Background: In computed tomography (CT), the spot geometry is one of the main sources of error in CT images. Since X-rays do not arise from a point source, artefacts are produced. In particular there is a penumbra effect, leading to poorly defined edges within a reconstructed volume. Penumbra models can be simulated gi...
Audrey Kueh, J. W. Warnett, Gregory J. Gibbons et al.· Warwick Research Archive Por...· 0 citations
Background: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-ge...
Modern Cyber-Physical Systems (CPS) demand high computational capability, strong security guarantees, and energy-aware operation, particularly in embedded, mobile, and autonomous platforms such as robotics, intelligent vehicles, and industrial controllers. Addressing these requirements calls for heterogeneous System-on...
With the rapid advancement and deep integration of the Internet of Things (IoT) and 5G technologies, mobile edge computing (MEC) has undertaken an increasingly important role in enhancing service quality. Leveraging their high mobility and flexible deployment, unmannedaerial vehicles (UAVs) extend MEC services to chall...
Zhao Tong, Shi-Yan Zhang, Jing Mei et al.· IEEE Transactions on Mobile...· 0 citations
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Many real-world problems involve hierarchical multi-objective optimization over coupled sequential decisions. A common strategy is to combine heuristic planning with deep reinforcement learning (DRL). While existing hybrid methods often show strong empirical performance, the coupled closed-loop learning dynamics betwee...
Wei Zhao, Kai Wang, Xiangyu Liu et al.· IEEE Transactions on Mobile...· 0 citations
Hierarchical federated learning (HFL) emerges as a promising solution for distributed systems in real-world IoT, compensating for the limitations of client and training data through multilevel aggregation. However, severe system heterogeneity, such as data heterogeneity, differentiated communication and computing power...
D. Wu, Changxin Lu, Zhi-Yuan Zhu et al.· IEEE Transactions on Mobile...· 0 citations
Artificial Intelligence-Generated Content is reshaping interactive content creation. However, provisioning diffusion-based interactive denoising services in mobile edge networks remains challenging, due to the demanding computation and communication resources. To address these challenges, in this paper, we investigate...
Yu-Xin Liang, Peng Yang, Zi-Qi Zhou et al.· IEEE Transactions on Mobile...· 1 citation
The exponential growth of edge devices and growing demand for low-latency, high-throughput applications have established edge computing as essential infrastructure. Edge computing enables near-data computation to alleviate device burdens, yet task offloading optimization remains challenging due to the heterogeneous, dy...
Zhao Tong, Shi-Zhen Xiao, Xi Zhang et al.· IEEE Transactions on Mobile...· 0 citations
Container-based microservices provide a lightweight and scalable foundation for edge computing, where layer sharing improves storage and throughput efficiency. However, the unpredictable nature of edge networks leads to variable layer download times and thus bandwidth uncertainty. This article addresses the problem of...
Zeynab Kabiri, Mahdi Dolati, Seyed Hamed Rastegar et al.· IEEE Internet of Things Jour...· 0 citations
As edge computing nodes and platform-class devices such as industrial gateways become increasingly prevalent, the x86-64 architecture is widely used in these deployments, making memory-safety protection against vulnerabilities in x86-64 commercial off-the-shelf (COTS) binaries a critical security requirement. However,...
Shi-Long Zhang, Ying-Ying Li, Guo-Qing Peng et al.· IEEE Internet of Things Jour...· 0 citations
Uncrewed aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a promising paradigm for future wireless communication systems by providing flexible and efficient computation offloading services to resource-limited ground user devices (UDs). However, the nonuniform user distribution and mobility, heter...
Jiahui Chai, Yun-Zhu Meng, Hao-Chuan Yue et al.· IEEE Internet of Things Jour...· 0 citations
The Federated Multi-Armed Bandit (FMAB) framework is proposed to facilitate collaborative model training in cloud-edge environments. Most existing FMAB-based systems assume that participants have personal datasets. This assumption becomes biased in scenarios with limited local data or when the discrepancy between histo...
Hang-Fan Li, Yang Xu, Yi-Bin Cai et al.· IEEE Transactions on Mobile...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
