This study proposes a compact Vivaldi-based phased array antenna (PAA) for the non-destructive detection of internal defects in tree trunks at varying heights. The proposed PAA comprises four specially designed Vivaldi elements operating from 0.6 to 1.4 GHz, enabling deep penetration into tree trunks while maintaining high resolution. To achieve a compact design with a narrow inter-element spacing of 0.4 and reduced mutual coupling, the outermost elements are slightly inclined, where is the wavelength at the center frequency. Additionally, a slotted metal reflector is placed behind the elements to improve impedance matching at low frequencies. The array is excited using Wilkinson power dividers to ensure stable amplitude and phase distribution. The measurement results show that the proposed PAA can steer its beam from -30 degrees to 30 degrees while maintaining an active voltage standing wave ratio (VSWR) below 2.5. It achieves a gain of 12-13 dBi while steering the beam. These characteristics, including a wide bandwidth, high gain, compact size, and beam-steering capability, make the proposed PAA highly suitable for scanning tree trunks and detecting defects at different heights. The proposed PAA was further validated on a real tree trunk, confirming its effectiveness for defect detection in practical scenarios.
Arithmetic circuits are the basic building blocks of frequently used computational systems. As the functions used in the computational system increase, the design constraint effects are proportional to the requirement. Approximate computing is a technique with the advantage of controlling design constraints by trading off accuracy. Approximation has major challenges in image processing, speech processing, and machine learning. So, an image-processing blending application, noise removal, or changing the transparency of images are designed using arithmetic circuits like adders and subtractors, respectively. Here, an 8-bit Dadda multiplier and a restoring divider are designed using approximate compressors and subtractors, respectively. The approximate full adders and full subtractors are implemented using tabular manipulation methods and logic-level pruning. The full adders are replaced either in the MSB or the LSB positions for better accuracy and error analysis. Image processing blending application, and background removal are performed using MATLAB, and design constraints are evaluated using Synopsys DC Compiler. The results show the logic-level pruning compressors, multipliers, and restoring dividers get a 40%-70% gain in PDP.
This investigation proposes a mathematical framework for the drain current as well as short-channel effects of an n-type heterojunction gate-all-around (GAA) nanowire tunnel field-effect transistor (TFET) architecture that incorporates a core-insulator within the channel (HJ-CI-GAA-NW-TFET). An analytical model for drain current is formulated employing Kane's interband tunneling model in conjunction with the shortest tunneling length approach. To derive the drain current and SCEs, numerical integration of the tunneling generation rate over the entire device volume is performed. The influence of variations of gate work function, diameter of the core-insulator, gate oxide thickness, and source doping concentration on several electrical parameters is systematically evaluated. Furthermore, the drive current is computed at a scaled channel length of 10 nm and under different gate-source voltages (V GS) spanning from 0.8 to 1 V. The analytical model developed yields results that agree well with Silvaco 3D TCAD simulations, reinforcing the validity of the analysis with just a 7% marginal deviation for different device parameters.
Behavioral models for Power Amplifier (PAs), particularly those based on Neural Networks (NNs), often suffer from high computational complexity when handling wideband signals. In this paper, we present a novel behavior model for wideband GaN PAs based on a Two-Sided Temporal Convolutional Neural Network (TST-CNN). The model uses a specialized convolutional layer to process the input data in order to extract the essential basis functions needed to accurately capture the dynamic memory effects and static nonlinearities of the PA. These enhanced features are then fed into a Fully Connected (FC) layer to establish the predictive model. Due to the efficient feature extraction enabled by the convolutional structure, the proposed architecture successfully handles strong memory effects without a significant increase in model complexity. The experimental results, obtained at a carrier frequency of 2.14 GHz using a 100 MHz wideband signal emulating 5th Generation (5G) wireless technologies, demonstrate that the TST-CNN outperforms current state-of-the-art models by achieving an Adjacent Channel Power Ratio (ACPR) of -52.1 dB. Furthermore, the TST-CNN requires only 237 coefficients, resulting in a high reduction in computational complexity compared with existing approaches, which makes it a very effective solution for future wideband communication systems.
This paper presents a developed iterative algorithm based on a Time-Domain Electric Field Integral Equation (TD-EFIE) formulation to analyze electromagnetic coupling in antenna systems. The primary objective is to determine the optimal immunity distance d that ensures mutual electromagnetic compatibility (EMC) between two antennas when one is subjected to atransient electromagnetic field. The proposed methodology employs the Method of Moments (MoM) in the time domain, utilizing Laguerre polynomials as temporal basis functions for stable recursive solution through a Marching-On-in-Degree (MOD) scheme. Space hybrid meshing techniques are applied to accurately model complex geometries. The core functionality of the algorithm consist in iteratively solving a coupled matrix system to compute transient induced currents, and comparing them with threshold values to determine the minimal safe separation distance. Validation through case studies-coupling between parabolic antennas and between a parabolic and a wire antenna demonstrates strong agreement with commercial simulator FEKO, confirming the algorithm's accuracy and computational efficiency. An evaluation of the proposed algorithms is conducted with respect to their computational complexity, CPU requirements, efficiency, and residual error. The resulting matrix system exhibits a computational complexity of where N and M denote spatial and temporal degrees of freedom, respectively. Compared with simulations performed using FEKO, the proposed algorithm achieves up to 40 & times; reduction in CPU time while maintaining strong accuracy agreement.
