Jeevan Kanesan

Analysis of Heat and Mass Transfer in a Vertical Annular Porous Cylinder Using FEM

The present study is intended to study heat and mass transfer in a vertical annular cylinder embedded with saturated porous medium. The inner surface of cylinder is maintained at uniform wall temperature and uniform wall concentration. The governing partial differential equations are non-dimensionalised and solved by using finite element method (FEM). The porous medium is discritised using triangular elements with uneven element size. Large number of smaller-sized elements are placed near the walls of the annulus to capture the smallest variation in solution parameters. The results are reported for both aiding and opposing flows. The effects of various non-dimensional numbers such as buoyancy ratio, Lewis number, Rayleigh number, aspect ratio, etc on heat and mass transfer are discussed. The temperature and concentration profiles are presented.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients’ benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

A survey on design of self-powered wireless sensor network

Supporting sensor application over wireless sensor node (WSN) is more challenging than over any other sensor due to the absence of a convenient way to maintenance the sensor node, especially in changing of energy source (batteries). In this paper, we solve this problem by using the ambient energy source (solar), design the efficient power conversion, and low-power consumption in WSN system. Some modifications have been introduced considering the differences between wired and wireless network. The modifications also consider the differences wide of WSN for detection. Simulation study is carried out using PSPICE to demonstrate the performance of the power conversion mechanism. MPLAB is used for the proposed logical control (PIC). We show that the proposed mechanism outperforms the wireless sensor node reception and transmission in every situation (different levels of darkness).

Ideology algorithm: a socio-inspired optimization methodology

This paper introduces a new socio-inspired metaheuristic technique referred to as ideology algorithm (IA). It is inspired by the self-interested and competitive behaviour of political party individuals which makes them improve their ranking. IA demonstrated superior performance as compared to other well-known techniques in solving unconstrained test problems. Wilcoxon signed-rank test is applied to verify the performance of IA in solving optimization problems. The results are compared with seven well-known and some recently proposed optimization algorithms (PSO, CLPSO, CMAES, ABC, JDE, SADE and BSA). A total of 75 unconstrained benchmark problems are used to test the performance of IA up to 30 dimensions. The results from this study highlighted that the IA outperforms the other algorithms in terms of number function evaluations and computational time. The eminent observed features of the algorithm are also discussed.

Power Amplifier Design Methodologies for Next Generation Wireless Communications

ABSTRACT As wireless communication standard continues to evolve accommodating the demand of high data rate operation, the design of radio frequency (RF) power amplifier (PA) becomes ever challenging. PAs are required to operate more efficiently while maintaining stringent linearity requirement. In this paper, the design concepts of the PA in addressing these challenges are reviewed. The concepts are divided into two main categories, namely the linearization technique and efficiency enhancement technique. The mandatory attempt in realizing a low-cost design is also discussed.

Design of Low-phase Noise, Low-power Ring Oscillator for OC-48 Application

Abstract A low-power, low-phase noise, high tuning range, and fully integrated inductorless RC-VCO (voltage-controlled oscillator) for OC-48 application is designed and simulated in standard 0.18 μm CMOS technology. The proposed inductorless RC-VCO has a simulated phase noise of −141 dBc/Hz at 1 MHz offset from the carrier frequency of 2.4 GHz, with a bias current of 605 μA and voltage headroom of 1.8 V. It has 50% tuning range at 2.4 GHz of operating frequency and consumes 1.09 mW of power. This RC-VCO shows a figure of merit performance of −208.2 dBc/Hz at the desired frequency.

A new electric dipole model for lightning-aircraft electrodynamics

Purpose – As commercial and military aircraft continue to be subject to direct lightning flashes, there is a great need to characterize correctly the electrical currents and electric potential fluctuations on an aircraft to determine alternative design approaches to minimizing the severity of the lightning-aircraft dynamics. Moreover, with the increased severity of thunderstorms due to global warming, the need arises even more to predict and quantify electrical characteristics of the lightning-aircraft electrodynamics, which is normally not measurable, using a reliable electric model of the aircraft. Such a model is advanced here. The paper aims to discuss these issues. Design/methodology/approach – The case considered in this paper is that of an aircraft directly attached to an earth flash lightning channel. The paper develops a new approach to modelling the aircraft using electric dipoles. The model has the power to represent sharp edges such as wings, tail ends and radome for any aircraft with differen...

Noise characteristics of grass-trimming machine engines and their effect on operators

Over the last few years, interaction of humans with noisy power-driven agricultural tools and its possible adverse after effects have been realized. Grass-trimmer engine is the primary source of noise and the use of motorized cutter, spinning at high speed, is the secondary source of noise to which operators are exposed. In the present study, investigation was carried out to determine the effect of two types of grass-trimming machine engines (SUM 328 SE and BG 328) noise on the operators in real working environment. It was found that BG-328 and SUM-328 SE produced high levels of noise, of the order of 100 and 105 dB(A), respectively, to which operators are exposed while working. It was also observed that situation aggravates when a number of operators simultaneously operate resulting in still higher levels of noise. Operators should be separated 15 meters from each other in order to avoid the combined level of noise exposure while working with these machines. It was found that SPL, of the grass-trimmer machine engines (BG-328 and SUM-328 SE), were higher than the limit of noise recommended by ISO, NIOSH, and OSHA for an 8-hour workday. Such a high level of noise exposure may cause physiological and psychological problems to the operators in long run.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities

Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.

Wideband LTE Power Amplifier with Integrated Novel Analog Pre-Distorter Linearizer for Mobile Wireless Communications

For the first time, a new circuit to extend the linear operation bandwidth of a LTE (Long Term Evolution) power amplifier, while delivering a high efficiency is implemented in less than 1 mm2 chip area. The 950 µm × 900 µm monolithic microwave integrated circuit (MMIC) power amplifier (PA) is fabricated in a 2 µm InGaP/GaAs process. An on-chip analog pre-distorter (APD) is designed to improve the linearity of the PA, up to 20 MHz channel bandwidth. Intended for 1.95 GHz Band 1 LTE application, the PA satisfies adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) specifications for a wide LTE channel bandwidth of 20 MHz at a linear output power of 28 dBm with corresponding power added efficiency (PAE) of 52.3%. With a respective input and output return loss of 30 dB and 14 dB, the PA’s power gain is measured to be 32.5 dB while exhibiting an unconditional stability characteristic from DC up to 5 GHz. The proposed APD technique serves to be a good solution to improve linearity of a PA without sacrificing other critical performance metrics.