Mutual spectral residual approach for multifocus image fusion

Multifocus image fusion is the process of obtaining a single image from multiple partially focused images such that the newly formed image consists of the well-defined information extracted from each source image. This paper proposes the use of saliency of the source images based on Mutual Spectral Residual. Spectral Residual brings out the unique/salient features of an image in frequency domain. The idea of proposed mutual spectral residual is to emphasize the relative unique features of a source image with respect to the other source images. The relative unique features are utilized to form saliency maps for each source image. These saliency maps can clearly indicate the focused and defocused parts of an image. Based on the saliency maps obtained, the image fusion takes place in spatial domain. Visual inspection and quantitative evaluation of the fused images obtained by the proposed method, using different evaluation metrics, demonstrate its effectiveness over several existing image fusion methods.     

Multi-objective optimization in wire-electro-discharge machining of TiC reinforced composite through Neuro-Genetic technique

This paper proposed a Neuro-Genetic technique to optimize the multi-response of wire electro-discharge machining (WEDM) process. The technique was developed through hybridization of a radial basis function network (RBFN) and non-dominated sorting genetic algorithm (NSGA-II). The machining was done on 5 vol% titanium carbide (TiC) reinforced austenitic manganese steel metal matrix composite (MMC). The proposed Neuro-Genetic technique was found to be potential in finding several optimal input machining conditions which can satisfy wide requirements of a process engineer and help in efficient utilization of WEDM in industry.     

An improved dynamic modeling of a multibody system with spherical joints

A dynamic modeling of multibody systems having spherical joints is reported in this work. In general, three intersecting orthogonal revolute joints are substituted for a spherical joint with vanishing lengths of intermediate links between the revolute joints. This procedure increases sizes of associated matrices in the equations of motion, thus increasing computational burden of an algorithm used for dynamic simulation and control. In the proposed methodology, Euler parameters, which are typically used for representation of a rigid-body orientation in three-dimensional Cartesian space, are employed to represent the orientation of a spherical joint that connects a link to its previous one providing three-degree-of-freedom motion capability. For the dynamic modeling, the concept of the Decoupled Natural Orthogonal Complement (DeNOC) matrices is utilized. It is shown in this work that the representation of spherical joints motion using Euler parameters avoids the unnecessary introduction of the intermediate links, thereby no increase in the sizes of the associated matrices with the dynamic equations of motion. To confirm the efficiency of the proposed representation, it is illustrated with the dynamic modeling of a spatial four-bar Revolute-Spherical–Spherical-Revolute (RSSR) mechanism, where the CPU time of the dynamic modeling based on proposed methodology is compared with that based on the revolute joints substitution. Finally, it is explained how a complex suspension and steering linkage can be modeled using the proposed concept of Euler parameters to represent a spherical joint.     

Bio-Inspired Artificial Fast-Adaptive and Slow-Adaptive Mechanoreceptors With Synapse-Like Functions

Development of artificial mechanoreceptors capable of sensing and pre-processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio-inspired artificial fast-adaptive (FA) and slow-adaptive (SA) mechanoreceptors with synapse-like functions are demonstrated for tactile perception. These mechanoreceptors integrate self-powered piezoelectric pressure sensors with synaptic electrolyte-gated field-effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric-ionic coupling effect based on P(VDF-TrFE) and an ionic liquid. Changes in post-synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre-synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.     

Improved aquila optimization-based parameter evaluation framework and meta-heuristic algorithm in radio-over-fiber SpatialMux multi-input multi-output long-term evolution system

In heterogeneous access network, Multiple-Input Multiple-Output (MIMO) radio-over-fiber (RoF) system is an efficient approach for multiple signal transmission with low cost and complexity. The performance of RoF fronthaul system in MIMO system will be varied with different nonlinear effects. By adjusting various transmission parameters, such as the input signal power or the laser bias current, the nonlinear impacts produced by the RoF system can be reduced. In this paper, a novel algorithm Improved Aquila Optimization (IAO) is proposed to optimize transmission circumstances of MIMO RoF system. It determines the appropriate bias current for both lasers and Radio Frequency (RF) signal power in a short period. The input signals are wavelength multiplexed with Intensity Modulation and Direct Detection (IM/DD) applied. The carrier as well as transmission frequency is governed by the MIMO-Long-Term Evolution (LTE) standard. The proposed system is implemented in MATLAB, and the performance is evaluated. The experimental results show that fast convergence and trade-off between noise and nonlinearity are obtained with varying bandwidth. In the experimental scenario, the maximum Error Vector Magnitude (EVM) of 1.88, 3.14, and signal-to-noise ratio (SNR) of 3.204, and 2.698 was attained for both quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) modulation. [Correction added on 24 April 2023, after first online publication: the SNR values were corrected in the preceding sentence.] For 100 iterations, the processing time was reduced to 0.137 s. When compared with the conventional state-of-the-art approaches, the accuracy and computational complexity of the proposed approach are improved.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Strain and electric field-modulated indirect-to-direct band transition of monolayer GaInS2

Journal of Computational Electronics - The strain- and electric field-dependent electronic and optical properties of monolayer GaInS2 have been calculated using density functional...     

Sink Mobility-Based Energy Efficient Routing Algorithm Variants in WSN

International Journal of Wireless Information Networks - In this work, energy efficient routing protocol variants considering different sink mobility in hierarchical cluster based wireless sensor...     

A quantum mutation-based backtracking search algorithm

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.