Designing a Ring Oscillator Using Nanotechnology through Cadence Virtuoso

This paper presents the design and simulation of a ring oscillator using nanotechnology and the Cadence Virtuoso platform. As feature sizes continue to shrink, new design methodologies are required to account for quantum effects that become prominent at the nanoscale. This paper utilizes predictive technology models for a 45nm process to design a three-stage ring oscillator with minimum channel lengths. The ring oscillator design is optimized through careful selection of transistor characteristics and layout considerations. Post-layout simulations demonstrate functionality with oscillation frequency and phase noise matching expected theoretical values. The completed design provides a demonstration of a basic analog circuit block implemented with nanoscale technology. &nbsp

Federated Learning for the Internet-of-Medical-Things: A Survey

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations