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Deep Convolutional Generative Adversarial Network for Inverse Kinematics of Self-Assembly Robotic Arm Based on the Depth Sensor

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IEEE Sensors Journal 2023年第1期23卷 758-765页

作者： Hsieh, Yi-Zeng Xu, Fu-Xiong Lin, Shih-Syun National Taiwan University of Science and Technology Department of Electrical Engineering Taipei City106335 Taiwan National Taiwan Ocean University Department of Computer Science and Engineering Keelung City202301 Taiwan

In this study, we propose a new deep convolutional generative adversarial kinematics network (DCGAKN) to establish inverse kinematics of self-assembly robotic arm. We design that the robot system uses a depth sensor detecting an object by you only look once v4 (YOLOv4) algorithm, and then, our proposed DCGAKN is with generator and discriminator of adversarial evolution training inverse kinematics model for controlling self-assembly robotic arm to solve the limited solution space to be more adaptive in the dynamic environment. The following are advancements of our proposed method: 1) generator neural network is trained by few-shot training data to control the self-assembly robotic arm to achieve high-accuracy position;2) generator is evaluated with discriminator not only depending on training data but also on adaptive evolutionary;3) the self-assembly robotic arm is like humanoid arm not traditional robotic arm structure and it is easy for self-assembly model to build inverse kinematics without computing inverse kinematics matrix;4) the object is detected by the depth information based on YOLOv4;and 5) through generator evolutionary, the activity range of robotic arm is not limited with training data range. The proposed DCGAKN is compared with convolutional neural network (CNN) and deep neural network (DNN) that the accuracy rate and distance error achieve 87% and 1.26 cm, respectively. The source code of this work is at: https://***/YiZengHsieh/DCGAKN. © 2001-2012 IEEE.

关键词： Generative adversarial networks

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The Intelligent Development of Smart Transmitter Estimation in Mobile Ad Hoc Network 2

The Intelligent Development of Smart Transmitter Estimation ...

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2nd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2023

作者： Hannah Jessie Rani, R. Rajat, Rajat Department of Electrical and Electronics Engineering Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Smart Transmitter (ST) parameter estimate presents a particular problem in Mobile Ad Hoc Networks (MANETs). Nodes in these networks must develop the ability to adapt to their dynamic environment, which includes elements like the number, distribution, and topology of nodes. As a result, a distributed approach must be used to estimate the ST parameters. Distributed estimate of ST parameters is a difficult task, particularly in dynamic mobile ad hoc networks. This study suggests an intelligent design for estimating mobile ad hoc network-wise transmitters. This is because the ST parameters must be changed to reflect the nodes' constant need to adapt to the environment's constant change. However, the ST parameters in these networks can be precisely estimated with the appropriate methods and instruments. © 2023 IEEE.

关键词： Transmitters

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A Scalable Open-Set ECG Identification System Based on Compressed CNNs

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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Enhanced memory window and efficient resistive switching in stabilized BaTiO3-based RRAM through incorporation of Al2O3 interlayer

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材料科学技术（英文版） 2025年第10期213卷 125-134页

作者： Akendra Singh Chabungbam Minjae Kim Atul Thakre Dong-eun Kim Hyung-Ho Park Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Centre of Functional Materials Vellore Institute of TechnologyTamil Nadu 632014India Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Aerogel Materials Research Center Yonsei UniversitySeoul 03722Korea

As artificial intelligence and big data become increasingly prevalent,resistive random-access memory(RRAM)has become one of the most promising alternatives for storing massive amounts of *** this study,we employed high-quality crystalline TiN/Al2O3/BaTiO3/Pt RRAM with an optimized thin Al2O3 in-terlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the inter-layer affects the memory window *** insertion of the Al2O3 interlayer,the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in ***,the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magni-tude,respectively,thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude,*** device also exhibits multilevel resistive switching behavior when varying the applied ***,we also developed a 6 × 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal ***,our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.

关键词： Resistive random-access memory Resistive switching Atomic layer deposition Al2O3 interlayer

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OPTIMAL ACTIVATION FUNCTIONS FOR THE RANDOM FEATURES REGRESSION MODEL 11

OPTIMAL ACTIVATION FUNCTIONS FOR THE RANDOM FEATURES REGRESS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Jianxin Bento, José Department of Electrical and Computer Engineering Rice University United States Department of Computer Science Boston College United States

The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AFs) that minimize a combination of the test error and sensitivity of the RFR under different notions of functional parsimony. We find scenarios under which the optimal AFs are linear, saturated linear functions, or expressible in terms of Hermite polynomials. Finally, we show how using optimal AFs impacts well established properties of the RFR model, such as its double descent curve, and the dependency of its optimal regularization parameter on the observation noise level. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Chemical activation

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Change Detection in Heterogeneous Remote Sensing Images Based on Copula-Supervised Neural Networks 2

Change Detection in Heterogeneous Remote Sensing Images Base...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Li, Weiming Wang, Xueqian Li, Gang Geng, Baocheng Varshney, Pramod K. Tsinghua University Department of Electronic Engineering Beijing100084 China The University of Alabama at Birmingham Department of Computer Science Birmingham United States Syracuse University Department of Electrical Engineering and Computer Science Syracuse United States

ISBN: (纸本)9798331515669

Change detection (CD) in heterogeneous remote sensing images has been extensively applied in response to emergencies. There have been substantial improvements during the past decades in addressing heterogeneous CD challenges, driven by the rapid advancements in deep neural networks (DNNs). However, most DNN-based heterogeneous CD methods often exhibit black-box behavior, limiting their interpretability and controllability in many practical CD applications. To overcome these issues, we propose a neural copula change detection (NCCD) framework, an approach for the heterogeneous CD problem that leverages an interpretable neural network supervised by copula theory. In NCCD, copula's mathematical characteristics are incorporated as loss functions to supervise a neural network in modeling dependence between bi-temporal heterogeneous remote sensing images. The trained neural network works as a copula function to estimate the degrees of dependence that serve as the indicator for identifying changes. Experimental results on two real heterogeneous CD datasets substantiate the effectiveness of our NCCD. © 2024 IEEE.

关键词： Change detection

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Image-based Criticality-Aware Fire Detection

Image-based Criticality-Aware Fire Detection

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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Causal Digital Twins for Physical System Control 59

Causal Digital Twins for Physical System Control

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59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Mohseni-Sehdeh, Saeed Saad, Walid Sakaguchi, Kei Yu, Tao Virginia Tech Bradley Department of Electrical and Computer Engineering AlexandriaVA22305 United States Institute of Science Tokyo Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9798331513269

Digital twins (DTs) are virtual representations of physical systems, replicating their behavior, dynamics, and interactions with the environment. Hosted on computational platforms, DTs have access to more extensive information and computational power than their physical counterparts (PTs), making them suitable for control applications. This paper proposes a novel DT-based control architecture that leverages causal learning to achieve autonomous control. In this framework, the DT employs causal inference to determine the desired system behavior and, subsequently, instructs the PT accordingly. By incorporating causal learning, a DT can gain self-training capabilities, enabling it to generate and analyze hypothetical scenarios based on cause-and-effect relationships. Simulation results show that DT-based control outperforms the traditional local control method by 49% in terms of the tracking performance measured through the mean squared error. Additionally, causal learning demonstrates 16% better tracking performance over statistical learning in a self-training scenario, as measured by the mean squared error. © 2025 IEEE.

关键词： Digital elevation model

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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A Multi-Modal Smart Switching Based Image Transmission Using Semantic Communication 17

A Multi-Modal Smart Switching Based Image Transmission Using...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Tiwari, Sanjana Tripathi, Sharda Joshi, Sandeep Bhatt, Priyanshu Natarajan, Balasubramaniam Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering 333031 India Kansas State University Department of Electrical & Computer Engineering 66506 United States

ISBN: (纸本)9798331531195

The conventional paradigm of communication primarily concentrates on the transmission of raw data, often disregarding its contextual meaning. However, to tackle the exponential growth in data demands along with the limited availability of transmission bandwidth, there is an increasing need to transition from Shannon's classical information-theoretic communication to a more advanced framework centered on semantics. This work presents a multi-modal semantic-based communication method for the transmission of high-definition images aimed at optimizing the transmitted data volume while maintaining a high throughput and mean intersection over union score. To this end, two architectural models are explored: a denser ResNet-based and a lightweight U-Net-based. Depending on the required QoS and resource availability, the raw image is either semantically segmented to obtain a fine-grained, pixel-level classification of the image or represented as label semantics, which provides only a higher-level, object-based, or region-based classification prior to its transmission. The experimental results show that such an adaptive semantic image processing approach leads to around 63% reduction in the transmitted data volume without compromising on the quality of image reconstruction. © 2025 IEEE.

关键词： Semantic Segmentation

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