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On-chip integration of GaN-based metal oxide semiconductor field effect transistor and ultraviolet light-emitting diode 21

On-chip integration of GaN-based metal oxide semiconductor f...

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21st China International Forum on Solid State Lighting and 10th International Forum on Wide Bandgap Semiconductors, SSLCHINA: IFWS 2024

作者： Ding, Yuling Yao, Xin Piao, Jinlong Wang, Yanzhe Wang, Fang Niu, Youtian Liu, Yuhuai Yan, Jiabin Ye, Ziqi Wang, Linning Liu, Pengzhan Wang, Yongjin College of electronic and electrical engineering School of Physics Henan Normal University Henan Xinxiang453007 China School of Physics Henan Normal University Henan Xinxiang453007 China National Center for International Joint Research of Electronic Materials and Systems International Joint-Laboratory of Electronic Materials and Systems of Henan Province Institute of Intelligence Sensing School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou450001 China GaN Optoelectronic Integration International Cooperation Joint Laboratory of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9798331541149

This paper presents an effective on-chip integration of a GaN-based metal-oxide-semiconductor field-effect transistor (MOSFET) and an ultraviolet light-emitting diode (UV LED) on a GaN-on-Si LED epitaxial (epi) wafer, designed and simulated using Silvaco TCAD. In the proposed structure, the drain of the MOSFET and the cathode of the UVLED share the same n-AlGaN layer, effectively reducing parasitic effects and improving device reliability and stability. The simulated MOSFET exhibits a maximum output current of 121 mA/mm and a peak transconductance (Gm) of 18.92 mS/mm. The designed UV LED emits at a wavelength of 392 nm with a threshold voltage of 3.1 V. Simulation results indicate that our MOSFET can effectively modulate the light output and output current of the integrated UV LED. This approach offers promising potential for applications in UV light communication and biomedicine. ©2024 IEEE.

关键词： Gallium nitride

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An Estimation Method of Fragmentation in Blast Muckpiles using Local and Global Modules of Deep Learning Based on 3D Point Clouds

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International Journal of the Society of Material engineering for Resources 2022年第1期25卷 78-84页

作者： Yoshino, Kohei Sato, Taiga Toriya, Hisatoshi Shishido, Hidehiko Hyongdoo, Jang Kawamura, Youhei Kitahara, Itaru Degree Programs in Systems and Information Engineering University of Tsukuba Ibaraki305-8577 Japan Graduate School of International Resource Sciences Akita University Akita010-8502 Japan Faculty of International Resource Sciences Akita University Akita010-8502 Japan Center for Computational Sciences University of Tsukuba Ibaraki305-8577 Japan Western Australian School of Mines: Minerals Energy and Chemical Engineering Curtin University Australia Division of Sustainable Resources Engineering Faculty of Engineering Hokkaido University Sapporo060-8628 Japan North China Institute of Science and Technology China

This paper proposes a method to estimate the particle (rock) size distribution of a muckpile using Deep Learning based on 3D shape information given by 3D photogrammetry. Optimization of blasting is crucial to increase the productivity of mining operations. However, since the internal structure of the ground (bedrock) is usually unknown, it is diffi cult to set the appropriate parameters, amounts of explosives, blasting location and timing. In order to solve the problem, research has been carried out to design and analyze the blasting procedures by measuring the particle size distribution after the blast. Ordinary works focus on developing accurate measurement methods of particle size distribution for the analysis. We aim to increase the measurement accuracy by combining 3D photogrammetry and Deep Learning for 3D shape data. The 3D muckpile model is generated using Structure from Motion (SfM), which is a reconstruction method that can generate 3D point clouds of the target object from multi-view images. The particle size distribution of muckpiles is estimated by using Deep Learning. The proposed network consists of "Local Module "that learns the local shape of rock and "Global Module "that learns the shape of the entire muckpile. From the experiments, it can be said that the fragmentation of the actual muckpiles can be eff ectively estimated by using the proposed method. ©2022 Soc. Mater. Eng. Resour. Japan.

关键词： Particle size analysis

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Performance Tradeoff Between Overhead and Achievable SNR in RIS Beam Training

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IEEE Transactions on Wireless Communications 2025年

作者： Laue, Friedemann Jamali, Vahid Schober, Robert Institute for Digital Communications Erlangen91058 Germany Fraunhofer Institute for Integrated Circuits IIS Division Communication Systems Germany Technical University of Darmstadt Department of Electrical Engineering and Information Technology Darmstadt64283 Germany

Efficient beam training is the key challenge in the codebook-based configuration of reconfigurable intelligent surfaces (RISs) because the beam training overhead can have a strong impact on the achievable system performance. In this paper, we study the performance tradeoff between overhead and achievable signal-to-noise ratio (SNR) in RIS beam training while taking into account the size of the targeted coverage area, the RIS response time, and the delay for feedback transmissions. Thereby, we consider three common beam training strategies: full search (FS), hierarchical search (HS), and tracking-based search (TS). Our analysis shows that the codebook-based illumination of a given coverage area can be realized with wide- or narrow-beam designs, which result in two different scaling laws for the achievable SNR. Similarly, there are two regimes for the overhead, where the number of pilot symbols required for reliable beam training is dependent on and independent of the SNR, respectively. Based on these insights, we reveal that the overhead for FS beam training can be significantly reduced by employing large RISs and wide beams. Moreover, we show that, depending on the RIS response time, feedback delay, and codebook size, FS beam training may outperform HS beam training. In addition, we derive an upper bound on the user velocity for which the overhead is generally negligible. Finally, we present numerical simulation results that verify our theoretical analysis. In particular, our results confirm the existence of the proposed SNR scaling laws and overhead regimes, demonstrate the benefits of wide beams and large RISs, reveal that fast RISs can lead to negligible overhead for FS beam training, and show that large feedback delays can significantly reduce the performance for HS beam training. © 2002-2012 IEEE.

关键词： Risk management

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Blockchain-Assisted UAV Data Free-Boundary Spatial Querying and Authenticated Sharing 26

Blockchain-Assisted UAV Data Free-Boundary Spatial Querying ...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Ya Lin, Xi Wu, Jun Pei, Bei Han, Yunyun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Collaborative Innovation Center of Shanghai Industrial Internet Shanghai China Waseda University Graduate School of Information Production and Systems Japan Ministry of Public Security Key Lab of Information Network Security Shanghai China Technology Co. Ltd Shanghai China

ISBN: (纸本)9798350331684

The flexibility and low cost of unmanned aerial vehicles (UAVs) offer great potential for them in areas such as disaster relief, energy line inspection, and traffic monitoring. Multiple UAVs form an airborne UAV network to share geo-tagged observation data for better collaborative missions. Blockchain can solve the security threats caused by the environment's untrustworthiness and the UAV networks' openness. However, the key to sharing data in blockchain-assisted UAV networks is identifying and understanding observational data and providing authentication query services in free boundary spatial. This paper proposes a blockchain-assisted UAV network data-sharing framework based on Non-Fungible Token (NFT). First, we design a marking and describing data method based on NFT to help geo-tagged data be effectively understood. Moreover, we propose a free-boundary spatial index tree to manage data and provide efficient queries. Furthermore, combined with the consensus mechanism and the blockchain transaction tree, the proposed sharing framework can provide query results authentication. Compared with the existing schemes, analysis and experiments demonstrate that our scheme could support the arbitrary expansion of UAV flight range and the random distribution of observation data in space, save at least 22% of storage overhead and reduce more than 36% of query time overhead. © 2023 IEEE.

关键词： Blockchain

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Superconducting isolators based on time-modulated coupled-resonator systems

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Physical Review Applied 2024年第5期21卷 054061-054061页

作者： Yi Zhuang Chandrashekhar Gaikwad Daria Kowsari Kater Murch Aravind Nagulu Department of Electrical and Systems Engineering Department of Physics Department of Physics & Astronomy Dornsife College of Letters Arts & Sciences Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA

We present a unified approach for designing isolators based on temporally modulated coupled resonator networks. Our method leverages standard superconducting quantum interference device–based resonators as building blocks, arranged in series-coupled resonators to realize a wide range of on-chip nonreciprocal devices. The devices operate by translating microwave power into out-of-band intermodulation products in a direction-dependent way. Our theoretical studies demonstrate the effectiveness of the proposed approach, achieving isolators with near-zero insertion losses and isolation greater than 20 dB. To validate our findings, we implemented and measured a series-coupled three-resonator superconducting isolator using a single-layer superconducting process. At a base temperature of 20 mK, our device exhibited an insertion loss of 1.3 dB in the forward direction and isolation of up to 25 dB at the center frequency and greater than 15 dB across a bandwidth of 250 MHz in the reverse direction. Our approach promises to enable the design of a broad range of high-performance nonreciprocal devices for superconducting circuits.

关键词： Non-reciprocal propagation Optoelectronics Quantum circuits Quantum control Quantum information with solid state qubits Superconducting devices Cavity resonators Microwave techniques

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Unleashing the Potential of Reinforcement Learning for Enhanced Personalized Education 53

Unleashing the Potential of Reinforcement Learning for Enhan...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Fernandes, Chelsea William Miari, Tahereh Rafatirad, Setareh Sayadi, Hossein California State University Department of Computer Engineering and Computer Science Long BeachCA United States Center for Information Systems and Technology Claremont Graduate University CA United States University of California Department of Computer Science DavisCA United States

ISBN: (纸本)9798350336429

Providing personalized content that meets the diverse needs of learners is a significant challenge for today's learning systems. Current educational platforms lack the capability to effectively support the needs of learners with varying intellectual abilities, learning pace, preferences, and academic backgrounds. To overcome this challenge, there is a pressing need for sustainable educational tools that can adapt to the individual needs of student learners. Our study proposes Reinforced-EDU, an intelligent and effective reinforcement learning-guided framework based on Q-Learning technique. The framework adaptively schedules course assignments and educational activities based on students' characteristics, preferences, skills, and academic backgrounds. It considers different student-centered academic factors to prescribe a suitable learning plan that maximizes the students' overall grade and satisfaction rate while reducing dropouts. We identify four distinct learning modes tailored towards effective logical reasoning that align best with the students' preferences and characteristics. Our proposed intelligent method, Reinforced-EDU, can dynamically assign an optimal learning path with 95% accuracy based on students' preferences and pre-knowledge. © 2023 IEEE.

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A tree detection method based on trunk point cloud section in dense plantation forest using drone Li DAR data

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Forest Ecosystems 2023年第1期10卷 37-45页

作者： Yupan Zhang Yiliu Tan Yuichi Onda Asahi Hashimoto Takashi Gomi Chenwei Chiu Shodai Inokoshi Center for Research in Isotopes and Environmental Dynamics University of Tsukuba1-1-1TennodaiTsukubaIbaraki305-8572Japan Graduate School of Systems and Information Engineering University of Tsukuba1-1-1TennodaiTsukubaIbaraki305-8572Japan Department of International Environmental and Agricultural Science Tokyo University of Agriculture and Technology3-5-8SaiwaieFuchuTokyo183-8509Japan

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone Li DAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone Li DAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with undercanopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha^(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone Li DAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m^(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy(F1-Score=0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

关键词： Tree detection Trunk sections Forest Drone LiDAR

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Nanoscale phase management of the 2D/3D heterostructure toward efficient perovskite solar cells

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Science Bulletin 2024年第18期69卷 2853-2861页

作者： Hao Gu Annan Zhu Junmin Xia Wang Li Jiahao Zheng Tao Yang Shengwen Li Nan Zhang Shiliang Mei Yongqing Cai Shi Chen Chao Liang Guichuan Xing Joint Key Laboratory of the Ministry of Education Institute of Applied Physics and Materials EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(IAM) Jiangsu National Synergetic Innovation Center for Advanced Materials(SICAM)Nanjing University of Posts&Telecommunications(NUPT)Nanjing 210023China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of PhysicsNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi’an Jiaotong UniversityXi’an 710049China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of TechnologyShenzhen 518055China Institute for Electric Light Sources School of Information Science and TechnologyFudan UniversityShanghai 200433China

The stabilization of the formamidinium lead iodide(FAPbI_(3))structure is pivotal for the development of efficient photovoltaic *** two-dimensional(2D)layers to passivate the threedimensional(3D)perovskite is essential for maintaining the a-phase of FAPbI_(3) and enhancing the power conversion efficiency(PCE)of perovskite solar cells(PSCs).However,the role of bulky ligands in the phase management of 2D perovskites,crucial for the stabilization of FAPbI_(3),has not yet been *** this study,we synthesized nanoscale 2D perovskite capping crusts with=1 and 2 Ruddlesden-Popper(RP)perovskite layers,respectively,which form a type-Ⅱ 2D/3D *** heterostructure stabilizes the a-phase of FAPbI_(3),and facilitates ultrafast carrier extraction from the 3D perovskite network to transport contact *** introduced tri-fluorinated ligands to mitigate defects caused by the halide vacancies and uncoordinated Pb^(2+)ions,thereby reducing nonradiative carrier recombination and extending carrier *** films produced were incorporated into PSCs that not only achieved a PCE of 25.39%but also maintained 95%of their initial efficiency after 2000 h of continuous light exposure without *** findings underscore the effectiveness of a phase-pure 2D/3D heterostructure-terminated film in inhibiting phase transitions passivating the iodide anion vacancy defects,facilitating the charge carrier extraction,and boosting the performance of optoelectronic devices.

关键词： Phase-pure 2D perovskite 2D/3D heterostructure Type-II energy alignment Carrier dynamic FAPbI_(3)

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A Large-Scale Multi-objective Evolutionary Algorithm Based on Two-layer Encoding Scheme for Neural Network Training 3

A Large-Scale Multi-objective Evolutionary Algorithm Based o...

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3rd International Conference on Automation, Robotics and Computer engineering, ICARCE 2024

作者： Yang, Wanting Liu, Jianchang Zheng, Tianzi College of Information Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (纸本)9798331529505

Evolutionary algorithms (EAs) have achieved great performance in solving multi-objective optimization problems (MOPs). However, there are more complex features in real-world MOPs. For example, the neural network training problem is characterized by large-scale decision variables and sparse Pareto-optimal solutions. This makes it difficult for most EAs to solve the neural network training problem. Therefore, this paper proposes a large-scale multi-objective evolutionary algorithm based on two-layer encoding scheme (MOEA- TES). In the proposed algorithm, the two-layer encoding scheme is employed. Moreover, a two-stage evolutionary strategy is designed for guiding the genetic operator. MOEA- TES is assessed by four real-world neural network training problems. Compared with five recent state-of-the-art algorithms, the proposed MOEA- TES achieves competitive results. © 2024 IEEE.

关键词： Cellular automata

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Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers

Enhancing Memory Capacity of Reservoir Computing with Delaye...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Soshi Hirayae Kanta Yoshioka Atsuki Yokota Ichiro Kawashima Yuichiro Tanaka Yuichi Katori Osamu Nomura Takashi Morie Hakaru Tamukoh School of Engineering Kyushu Institute of Technology Japan Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology Japan The School of Systems Information Science Future University Hakodate Japan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

To use reservoir computing (RC) for practical tasks, both a high memory capacity and nonlinearity are required; however, some RC models have the problem of a low memory capacity. We propose a delay mechanism for increasing the memory capacity in RC as well as a simple and small-scale digital circuit for implementing the delay mechanism. The proposed delay mechanism is integrated into the input layer of the RC model and is expected to be implemented in several RC models, such as material reservoirs and chaotic Boltzmann machine (CBM)-RC. We conducted experiments using a CBM-RC with a delay mechanism (CBM-RC-DL) and evaluated the performance improvement achieved by introducing a delay mechanism. We used CBM-RC as the base model because it is an appropriate model for the hardware implementation of large networks but has a low memory capacity. The experimental results for CBM-RC-DL indicated that the delay mechanism significantly increased the memory capacity of CBM-RC with the addition of a small-scale circuit. Furthermore, the entire synthesized CBM-RC-DL was sufficiently small-scale to be implemented in a field-programmable gate array for edge computing, and it outperformed conventional methods in nonlinear autoregressive moving average 10 (NARMA10)—a benchmark task for time-series data processing. The proposed delay mechanism can facilitate the use of many RC models because of its simple structure.

关键词： Computational modeling Memory management Shift registers Reservoirs Hardware Delays Integrated circuit modeling

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