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IoT Multi-control Bionic Manipulator's Design 3

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IoT Multi-control Bionic Manipulator's Design

3rd International Conference on Circuits, system and Simulation, ICCSS 2019

作者： Lili, Tang Qijun, Huang Wei, Huang Zhixing College of Hubei University Department of Computer and Information Engineering Wuhan China College of Wuhan University Department of Microelectronics Physics and Technology Wuhan China Fourth Laboratory System Design Institute of Hubei Aerospace Technology Academy Wuhan Wuhan China

ISBN: (纸本)9781728136578

This design uses STM32 microcontroller as the core controller of the mechanical arm, transplants Linux operating system to the S3C2440 development board and establishes the LAN server, realizes multiple functions and a variety of control mode: Android mobile app, personal PC in the local area network and gesture-sensing gloves can be used to control mechanical arm, it can also make the operation automatically. At the same time, because the server built, Linux system and QT interface transplanted are on S3C2440, so that the development board and all computers in the local area network can monitor the mechanical arm. This design is widely used in the field of intelligent industry, environmental exploration, intelligent home and so on. © 2019 IEEE.

关键词： Internet of things

Fabrication of highly reliable joint based on Cu/Ni/Sn double-layer powder for high temperature application

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Fabrication of highly reliable joint based on Cu/Ni/Sn doubl...

作者： Xu, Hongyan Shen, Yaochun Hu, Yihua Li, Jianqiang Xu, Ju Beijing Engineering Laboratory of Electrical Drive System and Power Electronic Device Packaging Technology Micro-Nano Fabrication Technology Department Institute of Electronic Engineering Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering Electronics and Computer Science University of Liverpool Brownlow Hill LiverpoolL69 3GJ United Kingdom State Key Laboratory of Multiphase Complex System Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China

A highly reliable three-dimensional network structure joint was fabricated based on Cu/Ni/Sn powder with double-layer coatings and transient liquid phase bonding (TLPB) technology for high temperature application. The Cu/Ni/Sn joint is characterized by Cu metal particles embedded in the matrix of (Cu,Ni)6Sn5/Ni3Sn4 intermetallic compounds (IMCs), with a low void ratio, and can be reflowed at low temperatures (6Sn5/ Ni3Sn4 IMCs, and the Cu/Ni/Sn joint had a lower void ratio and a higher shear strength than those of Cu/Sn. The mechanism of the Ni-coating layer inhibiting phase transformation was studied. The high reliable three-dimensional network structure joint based on Cu/Ni/Sn double-layer powder was fabricated for high temperature application. Copyright © International Microelectronics Assembly and Packaging Society

关键词： High temperature applications

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

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arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

Successive-Interference-Cancellation-Free NOMA for Indoor VLC: A Generalized Spatial Modulation Based Approach

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Successive-Interference-Cancellation-Free NOMA for Indoor VL...

IEEE International Conference on Communications Workshops, ICC

作者： Tengjiao Wang Fang Yang Changyong Pan Jian Song Zhu Han Department of Electronic Engineering Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen P. R. China University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Recently, visible light communication (VLC) has been regarded as one promising candidate to complement the conventional radio frequency communication in the future communication systems. In this paper, a novel successive-interference-cancellation-free non-orthogonal multiple access (NOMA) scheme is proposed for indoor VLC systems, based on the generalized spatial modulation technique. Different from the conventional NOMA schemes, information bits for users with diverse quality of service requirements are conveyed through different domains of the transmitted signals. Therefore, the intra-group interference among users is canceled and the successive interference cancellation is avoided to reduce the computational complexity at the receiver side. Theoretical analysis and simulation results are further provided to validate the superior performance of the proposed scheme over conventional orthogonal and non-orthogonal multiple access schemes.

关键词： Light emitting diodes NOMA Quality of service Optical transmitters Mutual information Detectors Silicon carbide

Cost Minimization for Cooperative Computation Framework in MEC Networks

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arXiv 2020年

作者： Pan, Yijin Pan, Cunhua Wang, Kezhi Zhu, Huiling Wang, Jiangzhou National Mobile Communications Research Laboratory Southeast University Nanjing211111 China School of Engineering and Digital Arts University of Kent United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer and Information Sciences Northumbria University United Kingdom

In this paper, a cooperative task computation framework exploits the computation resource in UEs to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs’ power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning

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arXiv 2023年

作者： Sivarajkumar, Sonish Tandale, Pratyush Bhardwaj, Ankit Johnson, Kipp W. Titus, Anoop Glicksberg, Benjamin S. Khader, Shameer Yadav, Kamlesh K. Subramanian, Lakshminarayanan Molecular Robotics Kerala India School of Computing and Information University of Pittsburgh Pennsylvania PA United States Health Informatics & Data Science Georgetown University WashingtonDC United States Department of Computer Science Courant Institute of Mathematical Sciences New York University New YorkNY United States Institute for Next Generation Healthcare Mount Sinai Health System New YorkNY United States Department of Preventive Cardiology DeBakey Heart & Vascular Center Houston Methodist HoustonTX United States Hasso Plattner Institute for Digital Health Icahn School of Medicine at Mount Sinai New YorkNY United States Faculty of Medicine Imperial College London London United Kingdom School of Engineering Medicine Texas A&M University HoustonTX United States Department of Translational Medical Sciences Center for Genomic and Precision Medicine Texas A&M University HoustonTX United States

Transcription factors (TFs) play a vital role in the regulation of gene expression thereby making them critical to many cellular processes. In this study, we used graph machine learning methods to create a compendium of TF cascades using data extracted from the STRING database. A TF cascade is a sequence of TFs that regulate each other, forming a directed path in the TF network. We constructed a knowledge graph of 81,488 unique TF cascades, with the longest cascade consisting of 62 TFs. Our results highlight the complex and intricate nature of TF interactions, where multiple TFs work together to regulate gene expression. We also identified 10 TFs with the highest regulatory influence based on centrality measurements, providing valuable information for researchers interested in studying specific TFs. Furthermore, our pathway enrichment analysis revealed significant enrichment of various pathways and functional categories, including those involved in cancer and other diseases, as well as those involved in development, differentiation, and cell signaling. The enriched pathways identified in this study may have potential as targets for therapeutic intervention in diseases associated with dysregulation of transcription factors. We have released the dataset, knowledge graph, and graphML methods for the TF cascades, and created a website to display the results, which can be accessed by researchers interested in using this dataset. Our study provides a valuable resource for understanding the complex network of interactions between TFs and their regulatory roles in cellular processes. Copyright © 2023, The Authors. All rights reserved.

关键词： Transcription factors

HARNESSING ARTIFICIAL INTELLIGENCE TO INFER NOVEL SPATIAL BIOMARKERS FOR THE DIAGNOSIS OF EOSINOPHILIC ESOPHAGITIS

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arXiv 2022年

作者： Larey, Ariel Aknin, Eliel Daniel, Nati Osswald, Garrett A. Caldwell, Julie M. Arva, Nicoleta C. Rochman, Mark Wasserman, Tanya Collins, Margaret H. Yang, Guang-Yu Rothenberg, Marc E. Savir, Yonatan Department of Computer Science Technion IIT Haifa3200003 Israel Department of Industrial Engineering Technion IIT Haifa3200003 Israel Department of Physiology Biophysics and System Biology Faculty of Medicine Technion IIT Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology Cincinnati Children's Hospital Medical Center University of Cincinnati College of Medicine CincinnatiOH United States Department of Pathology and Laboratory Medicine Ann and Robert H. Lurie Children's Hospital of Chicago Feinberg School of Medicine Northwestern University ChicagoIL United States Department of Pathology Northwestern University Feinberg School of Medicine ChicagoIL United States

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE diagnosis requires enumerating the density of esophageal eosinophils in esophageal biopsies, a somewhat subjective task that is time-consuming, thus reducing the ability to process the complex tissue structure. Previous artificial intelligence (AI) approaches that aimed to improve histology-based diagnosis focused on recapitulating identification and quantification of the area of maximal eosinophil density. However, this metric does not account for the distribution of eosinophils or other histological features, over the whole slide image. Here, we developed an artificial intelligence platform that infers local and spatial biomarkers based on semantic segmentation of intact eosinophils and basal zone distributions. Besides the maximal density of eosinophils (referred to as Peak Eosinophil Count [PEC]) and a maximal basal zone fraction, we identify two additional metrics that reflect the distribution of eosinophils and basal zone fractions. This approach enables a decision support system that predicts EoE activity and classifies the histological severity of EoE patients. We utilized a cohort that includes 1066 biopsy slides from 400 subjects to validate the system's performance and achieved a histological severity classification accuracy of 86.70%, sensitivity of 84.50%, and specificity of 90.09%. Our approach highlights the importance of systematically analyzing the distribution of biopsy features over the entire slide and paves the way towards a personalized decision support system that will assist not only in counting cells but can also potentially improve diagnosis and provide treatment prediction. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision support systems

Compressive x-ray material decomposition using structured illumination 12

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Compressive x-ray material decomposition using structured il...

12th SPIE Conference on Developments in X-Ray Tomography 2019

作者： Cuadros, Angela P. Ma, Xu Arce, Gonzalo R. Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9781510629202

ISBN: (纸本)9781510629196

This manuscript explores a new approach for spectral X-ray tomography that uses K-edge filtering structures to attain spectral and spatially coded illumination which enables the acquisition of compressive measurements for the reconstruction of energy-binned images. The system is coined compressive spectral X-ray imaging (CSXI). A multi-stage algorithm is proposed to solve the non-linear ill-posed problem using sparse and low-rank regularization constraints to exploit the structure of the spectral data cube. The proposed algorithm can reconstruct both the energy binned images as well as the material decomposition of the object given a set of basis materials. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image reconstruction

Compressive spectral image classification using 3D coded convolutional neural network

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arXiv 2020年

作者： Zhang, Hao Ma, Xu Zhao, Xianhong Arce, Gonzalo R. Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Hyperspectral image classification (HIC) is an active research topic in remote sensing. Hyperspectral images typically generate large data cubes posing big challenges in data acquisition, storage, transmission and processing. To overcome these limitations, this paper develops a novel deep learning HIC approach based on compressive measurements of coded-aperture snapshot spectral imagers (CASSI), without reconstructing the complete hyperspectral data cube. A new kind of deep learning strategy, namely 3D coded convolutional neural network (3D-CCNN) is proposed to efficiently solve for the classification problem, where the hardware-based coded aperture is regarded as a pixel-wise connected network layer. An end-to-end training method is developed to jointly optimize the network parameters and the coded apertures with periodic structures. The accuracy of classification is effectively improved by exploiting the synergy between the deep learning network and coded apertures. The superiority of the proposed method is assessed over the state-of-the-art HIC methods on several hyperspectral datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks