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Development of a BCI-Controlled Lower Limb Exoskeleton Simulator

Development of a BCI-Controlled Lower Limb Exoskeleton Simul...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Yousefi-Koma, Amir-Hossein Arzanpour, Siamak Park, Edward J. School of Mechatronic Systems Engineering Simon Fraser University SurreyBCV3T 0A3 Canada

ISBN: (纸本)9798350337020

Brain-computer interface (BCI) technology, particularly those based on electroencephalography (EEG), holds significant potential for controlling powered lower limb exoskeletons in rehabilitation contexts. This study introduces an EEG-based BCI system designed to decode anticipated gait direction, thereby enabling command of a self-balancing, overground exoskeleton. To evaluate the performance of the proposed system safely and effectively, we utilized a dynamic simulator (or a digital twin) of the physical exoskeleton, developed commercially for individuals with limited or no walking ability. Six healthy participants, wearing an EEG device, were instructed to initiate gait movements toward the direction indicated by on-screen arrow triggers (forward, backward, left, and right). A Convolutional Neural Network (CNN), operating on an 80%-20% Train-Test Ratio, was used to evaluate the system. The results demonstrated low error rates for the exoskeleton simulator, and an overall system accuracy of 0.75, reflecting the performance of the EEG-based BCI. Notably, the system had an average delay of 5 minutes in a real-time control setting, primarily attributed to its signal processing step. © 2023 ieee.

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Implementation of Speech processing Techniques for Human Emotion Recognition

Implementation of Speech Processing Techniques for Human Emo...

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2024 international conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Padmavathi, B.V.V. Ganesh, D. Sunil Kumar, M. Kiran Kumar, Mungara Talasila, Vamsidhar Neelambaram, B. Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Andhra Pradesh Vijayawada India Andhra Pradesh Tirupati India School of Technology Department of CSE Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Green Fields Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9798350381887

Emotion identification from speech signals has been a research issue in human machine interaction applications for many years. Emotions incredibly play a vital role in our mental functioning. SER is the process of identifying an individual speaker's emotional state through the speech stream. The purpose of emotion recognition using speech processing is to develop a system that cans precisely asses a person's emotional state based on their speech signals. They would use machine learning algorithms to analyze features such as pitch, tone, rhythm, and intensity of speech, and then classify the emotions expressed in the speech as happy, sad, angry, fearful, or neutral. The main applications of emotion recognition using speech processing include human-computer interaction, psychotherapy, and customer service. Inhuman-computer interaction, emotion recognition systems can provide more personalized and empathetic responses, enhancing the user experience. By analyzing their patients' facial expressions and body language, therapists using emotion recognition systems in psychotherapy can better meet the needs of their patients. In customer service, emotion recognition systems can help companies to improve their customer satisfaction by detecting and responding to negative emotions expressed by customers. Neutral, Anger, Being happy, & Sadness are some of the few universal emotions that can be taught to any intelligent system with limited processing resources and ability to recognize and synthesize. as required. Mel-frequency cepstral coefficients (MFCC), a chromogram, a Mel scale spectrogram are extracted together with Tonal Centroid and Spectral Contrast characteristics. Deep Neural Network is used to understand human emotions, advanced signal processing techniques, and sophisticated machine learning algorithms. © 2024 ieee.

关键词： Customer satisfaction

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Wrapped Haptic Display for Communicating Physical Robot Learning 5

Wrapped Haptic Display for Communicating Physical Robot Lear...

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5th international conference on Soft robotics (RoboSoft)

作者： Valdivia, Antonio Alvarez Shailly, Ritish Seth, Naman Fuentes, Francesco Losey, Dylan P. Blumenschein, Laura H. Purdue Univ Sch Mech Engn Lafayette IN 47901 USA Virginia Tech Collaborat Robot Lab Collab Dept Mech Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781665408288

Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it throughout the task. While prior works focus on how the robot learns, it is equally important that this learning is transparent to the human teacher. Visual displays that show the robot's uncertainty can potentially communicate this information;however, we hypothesize that visual feedback mechanisms miss out on the physical connection between the human and robot. In this work we present a soft haptic display that wraps around and conforms to the surface of a robot arm, adding a haptic signal at an existing point of contact without significantly affecting the interaction. We demonstrate how soft actuation creates a salient haptic signal while still allowing flexibility in device mounting. Using a psychophysics experiment, we show that users can accurately distinguish inflation levels of the wrapped display with an average Weber fraction of 11.4%. When we place the wrapped display around the arm of a robotic manipulator, users are able to interpret and leverage the haptic signal in sample robot learning tasks, improving identification of areas where the robot needs more training and enabling the user to provide better demonstrations. See videos of our device and user studies here: https://***/tX-2Tqeb9Nw

关键词： Training Visualization Uncertainty Pneumatic systems Manipulators Robot learning Haptic interfaces

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An Effective Wireless Communication Architecture Using Ultra Lightweight Automatic Modulation Classification Model over Cortex M4 Based Edge Device 4

An Effective Wireless Communication Architecture Using Ultra...

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4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Desai, Dev Gupta, Riya Shah, Shweta Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (纸本)9798350391183

The future advancement in computation, IoT and ultra-edge computing devices will require support of efficient communication methods. The presented research focuses on two parts. The first one focuses on a novel automatic modulation classification (AMC) technique developed using 1D-Convolutional Neural Network useful for STM32L4 series. The approach effectively balances performance with the stringent energy and computational constraints of the ultra-edge devices. The approached has shown 95.42% reduction in Memory requirement while achieving high classification accuracy of 79.01% along with having 99.63% at high SNR (10dB to 20dB), 92.90% at medium SNR (0dB to 10dB), and 44.78% at low SNR (-10dB to 0dB). The second part highlights a combination of multiple modulation schemes in a sequential manner, which is improvising the security of wireless communication. The model also demonstrates robust performance against noise and other channel impairments. This research provides a scalable, efficient AMC solution for ultra-edge devices, enhancing the practicality and security of intelligent communication systems. By optimising for the STM32L4 platform, this work represents a significant advancement in deploying advanced signal processing techniques on resource constrained devices. © 2024 ieee.

关键词： signal to noise ratio

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QR-Code Traffic Signs Recognition for ADAS Smart Driving System 6

QR-Code Traffic Signs Recognition for ADAS Smart Driving Sys...

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6th ieee international conference on Advanced systems and Emergent Technologies, IC_ASET 2023

作者： Jouini, Anis Cherif, Adnane Hasnaoui, Salem University Tunis El-Manar Laboratory of Signal Processing and Electric Systems Atssee Faculty of Science of Tunis Tunis Tunisia National Engineering School of Tunis University Tunis El-Manar Sys'Com Research Laboratory Tunis Tunisia

ISBN: (纸本)9798350321029

Today's self-driving cars recognize the meaning of road signs by taking a photo with a camera placed on the vehicle and analysing these images, but this process is difficult for machines since it consumes necessary time and memory for image processing (shapes and colors of signs). Using QR codes for road sign recognition is a promising method to improve road safety and driver navigation. QR codes store information about traffic signs, such as their meaning and location, and can be easily captured using an in-vehicle camera. This technology can be integrated with Advanced Driver Assistance systems (ADAS) to provide additional information to drivers in real time. For example, QR codes can be used to indicate speed limits, lane restrictions, weather conditions, or road obstacles. Drivers can also use QR codes to get information about nearby services and attractions. In this article, we present an intelligent QR-code traffic sign recognition system, which can detect the QR-code placed on road traffic signs. The result of the QR code detection is also used as parameters for ADAS (Advance driving assistant system) where it offers the possibility of an automated cruise control by classifying the speed signs or by activating the active suspension. When riding on a backstroke transmitting information to the microcontroller. © 2023 ieee.

关键词： Advanced driver assistance systems

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Modelling and tolerant control of the time-delay networked control system based on pole configuration 4

Modelling and tolerant control of the time-delay networked c...

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4th international conference on intelligent Control, Measurement and signal processing, ICMSP 2022

作者： Xu, Housheng Li, Jimeng Shenyang JianZhu University Department of Scienc Shenyang China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

For networkeded control systems with time delays, while considering the influence of external disturbances, a criterion is derived to ensure that the actuator is robust when a fault occurs, and a robust H_{infty} fault-tolerant controller design method is given. A fault diagnosis system is designed and fault-tolerant control is implemented using the closed-loop poles as fault information. The closed-loop system poles are used as input to an Extreme Learning Machine (ELM) fault diagnosis model to achieve fault-tolerant control of the different channels of the system actuator. Finally, a simulation example is used to verify the effectiveness of the described method. © 2022 ieee.

关键词： Closed loop systems

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Active IRS-Assisted Wireless Powered MEC systems with Non-linear Energy Harvesting Model 15

Active IRS-Assisted Wireless Powered MEC Systems with Non-li...

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15th ieee international conference on Wireless Communications and signal processing, WCSP 2023

作者： Chen, Pengcheng Yang, Yuxuan Jiang, Jie Lyu, Bin Yang, Zhen Jamalipour, Abbas Nanjing University of Posts and Telecommunications Nanjing210003 China University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9798350324662

intelligent reflecting surface (IRS) is a promising solution to assist the wireless power transfer (WPT) and task offloading in wireless powered mobile edge computing (WP-MEC) systems. However, to alleviate the double-fading attenuation of IRS-involved links, passive IRS is conventionally equipped with a large number of reflecting elements (REs), resulting in large surface size. To solve this issue, we investigate an active IRS-assisted WP-MEC system, in which active IRS is deployed to amplify the incident signals instead of just adjusting phase shifts as passive IRS. The beamforming at multi-antenna hybrid access point (HAP) is utilized for further performance improvement. To capture the sensitive properties of energy harvesting (EH) circuits, a practically non-linear EH model is adopted. The tasks are assumed to be bit-wise independent, and thus the partial offloading policy is adopted to the wireless devices (WDs). Accordingly, we propose an efficient compute-while-offload mode, i.e., the offloaded tasks are executed as soon as they are received by the HAP, thereby making full use of the finite edge computing capability. We aim to maximize the sum computational rate (SCR) of all the WDs by jointly optimizing time allocation, beamforming designs of active IRS and HAP, as well as offloading strategies of WDs. An algorithm based on alternating optimization (AO) is proposed to tackle the non-convexity of this problem. Numerical results demonstrate the superiority of deploying active IRS over passive IRS. © 2023 ieee.

关键词： active intelligent reflecting surface (active IRS) beamforming designs non-linear energy harvesting model Wireless powered mobile edge computing (WPMEC)

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Reliable ML-Based Image processing and Compression for an Onboard Imaging Pipeline 15

Reliable ML-Based Image Processing and Compression for an On...

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15th Space Computing conference

作者： Garrett, Tyler Wildenstein, Diego Wang, Eileen Gretok, Evan George, Alan Univ Pittsburgh NSF Ctr Space High Performance & Resilient Comp S Pittsburgh PA 15260 USA

ISBN: (纸本)9798350384543;9798350384536

Sensing and onboard-processing capabilities of next-generation spacecraft continue to evolve. Enabled by advances in avionic systems, large amounts of data can be collected and stored on orbit. Nevertheless, loss of signal, communication delays, and limited downlink rates remain a bottleneck for delivering data to ground stations or between satellites. This research investigates a multistage image-processing pipeline and demonstrates rapid collection, detection, and transmission of data using the Space Test Program - Houston 7 - Configurable and Autonomous Sensor processing Research (STP-H7-CASPR) experiment aboard the international Space Station as a case study. Machine-learning (ML) models are leveraged to perform intelligent processing and compression of data prior to downlink to maximize available bandwidth. Moreover, to ensure accuracy and preserve data integrity, a fault-tolerant ML framework is employed to increase pipeline reliability. This pipeline fuses the fault-tolerant Resilient TensorFlow framework with ML-based tile classification and the CNNJPEG compression algorithm. This research shows that the imaging pipeline is able to alleviate the impact of limited communication bandwidth by using reliable, autonomous data processing and compression techniques to achieve reduced transfer sizes of essential data. The results highlight the benefits provided by resilient classification and compression including minimized storage use and reduced downlink time. Furthermore, the findings of this research are used to assess the feasibility of such a system for future space missions. The combination of these approaches enables the system to achieve up to a 98.67% reduction in data size and downlink time as well as the capacity to capture imagery up to 75.19x longer time period for a given storage size, respectively, while maintaining reconstruction quality and data integrity.

关键词： Data Compression Fault-Tolerant Computing Resilient Computing Image processing Image Classification Machine Learning Deep Learning Onboard processing

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LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTRIAN CROSSING BEHAVIOR 29

LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTR...

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ieee international conference on Image processing (ICIP)

作者： Zhou, Chen AlRegib, Ghassan Parchami, Armin Singh, Kunjan Georgia Inst Technol Sch Elect & Comp Engn Ctr Signal & Informat Proc OLIVES Atlanta GA 30332 USA Ford Motor Co Dearborn MI USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In smart transportation, intelligent systems avoid potential collisions by predicting the intent of traffic agents, especially pedestrians. Pedestrian intent, defined as future action, e.g., start crossing, can be dependent on traffic surroundings. In this paper, we develop a framework to incorporate such dependency given observed pedestrian trajectory and scene frames. Our framework first encodes regional joint information between a pedestrian and surroundings over time into feature-map vectors. The global relation representations are then extracted from pairwise feature-map vectors to estimate intent with past trajectory condition. We evaluate our approach on two public datasets and compare against two state-of-the-art approaches. The experimental results demonstrate that our method helps to inform potential risks during crossing events with 0.04 improvement in F1-score on JAAD dataset and 0.01 improvement in recall on PIE dataset. Furthermore, we conduct ablation experiments to confirm the contribution of the relation extraction in our framework.

关键词： Intent Prediction Relation Learning Pedestrian Crossing

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Natural Language processing for Marketing 9

Natural Language Processing for Marketing

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9th international conference on signal processing and intelligent systems, ICSPIS 2023

作者： Dewi, Kania Evita Heryandi, Andri Widiastuti, Nelly Indriani Universitas Komputer Indonesia Informatic Engineering Department Bandung Indonesia

ISBN: (纸本)9798350369922

Promotion is one effort to increase consumers. The use of the Internet as a promotional medium has been widely used by companies because it can save costs in reaching consumers. Social media can be a platform for marketing, one of the social media that is widely used today is Twitter. However, promotions often fail if they are given to the wrong consumers. So it is necessary to identify consumers correctly when carrying out promotions. This research aims to identify users who are looking for a place to study, by analyzing messages sent via social media. In this research, the dataset from Twitter is processed first so that it is free from noise. The initial stages carried out in the research were deleting messages in the form of advertisements, filtering so that the data was clean of hashtags, links and symbols other than letter characters;tokenizing;stemming;remove stop words so that the data is clean of words that are felt to be unrelated to the topic. As a result of the initial stage, the dataset was then subjected to feature extraction using TF-IDF. The results of data set feature extraction are fed into machine learning to determine which users are looking for campuses and which users are just talking about campuses. The final result of this research is to get usernames from social media which will be promotional targets. The testing carried out in this research is more about testing the accuracy of machine learning to determine the right users who will be potential customers from campus. In this research, cross validation is used. The accuracy of Support Vector Machine (SVM) and Naive Bayes (NBC) reaches 96%. Even though in terms of accuracy it is very good, the recall and precision values are 0%. So the research objectives were not achieved. This happens because the dataset is unbalanced. Therefore, in future research it is necessary to consider the imbalance dataset problem. © 2023 ieee.

关键词： Feature extraction

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