With the shrinking of the technology nodes to deep submicron regime, billions of transistors are integrated on a single silicon chip. These higher levels of integration lead to exponential increase in the power dissipation. Hence, the researchers found different alternative design methodologies to reduce energy dissipation. Among several alternatives adiabatic logic is the most prominent design style to minimize the wastage of energy by recycling the energy. However, these adiabatic logic circuits exhibits hardware redundancy due to their dual rail nature and are explored towards hardware security. At the same time reliability is also playing a major role in designing digital VLSI circuits. The reliability is considered with respect to PVT variations and transient faults. However, their impact on the digital circuit reliability due to vulnerability to soft errors is not investigated systematically: 80% of the hardware faults are transient faults and arise during the run time of the circuit. So, in this work the reliability of different FinFET-based adiabatic logic families is evaluated by examining the impact of PVT variations by changing the temperature in the range of 0-100°C, amplitude of power clock by ± 30%VDD, and Fin thickness (Tfin) 10-30 nm. Further transient faults are injected at sensitive nodes to analyze resilience of adiabatic logic families with respect to faults. An algorithm is formulated for the step by step process. Simulation results revealed that among all the families considered, ECRL and MPFAL are capable to withstand more than 90% of the transient faults injected.
This paper presents the design and analysis of a microstrip patch antenna optimized for terahertz frequency operation, utilizing polytetrafluoroethylene as the substrate material. The proposed antenna achieves a main lobe magnitude of 20.2 dBV, signifying high directivity and efficient radiation performance. CST Microwave Studio-2019 software was employed to analyze the antenna parameters. A substrate etching technique was implemented to create split-surface resonant rings (SSRRs) on the substrate's surface. The performance was initially evaluated with a single SSRR etched on the substrate and subsequently enhanced by etching two SSRRs. The final simulation results exhibit exceptional return loss values at triple-band frequencies: -68.87 dB at 3.5 THz, -41.35 dB at 1.3 THz, and -37.63 dB at 1.7 THz. These outcomes affirm the antenna’s suitability for high-frequency terahertz applications.
This work evaluates the performance gain of a reconfigurable intelligent surface (RIS)-assisted massive multiple-input multiple-output (M-MIMO) cooperative non-orthogonal multiple access (C-NOMA) system under time-selective Nakagami-m fading channels, focusing on symbol error rate (SER) and outage probability (OP) in high-mobility environments. A baseline user-pairing strategy reveals that RIS phase shift cooperative relaying significantly enhances the end-to-end reliability of the far users compared to the traditional NOMA networks. However, performance is still limited due to the presence of the residual successive interference cancellation (SIC) and channel state information estimation errors. Simulation results demonstrate the significant impact of fading severity and Doppler-induced spectral broadening on the reliability of RIS-NOMA networks, exposing the constraints of conventional optimization-based and single-model deep-learning (DL) methodologies. To mitigate these problems, a convolutional neural network long short-term memory (CNN-LSTM) hybrid framework is considered. The proposed framework integrates CNNs for spatial feature extraction with LSTM units to capture temporal dependencies in time-selective fading channel conditions. Through extensive simulations, the results confirm that the proposed scheme outperforms conventional NOMA, stacked long short-term memory (S-LSTM), and bidirectional long-short term memory (Bi-LSTM) DL benchmarks. Specifically, it attains a near-user and far-user SER reduction of up to 3.5 × 10–3 and 1.39 × 10–2 at 20 dB SNR, respectively, while consistently attaining the minimal OP across all conditions. These outcomes indicate that DL-enabled spatiotemporal modeling, coupled with RIS-assisted beamforming, provides a robust, scalable, and energy-efficient solution for fifth-generation (5G) and beyond 5G (B5G) networks. The proposed framework is well-suited for time selective channel, high-speed vehicular communications and mission-critical applications defence application, facilitating the design of intelligent, low-latency, and resilient SIC estimators.
A single element, high directive gain is presented for penta-band mm wave applications. The dimensional size of the antenna is 12 × 20 × 0.8 mm3, which is designed and simulated on rogers 5880 with constant of 2.2. The five working bands of antenna are 20.7-21.3 GHz, 27.6-28.4 GHz, 33.8- 34.6 GHz, 35.6-36.9 GHz, 42.4-3.8 GHz. The applications of bands include satellite broadcasting (21GHz), mm wave communication (28 GHz), radio location (32 GHz), radar communication (36 GHz), and 5G & 6G wireless communications (43 GHz), respectively. And, the peak gain values of the working bands are 8.29, 7.03, 11.36, 8.14, and 7.82 dBi, these are at above-mentioned deep resonant frequencies. The high directive gain values are obtained with proper dimensional values of the structure and full ground. The E (co & cross) & H (co & cross) fields and 3D polar plots are studied and analyzed. The optimized values are evaluated with parametric analysis. The current distribution on the ground and top layers at various frequencies are also presented.
Nonorthogonal multiple access (NOMA) is a key multiple-access technique for 5G and beyond, enabling simultaneous multiuser transmission via power-domain multiplexing. However, conventional power domain NOMA often relies on fixed or slowly adaptive power-control updates. It increases power-adjustment latency and leads to inefficient interference handling and throughput loss under time-varying channels. This paper proposes a latency time improvement (LTI) framework that utilizes received signal strength indicator-driven feedback to guide adaptive multiphase power updates and reinforcement learning for long-term power control decisions. Users are grouped via K-means clustering; a two-stage coarse-fine adjustment reduces overshoot and correction delay, and tabular Q-learning selects power actions to accelerate convergence to signal-to-interference-plus-noise ratio targets. Simulation results under fading channels show that LTI reduces power-adjustment latency by 20%-30% while improving spectral efficiency, interference behavior, and bit error rate compared with classical power domain NOMA baselines.
Assessing car damages from an accident is a crucial process in the car insurance industry. At present, this task requires a manual inspection of each component. However, it is anticipated that smart devices will perform these evaluations more efficiently in the future. To address this, our research introduces an efficient YOLOv9 model featuring two novel modules. The RepNCSPELAN4 module splits input from initial convolutional layer into two paths and is processed through RepNCSP and convolutional layers before merging. This dual-path strategy enhances gradient flow and feature reuse, improving learning efficiency. The SPPELAN module utilizes the convolutional layer for channel adjustment and is followed by spatial pooling operations to capture multi-scale contextual information. This concatenates outputs and consolidates features through another convolutional layer, optimizing detailed feature extraction across spatial hierarchies. Experimental validation on the car parts dataset demonstrates superior performance, with our model achieving higher mAP (67.7%) on object detection and mAP (65.8%) on segmentation detection.
