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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Carrasco, Victor Diaz Melgar, Bryan Barzola Sayan, Luis Quispe Palomares, Ricardo National Technological University of Lima South School of Electronic Engineering and Telecommunications Lima Peru National Technological University of Lima South Research Group of Computing and Cognitive Neuroscience Lima Peru

ISBN: (纸本)9798331529833

The development of the intelligent system "Tawna Yachaq"is presented for the safe movement of a volunteer with visual disabilities. This system includes a cane with an obstacle detection system, an artificial vision system for the identification of objects and people, with an alert and position warning system in a controlled environment. The mechanical system consists of an intelligent system protection box developed in Fusion 360 software. The electronic-electrical design of the intelligent system, based on the Raspberry Pi 4B microprocessor. Includes a Neo 6M GPS module to provide real-time user location. Using HC-SR04 ultrasonic sensors and a buzzer module, it emits an alert sound to the user. In addition, an ESP32-Cam camera that uses computer vision algorithms detects predefined objects in the university campus environment. The system is powered by 7.4V Lion rechargeable batteries, with an LM2596 voltage regulator ensuring stable operation. A patent is proposed as future work. © 2025 IEEE.

关键词： Sustainable development

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Tawna Yachaq an Intelligent System for the safe movement of people with visual disabilities

Tawna Yachaq an Intelligent System for the safe movement of ...

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Víctor Díaz Carrasco Bryan Barzola Melgar Luis Quispe Sayán Ricardo Palomares School of Electronic Engineering and Telecommunications National Technological University of Lima South Lima Peru Research Group of Computing and Cognitive Neuroscience National Technological University of Lima South Lima Peru

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The development of the intelligent system "Tawna Yachaq" is presented for the safe movement of a volunteer with visual disabilities. This system includes a cane with an obstacle detection system, an artificial vision system for the identification of objects and people, with an alert and position warning system in a controlled environment. The mechanical system consists of an intelligent system protection box developed in Fusion 360 software. The electronic-electrical design of the intelligent system, based on the Raspberry Pi 4B microprocessor. Includes a Neo 6M GPS module to provide real-time user location. Using HC-SR04 ultrasonic sensors and a buzzer module, it emits an alert sound to the user. In addition, an ESP32-Cam camera that uses computer vision algorithms detects predefined objects in the university campus environment. The system is powered by 7.4V Lion rechargeable batteries, with an LM2596 voltage regulator ensuring stable operation. A patent is proposed as future work.

关键词： Visualization Renewable energy sources Regulators Software Real-time systems Wind turbines Intelligent systems Voltage control Sustainable development Protection

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Atypical brain function hierarchy in autism spectrum disorder: insights from a novel analytical approach based on neuronal oscillation pattern

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European Child and Adolescent Psychiatry 2025年 1-13页

作者： Zhang, Yunge Li, Huanjie Gu, Wenyu Gong, Guanyu Chen, Aiguo Zhou, Dongyue Song, Yang Lin, Lin Zheng, Siyu Deng, Zhou Bapi, Raju Surampudi Sun, Jin Cong, Fengyu Beckmann, Christian F. Central Hospital of Dalian University of Technology Dalian University of Technology Dalian China Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology) Ministry of Education Dalian China Graduate School of Dalian Medical University Dalian China The Institute for Translational Medicine Affiliated Zhongshan Hospital of Dalian University Dalian China Nanjing Sport Institute Nanjing China Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Radboud University Medical Centre Nijmegen Netherlands Cognitive Science Lab International Institute of Information Technology Hyderabad India Center of Women and Children’s Health Research Faculty of Medicine Dalian University of Technology - Dalian Women and Children’s Medical Group Dalian China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Hierarchy is the basic character of the human brain. Neuronal oscillation is one of the fundamental features of brain function, revealing abnormal hierarchical structures in psychiatric disorders from a system-level perspective. However, to date, no research has yet quantified the normal and abnormal brain functional hierarchy based on oscillation patterns. Therefore, this study aimed to quantify brain hierarchy based on neuronal oscillation patterns using the wide-scale information across multiple frequency bands of functional magnetic resonance imaging (fMRI) data and further investigate atypical oscillation patterns in autism spectrum disorder (ASD) at the system level. We analyzed resting-state fMRI data from the Autism Brain Imaging Data Exchange II, including 132 participants with ASD and 132 healthy controls. The energy distribution patterns (EDPs) across frequency bands were calculated for different brain networks using multivariate empirical mode decomposition and Hilbert Transform to represent oscillation patterns. The gradient analysis was applied to quantify the EDP segregation among networks, and the network median distance of gradients was compared between the two groups. The k-means clustering was applied to intuitively verify the atypical EDP in ASD. Across all participants, we observed that the EDPs of different brain regions were spatially coupled to the brain hierarchy. Compared to healthy controls, the ASD exhibited reduced segregation between unimodal and transmodal regions on both energy gradient and clustering analyses, correlating with social deficits. Our results quantitatively confirm that oscillation patterns can reflect the functional segregation among networks and provide novel evidence of the system-level imbalances in neuronal oscillations in ASD. © Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Autism spectrum disorder Energy distribution Functional gradient Functional hierarchy Multivariate empirical mode decomposition

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Making human connectome faster: GPU acceleration of brain network analysis

Making human connectome faster: GPU acceleration of brain ne...

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16th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2010

作者： Wu, Di Wu, Tianji Shan, Yi Wang, Yu He, Yong Xu, Ningyi Yang, Huazhong Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University China Microsoft Research Asia Hardware Computing Group China

ISBN: (纸本)9780769543079

The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous computation. As a result, most current studies of the Brain Networks are focused on a coarse scale based on Brain Regions. Networks on this scale usually consist around 100 nodes. The more accurate and meticulous voxel-base Brain Networks, on the other hand, may consist 20K to 100K nodes. In response to the difficulties of analyzing large-scale networks, we propose an acceleration framework for voxel-base Brain Network Analysis based on Graphics Processing Unit (GPU). Our GPU implementations of Brain Network construction and modularity achieve 24x and 80x speedup respectively, compared with single-core CPU. Our work makes the processing time affordable to analyze multiple large-scale Brain Networks. © 2010 IEEE.

关键词： Graphics processing unit

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Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis

Making Human Connectome Faster: GPU Acceleration of Brain Ne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Di Wu Tianji Wu Yi Shan Yu Wang Yong He Ningyi Xu Huazhong Yang Department of Electronic Engineering Tsinghua National Laboratory of Information Science and Technology Tsinghua University China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University China Hardware Computing Group Microsoft Research Asia China

关键词： Graphics processing unit Correlation Algorithm design and analysis Instruction sets Sparse matrices Eigenvalues and eigenfunctions Hardware

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A heterogeneous accelerator platform for multi-subject voxel-based brain network analysis 11

A heterogeneous accelerator platform for multi-subject voxel...

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IEEE International Conference on Computer-Aided Design

作者： Yu Wang Mo Xu Ling Ren Xiaorui Zhang Di Wu Yong He Ningyi Xu Huazhong Yang Department of Electronic Engineering Tsinghua National Laboratory for Information Science and Technology Tsinghua University China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University China Hardware Computing Group Microsoft Research Asia China

ISBN: (纸本)9781457713989

The research on understanding the human brain has attracted more and more attention. A promising method is to model the brain as a network based on modern imaging technologies and then to apply graph theory algorithms for analysis. In this work, we examine the computing bottleneck of this method, and propose a CPU-GPU heterogeneous platform to accelerate the process. We construct a statistical brain network from a sample of 198 people and get characteristics such as nodal degree and modularity. This is the first study of voxel-based brain networks on large samples. We also illustrate that domain-specific hardware platform can have a significant impact on neuroscience studies.

关键词： Graphics processing unit Correlation Algorithm design and analysis Humans Symmetric matrices Acceleration Computational modeling

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Spatial prepositions and vague quantifiers: Implementing the functional geometric framework

Spatial prepositions and vague quantifiers: Implementing the...

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International Conference Spatial Cognition 2004: Spatial Cognition IV - Reasoning, Action, Interaction

作者： Coventry, Kenny R. Cangelosi, Angelo Rajapakse, Rohanna Bacon, Alison Newstead, Stephen Joyce, Dan Richards, Lynn V. Centre for Thinking and Language School of Psychology University of Plymouth Drake Circus Plymouth PL4 8AA United Kingdom Adaptive Behaviour and Cognition Research Group School of Computing Comms and Electronics University of Plymouth Drake Circus Plymouth PL4 8AA United Kingdom Institute of Cognitive Neuroscience University College London Alexandra House 17 Queen Square WC1N 3AR United Kingdom

There is much empirical evidence showing that factors other than the relative positions of objects in Euclidean space are important in the comprehension of a wide range of spatial prepositions in English and other languages. We first the overview the functional geometric framework [11] which puts "what" and "where" information together to underpin the situation specific meaning of spatial terms. We then outline an implementation of this framework. The computational model for the processing of visual scenes and the identification of the appropriate spatial preposition consists of three main modules: (1) Vision Processing, (2) Elman Network, (3) Dual-Route Network. Mirroring data from experiments with human participants, we show that the model is both able to predict what will happen to objects in a scene, and use these judgements to influence the appropriateness of over/under/above/below to describe where objects are located in the scene. Extensions of the model to other prepositions and quantifiers are discussed. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Geometrical optics

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Deep Learning for Plant Classification in Precision Agriculture

Deep Learning for Plant Classification in Precision Agricult...

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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Carlos A. Mamani Diaz Edgar E. Medina Castaneda Carlos A. Mugruza Vassallo Department of Electronic and Telecommunication Engineering South Lima (UNTELS) Lima Peru Department of Electrical Engineering Federal University of Rio de Janeiro Rio de Janeiro Brazil Research group in Computing and Cognitive Neuroscience National University of Technology South Lima (UNTELS) Lima Peru

Deep learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multidisciplinary agriculture technologies domain. In this research, we present a deep learning classification system of diverse plants, in order to enable precision agriculture applications. This classification problem was achieved thanks to the public dataset “Plant Seedlings Dataset”, which contains images of approximately 960 unique plants belonging to 12 species at several growth stages. The database has been from Aarhus University Flakkebjerg research Station in collaboration between the University of Southern Denmark and Aarhus University. A classification comparison was used to determinate which of three pre-trained models; InceptionV3, VGG16 and Xception; reach the best accuracy performance for the database used in this work. Results determined that (1) Xception was the best model for plant classification obtaining 86.21%, overcoming other networks in 7.37% with a time processing around 741 seconds. (2) GPU hardware changes the classification model results impacting strongly in their accuracy score.

关键词： Agriculture Deep learning Production Companies Training Computational modeling

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25th Annual Computational neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC neuroscience 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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An Early Stage researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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