This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

The neuron can be referred to as the primary cell of the nervous system, responsible for transmitting messages through electrical signals between neurons or other cells. To comprehend the intricate behavior of neuron performance, a set of differential equations incorporating non-linear functions is necessary. This research introduces a novel method to improve the digital implementation of Wilson and Morris-Lecar neuron models, offering multiple benefits such as decreased hardware needs, enhanced processing speed and accuracy, and reduced implementation expenses. The proposed method simplifies the original model by converting all its differential equations into a single trigonometric function. This transformation greatly reduces computational complexity by eliminating the need for multipliers, resulting in a concise set of mathematical expressions. The digital implementation of this innovative approach can be efficiently achieved using the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the need for complex mathematical operations. To showcase the effectiveness of this approach, the suggested model is synthesized and implemented on a Field-Programmable Gate Array (FPGA) with successful results. The implementation outcomes reveal a significant improvement in operational frequency, approximately 3.28 and 4.68 times (for Wilson and Morris-Lecar, respectively) higher than the original model.

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), GrayWolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.

In this paper, a novel approach to design a photonic crystal exclusive-OR (XOR) gate with high performance using recurrent neural network (RNN) is proposed, integrating the principles of symmetry and asymmetry inherent in photonic structures. The proposed model realizes the nonlinearities and dispersion properties in photonic systems, optimizing the waveguide paths and interference patterns to improve the performance of the logic gate. Simulation results demonstrate that the presented RNN design not only achieves high fidelity in the logical operation but also significantly enhances the functionality of the all-optical gate. This integration of machine learning with photonic crystal technology opens a new era for developing compact, energy-efficient photonic circuits for high-speed optical computing. Finally, the performance of the designed all-optical gate is compared with other related methods, which shows that the proposed gate outperforms other works. The results show that the obtained output power of the proposed all-optical XOR gate is 0, 0.813, 0.82, and 1 × 10⁻⁷ in the logical states.

This paper introduces an innovative 4×4 dual-band Butler matrix (BM) characterized by compactness and an enhanced frequency ratio (K). The design employs meandered lines and an interdigital capacitor unit cell based composite right/left-handed transmission-line (CRLH-TL) metamaterial (MTM) structure. The BM integrates compact dual-band 3 dB branch-line couplers (BLC), a 0 dB crossover, and a dual-band 45° phase shifter on a single Rogers RT5880 substrate (with ε _r = 2.2 and h = 0.787 mm). Simulations and measurements results demonstrates reflection and isolation coefficients exceeding -20 dB at all ports, with a obtained insertion loss of -6±3 dB over the 0.7 GHz and 3.5 GHz frequency bands. The achieved phase differences of ±45°, ±135°, ±135°, and ±45° indicate a maximum average phase tolerance of ±4.5°. Importantly, the BM’s overall dimensions are 140 mm × 192 mm, resulting in a 78% size reduction compared to traditional BM designs. The proposed configuration is designed and simulated using CST Microwave Studio, with the agreement between simulated and measured parameters highlighting design reliability and effectiveness. Additionally, a performance evaluation comparing the proposed BM with existing circuits reveals its suitability for sub-6 GHz 5G dual-band antenna array beamforming networks (BFN) due to its compact size and improved band ratio.