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Dual-Functional Waveform Design With Local Sidelobe Suppression via OTFS Signaling

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 14044-14049页

作者： Zhang, Kecheng Yuan, Weijie Fan, Pingzhi Wang, Xianbin Southern Univ Sci & Technol Sch Syst Design & Intelligent Manu facturing Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Shenzhen Key Lab Robot & Comp Vis Shenzhen 518055 Peoples R China Southwest Jiaotong Univ CSNMT Int Coop Res Ctr MoST Informat Coding & Transmiss Key Lab Sichuan Prov Chengdu 61175 Peoples R China Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada

Integrated sensing and communication (ISAC) is viewed as a key technology in future wireless networks. One of the main challenges in realizing ISAC is developing dual-functional waveforms that can communicate with communication receivers and perform radar sensing simultaneously. In this paper, we consider the joint design of a dual-functional orthogonal time-frequency space (OTFS) signal and a receiving filter for the ISAC system. The problem of ISAC waveform design is formulated as the minimization of the weighted integrated sidelobe level (WISL) of the ambiguity function and the interference term from ISAC waveform, with constraints on signal-to-noise ratio loss. The majorization-minimization algorithm combined with alternating iterative minimization is implemented to solve the optimization problem. Simulation results show that the WISL and the interference term can be significantly decreased to guarantee achievable data rates and detection performance.

关键词： Sensors Optimization Interference Radar Symbols Information filters Delays Integrated sensing and communication OTFS weighted integrated sidelobe level majorization-minimization method

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Modeling Learners’ Programming Skills and Question Levels Through Machine Learning 22nd

Modeling Learners’ Programming Skills and Question Levels T...

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22nd International Conference on Human-Computer Interaction, HCI International 2020

作者： Kim, WooJeong Rhim, Soyoung Choi, John Y. J. Han, Kyungsik Ajou University Suwon Korea Republic of Coding Robot Lab Inc. Seoul Korea Republic of

ISBN: (纸本)9783030607029

Many universities have started to adopt online programming tools to support students’ programming practice, yet the services currently offered by the existing tools are somewhat passive with respect to considering a student’s programming skill level and providing appropriate code questions. To enhance students’ learning experience and improve their programming skills, it would be helpful to examine students’ programming abilities and provide them with the most suitable code questions and guidelines. Machine learning can play a role in modeling the level of students’ programming skills as well as the difficulty of questions by taking the students’ programming experience and code submissions into account. This paper presents a study on the development of machine learning models to classify the levels of students’ programming skills and those of programming questions, based on the data of students’ code submissions. We extracted a total of 197 features of code quality, code readability and system time. We used those features to build classification models. The model for the student level (four classes) and the question level (five classes) yielded 0.60 and 0.82 F1-scores, respectively, showing reasonable classification performance. We discuss our study highlights and their implications, such as group and question matching based on code submissions and user experience improvement. © 2020, Springer Nature Switzerland AG.

关键词： Students

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4D Single-particle tracking with asynchronous read-out single-photon avalanche diode array detector

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NATURE COMMUNICATIONS 2024年第1期15卷 6188页

作者： Bucci, Andrea Tortarolo, Giorgio Held, Marcus Oliver Bega, Luca Perego, Eleonora Castagnetti, Francesco Bozzoni, Irene Slenders, Eli Vicidomini, Giuseppe Ist Italiano Tecnol Mol Microscopy & Spect Genoa Italy Univ Genoa Dipartimento Informat Bioingn Robot Ingn Sistemi Genoa Italy Ist Italiano Tecnol Non Coding RNAs Physiol & Pathol Genoa Italy Sapienza Univ Dept Biol & Biotechnol Charles Darwin Rome Italy Ecole Polytech Fed Lausanne Lab Expt Biophys Lausanne Switzerland Univ Lausanne Ctr Integrat Genom Lausanne Switzerland

Single-particle tracking techniques enable investigation of the complex functions and interactions of individual particles in biological environments. Many such techniques exist, each demonstrating trade-offs between spatiotemporal resolution, spatial and temporal range, technical complexity, and information content. To mitigate these trade-offs, we enhanced a confocal laser scanning microscope with an asynchronous read-out single-photon avalanche diode array detector. This detector provides an image of the particle's emission, precisely reflecting its position within the excitation volume. This localization is utilized in a real-time feedback system to drive the microscope scanning mechanism and ensure the particle remains centered inside the excitation volume. As each pixel is an independent single-photon detector, single-particle tracking is combined with fluorescence lifetime measurement. Our system achieves 40 nm lateral and 60 nm axial localization precision with 100 photons and sub-millisecond temporal sampling for real-time tracking. Offline tracking can refine this precision to the microsecond scale. We validated the system's spatiotemporal resolution by tracking fluorescent beads with diffusion coefficients up to 10 mu m2/s. Additionally, we investigated the movement of lysosomes in living SK-N-BE cells and measured the fluorescence lifetime of the marker expressed on a membrane protein. We expect that this implementation will open other correlative imaging and tracking studies. Here, the authors upgrade a confocal laser scanning microscope with a single-photon array detector, achieving 40 nm lateral and 60 nm axial localisation precision with 100 photons and a sub-millisecond temporal sampling for real-time single-particle tracking with fluorescence lifetime measurement.

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Industrial Applications of Ultrahigh Definition Video coding With an Optimized Supersample Adaptive Offset Framework

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第12期16卷 7613-7623页

作者： Wang, Miaohui Xie, Wuyuan Zhang, Jia Qin, Jing Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Guangdong Lab Artificial Intelligence & Digital E Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China IQIYI Inc Coding Algorithm Grp Shenzhen 518000 Peoples R China Hong Kong Polytech Univ Kowloon Hong Kong Peoples R China

This article presents an efficient superblock-based sample adaptive offset (superSAO) that jointly exploits block-wise filter partition and probabilistic band interval segmentation to improve quality-of-experience of industrial video applications. Specifically, we investigate the partition flexibility of a superSAO block whose root size is up to 256 x 256, and propose to optimize the block-wise SAO filter by considering computation complexity and compression efficiency. Furthermore, we segment the band interval by equal probability of the sample intensity distributions, which facilitates the computation of better band offsets to attenuate ringing artifact due to quantization errors or encoded motion vectors. Experimental results show that the proposed superSAO method outperforms state-of-the-art approaches by obtaining 4.6% bandwidth reduction on average for the low delay and high-compression video applications.

关键词： Encoding Video coding Streaming media Indexes Informatics Quality of experience UHDTV High efficiency video coding (HEVC) in-loop filter sample adaptive offset (SAO) super-block video coding ultrahigh definition (UHD)

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Hippocampal Neurogenesis Reduces the Dimensionality of Sparsely Coded Representations to Enhance Memory Encoding

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2019年 12卷 99页

作者： DeCostanzo, Anthony J. Fung, Chi Chung Alan Fukai, Tomoki RIKEN Ctr Brain Sci Lab Neural Coding & Brain Comp Saitama Japan Ascent Robot Inc Tokyo Japan

Adult neurogenesis in the hippocampal dentate gyrus (DG) of mammals is known to contribute to memory encoding in many tasks. The DG also exhibits exceptionally sparse activity compared to other systems, however, whether sparseness and neurogenesis interact during memory encoding remains elusive. We implement a novel learning rule consistent with experimental findings of competition among adult-born neurons in a supervised multilayer feedforward network trained to discriminate between contexts. From this rule, the DG population partitions into neuronal ensembles each of which is biased to represent one of the contexts. This corresponds to a low dimensional representation of the contexts, whereby the fastest dimensionality reduction is achieved in sparse models. We then modify the rule, showing that equivalent representations and performance are achieved when neurons compete for synaptic stability rather than neuronal survival. Our results suggest that competition for stability in sparse models is well-suited to developing ensembles of what may be called memory engram cells.

关键词： dimensionality reduction hippocampus pattern separation neuromorphic computing feed-forward neural network synaptic pruning synaptic turnover synaptic plasticity

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MedShapeNet - a large-scale dataset of 3D medical shapes for computer vision

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2025年第1期70卷 71-90页

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Viet Duc Vu Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Maechler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Kuestner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Loeffler, Maximilian T. Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nuernberger, Andreas Pedrosa, Joo Ferreira, Carlos Aresta, Guilherme Cunha, Antonio Campilho, Aurelio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Roehrig, Rainer Hoelzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, Andre Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hoerst, Fabian Brehmer, Alexan Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Girardetstr 2 D-45131 Essen Germany Graz Univ Technol Inst Comp Graph & Vis ICG Inffeldgasse 16c A-8010 Graz Austria Comp Algorithms Med Lab Cafe Graz Austria Univ Med Essen AoR Canc Res Ctr Cologne Essen CCCE Hufelandstr 55 D-45147 Essen Germany Univ Med Essen Univ Hosp Essen Ctr Virtual & Extended Real Med ZvRM Hufelandstr 55 D-45147 Essen Germany Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Swiss Fed Inst Technol Lausanne EPFL Comp Vis Lab Lausanne Switzerland Univ Hosp RWTH Aachen Dept Oral & Maxillofacial Surg Aachen Germany Shanghai Jiao Tong Univ Renji Hosp Sch Med Dept Radiol Shanghai Peoples R China AGH Univ Sci & Technol Dept Measurement & Elect Krakow Poland Univ Appl Sci Western Switzerland HES SO Valais Inst Informat Syst Sierre Switzerland Univ Basel Dept Biomed Engn Ctr Med Image Anal & Nav CIAN Allschwil Switzerland Univ Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA ZhejiangLab Res Ctr Connected Healthcare Big Data Hangzhou Zhejiang Peoples R China Graz Univ Technol Inst Mech Graz Austria Justus Liebig Univ Giessen Univ Hosp Giessen Dept Diagnost & Intervent Radiol Giessen Germany Mehran Univ Engn & Technol Dept Mech Engn Jamshoro Sindh Pakistan Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Hosp Canadian Surg Technol & Adv Robot CSTAR London ON Canada Shanghai Jiao Tong Univ Inst Biomed Mfg & Life Qual Engn Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai Peoples R China Med Univ Graz Dept Cardiac Surg Graz Austria Techn Univ Munchen Univ Klinikum Geschaftsfuhrender Oberarzt Abt Intervent & Diagn Munich Germany Icometrix Leuven Belgium Tech Univ Munich Dept Informat Garching Germany Univ Zurich Dept Quantitat Biomed Zurich Switzerland Tech Univ Dortmund Fac Stat Dortmund Germany Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Univ Techn Univ Munchen Geschaftsfuhrender Oberarzt Abt Interv

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://***/.

关键词： 3D medical shapes benchmark anatomy education shapeomics augmented reality virtual reality

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Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019

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LANCET PUBLIC HEALTH 2022年第8期7卷 E657-E669页

作者： Peden, Amy E. Cullen, Patricia Francis, Kate Louise Moeller, Holger Peden, Margaret M. Ye, Pengpeng Tian, Maoyi Zou, Zhiyong Sawyer, Susan M. Aali, Amirali Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen Abdelmasseh, Michael Abdoun, Meriem Abd-Rabu, Rami Abdulah, Deldar Morad Abdurehman, Ame Mehadi Abebe, Getachew Abebe, Ayele Mamo Abedi, Aidin Abidi, Hassan Aboagye, Richard Gyan Ali, Hiwa Abubaker Abu-Gharbieh, Eman Adane, Denberu Eshetie Adane, Tigist Demssew Addo, Isaac Yeboah Adewole, Ololade Grace Adhikari, Sangeet Adnan, Mohammad Adnani, Qorinah Estiningtyas Sakilah Afolabi, Aanuoluwapo Adeyimika Bachelor Afzal, Saira Afzal, Muhammad Sohail Aghdam, Zahra Babaei Ahinkorah, Bright Opoku Ahmad, Araz Ramazan Ahmad, Tauseef Ahmad, Sajjad Ahmadi, Ali Ahmed, Haroon Ahmed, Muktar Beshir Ahmed, Ali Ahmed, Ayman Ahmed, Jivan Qasim Rashid, Tarik Ahmed Aithala, Janardhana P. Aji, Budi Akhlaghdoust, Meisam Alahdab, Fares Alanezi, Fahad Mashhour Alemayehu, Astawus Al Hamad, Hanadi Ali, Syed Shujait Ali, Liaqat Alimohamadi, Yousef Alipour, Vahid Aljunid, Syed Mohamed Almidani, Louay Almustanyir, Sami Altirkawi, Khalid A. Alvis-Zakzuk, Nelson J. Ameyaw, Edward Kwabena Amin, Tarek Tawfik Amir-Behghadami, Mehrdad Amiri, Sohrab Amiri, Hoda Anagaw, Tadele Fentabil Andrei, Tudorel Andrei, Catalina Liliana Anvari, Davood Anwar, Sumadi Lukman Anyasodor, Anayochukwu Edward Arabloo, Jalal Arab-Zozani, Morteza Arja, Asrat Arulappan, Judie Arumugam, Ashokan Aryannejad, Armin Asgary, Saeed Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Attia, Sameh Aujayeb, Avinash Awedew, Atalel Fentahun Azadnajafabad, Sina Azangou-Khyavy, Mohammadreza Azari, Samad Jafari, Amirhossein Azari Azizi, Hosein Azzam, Ahmed Y. Badiye, Ashish D. Baghcheghi, Nayereh Bagherieh, Sara Baig, Atif Amin Bakkannavar, Shankar M. Balta, Asaminew Birhanu Banach, Maciej Banik, Palash Chandra Bansal, Hansi Bardhan, Mainak Barone-Adesi, Francesco Barrow, Amadou Bashiri, Azadeh Baskaran, Pritish Basu, Saurav Bayileyegn, Nebiyou Simegnew Bekel, Abebe Ayalew Bekele, UNSW Sydney Kensington NSW 2052 Australia Univ New South Wales Sch Publ Hlth & Community Med Sydney NSW Australia Univ New South Wales Populat Hlth Dept Sydney NSW Australia Univ New South Wales Sch Populat Hlth Sydney NSW Australia Univ New South Wales George Inst Global Hlth Sydney NSW Australia Univ New South Wales Ctr Social Res Hlth Sydney NSW Australia James Cook Univ Sch Publ Hlth Med & Vet Sci Townsville NSW Australia George Inst Global Hlth Global Womens Hlth Div Newtown NSW Australia George Inst Global Hlth Injury Div Newtown NSW Australia George Inst Global Hlth Dept Injury Newtown NSW Australia Murdoch Childrens Res Inst Ctr Adolescent Hlth Melbourne Vic Australia Imperial Coll London Sch Publ Hlth London England Imperial Coll London Dept Primary Care & Publ Hlth London England Imperial Coll London WHO Collaborating Ctr Publ Hlth Educ & Training London England George Inst Global Hlth TGI UK London England Chinese Ctr Dis Control & Prevent Natl Ctr Chron & Noncommunicable Dis Control & Pr Beijing Peoples R China Harbin Med Univ Sch Publ Hlth Harbin Peoples R China Peking Univ Inst Child & Adolescent Hlth Beijing Peoples R China Univ Melbourne Dept Paediat Parkville Vic Australia Murdoch Childrens Res Inst Ctr Adolescent Hlth Parkville Vic Australia Mashhad Univ Med Sci Fac Med Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci E Learning Ctr Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci Dept Nursing Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci Dept Med Informat Mashhad Razavi Khorasan Iran Mashhad Univ Med Sci Clinial Res Dev Unit Mashhad Razavi Khorasan Iran Shiraz Univ Med Sci Hlth Policy Res Ctr Shiraz Iran Shiraz Univ Med Sci Hlth Informat Management Dept Shiraz Iran Shiraz Univ Med Sci Dept Epidemiol Shiraz Iran Shiraz Univ Med Sci Dept Phys Therapy Shiraz Iran Shahid Beheshti Univ Med Sci Social Determinants Hlth Res Ctr Tehran Iran Shahid Beheshti Univ Med Sci Dept Epidemiol Tehra

Background Globally, transport and unintentional injuries persist as leading preventable causes of mortality and morbidity for adolescents. We sought to report comprehensive trends in injury-related mortality and morbidity for adolescents aged 10-24 years during the past three decades. Methods Using the Global Burden of Disease, Injuries, and Risk Factors 2019 Study, we analysed mortality and disability-adjusted life-years (DALYs) attributed to transport and unintentional injuries for adolescents in 204 countries. Burden is reported in absolute numbers and age-standardised rates per 100 000 population by sex, age group (10-14, 15-19, and 20-24 years), and sociodemographic index (SDI) with 95% uncertainty intervals (UIs). We report percentage changes in deaths and DALYs between 1990 and 2019. Findings In 2019, 369 061 deaths (of which 214337 [58%] were transport related) and 31.1 million DALYs (of which 16.2 million [52%] were transport related) among adolescents aged 10-24 years were caused by transport and unintentional injuries combined. If compared with other causes, transport and unintentional injuries combined accounted for 25% of deaths and 14% of DALYs in 2019, and showed little improvement from 1990 when such injuries accounted for 26% of adolescent deaths and 17% of adolescent DALYs. Throughout adolescence, transport and unintentional injury fatality rates increased by age group. The unintentional injury burden was higher among males than females for all injury types, except for injuries related to fire, heat, and hot substances, or to adverse effects of medical treatment. From 1990 to 2019, global mortality rates declined by 34.4% (from 17.5 to 11.5 per 100 000) for transport injuries, and by 47.7% (from 15.9 to 8.3 per 100000) for unintentional injuries. However, in low-SDI nations the absolute number of deaths increased (by 80.5% to 42 774 for transport injuries and by 39.4% to 31 961 for unintentional injuries). In the high-SDI quintile in 2010-19, the

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