Abstract: Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the ...

Abstract: Federated learning (FL) has emerged as a popular distributed machine-learning paradigm. It involves many rounds of iterative communication between nodes to exchange model parameters. With ...

Abstract: As 5G networks proliferate globally, the need for accurate, reliable, and scalable positioning solutions has become increasingly critical across industries, such as Internet of Things (IoT), ...

Abstract: Typically, deep network-based full-reference image quality assessment (FR-IQA) models compare deep features from reference and distorted images pairwise, overlooking correlations among ...

Abstract: It is my big honor to be appointed as the new Editor-in-Chief of IEEE Transactions on Engineering Management (IEEE-TEM). In this editorial article, some updates of the journal are presented.

Abstract: This article proposes a novel speed observer-based sensorless control strategy for permanent magnet synchronous motors (PMSMs) operating in the low-speed range. First, a stable sliding mode ...

Abstract: Modular Self-Reconfigurable Robots offer exceptional adaptability and versatility through reconfiguration, but traditional rigid robot designs lack the compliance necessary for effective ...

Abstract: A SAR architecture is proposed that employs a predictive technique to increase the conversion speed. In this new technique, the comparator operates in parallel with the logic and DAC, ...

Abstract: Extreme events can interrupt both electricity and gas supply in an integrated electric-gas distribution system (IEGDS). This work proposes a two-stage resilient preparation and restoration ...

Abstract: In this paper, we investigate a novel integrated sensing and communication (ISAC) system aided by movable antennas (MAs). A bistatic radar system, in which the base station (BS) is ...

Abstract: For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The ...

Abstract: Dynamic multimodal optimization problems (DMMOPs) represent the multimodal optimization problems that the optimal solution changes over time. Due to the wide application of DMMOPs in reality ...