Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

Solar/UV-induced photocatalytic degradation of three commercial textile dyes.

The photocatalytic degradation of three commercial textile dyes with different structure has been investigated using TiO(2) (Degussa P25) photocatalyst in aqueous solution under solar irradiation. Experiments were conducted to optimise various parameters viz. amount of catalyst, concentration of dye, pH and solar light intensity. Degradation of all the dyes were examined by using chemical oxygen demand (COD) method. The degradation efficiency of the three dyes is as follows: Reactive Yellow 17(RY17) > Reactive Red 2(RR2) > Reactive Blue 4 (RB4), respectively. The experimental results indicate that TiO(2) (Degussa P25) is the best catalyst in comparison with other commercial photocatalysts such as, TiO(2) (Merck), ZnO, ZrO(2), WO(3) and CdS. Though the UV irradiation can efficiently degrade the dyes, naturally abundant solar irradiation is also very effective in the mineralisation of dyes. The comparison between thin-film coating and aqueous slurry method reveals that slurry method is more efficient than coating but the problems of leaching and the requirement of separation can be avoided by using coating technique. These observations indicate that all the three dyes could be degraded completely at different time intervals. Hence, it may be a viable technique for the safe disposal of textile wastewater into the water streams.     

A KBES for integrated engineering

The expert process of engineering involves application of knowledge fundamental to the engineering process at one end, and of compiled knowledge in the form of heuristics gained through many years of experience at the other. Developments in Artificial Intelligence have made the process of computer aided engineering richer by enabling the simulation of this human process of engineering, which until now has been difficult to capture. Knowledge Based Expert Systems (KBES) in engineering developed so far have addressed only narrow domains of the entire process of engineering. This paper discusses a KBES shell (IES) that integrates various AI technologies, which makes it possible to implement new problem-solving strategies required for efficient handling of the entire engineering process. Issues in the integrated process of engineering are discussed first, in order to evolve a specification for the shell. The blackboard architecture is shown to be the ideal backbone for such a shell. The details of inference techniques, the knowledge based backtracking mechanism and the DBMS used in the IES are presented. The application of the IES is demonstrated using the domain of integrated engineering of steel industrial structures as an example. This application encompasses the engineering activities of conceptual design, detailed design, documentation and construction planning.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

Esterification of valeric acid over PTA supported mesoporous Al-SBA-15 as efficient solid acid catalysts

Journal of Porous Materials - Valeric acid can be produced by selective hydrogenation of biomass-derived levulinic acid. The present work aims to synthesize ethyl valerate (EV), a fuel, fuel...     

Epoxy‐tert‐butyl poly(cyanoarylene ether) blends: Phase morphology,fracture toughness,and mechanical properties

Tert‐butyl hydroquinone–based poly(cyanoarylene ether) (PENT) was synthesized by the nucleophilic aromatic substitution reaction of 2,6‐dichlorobenzonitrile with tert‐butyl hydroquinone using N‐methyl‐2‐pyrrolidone (NMP) as solvent in the presence of anhydrous potassium carbonate in a nitrogen atmosphere at 200°C. PENT‐toughened diglycidyl ether of bisphenol A epoxy resin (DGEBA) was developed using 4,4′‐diaminodiphenyl sulfone (DDS) as the curing agent. Scanning electron micrographs revealed that all blends had a two‐phase morphology. The morphology changed from dispersed PENT to a cocontinuous structure with an increase in PENT content in the blends from 5 to 15 phr. The viscoelastic properties of the blends were investigated using dynamic mechanical thermal analysis. The storage modulus of the blends was less than that of the unmodified resin, whereas the loss modulus of the blends was higher than that of the neat epoxy. The tensile strength of the blends improved slightly, whereas flexural strength remained the same as that of the unmodified resin. Fracture toughness was found to increase with an increase in PENT content in the blends. Toughening mechanisms like local plastic deformation of the matrix, crack path deflection, crack pinning, ductile tearing of thermoplastic, and particle bridging were evident from the scanning electron micrographs of failed specimens from the fracture toughness measurements. The thermal stability of the blends were comparable to that of the neat resin. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3536–3544, 2006     

Comparison of TiAIN,AICrN, and AICrN/TiAIN coatings for cutting-tool applications

Monolayer and bilayer coatings of TiAlN, AlCrN, and AlCrN/TiAlN were deposited onto tungsten carbide inserts using the plasma enhanced physical vapor deposition process. The microstructures of the coatings were characterized using scanning electron microscopy （SEM） and atomic force microscopy （AFM）. The SEM micrographs revealed that the AlrN and AlCrN/TiAlN coatings were uniform and highly dense and contained only a limited number of microvoids. The TiAIN coating was non-uniform and highly porous and contained more micro droplets. The hardness and scratch resistance of the specimens were measured using a nanoindentation tester and scratch tester, respectively. Different phases formed in the coatings were analyzed by X-ray diffraction （XRD）. The AlCrN/TiAlN coating exhibited a higher hardness （32.75 GPa）, a higher Young＇s modulus （561.97 GPa）, and superior scratch resistance （LcN = 46 N） compared to conventional coatings such as TiAlN, A1CrN, and TiN.     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.