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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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MT8 Cost-Consequences Analysis (CCA) of Echocardiography Digital Artificial Intelligence (AI) Monitoring Application Developed at Hosmartai (Horizon 2020 Funded)

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Value in Health 2024年第12期27卷 S488-S488页

作者： Chatzikou, M Latsou, D Apostolidis, G Charisis, V Hadjidimitriou, S Hadjileontiadis, L Pagkourelias, E Vassilikos, V Pharmecons Easy Access LtD York UK Signal Processing & Biomedical Technology Unit Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Biomedical Engineering Khalifa University Abu Dhabi Abu Dhabi United Arab Emirates 3rd Cardiology Department Hippokrateion Hospital Medical School Aristotle University of Thessaloniki Thessaloniki Greece

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Review of Image-Guided Percutaneous Coronary Interventions

Review of Image-Guided Percutaneous Coronary Interventions

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2019 Advances in Science and engineering Technology International Conferences, ASET 2019

作者： Alatoom, Aseel Farhat, Jumana Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering Biomedical Engineering Graduate Program Biosciences and Bioengineering Research Institute American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781538682715

Image-guided surgery (IGS) involves using surgical instruments tracking system to guide the surgeon during invasive surgical procedures. The use of preoperative or intraoperative images is very essential in providing navigation for different medical applications. In this paper, a literature review of different imaging modalities that aid in the diagnosis and treatment of coronary artery disease (CAD) is proposed. The imaging modalities discussed include x-ray fluoroscopy, coronary computed tomography angiography (CCTA), fractional flow reserve (FFR), cardiac single-photon emission computed tomography (SPECT), cardiovascular magnetic resonance imaging (CMRI) and ultrasound. Additionally, pseudoaneurysms, which can result from percutaneous coronary interventions (PCI) procedure, are reviewed as well as the treatment procedure. Finally, a comparison between the different imaging modalities is carried out, based on the number of papers that have been published, in addition to the sensitivity and specificity for each modality according to PubMed and Web of Science databases. © 2019 IEEE.

关键词： Ultrasonics

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Development of automatic rice plant pest detection system based on convolutional neural network

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AIP Conference Proceedings 2023年第1期2482卷

作者： Matthew Sjarah Winda Astuti Lie Zener Ari Fadli 1)Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480 2)Electrical Departement Faculty of Engineering Jenderal Soedirman University Purwokerto Jawa Tengah Indonesia 53122

Rice is the most important food crop in Indonesia, one of the plants that is quite significant and as a daily staple food for the people of Indonesia. The Indonesia government through the ministry of Agriculture tries to maintain the rice production by maintaining of the rice field. One the challenging situation that can involve the rice production is pest. Pests became one of the reasons which can reduce rice production. The decrease in rice production due to pest attack is an important problem in rice plant care. In this work, a pest detection system in rice plants developed using an intelligent system technique. The system involved image processing and intelligent technique. The system recognizes the kind of pests of the rice plant based on the feature of the image of the pets. Rice plant pest detection systems based on the pest image proceed by image procession technique and Convolution Neural Network (CNN). The system is working properly, since it resulting the training and testing accuracy of 99% and 90%, respectively.

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Model-centric volumetric point cloud attributes

arXiv

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arXiv 2021年

作者： de Queiroz, Ricardo L. Dorea, Camilo Freitas, Davi R. Krivokuća, Maja Sandri, Gustavo P. Computer Science Department Universidade de Brasilia Brasilia Brazil Post-Graduate Program at the Electrical Engineering Science Department Universidade de Brasilia Brasilia Brazil INRIA Rennes France Federal Institute of Brasilia Brasilia Brazil

Point clouds have recently gained interest, especially for real-time applications and for 3D-scanned material, such as is used in autonomous driving, architecture, and engineering, to model real estate for renovation or display. Point clouds are associated with geometry information and attributes such as color. Be the color unique or direction-dependent (in the case of plenoptic point clouds), it reflects the colors observed by cameras displaced around the object. Hence, not only are the viewing references assumed, but the illumination spectrum and illumination geometry is also implicit. We propose a model-centric description of the 3D object, that is independent of the illumination and of the position of the cameras. We want to be able to describe the objects themselves such that, at a later stage, the rendering of the model may decide where to place illumination, from which it may calculate the image viewed by a given camera. We want to be able to describe transparent or translucid objects, mirrors, fishbowls, fog and smoke. Volumetric clouds may allow us to describe the air, however "empty", and introduce air particles, in a manner independent of the viewer position. For that, we rely on some eletromagnetic properties to arrive at seven attributes per voxel that would describe the material and its color or transparency. Three attributes are for the transmissivity of each color, three are for the attenuation of each color, and another attribute is for diffuseness. These attributes give information about the object to the renderer, with whom lies the decision on how to render and depict each object. © 2021, CC BY.

关键词： Color

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A Fog Computing Framework for Cognitive Portable Ground Penetrating Radars

A Fog Computing Framework for Cognitive Portable Ground Pene...

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2019 IEEE International Conference on Communications, ICC 2019

作者： Wu, Dalei Omwenga, Maxwell M. Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781538680889

With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing. © 2019 IEEE.

关键词： Fog computing

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RSAC: Regularized subspace approximation classifier for lightweight continuous learning

arXiv

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arXiv 2020年

作者： Ho, Chih-Hsing Tsai, Shang-Ho Department of Electrical Engineering and Computer Science Honor Program National Chiao Tung University Hsinchu Taiwan Department of Electrical Engineering National Chiao Tung University Hsinchu Taiwan

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Approximation algorithms

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Autonomous cognitive gpr based on edge computing and reinforcement learning 2

Autonomous cognitive gpr based on edge computing and reinfor...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Omwenga, Maxwell M. Wu, Dalei Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781728129778

Autonomous cognitive ground penetrating radar (ACGPR), carried by drones or other robotic platforms, may perform robust and accurate subsurface object detection and recognition in varying environments based on real-time data processing and decision making. However, limited system computing resources and intelligence generating capability pose significant challenges for the operations of such systems. To address these challenges, in this paper we propose an ACGPR enabled by edge computing (EC) and reinforcement learning. Specifically, an edge computing based system architecture is presented to utilize edge resources for real-time intelligence generation. A reinforcement learning approach is developed as the decision-making model for the ACGPR to adaptively adjust its operational parameters. Simulation results show the accuracy and efficacy of the proposed ACGPR system. The framework also provides insight into the design of autonomous cognitive industrial Internet of things (IoT) supported by edge computing and machine learning. © 2019 IEEE.

关键词： Ground penetrating radar systems

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Deterministic Learning with Probabilistic Analysis on Human-Robot Shared Contro

Deterministic Learning with Probabilistic Analysis on Human-...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Xiaotian Chen Paolo Stegagno Chengzhi Yuan Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, Deterministic Learning (DL). With DL the robot is able to learn and recognize four specifically designed body gestures, which represent four corresponding moving directions (i.e., left, right, forward, and backward) of the controlled UGV. A Kinect camera is employed to collect human body skeleton data of a user. Eight specifically-designed features are extracted and utilized to train radial basis function neural networks (RBFNNs). The dynamics of the human arm waving motion is guaranteed to be accurately identified, represented, and stored as an RBFNN model with converged constant NN weights, which facilitates rapid recognition in the online identification phase. However, learning time of and storage space of RBFNNs grow exponentially with the number of features. In order drastically reduce required computations and storage space, we propose to split the features in subgroups, and use each subgroup to learn a smaller independent. In the online identification phase, the trained RBFNNs are used to analyze and identify any new incoming gestures. The identification results of all RBFNNs are then fused together following a probabilistic approach, and the gestures of the user are interpreted as commands for the UGV.

关键词： Feature extraction Probabilistic logic Data mining Dynamics Artificial neural networks Robots

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Sequence-Dependent Tyrosine-Containing Peptide Nano-Assemblies for Sensing Tyrosinase and Melanoma

SSRN

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SSRN 2022年

作者： Guo, Jun Zhao, Ruoyang Gao, Feng Li, Xiaofang Wang, Liping Zhang, Feng Quantum Biophotonic Lab School of Optical-Electrical and Computer-Engineering University of Shanghai for Science and Technology Shanghai200093 China Wenzhou Institute University of Chinese Academy of Sciences Wenzhou325001 China Oujiang Laboratory Zhejiang Wenzhou325000 China Pharmacy Laboratory Inner Mongolia International Mongolian Hospital Hohhot010065 China Department of Biomedical Engineering School of Basic Medical Sciences Guangzhou Medical University Guangzhou511436 China

Tyrosine-containing peptide nano-assemblies have received tremendous attention because of their potential applications in biomedicine and nanomaterial fields. However, a current outstanding challenge is to direct the equilibrium between oxidative polymerization of precursors and the non-covalent assembly to precisely tune their specific nano-structures and functionalities through the rational design of peptide sequences. With a simple library of tripeptides containing tyrosine, glycine and lysine, here we demonstrate how amino acid sequence encodes the property of tripeptide nano-assemblies by modulating the enzymatic oxidation of tyrosinase with the accompanied self-assembly, and thus selects the pathways towards fluorescent or melanin-like nano-assemblies. The fluorescence of tripeptide nano-assemblies has been demonstrated in sensing both tyrosinase and melanoma. Our findings will provide inspiration of peptide sequence design for generating the complex bioactive peptide nanomaterials for biomedical applications. © 2022, The Authors. All rights reserved.

关键词： Amino acids

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