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The flow of fuel and oxidant through a PEMFC is analyzed for prediction of maldistribution. Flow distribution of both fuel and oxidant from the port to the individual cells critically control the performance of a PEMFC stack in combination. The distribution of fluids was simulated by analytical approach utilizing flow channeling model of a manifold. A detailed numerical modeling is also carried out considering flow in each cell between the electrodes as flow through an equivalent porous medium offering identical resistance. The results show a close match between the analytical and numerical results'. The parametric study reveals that flow rate and port size plays major role determining maldistribution of the ,fluids', which can be considerably skewed when large numbers of cells are stacked for larger power output.

A detailed experimental study on flow maldistribution from port to channel of a plate heat exchanger is presented. In general, flow maldistribution brings about an increase in pressure drop across the heat exchanger. This increase is found to depend on flow rate, number of channels and port size. Experiments show that analytical predictions of pressure drop including maldistribution effect are quite accurate for practical purposes. The results indicate that under identical conditions, maldistribution is more severe in Z-type plate heat exchanger compared to U type. Experiments are also carried out under nonisothermal realistic operating conditions, which show increased flow maldistribution at elevated temperature. Finally predictions are made for industrial plate heat exchangers, which show the limitation of adding additional plates beyond a certain limit. An insight to the physical aspects of maldistribution and its possible reduction through proper design strategy is also indicated. [DOI: 10.1115/1.1779664]

In recent times, the medical imaging becomes an indispensable tool in clinical practice. Due to the large volume of medical images, compression is needed to lessen the redundancies in the image and also to represent the image in shorter manner for effective transmission. In this paper, Linde–Buzo–Gray (LBG) algorithm was developed with vector quantization (VQ) for compressing the images, and it results in decent image quality. To further increase the image quality, optimization techniques [particle swarm optimization (PSO) and firefly algorithm (FA)] were used in LBG method to optimize the codebook for generating the global codebook. In the proposed work, LBG method was used to get the local codebooks and the obtained local codebooks were optimized by utilizing PSO. The optimized codebooks from PSO were again optimized by using FA that results in good quality of the image. In the experimental phase, the performance of the proposed work was compared with individual optimization techniques like PSO and FA. From the experimental study, the proposed work showed 1.2–6 dB improvement in image compression related to other existing approaches. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is the trending domain in addressing the inadequate bands for communication, and spectrum sensing is the hectic challenge need to be addressed extensively. In the conventional CRs, the communication is restricted to the secondary users (SUs) in the allocated bands causing the underutilization of the available band. Thus, with the aim to afford higher throughput and spectrum efficiency, this paper introduces the hybrid mixture model for spectrum sensing in the multiple-input–multiple-output (MIMO) systems and the effectiveness is evaluated based on the evaluation parameters, such as detection probability and probability of false alarm. The signal received through the orthogonal frequency-division multiplexing (OFDM) antenna is employed for analyzing the spectral availability for which the energy and Eigen statistics of the signal is generated, which forms the input to the Hybrid mixture model. The developed Hybrid mixture model is the integration of the Gaussian Mixture Model (GMM) and Whale Elephant-Herd Optimization (WEHO). The GMM is subjected to the optimal tuning using the WEHO, which is the modification of the standard Whale Optimization Algorithm (WOA) with the Elephant-Herd Optimization (EHO). The analysis reveals that the proposed spectrum sensing model acquired the maximal detection probability and minimal false alarm probability of 99.9% and 46.4%, respectively. The proposed hybrid mixture model derives the spectrum availability and ensures the effective communication in CR without any interference. [ABSTRACT FROM AUTHOR]

For the past few years, the automated addressing of changes in remote sensing images plays a significant role. However, the change detection (CD) model often suffers from the issue of speckle noise. More investigations have been proceeded to overcome this obstacle. This paper also considers the same issue and proposes a new CD model in synthetic aperture radar (SAR) images. Here, two SAR images that are captivated at different times will be considered as the input of the detection process. At first, discrete wavelet transform is incurred for image fusion, where the coefficients are optimally selected through a hybrid model that hybridizes the gray wolf optimization and dragonfly (DA) optimization. At last, the fused images after inverse transform are clustered via the fuzzy c-mean (FCM) clustering approach, and a similarity measure is performed between the segmented image and the ground truth image. The proposed model, wolf hunting-based DA with FCM, compares its performance over other conventional methods in terms of measures like accuracy, specificity, sensitivity, precision, negative predictive value, F 1 score and Matthews correlation coefficient. Similarly, the negative measures are false positive rate, false negative rate and false discovery rate, and the betterment is proven. [ABSTRACT FROM AUTHOR]

An experimental and theoretical study of the effect of flow maldistribution from port to channel on the thermal performance of single and multipass plate heat exchangers is presented. The results indicate that under identical conditions, maldistribution is more severe in Z-type late heat exchanger compared to U type.

Tuberculosis (TB) is a global health problem and an infectious disease for people with low immunity and HIV/AIDS patients. Unfortunately diagnosing tuberculosis is still a major challenge. In any medical diagnosis, classifier plays an important role. In order to improve the speed of the classifier; it is required to use the selected features. To reduce the dimensionality of feature vector the authors have used RST based Multi Kernel Fuzzy C-Means (MKFCM) method for selecting features which are indispensable. Features those are not selected are treated as redundant or superfluous and are removed from the final feature vector. The performance of proposed methodology is analyzed in terms of accuracy, performance curve and regression plots. As a result, the suggested method gives the better performance. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present an image compression method using feedforward backpropagation neural networks. Medical imaging is an efficient source for better diagnosis of the disease and also helps in assessing the severity of the disease. But due to the increasing size of the medical images, transferring and storage of images require huge bandwidth and storage space. Therefore, it is essential to derive an effective compression algorithm, which have minimal loss, time complexity, and increased reduction in size. With the concept of Neural Network, data compression can be achieved by producing an internal data representation. The training algorithm and development architecture gives less distortion and considerable compression ratio and also keeps up the capability of hypothesizing and is becoming important. The performance metrics of three algorithms, Levenberg Marquardt algorithm, Resilient backpropagation algorithm, and Gradient Decent algorithm have been computed on Magnetic Resonance Imaging (MRI) images and it is observed that Levenberg Marquardt algorithm is more accurate when compared to the other two algorithms. [ABSTRACT FROM AUTHOR]