Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

A comprehensive survey on image enhancement techniques with special emphasis on infrared images

Capturing of infrared images is an easy task but perceptual visualization is difficult due to environmental conditions such as light rain, partly cloudy, mostly cloudy, haze, poor lightening conditions, noise generated by the sensors, geographical distance and appearances of the objects. To improve the human perception and quality of the infrared images for further processing like image analysis, image enhancement is an essential process. This paper provides a detailed review of various image enhancement techniques from contrast stretching to optimization methods used in infrared images. It also discusses the existing infrared image enhancement techniques as group such as histogram based methods, filter based methods, transform domain based methods, morphological based methods, saliency extraction methods, fuzzy based methods, learning methods, optimization methods and its popular algorithms also address the countless issues. Some of the existing image enhancement methods (Histogram Equlization, Max-median filter, Top-Hat transform) and infrared image enhancement methods (multi-scale top-hat transform, adaptive infrared image enhancement) are implemented along with the adaptive fuzzy based infrared image enhancement method and its obtained results evaluation is done on subjective and objective ways. From the results observed that the fuzzy based method works well for both subjective and objective evaluation. The paper aims to provide a complete study on image enhancement techniques and how they specially utilized while dealing with infrared images. In addition, the paper helps the researchers to select the suitable infrared image enhancement techniques for their infrared image application needs.

     

Compact Wideband Meandered Slotted Multi Frequency Microstrip Antenna

A compact wideband multi frequency microstrip antenna for wireless communication is proposed in this paper. The antenna is designed by introducing meandered slot on the patch and a pair of spur lines along the triangular notch on the finite ground plane. The overall size of the fabricated antenna is very small and low profile as the total dimension is 20?×?16 mm². The proposed antenna operates at 3.7 GHz, 4.27 GHz and 5.1 GHz which may be suitable for WiMAX and WLAN applications. In addition with multi frequency operation a wide bandwidth (VSWR?≤?2) has been achieved from 6 to 13.7 GHz i.e. 78.2% bandwidth of center frequency, which is suitable for X-band communication and ITU band applications. The meandered slot on the patch causes multi frequency operation of the antenna with 60% compactness and the spur line along with triangular notch on finite ground plane cause bandwidth enhancement.

     

L1 norm based pedestrian detection using video analytics technique

Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. In general, detection is made with two different phases, feature extraction and classification. Also, features for detection of pedestrian are already are available such as optimal feature model. But still required is an improvement in detection by reducing the execution time and false positive. The proposed model has three different phases, that is, background subtraction, feature extraction, and classification. In spite of giving entire information into feature extraction, the system gives only a useful information (foreground image) by twin background model. Then the foreground image moves to the feature extraction and classifies the pedestrian. For feature extraction, histogram of orientation gradient (HOG) L₁ normalization has been used. This will increase the detection accuracy and reduce the computation time of a process. In addition, false positive rate has been minimized.     

Ab initio study of mono-layer 2-D insulators (X-(OH)2 and h-BN) and their use in MTJ memory device

The paper presents an ab initio study of the 2-D insulators and their effect on the performance of a magnetic tunnel junction memory (MTJ) device. MTJ devices has been considered as an alternate to the charge based data storage cells due to its spin-polarised operation and high scaling probability. The use of 2-D insulators like X-(OH)₂ (X: Ca and Mg) and h-BN (hexagonal-Boron Nitride) in such device would be interesting. The authors have calculated the band structures, density of states and effective mass of electrons and holes for the mono-layer of these three non-conventional 2-D insulators using the first principle calculations in density functional theory framework using Quantumwise ATK tool. The ab initio calculation yielded band gap (E_g) of 4.633, 4.685 and 4.249 eV for h-BN, Ca(OH)₂ and Mg(OH)₂, respectively. The effective mass of electrons was calculated as 0.621, 0.604 and 0.478 for single layer h-BN, Ca(OH)₂ and Mg(OH)₂, respectively. While for holes it is 0.834, 0.446 and 0.407, respectively for h-BN, Ca(OH)₂ and Mg(OH)₂. The MTJ device properties as tunneling-magneto resistance, differential TMR, parallel and anti-parallel resistance, differential resistance and spin transfer torque components (in-plane and out-of-plane) with these materials as composite dielectric has been reported in this paper using MTJ Lab tool. The performance of MTJ memory device with h-BN based composite dielectric is found better.

     

Remote sensing of alpine treeline ecotone dynamics and phenology in Arunachal Pradesh Himalaya

Global warming is inducing the elevational alpine treeline ecotone (ATE) to migrate to higher elevations in the Himalaya. Prior research on ATE dynamics has been primarily based on field inventory and studied at the community level. The potential of using remote sensing and geographic information system for the delineation of the treeline ecotone has been explored. In this study, we used satellite-derived Normalized Difference Vegetation Index (NDVI) data from Landsat-1/2 Multispectral Scanner (MSS), Resourcesat-1/2 Linear Imaging Self Scanning Sensor (LISS-III), and National Oceanographic and Atmospheric Administration-Advanced Very High-Resolution Radiometer (NOAA-AVHRR) to investigate long-term ATE dynamics. Satellite remote sensing of treeline in Arunachal Pradesh Himalaya revealed an upward shift over the past four decades. The ATE has shifted c. 452 m ± 74 m upward in vertical dimension at a rate c. 113 m decade^?1. Furthermore, the land surface phenology along ATE and forest area has changed significantly over the past 33 years. The significant positive trend in length of the growing season (LOS; p < 0.05) and delay in the end of the growing season (EOS) was observed. The start of the growing season (SOS) had a negative tendency with non-significant linear trend. The treeline upward shift and significant lengthening of the growing season at ATE and forest area indicate changing climatic patterns and processes.     

Distance metric learning-based kernel gram matrix learning for pattern analysis tasks in kernel feature space

Approaches to distance metric learning (DML) for Mahalanobis distance metric involve estimating a parametric matrix that is associated with a linear transformation. For complex pattern analysis tasks, it is necessary to consider the approaches to DML that involve estimating a parametric matrix that is associated with a nonlinear transformation. One such approach involves performing the DML of Mahalanobis distance in the feature space of a Mercer kernel. In this approach, the problem of estimation of a parametric matrix of Mahalanobis distance is formulated as a problem of learning an optimal kernel gram matrix from the kernel gram matrix of a base kernel by minimizing the logdet divergence between the kernel gram matrices. We propose to use the optimal kernel gram matrices learnt from the kernel gram matrix of the base kernels in pattern analysis tasks such as clustering, multi-class pattern classification and nonlinear principal component analysis. We consider the commonly used kernels such as linear kernel, polynomial kernel, radial basis function kernel and exponential kernel as well as hyper-ellipsoidal kernels as the base kernels for optimal kernel learning. We study the performance of the DML-based class-specific kernels for multi-class pattern classification using support vector machines. Results of our experimental studies on benchmark datasets demonstrate the effectiveness of the DML-based kernels for different pattern analysis tasks.     

Study of Stratospheric Sudden Warming (SSW) over the tropical and subtropical regions of India using Rayleigh lidar

The Stratospheric Sudden Warming (SSW) is one of the most spectacular phenomena in the atmosphere and has impacts on the Earth’s lower, middle, and upper atmospheres. In this study, two major SSW episodes associated with vortex displacement and vortex splitting that occurred in the years 1998 and 1999, respectively, are investigated for the first time over Mt. Abu using lidar observations. Analyses show that ground-based lidar and satellite observations from the Halogen occultation experiment (HALOE) on board the upper atmospheric research satellite (UARS) can capture the effect of SSW events. Lidar measurements are able to capture SSW warming and its decay very accurately. The impact of SSW is further investigated in the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim-reanalysed potential vorticity. Moreover, a detailed study has been presented to understand the latitudinal variation of SSW warming and the associated mesospheric cooling over the Indian region. The results showed that warming is higher over the northern Indian region (35° N, 77° E) compared with the southern Indian region (5° N, 77° E).     

Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease

Expert’s knowledge base systems are not effective as a decision-making aid for physicians in providing accurate diagnosis and treatment of heart diseases due to vagueness in information and impreciseness and uncertainty in decision making. For this reason, automatic diagnostic fuzzy systems are very time demanding to improve the diagnostic accuracy. In this paper, we have developed an automatic fuzzy diagnostic system based on genetic algorithm (GA) and a modified dynamic multi-swarm particle swarm optimization (MDMS-PSO) for prognosticating the risk level of heart disease. Our proposed fuzzy diagnostic system (FS) works as follows: i) Preprocess the data sets ii) Effective attributes are selected through statistical methods such as Correlation coefficient, R-Squared and Weighted Least Squared (WLS) method, iii) Weighted fuzzy rules are formed on the basis of selected attributes using GA, iv) MDMS-PSO is employed for the optimization of membership functions (MFs) of FS, v) Build the ensemble FS from the generated fuzzy knowledge base by fusing the different local FSs. Finally, to ascertain the efficiency of the adaptive FS, the applicability of the FS is appraised with quantitative, qualitative and comparative analysis on the publicly available different real-life data sets. From the empirical analysis, we see that this hybrid model can manage the knowledge vagueness and decision-making uncertainty precisely and it has yielded better accuracy on the different publicly available heart disease data sets than other existing methods so that it justifies its adaptability with different data sets.     

Fin shape influence on analog and RF performance of junctionless accumulation-mode bulk FinFETs

Microsystem Technologies - The non-planar 3D structure of multi-gate FinFETs makes them able to be scaled down to 20&nbsp;nm and beyond and also have greater performance. But any variation of...