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.

     

Design and Implementation of Routing Algorithm to Enhance Network Lifetime in WBAN

Today’s era is the era of smart and remote applications exploiting advancement in sensors, cloud, Internet of things etc. Major application is in healthcare monitoring and support using wireless body area network (WBAN) in which sensor nodes sense vital physiological parameters and send to server through sink i.e. smart phone nowadays for seamless monitoring. The most significant issue in such applications is energy efficiency which leads to enhanced network life time that ensures uninterrupted seamless services. From source to sink data transmission may occur considering three different scenarios: source to sink single hop direct data transmission irrespective of in-between node distance, source to sink multi hop data transmission in which transmission range of source node is fixed at a threshold to find next forwarder node and transmission range of source node is incremented by affixed value until data gets transmitted to sink. In this work WBAN having different network configurations based on fixed or random positions of nodes have been simulated. Different scenarios with fixed and varying number of nodes are framed and simulated using MATLAB 2020a for performance evaluation of proposed algorithm in terms of energy consumption, network lifetime, path loss etc. due to data transmission from source to sink. Experimental results show that incremental approach is better than direct one in terms of energy consumption, path loss and network lifetime. While selecting transmission range of a source node, it is considered to keep Specific Absorption Rate (SAR) lower to reduce impact on human tissue.

     

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.     

Sliding Mode Control of Coupled Tank Systems Using Conditional Integrators

For the problem of set point regulation of the liquid level in coupled tank systems, we present a continuous sliding mode control(SMC) with a "conditional integrator", which only provides integral action inside the boundary layer. For a special choice of the controller parameters, our design can be viewed as a PID controller with anti-windup and achieves robust regulation.The proposed controller recovers the transient response performance without control chattering. Both full-state feedback as well as output-feedback designs are presented in this work. Our output-feedback design uses a high-gain observer(HGO) which recovers the performance of a state-feedback design where plant parameters are assumed to be known. We consider both interacting as well as non-interacting tanks and analytical results for stability and transient performance are presented in both the cases. The proposed controller continuous SMC with conditional integrators(CSMCCI) provides superior results in terms of the performance measures as well as performance indices than ideal SMC, continuous SMC(CSMC) and continuous SMC with conventional integrator(CSMCI). Experimental results demonstrate good tracking performance in spite of unmodeled dynamics and disturbances.     

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.     

A novel semi-folded parallel successive cancellation-based polar decoder for optimal-register allocation

The Journal of Supercomputing - Efficient compression and reliable data transmission are the major areas in the information theory that governs various applications in mobile communication,...     

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.     

Fuzzy least squares twin support vector clustering

In this paper, we have formulated a fuzzy least squares version of recently proposed clustering method, namely twin support vector clustering (TWSVC). Here, a fuzzy membership value of each data pattern to different cluster is optimized and is further used for assigning each data pattern to one or other cluster. The formulation leads to finding k cluster center planes by solving modified primal problem of TWSVC, instead of the dual problem usually solved. We show that the solution of the proposed algorithm reduces to solving a series of system of linear equations as opposed to solving series of quadratic programming problems along with system of linear equations as in TWSVC. The experimental results on several publicly available datasets show that the proposed fuzzy least squares twin support vector clustering (F-LS-TWSVC) achieves comparable clustering accuracy to that of TWSVC with comparatively lesser computational time. Further, we have given an application of F-LS-TWSVC for segmentation of color images.