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.

The neuron is sometimes referred to as the "head" or "central" cell of the nervous system since it has the ability to communicate with other neurons or cells via electrical impulses. The hardware realization and simulation of these neurons are critical in neuromorphic engineering. In this paper, we made a device which generates 4 different spiking patterns of nervous system as a Spike Generator (SG) using a hybrid approximation of the target model called the Piece-Wised Power-2 Based Izhikevich Model (PWP2BIM). This proposed modelworks in a low-cost state to achieve a correct digital implementation of the Izhikevich model, one of the main neuron models (i.e. decreasing hardware resources and enhancing speed and accuracy). The proposed model successfully reproduces the behavioral traits of the initial neuron model. To verify the results of the mathematical simulation, the proposed model was synthesized and implemented on the Zynq XC7Z010 (3CLG400) reconfigurable board (FPGA). The findings of hardware synthesis and applications of the suggested paradigm demonstrate that certain biological behaviors may be duplicated more effectively and at a significantly lower cost. The suggested model’s frequency can be increased using this technique (implemented on the Zynq board) at least by 3.6 times compared to the original model, and power consumption can be decreased by 28%. High-frequency design of neuronal models with low-cost attributes is required for application-based types of equipment in case of high-speed operations of these components. Thus, using our approach, the desired goals of application-based features are to be fulfilled. In addition, because the suggested model uses fewer hardware resources than the original model, it is feasible to construct a significantly higher number of neurons (approximately 5 times) on a single Zynq board.

The Adex neuron model is implemented in a system using the high-matching method under the name Digital-High-Matching Adex Neuron (D-HMAN). Comparing this model to all other comparable efforts, it has reduced implementation costs while accurately reproducing various spiking characteristics, similar to those of biological neurons. High computational performance, minimal hardware cost, and strong resemblance to the original model are all evidenced by experimental findings. In comparison to the original Adex neuron, the suggested D-HMAN version on FPGA uses a lot less hardware and also higher levels of speed and frequency (618.24 vs. 348.71 MHz in Zynq board). With this update, the original model’s temporal patterns of neuron firing and the ever-changing nature of neural activity are replicated by performing computations using affordable fixed-point calculations. For large-scale neuromorphic design aiming for low-cost hardware realization, this makes the suggested model an attractive contender. The suggested model is implemented on the large-scale level in a digital form as a case study.

The development of prostheses and treatments for illnesses and recovery has recently been centered on hardware modeling for various delicate biological components, including the nervous system, brain, eyes, and heart. The retina, being the thinnest and deepest layer of the eye, is of particular interest. In this study, we employ the Nyquist-Based Approximation of Retina Rod Cell (NBAoRRC) approach, which has been adapted to utilize Look-Up Tables (LUTs) rather than original functions, to implement rod cells in the retina using cost-effective hardware. In modern mathematical models, numerous nonlinear functions are used to represent the activity of these cells. However, these nonlinear functions would require a substantial amount of hardware for direct implementation and may not meet the required speed constraints. The proposed method eliminates the need for multiplication functions and utilizes a fast, cost-effective rod cell device. Simulation results demonstrate the extent to which the proposed model aligns with the behavior of the primary rod cell model, particularly in terms of dynamic behavior. Based on the results of hardware implementation using the Field-Programmable Gate Arrays (FPGA) board Virtex-5, the proposed model is shown to be reliable, consume 30 percent less power than the primary model, and have reduced hardware resource requirements. Based on the results of hardware implementation using the reconfigurable FPGA board Virtex-5, the proposed model is reliable, uses 30 % less power consumption than the primary model in the worth state of the set of approximation method, and has a reduced hardware resource requirement. In fact, using the proposed model, this reduction in the power consumption can be achieved.Finally, in this article, by using the LUT which is systematically sampled (Nyquist rate), we were able to remove all costly operators in terms of hardware (digital) realization and achieve very good results in the field of digital implementation in two scales of network and single neuron.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion.

Microstrip Diplexers play an important role in modern wireless communication systems. In this paper, a novel compact microstrip diplexer based on spiral cells is presented. The proposed resonator primarily consists of two spiral thin lines connected to a pair of coupled lines. This novel resonator is analyzed mathematically to find its behavior and tune the dimensions of the final layout easily. Using the analyzed resonator, two bandpass filters (BPFs) are designed. Then, a novel high-performance microstrip diplexer is obtained by designing and integrating these two BPFs. The center frequencies of the first and second channels of the proposed diplexer are 1.86 GHz and 4.62 GHz, respectively. The proposed diplexer boasts a remarkably small size of 0.004 λg2 and features flat channels with low insertion losses of only 0.048 dB and 0.065 dB for the first and second channels, respectively. The maximum group delays of S21 and S31 are 0.31 ns, 0.86 ns, respectively, which are good values for a modern communication system. Meanwhile, inside its passbands for some frequency ranges, its group delays are negative. Thus, using this diplexer can decrease the signal dispersion. The 1st and 2nd passbands are wide with 47.3% and 47.1% fractional bandwidths (FBW), respectively. Therefore, this diplexer can be easily and successfully used in designing high-performance RF communication systems.