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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Cross-Language Transfer Learning-based Lhasa-Tibetan Speech Recognition

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computers, Materials & Continua 2022年第10期73卷 629-639页

作者： Zhijie Wang Yue Zhao Licheng Wu Xiaojun Bi Zhuoma Dawa Qiang Ji School of Information Engineering Minzu University of ChinaBeijing100081China School of Chinese Ethnic Minority Languages and Literatures Minzu University of ChinaBeijing100081China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic InstituteTroyNY 12180-3590USA

As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until ***,along with the relatively small Tibetan corpus,has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end *** paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training *** demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects:modeling unit selection,transfer learning method,and source language *** results show that the Chinese-Tibetan multi-language learning method using multilanguage character set as the modeling unit yields the best performance on Tibetan Character Error Rate(CER)at 27.3%,which is reduced by 26.1%compared to the language-specific *** our method also achieves the 2.2%higher accuracy using less amount of data compared with the method using Tibetan multi-dialect transfer learning under the same model structure and data set.

关键词： Cross-language transfer learning low-resource language modeling unit Tibetan speech recognition

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Dynamic Environment-based Visual User Interface System for Intuitive Navigation Target Selection for Brain-actuated Wheelchairs

Dynamic Environment-based Visual User Interface System for I...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ricardo Pereira Aniana Cruz Luís Garrote Gabriel Pires Ana Lopes Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Portugal

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Visual user interface paradigms are one of the key modules for brain-actuated wheelchairs, which are a class of promising assistive devices that can increase the autonomy and mobility of people suffering from severe motor impairments. This work proposes a Dynamic Environment-based Visual Interface System (DEVIS) for intuitive navigation target selection for brain-actuated wheelchairs. It is composed of a novel Dynamic Visual Interface (DVI), an RGB image-based perception module (indoor scene classification, object detection and classification, and object tracking), and a P300-based BCI. The DVI displays potential navigation goals in three forms of visual cues: an RGB camera image streaming with object bounding boxes overlaid on objects detected and tracked, three global points of interest (indoor places), and two static commands. Hence, the DVI allows a user to select, through the P300-based BCI, navigation targets/commands that are flashing independently and randomly to create an oddball paradigm. Experimental evaluations were carried out in a dynamic setting with 5 participants, who were asked to select targets displayed in the DVI. The dynamic setting was obtained using RGB image sequences of the ISR-RGB-D Dataset, which represents a mission performed by a mobile robotic platform. The target selection was performed through a P300-based BCI, using non-self-paced and self-paced modalities. The obtained results show that the proposed DEVIS can run in real-time and that users are able to correctly select targets with high BCI accuracy rates.

关键词： Visualization Target tracking Navigation Wheelchairs User interfaces Streaming media Real-time systems

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Action recognition through fusion of sEMG and skeletal data in feature level

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Journal of Ambient Intelligence and Humanized Computing 2022年第9期13卷 4125-4134页

作者： Wang, Xinming Ding, Weili Bian, Shuangshuang Liu, Honghai Department of Automation Institute of Electrical Engineering Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Yanshan University 438 West of Hebei Avenue Haigang District Qinhuangdao066004 China School of Mechanical Engineering and Automation State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen Nanshan District Shenzhen518055 China

Human action can be recognized through a unimodal way. However, the information obtained from a single mode is limited due to the fact that a single mode contains only one type of physical attribute. Therefore, it is motivational to improve the accuracy of actions through fusion of two different complementary modality, which are the surface electromyography (sEMG) and the skeletal data. In this paper, we propose a general framework of fusion of sEMG signals and skeletal data. Firstly, vector of locally aggregated descriptor (VLAD) was extracted from sEMG sequences and skeletal sequences, respectively. Secondly, features obtained from sEMG and skeletal data are mapped through different weighted kernels using multiple kernel learning. Finally, the classification results are obtained through the model of multiple kernel learning. A dataset of 18 types of human actions is collected via KinectV2 and Thalmic Myo armband to verify our ideas. The experimental results show that the accuracy of human action recognition are improved by combining skeletal data with sEMG signals. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Musculoskeletal system

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A Compositional Resilience Index for Computationally Efficient Safety Analysis of Interconnected systems 62

A Compositional Resilience Index for Computationally Efficie...

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62nd IEEE Conference on Decision and Control, CDC 2023

作者： Niu, Luyao Maruf, Abdullah Al Clark, Andrew Mertoguno, J. Sukarno Poovendran, Radha University of Washington Network Security Lab Department of Electrical and Computer Engineering SeattleWA98195-2500 United States California State University Department of Electrical and Computer Engineering Los AngelesCA90032 United States McKelvey School of Engineering Washington University in St. Louis Electrical and Systems Engineering Department St. LouisMO63130 United States School of Cybersecurity and Privacy Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798350301243

Interconnected systems such as power systems and chemical processes are often required to satisfy safety properties in the presence of faults and attacks. Verifying safety of these systems, however, is computationally challenging due to nonlinear dynamics, high dimensionality, and combinatorial number of possible faults and attacks that can be incurred by the subsystems interconnected within the network. In this paper, we develop a compositional resilience index to verify safety properties of interconnected systems under faults and attacks. The resilience index is a tuple serving the following two purposes. First, it quantifies how a safety property is impacted when a subsystem is compromised by faults and attacks. Second, the resilience index characterizes the needed behavior of a subsystem during normal operations to ensure safety violations will not occur when future adverse events occur. We develop a set of sufficient conditions on the dynamics of each subsystem to satisfy its safety constraint, and leverage these conditions to formulate an optimization program to compute the resilience index. When multiple subsystems are interconnected and their resilience indices are given, we show that the safety constraints of the interconnected system can be efficiently verified by solving a system of linear inequalities. We demonstrate our developed resilience index using a numerical case study on chemical reactors connected in series. © 2023 IEEE.

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Safety Embedded Adaptive Control Using Barrier States

arXiv

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arXiv 2025年

作者： AL-Sunni, Maitham F. Almubarak, Hassan Dolan, John M. Electrical and Computer Engineering Department Carnegie Mellon University PittsburghPA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Robotics Institute Carnegie Mellon University PittsburghPA United States

In this work, we explore the application of barrier states (BaS) in the realm of safe nonlinear adaptive control. Our proposed framework derives barrier states for systems with parametric uncertainty, which are augmented into the uncertain dynamical model. We employ an adaptive nonlinear control strategy based on a control Lyapunov functions approach to design a stabilizing controller for the augmented system. The developed theory shows that the controller ensures safe control actions for the original system while meeting specified performance objectives. We validate the effectiveness of our approach through simulations on diverse systems, including a planar quadrotor subject to unknown drag forces and an adaptive cruise control system, for which we provide comparisons with existing methodologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Self adjusting control systems

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Foundry Manufactured 6-Bit Resolution, 150μm Long Slow-Light Electro-Optic Modulator for On-Chip Photonic Tensor Computing

Foundry Manufactured 6-Bit Resolution, 150μm Long Slow-Ligh...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Meng Zhang Amir Begović Dennis Yin Nicholas Gangi Jiaqi Gu Rena Huang Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Arizona State University School of Electrical Computer and Energy Engineering Tempe AZ USA

We demonstrate 6-bit DAC resolution with an ultra-compact slow-light electro-optic modulator. The 10× modulation length reduction enables 31 × compute density, 1.17 × energy efficiency, and 36.1 × ... 详细信息

ISBN: (纸本)9798350369311

关键词： Tensors Scientific computing Scalability Energy resolution Modulation Machine learning Electro-optic modulators Energy efficiency System-on-chip Photonics

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An Effective Two-time Scale Composite Control Contraction Based Chaotic Trajectory Tracking of Two-link Flexible Manipulator

An Effective Two-time Scale Composite Control Contraction Ba...

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IEEE Conference on Decision and Control

作者： Kshetrimayum Lochan Asim Khan Binoy Krishna Roy Bidyadhar Subudhi Lakmal Seneviratne Irfan Hussain Department of Mechanical Engineering Khalifa University Center for Robotics and Autonomous Systems (KUCARS) Khalifa University United Arab Emirates Department of Electrical Engineering National Institute of Technology Silchar India Department of Electrical Engineering National Institute of Technology Warangal India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, a two-link flexible manipulator n-dimensional model is developed using the assumed modes method. Based on this model, the manipulator dynamics are segregated into two subsystems by the two-time scale decomposition method of singular perturbation. Subsequently, a contraction-based control theory and a backstepping control of composite controller are investigated for the desired chaotic trajectory tracking along with tip deflection vibration suppression. In the two subsystems, the slow subsystem is involved in the modelling of the joint angles, and the fast subsystem is for corrected flexible modes of vibration suppression. In order to guarantee strict stability, Lyapunov’s stability is realized for closed-loop system uniform boundedness. Thus, by choosing the control parameters appropriately, the system states converge to a neighborhood of asymptotic stability. Eventually, extensive validation by comparative simulations of the Quanser model of the two-link flexible manipulator is carried out to demonstrate and indicate the effectiveness of the proposed composite controller in terms of faster tip deflection vibration suppression and better trajectory tracking.

关键词： Vibrations Asymptotic stability Backstepping Trajectory tracking Perturbation methods Stability analysis Robustness Control theory Complexity theory Manipulator dynamics

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