This paper introduces a novel tunable oscillator-based impulse radio ultra-wideband transmitter, implemented in 28 nm HPC TSMC CMOS technology. The transmitter employs a differential ring voltage-controlled oscillator to achieve wide tuning across low and high-frequency bands, ensuring compliance with FCC, ECC, Japan's spectrum masks, and IEEE 802.15.6 standard. Operating at a data rate of 40 Mbps, it features low power consumption below 1 mW and an ultra-compact design footprint of just 0.0019 mm 2 , making it ideal for neural recording implants. The transmitter achieves energy efficiencies of 2.75% and 1.52% in the lower and upper bands, respectively, with output power of approximately -15 dBm and -17.8 dBm. Additionally, it attains a Figure-of-Merit of 17.4 and 9.5 (1/ mm 2 ) in the upper and lower bands, respectively, highlighting its superior area and power efficiency.
The Multiple-Value Logic (MVL) circuit significantly enhances propagation delay and energy consumption compared to the binary circuit. Additionally, the Carbon Nanotube Field-Effect Transistor (CNTFET) demonstrates enhancements in energy efficiency and circuit speed when compared to other transistor technologies. Therefore, this paper proposes two different designs of Ternary Decoders (TDecoders) using CNTFET, which can create additional ternary circuits such as ternary adders, multipliers, and multiplexers. The investigation and proposed TDecoders were simulated and tested with the HSpice simulator. The simulation results demonstrate the approach's merits in reducing energy consumption by over 65% and 10% compared to the standard design and the best-case design, respectively.
This paper presents a novel MEMS and ASIC-based sensor system for simultaneous detection of magnetic fields and patient movement. The prototype system integrates a three-channel current-reuse amplifier and three MEMS cantilevers on independent PCBs. The amplifier, fabricated in 350 nm CMOS technology, exhibits a closed-loop gain of 10 V/V, a low power consumption, and a noise floor of 18 nV/root Hz. In combination with three MEMS cantilevers (3 mm x 1 mm) in a 3D-configuration this setup is able to detect vibrations and movement. The cross-axis sensitivity of this setup was measured as 20% for one directional vibration on a shaker. The system successfully detected the 3D-acceleration of human footsteps.
The quality of training data is a critical determinant of the performance, reliability, and predictive accuracy of any AI model. Federated learning provides a promising framework for developing global AI models in distributed computing environments, where individual data nodes can maintain data privacy while contributing to a shared model. Although each node can locally enhance the quality of its data, the distributed nature of the system does not guarantee global data quality. One of the main challenges arises from heterogeneity introduced by federated learning is data quality, which often result in global data degradation. Despite the increasing focus on the challenge of Non Independently and Identically Distributed (NIID) datasets, data quality is often regarded as a subset of this problem and not fully explored in its own right. In this paper, we introduce a comprehensive node scoring approach designed to enhance quality awareness without relaying on a shared reference dataset. The scoring approach focuses on assessing global data quality with respect to variations in data volume, label imbalance, global duplicates, and label preference skew. The proposed Quality scoring is utilized to either determine nodes respective weights in Fedq aggregation method. It is used also in sorting and sampling nodes for further selections. To evaluate the proposed node scoring method, the paper introduces a simulation model based on the MNIST dataset. The introduced model simulates extreme low level of data quality nature of the federated data and uses this data to evaluate the proposed scoring approach versus the baseline FedAvg and Irrelevance score in terms of global model accuracy, model stability and model convergence with particular attention to the content diversity score, which quantifies global duplicity.
This abstract provides an overview of an article focusing on the integration of the Internet of Vehicles (IoV) with electric vehicle (EV) charging infrastructure to enhance transportation sustainability and efficiency. It emphasizes the importance of efficient EV charging planning in identifying suitable charging stations while addressing constraints such as State-of-Charge (SOC) and driving direction. The proposed IoV-based scheduling scheme aims to minimize travel distance and charging costs, meet EV charging time requirements, and optimize charging station resource utilization. The study employs artificial neural networks to determine the optimal charging moment, with simulations conducted using Matlab-Simulink and a DSP board for trained data. Morocco is selected as the study area for this research.
