This study presents a high-efficiency circular microstrip patch antenna designed for radio frequency identification (RFID) applications simulation results illustrate the performance of a circular microstrip patch antenna operating at 900 MHz. Microstrip antennas are renowned for their ability to meet the requirements of compact, lightweight designs, ensuring compatibility, and ease of integration. This research focuses on the development of a circular microstrip antenna, formed as a circular patch on a 0.035 mm thick FR-4 substrate. The design was realized using a substrate with a relative permittivity (εr) of 4.3, a loss tangent (tan δ) of 0.021 and a substrate height (h) of 1.6 mm. The antenna dimensions are small, measuring 58×45 mm, with a circular patch radius of 17 mm. The antenna operates over a frequency range from 0.5 GHz to 2 GHz. Key performance parameters include a return loss of -49.8 dB, a wide bandwidth of 150 MHz, a voltage standing wave ratio (VSWR) of 1.009, a gain of 2.161 dB, and a directivity of 2.200 dBi. Antenna design and simulation were carried out using computer simulation technology (CST) Studio Suite Software, specifically adapted to RFID applications.

This paper presents NeuroAdaptiveNet, an FPGA-based neural network framework that dynamically self-adjusts its architectural configurations in real time to maximize performance across diverse datasets. The core innovation is a Dynamic Classifier Selection mechanism, which harnesses the k-Nearest Centroid algorithm to identify the most competent neural network model for each incoming data sample. By adaptively selecting the most suitable model configuration, NeuroAdaptiveNet achieves significantly improved classification accuracy and optimized resource usage compared to conventional, statically configured neural networks. Experimental results on four datasets demonstrate that NeuroAdaptiveNet can reduce FPGA resource utilization by as much as 52.85%, increase classification accuracy by 4.31%, and lower power consumption by up to 24.5%. These gains illustrate the clear advantage of real-time, per-input reconfiguration over static designs. These advantages are particularly crucial for edge computing and embedded applications, where computational constraints and energy efficiency are paramount. The ability of NeuroAdaptiveNet to tailor its neural network parameters and architecture on a per-input basis paves the way for more efficient and accurate AI solutions in resource-constrained environments.

Blind Source Separation (BSS) is termed as the extraction of source signals from mixed data without or with little knowledge about the source signals. It is used in many fields of research such as military, medical and industry. Among the many challenges that must be overcome to apply BSS to a real system, implementation restrictions are the most difficult. Software implementations require a significant amount of overhead to make them applicable to real-time systems and have a lower maximum speed of operation than hardware implementations. When speed is desired, the algorithm should be implemented on specialized hardware such as a Field Programmable Gate Array(FPGA) which allows the user to take advantage of parallel computational abilities. This paper describes the methodology for implementing real-time DSP applications on FPGA and the concept of hardware software cosimulation of the Second Order Blind Identification (SOBI) algorithm using MATLAB Simulink and Xilinx System Generator (XSG). Performances of efficient architectures are implemented on FPGA ZYBO Z7 Zynq-7020.

High-efficiency rectifier achieves 63% power conversion in low start-up voltage for UHF RFID tags in 180 nm CMOS technology

This paper proposes a robust control approach for autonomous vehicle path-tracking based on sideslip angle estimation. First, artificial intelligence techniques, namely artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), are employed for sideslip angle estimation. The optimal model structure is determined based on statistical error metrics, including mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE). Next, the controller is designed using a Takagi–Sugeno fuzzy multi-model combined with a Line Integral Lyapunov Function (LILF) and H∞ performance, leading to a convex optimization problem formulated as linear matrix inequalities (LMI), from which optimal gains are obtained. The approach is validated through MATLAB/Simulink and CarSim co-simulations under various scenarios and compared with Sliding Mode Control (SMC), LQF-based control, and PID methods. Results show that the ANFIS-based method achieves better accuracy and robustness across different conditions.