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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Jianwei Yi, Yi Zhi, Yujie Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering Dalian University of Technology Dalian China School of Artificial Intelligence Dalian University of Technology Dalian China Electronics Standardization Institute China

ISBN: (纸本)9781665450515

With the development of integrated circuit technology, there are more and more researches on system-on-chip (SOC). In general, many interface modules need to be integrated into SOC chip to implement specific functions, among which audio interface module is one of the most widely used IP. Audio technology is an important part of multimedia technology. In the digital audio transmission technology, many transmission protocol standards have been proposed. I2S(Inter-IC Sound) is one of the most popular protocols because of its simple structure, good stability and wide application range. However, the IP of the standard I2S protocol interface itself does not have any data processing function. In this paper, an I2S-SC(I2S-sidetone controller) is implemented, which has the function of signal processing. Compared with the standard I2S protocol interface IP, the I2S - SC implemented in this paper has the unique sidetone echo function and loopback mechanism, which can support the actual call scenario. At the same time, it combines the functions of half-duplex transmission mode and full-duplex transmission mode, and can transmit data with high efficiency. In addition, it uses the TDM(Time Division Multiplexed) function to reduce the chip design area, and through the internal power saving mechanism to reduce power consumption. Finally, In order to facilitate the use of I2S - SC In AMBA systems, this IP provides an APB bus slave interface, which supports two external DP-Rams as FIFO (first-in-first-out). © 2022 IEEE.

关键词： system-on-chip

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Enhancing Performance of Linked Independent Component Analysis: Investigating the Influence of Subjects and Modalities 4

Enhancing Performance of Linked Independent Component Analys...

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4th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Xu, Huashuai Li, Huanjie Karkkainen, Tommi Cong, Fengyu Dalian University of Technology School of Biomedical Engineering Dalian China Dalian University of Technology School of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering School of Artificial Intelligence Faculty of Electronic Information and Electrical Engineering Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland

ISBN: (纸本)9798350342710

In recent years, neuroimaging studies have increasingly been acquiring multiple modalities of data. The benefit of integrating multiple modalities through fusion lies in its ability to combine the unique strengths of each modality when analyzed collectively, as opposed to examining each one individually. In 2011, Adrian R. Groves proposed the Linked independent component analysis (LICA) method, which simultaneously models and discovers common features across multiple modalities. LICA has emerged as a powerful technique for analyzing multivariate data, particularly in neuroimaging and biomedical signal processing. The performance of LICA can be affected by the number of subjects and modalities. However, the detailed influence of the number of subjects and modalities on its performance remains an open question. In this study, we test the effects of the number of subjects and modalities on the performance of LICA using both simulated multimodal MRI data and the real multimodal MRI datasets from Autism Brain Imaging Data Exchange II (ABIDE II). Simulated data were utilized to evaluate the influence of subjects and modalities' variabilities. Real multi-site MRI data were used to demonstrate the advantages of multimodal fusion in identifying site-related components and removing site effects. Based on the simulation results, we found that increasing the number of modalities and subjects can improve the results when LICA can not recover the spatial maps or subject courses well. The correlation among subject courses from various modalities, the number of modalities, and the choice of components for decomposition all affect the linking performance of LICA. Our results from real-world datasets also demonstrated the advantages of multimodal fusion by LICA: 1) identify more site-related components;2) remove more site effects. © 2023 IEEE.

关键词： Independent component analysis

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A High-Performance Hardware Accelerator for Sparse Convolutional Neural Network on FPGA 8

A High-Performance Hardware Accelerator for Sparse Convoluti...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zhang, Jianwei Xu, Fen Li, Jinghu School of Biomedical Engineering School of Artificial Intelligence Dalian University of Technology Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System Dalian China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China

ISBN: (纸本)9781665450515

Recently convolutional neural networks (CNNs) have developed rapidly and shown excellent performance in a wide range of applications. The design of hardware accelerators for convolutional neural networks has attracted great attention. With the number of layers in a CNN network deepens, the amount of model computation increases. The high computation and memory requirements can limit the deployment of CNNs on resource-limited hardware accelerators such as FPGAs, and it is urgent to use pruning technology to compress network models for easy deployment on FPGAs. In this paper, we use a sparse way based on the combination of convolutional kernel pruning and weight pruning to compress CNN models, together with 16-bit fixed-point quantization, skip zero-value processing, and independent multiply-and-accumulate (MAC) modules to implement a FPGA-based high-performance hardware accelerator design for sparse convolutional neural networks. The LeNet-5 network is implemented as an example on the Xilinx Virtex VC-707 FPGA board, and the experimental result from inference of 10,000 images of the MNIST test set shows that when the network sparsity is 87.46%, the inference time is 82.43% less than the CPU, the energy efficiency ratio is 264.85 times that implemented on the CPU. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Development And Optimization of Airborne Display Scene Based on SCADE 4

Development And Optimization of Airborne Display Scene Based...

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4th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2022

作者： Song, Qian Zhang, Li Tian, Ze Xi'an Polytechnic University School of Electronic Information Shaanxi xi'an710600 China Key Laboratory of Aviation Science and Technology on Integrated Circuit and Micro-System Design Shaanxi xi'an710068 China

ISBN: (纸本)9798350320008

With the rapid development of avionics display field, the traditional graphics software development method can no longer meet the needs of high security and efficiency. This paper studies a development method based on SCADE Display, a high security graphics application development tool, analyses the principle and characteristics of SCADE Display, applies SCADE Display to design airborne display scene models, and tests, analyses and optimizes the process on a home-made test platform. Finally, the experimental results show that the optimized solution can effectively improve the development efficiency and graphics frame rate, and also verify the adaptability and functionality of the home-made platform. © 2022 IEEE.

关键词： Software design

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4DFA: Four-Dimensional Full-Anatomy Reconstruction of Individualized Digital Human Models Based on Motion Videos 2nd

4DFA: Four-Dimensional Full-Anatomy Reconstruction of Indivi...

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2nd World Conference on Intelligent and 3D Technologies, WCI3DT 2023

作者： Zhao, Rui Mi, Jiachen Jiang, Yanxin Chen, Zhefu Wang, Hongkai School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Dalian116024 China co LTD Dalian116023 China School of Kinesiology and Health Promotion Dalian University of Technology Dalian116024 China Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System Liaoning China

ISBN: (纸本)9789819970100

Reconstructing personalized models of the human body is a key step for digital twin modeling of sports motion. There have been many studies reconstructing the body surface models based on video sequences, but these algorithms lack the modeling of internal structures (bones, muscles, blood vessels, etc.). Some researchers map internal organ structures into the 3D point cloud scanning of the body surface, but such mapping does not guarantee realistic tissue deformation and is not developed for video processing. This paper focuses on the video-based reconstruction of a personalized full-anatomy digital model in sports motion. Our method creates a four-dimensional (4D) model of the moving process composed of the 3D models of each time moment. We first use a deep network to regress the body surface of each time point and then estimate the internal structures by registering a previously constructed deformable human anatomy atlas to the body surfaces. To mimic realistic internal structure deformation, we applied articulated linear transform to the bony structures to avoid bone shape distortion and applied smooth nonlinear transform to the soft tissue to mimic the motion deformation. We also developed an intersection removal method to prevent the possible intersection between bones and soft tissues during the deformation. Our method is tested with athletes’ sports videos and automatically generates full-anatomy motion models which cannot be achieved by previous methods. Our method also surpasses the existing video-based body surface modeling method by providing additional internal structures. The 4D human motion model constructed by our method is useful in sports modeling, biomechanics simulation, wearable device design, etc. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blood vessels

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Self-supervised Contrastive Pre-training integrated with Multi-level Co-attention for Survival Prognosis from Whole Slide Images

Self-supervised Contrastive Pre-training Integrated with Mul...

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International Conference on Image, Vision and Intelligent systems, ICIVIS 2023

作者： Gao, Junxiu Hao, Xinyu Jin, Shan Xu, Hongming School of Biomedical Engineering Faculty of Medicine Dalian University of Technology Dalian116024 China Faculty of Information Technology University of Jyväskylä Jyväskylä40014 Finland Key Laboratory of Integrated Circuit and Biomedical Electronic System Liaoning Province Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789819708543

Survival analysis is of paramount importance in guiding the development of optimal treatment strategies for cancer patients. Because of the rich prognostic information contained in whole slide images (WSIs), multiple instance learning (MIL) approaches integrated with WSI analysis have been widely used in survival risk prediction. However, existing MIL methods often fail to encompass the complete range of histological image features, including critical information from local tissue regions, which limits their performance in survival prognosis. To address this limitation, we employ a self-supervised learning mechanism to train a feature extractor which can capture the intricate characteristics of WSIs. Furthermore, we introduce an attention mechanism that incorporates local patches and clusters to guide the fusion of multi-level features for survival outcome prediction. The proposed method demonstrates excellent performance on the widely recognized TCGA-COAD dataset. Experimental findings indicate that the integration of pre-trained feature extractors with MIL method and the fusion of multi-level histological features yield notable advancements in survival risk predictions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supervised learning

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Low-Dose Mouse Micro-CT Image Segmentation Based on Multi-Resolution Multi-Organ Shape Prior Knowledge Model 3

Low-Dose Mouse Micro-CT Image Segmentation Based on Multi-Re...

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3rd International Conference on Computers, Information Processing and Advanced Education, CIPAE 2022

作者： Chen, Zhonghua Wang, Hongkai Cong, Fengyu Kettunen, Lauri School of Biomedical Engineering Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland School of Biomedical Engineering Faculty of Electronic Information and Electrical Engineering School of Artificial Intelligence Faculty of Electronic Information and Electrical Engineering Key Laboratory of Integrated Circuit and Biomedical Electronic System Dalian University of Technology Dalian China

ISBN: (纸本)9781665468121

Automatic segmentation of computed tomography (CT) images of mice is a step toward computer-assisted preclinical image analysis. Due to the low image quality of micro-CT images, fully-automatic methods may not achieve robust segmentation. For this reason, human interventions are needed to achieve higher segmentation accuracy. In this paper, we propose a human interactive segmentation method incorporating anatomical prior knowledge for multiple abdominal organs in mouse micro-CT images. The method automatically fits a multi-organ shape model to the user-sketched partial boundary contours. Segmentation accuracy is validated by comparing the proposed method against existing shape models. The robustness of our proposed method was evaluated with different users. Finally, the results suggest the proposed method generates accurate segmentation with good robustness. © 2022 IEEE.

关键词： Image segmentation

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Survey of Shared Register File design for Unified Shader Array in GPUs 9

Survey of Shared Register File design for Unified Shader Arr...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Ze, Tian Jun, Zhang Xianglong, Ren Feihu, Fan Yue, Chen Xi'an Xiang Teng Micro-Electronic Technology Co. Ltd Xi'an710068 China Key Laboratory of Aviation Science and Technology on Integrated Circuit and Micro-System Desigen Xi'an710068 China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Unified Shader Array (USA) is the computing core of the Unified Shader Array Graphic Processing Unit (GPU), and the shader cores is the basic shader Unit of the Unified Shader Array. In order to support the large-scale thread-level parallelism of GPU, a large number of streaming processors composed of shader cores are set in GPU. The streaming processors (SP) can enable GPU to implement thread-level parallelism in the way of Single Instruction Multiple Data (SIMD) or Single Instruction Multiple Threads (SIMT). At the same time, in order to reduce the cost caused by context switching, GPU deployments a large scale of register file resources at the bottom level for the use of the streaming processor. So the management and use of register file is of great significance to the performance of GPU. The unique register file for each shader cores in the traditional GPU simplifies the register management hardware, but at the cost of insufficient register utilization. It will cause the disadvantage of limited size of shader cores. This paper introduces the current research status of various technologies at home and abroad from the aspects of register file sharing, high resource utilization and low power consumption. © 2022 IEEE.

关键词： Graphics processing unit

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit photonic neural computing semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic

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A TD-Learning Based Bionic Cerebellar Model Controller For Humanoid Robots

A TD-Learning Based Bionic Cerebellar Model Controller For H...

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2022 International Symposium on Biomimetic Intelligence and Robotics, ISBIR 2022

作者： Li, Honghui Liu, Rong Wang, Yongxuan Liu, Yin Chen, Yaru Wang, Jiaxing Gu, Jason Liaoning Key Lab of Integrated Circuit and Biomedical Electronic System School of Biomedical Engineering Dalian University of Technology Liaoning Dalian116024 China School of Information Engineering Dalian University Liaoning Dalian116024 China Department of Electrica Dalhousie University Halifax Canada

The cerebellum is a crucial component of the human body that plays a vital role in human walking. To design a robot gait controller by referring the working mechanism of the cerebellum is one of the hotspots in the bionic control field. This paper designs a bionic cerebellar motion control model to control the slope gait of a humanoid robot. The cerebellum model refers to the connection method between neurons in a human cerebellum, and expresses from a bionic perspective how the neurons in the cerebellum process external information and generate control commands during walking. Inspired by how human walking is learned, this model employs reinforcement learning in the learning process of the bionic cerebellar model. A corresponding simulation environment is also designed to train and test the cerebellar control model's effectiveness when regulating a robot's slope walking stability. The simulation experimental results demonstrate that the cerebellum model can achieve stable control of the walking motion of the humanoid robot after training, verifying its effectiveness, and laying a foundation for further realization of human-like artificial intelligence. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and Robotics.

关键词： Anthropomorphic robots

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