architecture in Multi User OFDM System

Fri, 01 Jul 2016 00:00:00 GMT

Title: A Low Power and Low Latency Inter Carrier Interference Cancellation Architecture in Multi User OFDM System Authors: Kumar, M N; Arasu, G T Abstract: The Inter carrier interference (ICI) and inter block interference (IBI) are the two signal degradations on multi user orthogonal frequency division multiplexing (OFDM) communication system. In this paper, a noval methodology is proposed for the detection and reduction of ICI in received OFDM symbols. The vector precoding technique is proposed to reduce the ICI and more bandwidth efficient than other precoding techniques to eliminate ICI. The precoding architecture is used to determine the level of interference in the received OFDM symbols. The proposed interference cancellation architecture is designed and implemented in VLSI for analyzing its low power, bit error rate and energy consumption with conventional methods. Page(s): 427-431

Organoleptic Evaluation of Tungrymbai and Bekang, Naturally Fermented Soybean Foods, Produced by using Selected Species of Bacillus

Fri, 01 Jul 2016 00:00:00 GMT

Title: Organoleptic Evaluation of Tungrymbai and Bekang, Naturally Fermented Soybean Foods, Produced by using Selected Species of Bacillus Authors: Chettri, R; Tamang, J P Abstract: Tungrymbai and bekang are naturally fermented soybean foods of Meghalaya and Mizoram, respectively. Three strains of Bacillus viz. Bacillus subtilis, B. licheniformis and B. pumilus previously isolated from naturally fermented products tungrymbai and bekang, and were used singly and/or in mixture as starter culture(s) for the production of tungrymbai and bekang under optimized laboratory conditions. None of the strains of Bacillus used singly as starter could produce organoleptically acceptable tungrymbai and bekang. The sensory evaluation result showed that tungrymbai and bekang prepared in the laboratory by cell suspension mixture of pure culture of Bacillus spp. was more acceptable than conventionally prepared products. Page(s): 416-419

A Performance Analysis of GA-ELM Classifier in Classification of Abnormality Detection in Electrical Impednce Tomography (EIT) Lung Images

Fri, 01 Jul 2016 00:00:00 GMT

Title: A Performance Analysis of GA-ELM Classifier in Classification of Abnormality Detection in Electrical Impednce Tomography (EIT) Lung Images Authors: Prabu, R; Harikumar, R Abstract: This article presents the performance analysis of Genetic Algorithm (GA) - Extreme Learning Machine (ELM) classifier by comparing with Genetic Algorithm (GA)-Support Vector Machine (SVM) classifier for detecting the abnormalities from Electrical impedance tomography images. The machine learning algorithms, Extreme Learning Machine (ELM), and Support Vector Machine (SVM) are used for classification and a Genetic Algorithm (GA) is used as feature selector to reduce the high dimensional features needed for classification. The Gray Level Co-occurrence Matrix (GLCM) and intensity histogram are used for texture feature extraction from the EIT images. The EIT lung images are reconstructed using one step linearized Gauss-Newton (GN) algorithm. Detection of lung injury is one of the critical issue where excessive care has to be taken for better diagnosis and treatment. Any classifier needs to detect the non-ventilated regions with respect to efficiency and performance. The performance analysis of these two classifiers are analyzed based on the benchmark parameters such performance index, sensitivity, specificity, average detection and F-score. From the experimental results it is evident, that the Extreme Learning Machine has performed well compared with the Support Vector Machine and also Extreme Learning Machine classification performance has been increased for genetic algorithm based feature selection. Page(s): 404-411