FedViT: Federated continual learning of vision transformer at edge

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral part of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge...

Integrating the flow regime and water quality effects into a niche-based metacommunity dynamics model for river ecosystems

Aquatic community dynamics are closely dominated by flow regime and water quality conditions, which are increasingly threatened by dam regulation, water diversion, and nutrition pollution. However, further understanding of the ecological impacts of flow regime and water quality conditions on aquatic multi-population dynamics has rarely been...

FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge devices....

EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods...

An Output Bandwidth Optimized 200-Gb/s PAM-4 100-Gb/s NRZ Transmitter With 5-Tap FFE in 28-nm CMOS

This article presents a 200-Gb/s pulse amplitude-modulation four-level (PAM-4) and 100-Gb/s non-return-to-zero (NRZ) transmitter (TX) in 28-nm CMOS technology. To achieve the target data rate, the output bandwidth and swing of the proposed TX are optimized by minimizing the output capacitance of the 4:1 multiplexer (MUX) and driver stage with...

Large Region Targets Observation Scheduling by Multiple Satellites Using Resampling Particle Swarm Optimization

The last decades have witnessed a rapid increase of Earth observation satellites (EOSs), leading to the increasing complexity of EOSs scheduling. On account of the widespread applications of large region observation, this article aims to address the EOSs observation scheduling problem for large region targets. A rapid coverage calculation...

An online data driven fault diagnosis and thermal runaway early warning for electric vehicle batteries

Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early...

Federated Learning With Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and...

A Ring-Oscillator Sub-Sampling PLL With Hybrid Loop Using Generator-Based Design Flow

We present a ring-oscillator-based sub-sampling phase-locked loop (PLL) using a generator-based design flow. A hybrid loop with a delta-sigma ($\Delta \Sigma$) modulator is applied to reduce the loop filter (LF) area and the control ripple. The generator automatically produces the ring oscillator and PLL to meet the provided specifications. The...

EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Edge-cloud jobs are rapidly prevailing in many application domains, posing the challenge of using both resource-strenuous edge devices and elastic cloud resources. Efficient resource allocation on such jobs via scheduling algorithms is essential to guarantee their performance, e.g. latency. Deep reinforcement learning (DRL) is increasingly...

Lightweight and Accurate DNN-Based Anomaly Detection at Edge

Deep neural networks (DNNs) have been showing significant success in various anomaly detection applications such as smart surveillance and industrial quality control. It is increasingly important to detect anomalies directly on edge devices, because of high responsiveness requirements and tight latency constraints. The accuracy of DNN-based...

Temperature-sensing performance of polymer-derived SiAlCN ceramics up to 1000 °C

Temperature sensors that can operate in high-temperature and harsh environments are highly desired. However, this is a great challenge for sensing materials to operate under extreme working conditions because of oxidation and/or corrosion at high temperature. In this study, polymer-derived SiAlCN ceramics were prepared as sensing materials to...

A Looseness Detection Method for Railway Catenary Fasteners based on Reinforcement Learning Refined Localization

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too...

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging....

Knitting 4D garments with elasticity controlled for body motion

In this paper, we present a new computational pipeline for designing and fabricating 4D garments as knitwear that considers comfort during body movement. This is achieved by careful control of elasticity distribution to reduce uncomfortable pressure and unwanted sliding caused by body motion. We exploit the ability to knit patterns in...

Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers

Cluster schedulers provide flexible resource sharing mechanism for best-effort cloud jobs, which occupy a majority in modern datacenters. Properly tuning a scheduler's configurations is the key to these jobs' performance because it decides how to allocate resources among them. Today's cloud scheduling systems usually rely on cluster operators...

SlimML: Removing Non- critical Input Data in Large-scale Iterative Machine Learning

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data...

Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for...