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A body-machine interface for training selective pelvis movements in stroke survivors: a pilot study

A body-machine interface for training selective pelvis movem...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society

作者： S. Summa C. Pierella P. Giannoni A. Sciacchitano S. Iacovelli A. Farshchiansadegh F. A. Mussa-Ivaldi M. Casadio the Department of Informatics Bioengineering Robotics and System Engineering at the University of Genoa 16145 Genoa Italy the ART Education and Rehab Center Genoa Italy the Sensory Motor Performance Program Rehabilitation Institute of Chicago Chicago IL 60611 USA. the Sensory Motor Performance Program Rehabilitation Institute of Chicago Chicago IL 60611 USA

ISBN: (纸本)9781424492695

the body-machine interfaces (BMIs) map the subjects' movements into the low dimensional control space of external devices to reach assistive and/or rehabilitative goals. this work is a first proof of concept of this kind of BMI as tool for rehabilitation after stroke. We designed an exercise to improve the control of selective movements of the pelvis in stroke survivors, increasing the ability to decouple the motion in the sagittal and frontal planes and decreasing compensatory adjustments at the shoulder girdle. A Kinect sensor recorded the movements of the subjects. Subjects played different games by controlling the vertical and horizontal motion of a cursor on a screen with respectively the lateral tilt and the ante/retroversion of their pelvis. We monitored also the degrees of freedom not directly involved in cursor control, thus subjects could complete the task only with a correct posture. Our preliminary results highlight significant improvement not only in cursor control, but also in the Trunk Impairment Scale (TIS) and in the Five Times Sit to Stand Test (5xSST).

关键词： TIS 5xSST BMI Pelvis cursor control Movements proof of concept Stroke Pilot Studies Body Mass Index Horizontal motion BMI CONSOLE first proofs Survivors nonius

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Multi-Armed Bandit-Aided Near-Optimal Over-the-Air Updates in Multi-Band V2X Systems

Multi-Armed Bandit-Aided Near-Optimal Over-The-Air Updates i...

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IEEE international conference on Computer Communication and the Internet (ICCCI)

作者： Sherief Hashima Mostafa M. Fouda Kohei Hatano Eiji Takimoto Zubair Md Fadlullah Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Informatics Kyushu University Fukuoka Japan Department of Computer Science Western University London ON Canada

As autonomous and connected vehicles continue to garner much research attention, the automotive Over-the-Air (OTA) updates recently emerged as an important research topic. OTA is crucial to disseminate critical updates for safety and stability of on-board sensing and operational systems. In beyond 5G(BSG) systems, OTA may be regarded as cached and a service provided by cellular base stations and roadside units (RSUs). However, for large-size OTA dissemination, the Electronic control Units (ECUs) of vehicles need to download scheduled segments of the OTA payload from the serving RSU in an opportunistic manner, i.e., while stopping at the traffic signal or waiting in traffic. To maximize the downloadable payload per vehicle served by a RSU within a limited time window, we consider multi-band RSUs and ECUs as transmitting and receiving nodes, respectively. We consider legacy RF (radio frequency), mmWave (millimeter Wave), and visible light communication (VLC) bands at the RSU to provide large capacity links to the ECUs, respectively. However, the sub-channels of these frequency bands suffer from different blockage characteristics. We formulate this as a tradeoff problem in this paper in the presence of vehicular blockers, and propose a thompson Sampling (TS)-based opportunistic band selection to alleviate the computational burden on both the communicating RSU and ECU nodes. Based on extensive computer-based simulations, we demonstrate the performance of our proposal in contrast with an optimal (centralized) baseline, as well as other comparable heuristic-based solutions.

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SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds

SPARROW: Smart Precision Agriculture Robot for Ridding of We...

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Emerging Technology (INCET), international conference for

作者： Dhanushka Balasingham Sadeesha Samarathunga Gayantha Godakanda Arachchige Anuththara Bandara Sasini Wellalage Dinithi Pandithage Mahaadikara M.D.J.T Hansika Rajitha De Silva Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka City of Melbourne Australia Lincoln Agri-Robotics University of Lincoln United Kingdom

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

the advancements in precision agriculture are vi-tal to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemicalusage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. this proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds.

关键词： Precision agriculture Laser radar Costs Trajectory planning Navigation Spraying Crops

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Auto Landing System on A Fixed Wing Unmanned Aerial Vehicle Using Linear Quadratic Approach

Auto Landing System on A Fixed Wing Unmanned Aerial Vehicle ...

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international conference on Science and Technology-Computer (ICST)

作者： Andi Dharmawan Ahmad Ashari Ariesta Martiningtyas Handayani Riarsari Meirani Utami I Made Tresnayana Electronics and Instrumentation Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Computer System and Networking Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Informatics Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

the primary control issue on Unmanned Aerial Vehicle (UAV) is to control the rotation motion in the form of pitch, roll, and yaw. these rotational movements are used for various flight phases. the flight phases are at least divided into 3, consisting of taking off, cruising, and landing phases. the landing phase is one of the most critical phases. If the flight control of a UAV in this phase is not optimal, then it can crash. the landing phase control system in this research uses the Linear Quadratic Regulator (LQR) method. this method works on a full-state feedback control system. this control system will produce style and torque, both of which will be converted to PWM (Pulse Width Modulation) signals and the servos deflection angle values. the PWM signals play a role in regulating how fast the brushless motor rotates. the control system is used to maintain the stability of the UAV upon landing so that the UAV does not experience undesirable things. Landing phase testing results show that the control system has been able to land the UAV with an optimal control response.

关键词： Unmanned aerial vehicles control systems Sensors Nose Force Angular velocity Elevators

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System Identification in Adaptive Inverse control for Electrohydraulic Shaking Table Using Variable Tap-length NLMS Algorithm

System Identification in Adaptive Inverse Control for Electr...

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2017 5th international conference on Mechanical, Automotive and Materials Engineering（CMAME 2017）

作者： Man Wang Dacheng Cong Zhidong Yang Changhong Gao State Key Laboratory of Robotics and System School of Mechatronics EngineeringHarbin Institute of Technology

Adaptive inverse control（AIC） is an advanced control technology which can efficiently learn and control the severely nonlinear and real-time plant,such as the electrohydraulic shaking table（EST）.the foundation of AIC is the system identification of the plant via an adaptive *** traditional adaptive filter theory is known as a fixed tap-length（length of the weight vector） FIR（Finite impulse response） filter whose weights change by adaptive algorithm *** mismatch between plant and the fixed taplength will cause inaccuracy or compute *** paper introduce a variable tap-length NLMS（VTLNLMS） algorithm to find the optimum tap-length（OPTL） of the model of multi-DOF EST,then use AIC to improve the accuracy of time wave *** dynamics response nonlinear model of the EST is built to reflect the experimental *** nonlinear EST model and referred algorithm are simulated in MATLAB/*** show the AIC with OPTL is effective in the EST.

关键词： Adapative Inverse control Electrohydraulic Shaking Table Variable Tap-length Nl MS Nonliner System System Identification

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Sunflower Sentry: Advanced Hybrid Model for Early Disease Detection

Sunflower Sentry: Advanced Hybrid Model for Early Disease De...

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Data Intelligence and Cognitive informatics (ICDICI), international conference on

作者： Yashu Amanveer Singh Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Improving agricultural output and guaranteeing food security depends on early identification of plant diseases and precise categorisation of them. In this work, seven major diseases of sunflower such as Downy Mildew, Rust, Verticillium Wilt, Phoma Black Stem, Alternaria Leaf Spot, Sclerotinia Stem Rot, and Phomopsis Stem Canker are considered where From the advantages of both methodologies, the suggested model consists of Random Forest (RF) as well as Convolutional Neural Networks (CNN). Two sets of convolutional and max pooling layers make up the CNN architecture; then, a flattening layer. After that, a Random Forest with 14 trees—each with a maximum depth of 8—classified the obtained characteristics. From several web sources, 4,356 photos of sunflower leaves were gathered and preprocessed using cleaning, resizing, and augmentation. With an overall accuracy of 81.54%, the model was quite strong in identifying and grouping the disorders. For Phomopsis Stem Canker and Sclerotinia Stem Rot, accuracy is shown especially by precision, recall, and F1-score measurements. the confusion matrix study emphasised, even more, the strengths and opportunities for the development of the approach. Early intervention and efficient disease control made possible by this hybrid model provide a great tool for contemporary agriculture, hence allowing sustainable farming methods and maybe higher crop yields.

关键词： Radio frequency Plant diseases Accuracy Food security Forestry Data models Convolutional neural networks Cognitive informatics Crop yield Farming

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Oral Ulcer Classification System Using CNN

Oral Ulcer Classification System Using CNN

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international conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Ahmad Ikmal Naim Mod Azam Azliza Mohd Ali Wannoraini Abdul Latif Wan NurHazirah Wan Ahmad Kamil Mukarramah Zainal Nurzeatul Hamimah Abdul Hamid College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Jasin Melaka Malaysia Faculty of Dentistry Universiti Teknologi MARA Kampus Sungai Buloh Sungai Buloh Selangor Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Shah Alam Selangor Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

this study presents a Convolutional Neural Network (CNN) model developed to address the limitations of current diagnostic methods for oral ulcers. Specifically, it targets the classification of three types of oral ulcers: Oral Malignant Ulcer, Recurrent Aphthous Ulcer, and Traumatic Ulcer. Traditional diagnostic approaches often suffer from limitations such as subjective interpretation, variability in diagnostic accuracy, and inadequate automation. To overcome these challenges, our approach involves constructing a CNN from scratch and rigorously optimizing key hyperparameters, including batch size and learning rate. Under optimal conditions, with a batch size of 12 and a learning rate of 0.001, our CNN achieved an accuracy of 98.61% and a loss rate of 4.78%. In contrast, alternative configurations resulted in an accuracy of 90.62% and a loss of 17.12 % , highlighting the model's sensitivity to parameter adjustments. these results demonstrate that careful tuning of CNN hyperparameters can significantly improve diagnostic performance and accuracy. the findings suggest that the proposed CNN model offers a promising enhancement over traditional methods, with the potential to improve diagnostic precision and clinical outcomes for oral ulcers. Future work will focus on validating the model in real-world clinical settings and exploring its applicability to other medical imaging domains, with the aim of broadening its impact and utility in healthcare diagnostics.

关键词： Image quality Accuracy Sensitivity Squamous cell carcinoma Transfer learning Medical services Convolutional neural networks Tuning Medical diagnostic imaging Cancer

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Feature extraction and selection for automatic sleep staging using EEG

Feature extraction and selection for automatic sleep staging...

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作者： Simões, Hugo Pires, Gabriel Nunes, Urbano Silva, Vitor Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Electrical Engineering University of Coimbra - Polo II Coimbra Portugal

ISBN: (纸本)9789898425027

Sleep disorders affect a great percentage of the population. the diagnostic of these disorders is usually made by a polysomnography, requiring patient's hospitalization. Low cost ambulatory diagnostic devices can in certain cases be used, especially when there is no need of a full or rigorous sleep staging. In this paper, several methods to extract features from 6 EEG channels are described in order to evaluate their performance. the features are selected using the R-square Pearson correlation coefficient (Guyon and Elisseeff, 2003), providing this way a Bayesian classifier with the most discriminative features. the results demonstrate the effectiveness of the methods to discriminate several sleep stages, and ranks the several feature extraction methods. the best discrimination was achieved for relative spectral power, slow wave index, harmonic parameters and Hjorth parameters.

关键词： Correlation methods

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Game Scene Construction for Lower Limb Rehabilitation Robot Based on Virtual Reality

Game Scene Construction for Lower Limb Rehabilitation Robot ...

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international conference on Electromechanical control Technology and Transportation (ICECTT)

作者： Yongfei Feng Di Jin Qianjun Shao Jianye Niu Luige Vladareanu Hongbo Wang Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Hebei University of Technology Tianjin China Robotics and Mechatronic Department Institute of Solid Mechanics of the Romanian Academy Bucharest Romania Parallel Robot and Mechatronic System Laboratory of Hebei Province and Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education Yanshan University Qinhuangdao China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199290

At present, the number of patients with lower extremity dysfunction is increasing year by year. However, traditional rehabilitation training cannot meet the needs of patients for rehabilitation training. Rehabilitation robot is a good solution to train patients instead of the rehabilitation physician. Based on clinical practice experience, a lower limb rehabilitation training system based on virtual reality (VR) is designed, which combined the VR technology and rehabilitation evaluation program to improve the rehabilitation efficiency of patients. According to the functional requirements of the lower limb rehabilitation robot (LLR-RO), the construction of rehabilitation robot control system and sensor system is completed. Based on the Unity3D development environment, a VR scenes of space shooting is planned and created for rehabilitation training VR technology which enables patients to complete the training process while maintaining a pleasant mood. through the software and hardware debugging, the feasibility, data transmission capability of VR-based remote rehabilitation training system and the synchronization performance of VR scenes and rehabilitation robots are verified. In the future, more virtual reality scenes will be studied to adapt to patients of different genders and ages.

关键词： Games Training Rehabilitation robotics Space vehicles Legged locomotion Aircraft Aerospace electronics

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the Role of AI for Smart Environments based on Big Data and IoT Applications

The Role of AI for Smart Environments based on Big Data and ...

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Petya Popova Mariana Petrova Dept. of Business Informatics Tsenov Academy of Economics Svishtov Bulgaria Institute for Scientific Research Tsenov Academy of Economics Svishtov Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

the modern business environment is characterized by a high degree of variability, uncertainty, and complexity. To cope with the challenges of the environment, to take advantage of new opportunities and to function effectively, organizations rely on innovative techniques, approaches, and models. In this study, based on a semi-systematic literature review method, the entry of AI into the systems based on Internet of things (IoT) and Big Data and the role of Artificial Intelligence (AI) for the construction of the so-called smart environment were investigated. the intelligent environment, from the point of view of business, is an ecosystem that integrates various leading technologies to organize and maintain efficient and highly effective operational structures. AI transforms the data-driven business intelligent environment into an environment that is not only informed and intelligent but is also capable of real-time optimization. I.e., AI transforms traditional business systems into dynamic and highly adaptive systems that can respond to changing conditions in real time. the report explores AI's role as an engine for business with a focus on new opportunities and challenges in its use. the report highlights the critical role of AI in optimizing business intelligent environment to smart environment by leveraging Big Data and IoT. It highlights how new AI driven operations and new insights are improving automation, decision-making, and overall efficiency, modeling intelligent systems that adapt in real-time. these systems subsequently transform industries and improve people's quality of life.

关键词： automation Decision making Transforms Big Data Real-time systems Internet of things Business intelligence Artificial intelligence Intelligent systems Business

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