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A 2.2-3.6 GHz CMOS Reconfigurable Fourth-Order Bandpass Filter With Compact Size and High Selectivity Using Transformer-Type Resonators

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IEEE Transactions on Microwave Theory and Techniques 2023年第1期71卷 218-229页

作者： Li, Kun Liu, Bin Chi, Pei-Ling Wang, Yong Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu Sichuan610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan Nanhu Laboratory Research Center of Advanced Rf Chips and Systems Jiaxing Zhejiang314000 China

A compact reconfigurable CMOS bandpass filter (BPF) with high selectivity is proposed using transformer-type resonators in this article. The transformer-type resonator is constructed by a transformer loaded with parallel varactors for frequency tuning, and two varactors are then added across primary wind and secondary wind for coupling tuning. With the transformer-type structure, one resonator is formed inside another resonator, thus fully utilizing the chip area and resulting in a miniaturized structure. Magnetic and electrical couplings are formed within the transformer-type resonator and facilitate the bandwidth (BW) tuning. The quality factor (Q) of each resonator is improved by cross-coupled negative resistance structure. Meanwhile, two pairs of transformer-type resonators are placed side-by-side and naturally form a quadruplet structure;as a result, two transmission zeros (TZs) are introduced at each side of the passband thus significantly improving the selectivity and stopband rejection of the BPF. The proposed tunable on-chip BPF is manufactured by a commercial 55 nm bulk CMOS technology with a core area of 1.48 × 0.54 mm2. The filter center frequency (CF) can be tuned from 2.2 to 3.6 GHz, while the 3 dB fractional BW (FBW) can be tuned from 14% to 33%. The shape factor of the proposed filter is 1.4-1.9. © 1963-2012 IEEE.

关键词： Varactors

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STARS Assisted Semi-Grant-Free NOMA Communications

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4904-4919页

作者： Xie, Jin Yue, Xinwei Zhao, Jingjing Zou, Yixuan Liu, Yuanwei Liu, Rongke Ding, Zhiguo Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Ministry of Information Industry Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100101 China Beihang University School of Electronic and Information Engineering Beijing100191 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput;2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios;and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced. © 2002-2012 IEEE.

关键词： Laplace transforms

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Grid-Forming Inverters for Enhancing Stability and Resilience in Distribution Networks Under Transients and Restoration

Grid-Forming Inverters for Enhancing Stability and Resilienc...

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Innovative Smart Grid Technologies (ISGT)

作者： Alaa Selim Junbo Zhao Gab-Su Seo Fei Ding Bai Cui Department of Electrical and Computer Engineering University of Connecticut Power Systems Engineering Center National Renewable Energy Laboratory Golden CO Department of Electrical and Computer Engineering Iowa State University

The increasing integration of distributed energy resources in distribution networks has significantly reduced system inertia, posing challenges to grid stability during transient events, such as Cold Load Pickup (CLPU). This paper proposes the implementation of grid-forming inverters (GFMs) to enhance the stability of low-inertia distribution systems. The paper also investigates the performance of the GFM-based approach in maintaining voltage profiles along the distribution feeder, especially during CLPU scenarios without synchronous machines. A comparison is made between the GFM-based system and the conventional synchronous machine setup. The synchronous machine is chosen as a reference point for evaluating the effectiveness of the GFM-based approach. The GFM-based system’s performance is assessed under various challenging scenarios, including short-circuit faults, islanding, and a combination of these through extensive simulations. The results demonstrate the potential of GFMs to stabilize a distribution system and outperform the traditional rigid synchronous machine during transient events.

关键词：

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Prediabetes detection in unconstrained conditions using wearable sensors

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Clinical Nutrition Open Science 2024年 58卷 163-174页

作者： Tatli, Dimitra Papapanagiotou, Vasileios Liakos, Aris Tsapas, Apostolos Delopoulos, Anastasios Multimedia Understanding Group Dpt. of Electrical and Computer Engineering Faculty of Engineering AUTH Greece Embedded Systems Laboratory Institute of Electrical and Micro Engineering EPFL Lausanne Switzerland IMPACT Research Group Dpt. of Medicine Karolinska Institutet Huddinge Sweden Diabetes Centre Second Medical Department at Ippokratio General Hospital Thessaloniki Greece

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used. © 2024 The Authors

关键词： Continuous glucose monitors Machine learning Prediabetes Signal processing

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Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite Imagery 1

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Kåreborn, Liv Ingerstad, Erica Berg, Amanda Karlsson, Justus Haglund, Leif Maxar International Sweden AB Ebbegatan 13 Linköping582 13 Sweden AI Sweden Folkungagatan 44 Stockholm118 26 Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden

ISBN: (数字)9783031851872

ISBN: (纸本)9783031851865

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model’s ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Data-Driven Adaptive Control for Frequency Perturbations in Power systems with PV-SmartParks

Data-Driven Adaptive Control for Frequency Perturbations in ...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES Companion), IEEE Symposia on

作者： Ali Arzani Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University SC USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798331519681

ISBN: (纸本)9798331519698

Seamless operation of PV plants during both transient and dynamic conditions is imperative for all stakeholders including energy control centers. Fast and dampened transient response, low steady-state error, and stability are sought for power system operation with PV plants. For PV power perturbation at point of interconnection (POI), this can be further supported by deploying auxiliary fast-acting non-synchronous generation such as SmartParks (SPs). SmartParks are parking lots with a large number of electric vehicles performing bidirectional power transfers, namely, vehicle-to-grid and grid-to-vehicle. In this study, a data-driven adaptive frequency controller (DD-AFC) was developed for SPs to compensate for frequency perturbations at PV-POI. The DD-AFC was incorporated into the controller of SPs to provide a fast response for frequency perturbations caused by PV-site power perturbations. The adaptiveness of the frequency droop comes from data-driven empirical characterization of the PV plant operation at the PV-site location using time-series data analytics. The DD-AFC supports fast-frequency control response by SPs for mitigating fast power perturbation at PV-POIs. The effectiveness of the DD-AFC was illustrated via real-time simulations of a bulk power system with a 210MW p PV plant and 100MW p of SmartParks in RTDS.

关键词： Automatic frequency control Time-frequency analysis Vehicle-to-grid Transient response Data analysis Perturbation methods Power system stability Adaptive control Transient analysis Vehicle dynamics

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Analyzing Infrared Linescan Profiles of Steel Strips for Enhanced Cooling Pattern Prediction

Analyzing Infrared Linescan Profiles of Steel Strips for Enh...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Rubén Usamentiaga Adrian G. Gayo Francisco J. DelaCalle Dario G. Lema Stefano Sfarra Hai Zhang Department of Computer Science and Engineering University of Oviedo Gijón Asturias Spain Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila L'Aquila Italy Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory (CVSL) Laval University Quebec Canada

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Real-time temperature information is crucial for optimizing cooling processes during steel strip rolling, ensuring the attainment of desired microstructural properties and surface quality at an optimal cooling rate. Infrared line scanners emerge as the preferred choice for temperature measurement in highspeed rolling operations, delivering temperature readings with high resolution and enabling the capture of detailed temperature profiles. By analyzing these profiles, cooling systems can be finely adjusted and precisely controlled to optimize the rolling operation. However, developing effective cooling strategies becomes challenging when dealing with temperature profiles comprising numerous discrete data points, often numbering in the thousands per profile. This study presents an innovative approach that integrates the detection of steel strip boundaries within temperature profiles and subsequent temperature pattern characterization using polynomial fitting. A significant advantage is demonstrated by leveraging the coefficients of Legendre polynomials, which provide a concise description of temperature profile shapes, facilitating straightforward approaches to cooling strategies. By integrating boundary detection with temperature characterization, the system enhances its ability to predict tailored cooling patterns, optimizing cooling efficiency, and enhancing product quality in the manufacturing process. Rigorous testing using both synthetic data and real-world applications in cold and hot rolling validates the proposed system's practical utility and reliability. These results underscore its potential to enhance efficiency and quality in industrial steel manufacturing operations.

关键词： Temperature measurement Temperature sensors Strips Cooling Shape Polynomials Real-time systems Steel Monitoring Testing

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Improved High-Speed Thermal Analysis Based on Two-Step Simulation for High-Frequency Transformers 14

Improved High-Speed Thermal Analysis Based on Two-Step Simul...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Yi, Zheyuan Sun, Kai Li, Qiang Liu, Zengyang Tsinghua University State Key Laboratory of Power System Department of Electrical Engineering Beijing China Delta Electronics Shanghai Co. Ltd. Shanghai China University Blacksburg Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State BlacksburgVA United States

ISBN: (纸本)9798331516116

Thermal analysis is a crucial and time-consuming step in the optimization design process of high-frequency transformers (HFTs). This paper proposes an improved thermal analysis method based on heat dissipation-conduction two-step simulation, significantly reducing the computation time while ensuring the accuracy of thermal analysis. Simulation results validate the accuracy and of the proposed method and demonstrate that the proposed method saves 82% of computation time for a single thermal analysis case compared to direct CFD. Experimental results further verify the accuracy of the proposed method. An exemplary application of the proposed method in the optimization design of the HFT illustrates a 98% reduction in computation time for thermal analysis during the optimization process. © 2025 IEEE.

关键词： Thermal modeling

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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