Multimodal image/video fusion rule using generalized pixel significance based on statistical properties of the neighborhood

Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications such as multifocus, multiexposure, multispectral ( $IR$ -visible) and multimodal medical (CT and MRI) image and video fusion. While there exist many fusion techniques for each of these applications, it is difficult to formulate a common fusion technique that works equally well for all these applications. This is mainly because of the different characteristics of the images involved in various applications and the correspondingly different requirements on the fused image. In this work, we propose a common generalized fusion framework for all these classes, based on the statistical properties of local neighborhood of a pixel. As the eigenvalue of the unbiased estimate of the covariance matrix of an image block depends on the strength of edges in that block, we propose to employ it to compute a quantity we call as the significance of a pixel. This generalized pixel significance in turn can be used as a measure of the useful information content in that block, and hence can be used in the fusion process. Several data sets were fused to compare the results with various recently published methods. The analysis shows that for all the image types into consideration, the proposed methods improve the quality of the fused image, both visually and quantitatively, by preserving all the relevant information.     

DNA-CTMA/a-Si:H bio-hybrid photodiode: A light-sensitive photosensor

Recently, considerable interest have occurred in the development of an organic-inorganic-based bio-hybrid photodiodes (Bio-HPDs) with metal-free, eco-friendly, and cost-competitive features for light-sensitive devices. This paper reports a bio-inspired optical absorber material for the fabrication of Bio-HPDs using n-type hydrogenated amorphous silicon (a-Si:H) and a natural deoxyribonucleic acid (DNA)-cetyltrimethylammonium chloride (CTMA) biomaterial. a-Si:H is inexpensive and abundant, and DNA-CTMA is metal-free and eco-friendly. A DNA-CTMA coating on n-type a-Si:H leads to a chemically stable material with increased absorption and effective ties of dangling bonds and interface state density. Analysis results showed that the rectification ratio (RR) of the Bio-HPD is found to be 4 times higher than reference PD. This indicates that the effective RR is improved by the DNA-CTMA layer since it creates molecular charge interactions between DNA-CTMA layer and a-Si:H substrate. Moreover, Bio-HPD shows a light photosensitivity (I_photo/I_dark) of 474 with more reliable and has longer life time. In addition, the formation and feasible charge transport mechanisms are discussed. This biomaterial can be used for the development of commercially viable and environmentally safe large-scale Bio-HPDs applications.     

A delay and energy aware coordination mechanism for WSAN

In this paper, a delay and energy aware coordination mechanism (DEACM) has been devised for wireless sensor–actor networks. In DEACM, a two‐level hierarchical K‐hop clustering mechanism is used to organize the sensors and actors for communication. In the first level, sensors form a K‐hop cluster using actors as cluster heads, and sink is made as the cluster head in the second level to form a cluster among actors. Sensor nodes, which are 1‐hop away from the actors, also called as relay nodes are elected as backup cluster head (BCH) based on the residual energy and node degree. BCH collects the data from sensors when an actor is away to perform actions in the affected area. The scheme is evaluated through exhaustive simulation in NS2 along with other existing schemes. Different parameters like average event waiting time, event reliability, and average energy dissipation are compared, varying the number of sensors, actors, and data transfer rate. In general, it is observed that the proposed DEACM outperforms other existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

Significance of image representation for face verification

In this paper we discuss the significance of representation of images for face verification. We consider three different representations, namely, edge gradient, edge orientation and potential field derived from the edge gradient. These representations are examined in the context of face verification using a specific type of correlation filter, called the minimum average correlation energy (MACE) filter. The different representations are derived using one-dimensional (1-D) processing of image. The 1-D processing provides multiple partial evidences for a given face image, one evidence for each direction of the 1-D processing. Separate MACE filters are used for deriving each partial evidence. We propose a method to combine the partial evidences obtained for each representation using an auto-associative neural network (AANN) model, to arrive at a decision for face verification. Results show that the performance of the system using potential field representation is better than that using the edge gradient representation or the edge orientation representation. Also, the potential field representation derived from the edge gradient is observed to be less sensitive to variation in illumination compared to the gray level representation of images.     

The road to high efficiency organic light emitting devices

Advances leading to very high efficiency organic light emitting devices for use in flat panel displays and solid state illumination are described. The topics addressed in the Special Issue on High Efficiency Organic Light Emitting Devices are introduced.     

Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

Wireless Personal Communications - The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future...     

Effect of Oxygen Flow Rate on Structural,Electrical and Optical Properties of Zinc Aluminum Oxide Thin Films Deposited by DC Magnetron Sputtering

Semiconductors - Zinc Aluminum Oxide thin films were deposited on glass substrates by reactive DC magnetron sputtering method by varying oxygen flow rates from 1 to 4 sccm. Glancing angle X-ray...     

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.     

Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Heterogeneous load balancing clustering protocol for extending the lifetime of wireless sensor networks

Clustering is an indispensable strategy that helps towards the extension of lifetime of each sensor nodes with energy stability in wireless sensor networks (WSNs). This clustering process aids in sustaining energy efficiency and extended network lifetime in sensitive and critical real-life applications that include landslide monitoring and military applications. The dynamic characteristics of WSNs and several cluster configurations introduce challenge in the process of searching an ideal network structure, a herculean challenge. In this paper, Hybrid Chameleon Search and Remora Optimization Algorithm-based Dynamic Clustering Method (HCSROA) is proposed for dynamic optimization of wireless sensor node clusters. It utilized the global searching process of Chameleon Search Algorithm for selecting potential cluster head (CH) selection with balanced trade-off between intensification and extensification. It determines an ideal dynamic network structure based on factors that include quantity of nodes in the neighborhood, distance to sink, predictable energy utilization rate, and residual energy into account during the formulation of fitness function. It specifically achieved sink node mobility through the integration of the local searching capability of Improved Remora Optimization Algorithm for determining the optimal points of deployment over which the packets can be forwarded from the CH of the cluster to the sink node. This proposed HCSROA scheme compared in contrast to standard methods is identified to greatly prolong network lifetime by 29.21% and maintain energy stability by 25.64% in contrast to baseline protocols taken for investigation.