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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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Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

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CSEE Journal of Power and Energy systems 2023年第1期9卷 8-14页

作者： Qing-Hua Wu Anjan Bose Chanan Singh Joe H.Chow Gang Mu Yuanzhang Sun Zhaoxi Liu Zhigang Li Yang Liu School of Electric Power Engineering South China University of TechnologyGuangzhou 510640China School of Electrical Engineering&Computer Science Washington State UniversityPullmanWA 99164USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNYUSA Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)Jilin 132012China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is *** conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power *** analysis and control methods are needed for power systems to cope with the ongoing *** the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power ***,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

关键词： Distributed energy resources high converter penetration power system control renewable energy generation stability control transient stability

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UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

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CAAI Transactions on Intelligence technology 2024年第3期9卷 756-768页

作者： Wei Xie Lianghao Jin Shiqi Hua Hao Sun Bo Sun Zhigang Tu Jun Liu Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal UniversityWuhanChina School of Computer Science Central China Normal UniversityWuhanChina National Language Resources Monitoring and Research Center for Network Media Central China Normal UniversityWuhanChina The First Affiliated Hospital of Dalian Medical University DalianChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Information Systems Technology and Design Pillar Singapore University of Technology and DesignSingaporeSingapore

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality *** lesions are generally diffusely distributed,showing a variety of scales with irregular *** complex characteristics of lesions make SAH segmentation a challenging *** cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH ***,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale ***,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder ***,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed *** experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

关键词： image segmentation medical image processing

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Revisiting the thermal ageing on the metallised polypropylene film capacitor:from device to dielectric film

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High Voltage 2023年第2期8卷 305-314页

作者： Yushuang He Feipeng Wang Guoqiang Du Lei Pan Kaizheng Wang Reimund Gerhard Ronald Plath Pawel Rozga Pavel Trnka State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Faculty of Electrical Engineering Kunming University of Science and TechnologyKunmingChina Soft Transducer Materials and Systems Universität PotsdamPotsdamGermany Faculty IV Electrical Engineering and Computer Science Technische Universität BerlinBerlinGermany Institute of Electrical Power Engineering Lodz University of TechnologyLodzPoland Faculty of Electrical Engineering University of West BohemiaPilsenCzech Republic

Metallised polypropylene film capacitors(MPPFCs)are widely used in power electronics and are generally degraded by elevated *** work aims to determine the relationships between the structural changes of MPPFC and the microstructural variations of the PP film during the thermal ageing of MPPFC at 100℃ for 38 *** capacitance of MPPFC has a slight decrease during thermal ***,the breakdown voltage of the MPPFC decreases by 39.4%by the *** partial discharge(PD)number of MPPFC increases linearly with ageing *** tear-down analysis of the MPPFC reveals that the molecular structure of the PP film has not been altered but has led to molecular chain scission and the generation of some polar fragments/***,the relative permittivity of the PP films rises as the ageing time ***,thermal ageing causes the conversion of aluminum to alumina in the metallised electrode,which is hydrophilic for polar groups and leads to an adhesion effect between the metallised electrodes and the PP *** angle measurements prove that the surface hydrophilicity of the PP sample increased after thermal ***,the PD/breakdown voltage in the MPPFC increases/decreases due to the uneven adhesion of the metallised PP film.

关键词： film thermal ageing

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SparSynergy: Unlocking Flexible and Efficient DNN Acceleration Through Multi-Level Sparsity

SparSynergy: Unlocking Flexible and Efficient DNN Accelerati...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jingkui Yang Mei Wen Junzhong Shen Jianchao Yang Yasong Cao Jun He Minjin Tang Zhaoyun Chen Yang Shi College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

To more effectively address the computational and memory requirements of deep neural networks (DNNs), leveraging multi-level sparsity-including value-level and bit-level sparsity-has emerged as a pivotal strategy. While substantial research has been dedicated to exploring value-level and bit-level sparsity individually, the combination of both has largely been overlooked until now. In this paper, we propose SparSynergy, which-to the best of our knowledge-is the first accelerator that synergistically integrates multi-level sparsity into a unified framework, maximizing computational efficiency and minimizing memory usage. However, jointly considering multi-level sparsity is non-trivial, as it presents several challenges: (1) increased hardware overhead due to the complexity of incorporating multiple sparsity levels, (2) bandwidth-intensive data transmission during multiplexing, and (3) decreased throughput and scalability caused by bottlenecks in bit-serial computation. Our proposed SparSynergy addresses these challenges by introducing a unified sparsity format and a cooptimized hardware design. Experimental results demonstrate that SparSynergy achieves a 5.38 x geometric mean improvement in the energy-delay product (EDP) when compared with the tensor core, across workloads with varying degrees of sparsity. Furthermore, SparSynergy significantly improves accuracy retention compared to state-of-the-art accelerators for representative DNNs.

关键词： Multiplexing Accuracy Tensors Scalability Memory management Artificial neural networks Life estimation Throughput Computational efficiency Optimization

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SparSynergy: Unlocking Flexible and Efficient DNN Acceleration Through Multi-Level Sparsity

SparSynergy: Unlocking Flexible and Efficient DNN Accelerati...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Yang, Jingkui Wen, Mei Shen, Junzhong Yang, Jianchao Cao, Yasong He, Jun Tang, Minjin Chen, Zhaoyun Shi, Yang College of Computer Science and Technology National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha China

ISBN: (纸本)9783982674100

关键词： Data storage equipment

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Refining Boundary Sample Space for Differentiable Rendering 16

Refining Boundary Sample Space for Differentiable Rendering

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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WinAcc: Window-based Acceleration of Neural Networks Using Block Floating Point

WinAcc: Window-based Acceleration of Neural Networks Using B...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Ju, Xin He, Jun Wen, Mei Feng, Jing Cao, Yasong Shen, Junzhong Chen, Zhaoyun Shi, Yang School of Computer National University of Defense Technology Key Laboratory of Advanced Microprocessor Chips and Systems Changsha410073 China

ISBN: (纸本)9783982674100

Deep Neural Networks (DNNs) impose significant computational demands, necessitating optimizations for computational and energy efficiencies. Per-vector scaling, which applies a scaling factor to blocks of elements using narrow integer types, effectively reduces storage and computational overhead. However, the frequent occurrence of floating-point accumulations between vectors limits further improvements in energy efficiency. State-of-the-art accelerators address this challenge by grouping and summing vector products based on their exponent differences, thereby reducing the overhead associated with intra-group shifting and accumulation. Nevertheless, this approach increases the complexity of register usage and grouping logic, leading to limited energy benefits and hardware efficiency. In this context, we introduce WinAcc, a novel algorithm and architecture co-designed solution that utilizes a low-cost accumu-lator to handle the majority of data in DNNs, offering low area overhead and high energy efficiency gains. Our key insight is that the data of DNNs follows a Laplace-like distribution, which enables the use of a customized data format with a narrow dynamic range to encode most of the data. This allows for the design of a low-cost accumulator with narrow shifters and adders, significantly reducing reliance on floating-point accumulator and consequently improving energy efficiency. Compared with state-of-the-art architecture Bucket, WinAcc achieves 33.95% energy reduction across seven representative DNNs and reduces area by 9.5% while maintaining superior model performance. © 2025 EDAA.

关键词： Deep neural networks

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A novel cooperative collision avoidance system for vehicular communication based on deep learning

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International Journal of Information technology (Singapore) 2024年第3期16卷 1661-1675页

作者： Farhat, Wajdi Ben Rhaiem, Olfa Faiedh, Hassene Souani, Chokri Laboratory of Microelectronics and Instrumentation University of Monastir Monastir Tunisia Higher Institute of Applied Sciences and Technology Sousse University Sousse Tunisia Computer Science Department College of Science Northern Border University Arar Saudi Arabia Digital Research Center of SFax (CRNS) SM@RTS (Laboratory of Signals systeMs aRtificial Intelligence and neTworkS) Sfax Tunisia

Global road traffic injuries represent a significant safety challenge, with the highest fatality rates worldwide stemming from a combination of drivers' reckless behavior and the ever-expanding number of vehicles on the roads. In light of these factors, Autonomous Vehicles (AVs) have received a lot of attention as a technology that has the potential to revolutionize various industries and solve a lot of problems. The rise of the Internet of Vehicles (IoV) and Connected Vehicles (CVs) enables AVs to perceive and react swiftly in complex road scenarios, prioritizing safety. These vehicles prioritize individual safety in all situations, significantly enhancing road safety. Mobile Edge Computing (MEC) plays a crucial role thanks to its capacity for minimal latency and high bandwidth capabilities, enabling critical vehicular applications. In this paper, we introduce a cooperative collision avoidance system based on MEC, named 2CAS-MEC, that proactively identifies and locates road hazards. Our system is based on MEC servers positioned alongside roads, it consistently analyzes traffic and hazard data, issuing targeted alerts to nearby vehicles regarding potential risks. Particularly, our work introduces an advanced deep learning system that utilizes 5G, mobile edge computing, and cloud intelligence to prevent vehicle collisions. The experimental results show that our system outperforms the existing related works and exhibits the anticipated performance, effectively helping drivers in accident avoidance. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cloud Connected and autonomous vehicles Deep learning IOV MEC Vehicle to-everything (V2X)

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Deep Learning for Automatic Text Summarization

Deep Learning for Automatic Text Summarization

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2024 International Conference on Telecommunications and Intelligent systems, ICTIS 2024

作者： Ghibeche, Youcef Sellam, Abdallah Nouri, Nabil Abdelkader Laggoun, Saad Khadroun, Nizar Omar Idris University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Science and Technology Djelfa17000 Algeria University Center El Cherif Bouchoucha Institute of Science Algeria University of Djelfa Faculty of Exact Sciences and Computer Science Djelfa17000 Algeria

ISBN: (纸本)9798331527396

In today's digital age, the rapid accumulation of textual information necessitates effective automatic text summarization. The demand for computer systems capable of summarizing vast amounts of information has grown exponentially, aiding users in extracting key insights and making informed decisions. This study focuses on the development of a robust abstractive text summarization model utilizing Deep Learning and Seq2Seq models with LSTM networks and attention mechanisms. Through the construction of a Seq2Seq-based framework incorporating LSTM layers and attention mechanisms, the model effectively captures semantic relationships and contextual dependencies within the input text. Training strategies, optimization techniques, and word embedding methods, including pretrained word embedding like GloVe, were employed to enhance the model's ability to generate accurate and concise summaries. © 2024 IEEE.

关键词： Contrastive Learning

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