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Volume 15
Issue 3
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J. Low Power Electron. Appl., Volume 15, Issue 3 (September 2025) – 3 articles

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17 pages, 4316 KiB  
Article
A Coverage Path Planning Method with Energy Optimization for UAV Monitoring Tasks
by Zhengqiang Xiong, Chang Han, Xiaoliang Wang and Li Gao
J. Low Power Electron. Appl. 2025, 15(3), 39; https://doi.org/10.3390/jlpea15030039 - 9 Jul 2025
Abstract
Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper [...] Read more.
Coverage path planning solves the problem of moving an effector over all points within a specific region with effective routes. Most existing studies focus on geometric constraints, often overlooking robot-specific features, like the available energy, weight, maximum speed, sensor resolution, etc. This paper proposes a coverage path planning algorithm for Unmanned Aerial Vehicles (UAVs) that minimizes energy consumption while satisfying a set of other requirements, such as coverage and observation resolution. To deal with these issues, we propose a novel energy-optimal coverage path planning framework for monitoring tasks. Firstly, the 3D terrain’s spatial characteristics are digitized through a combination of parametric modeling and meshing techniques. To accurately estimate actual energy expenditure along a segmented trajectory, a power estimation module is introduced, which integrates dynamic feasibility constraints into the energy computation. Utilizing a Digital Surface Model (DSM), a global energy consumption map is generated by constructing a weighted directed graph over the terrain. Subsequently, an energy-optimal coverage path is derived by applying a Genetic Algorithm (GA) to traverse this map. Extensive simulation results validate the superiority of the proposed approach compared to existing methods. Full article
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16 pages, 5447 KiB  
Article
A Gate Driver for Crosstalk Suppression of eGaN HEMT Power Devices
by Longsheng Zhang, Kaihong Wang, Shilong Guo and Binxin Zhu
J. Low Power Electron. Appl. 2025, 15(3), 38; https://doi.org/10.3390/jlpea15030038 - 6 Jul 2025
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Abstract
The eGaN HEMT power devices face serious crosstalk problems when applied to high-frequency bridge circuits, thereby limiting the switching performance of these devices. To address this issue, a gate driver is proposed in this paper that can suppress both positive and negative crosstalk [...] Read more.
The eGaN HEMT power devices face serious crosstalk problems when applied to high-frequency bridge circuits, thereby limiting the switching performance of these devices. To address this issue, a gate driver is proposed in this paper that can suppress both positive and negative crosstalk of eGaN HEMT power devices, offering the advantages of simple control and easy integration. The basic idea is to suppress positive crosstalk by constructing a negative voltage capacitor, and to suppress negative crosstalk by reducing the impedance of the gate loop. To verify the capability of the proposed gate driver, double-pulse and synchronous Buck test platforms are constructed. The experimental results clearly demonstrate that the proposed gate driver reduces the positive and negative crosstalk spikes by 2.03 V and 1.54 V, respectively, ensuring that the positive and negative crosstalk spikes fall within a safe operating range. Additionally, the turn-off speed of the device is enhanced, leading to a reduction in switching loss. Full article
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29 pages, 2379 KiB  
Article
An Analog Architecture and Algorithm for Efficient Convolutional Neural Network Image Computation
by Jennifer Hasler and Praveen Raj Ayyappan
J. Low Power Electron. Appl. 2025, 15(3), 37; https://doi.org/10.3390/jlpea15030037 - 25 Jun 2025
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Abstract
This article presents an energy-efficient IC architecture implementation of an analog image-processing ML system, where the primary issue is analog architecture development for existing energy-efficient analog computing devices. An architecture is developed for image classification, transforming a typical imager input into a classified [...] Read more.
This article presents an energy-efficient IC architecture implementation of an analog image-processing ML system, where the primary issue is analog architecture development for existing energy-efficient analog computing devices. An architecture is developed for image classification, transforming a typical imager input into a classified result using a particular NN algorithm, a convolutional NN (ConvNN). These efforts show the need to continue to develop energy-efficient analog architectures alongside efficient analog circuits to fully exploit the opportunities of analog computing for system application. Full article
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