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arXiv 2023年

作者： Althoefer, Kaspar Ling, Yonggen Li, Wanlin Qian, Xinyuan Lee, Wang Wei Qi, Peng Queen Mary University of London London United Kingdom Tencent Robotics X Lab Shenzhen China Beijing China The Department of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai China

In conjunction with huge recent progress in camera and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information. © 2023, CC BY.

关键词： Silicones

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Risk-Sensitive Inhibitory control for Safe Reinforcement Learning

Risk-Sensitive Inhibitory Control for Safe Reinforcement Lea...

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IEEE Conference on Decision and control

作者： Armin Lederer Erfaun Noorani John S. Baras Sandra Hirche Chair of Information-oriented Control (ITR) School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent years for ensuring the safety of control. Realized using control barrier functions or predictive safety filters, these approaches can effectively ensure the satisfaction of state constraints through an online adaptation of nominal control laws, e.g., obtained through reinforcement learning. While the focus of these realizations of inhibitory control has been on risk-neutral formulations, human studies have shown a tight link between response inhibition and risk attitude. Inspired by this insight, we propose a flexible, risk-sensitive method for inhibitory control. Our method is based on a risk-aware condition for value functions, which guarantees the satisfaction of state constraints. We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success. By enforcing the derived safety conditions online using the learned value function, risk-sensitive inhibitory control is effectively achieved. The effectiveness of the developed control scheme is demonstrated in simulations.

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Image Definition Evaluation Function Based on Improved Maximum Local Variation and Focusing Window Selection 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Shiyun Chen, Jian Wan, Jiaze Li, Zuoyong Lin, Li School of Electronic Electrical Engineering and Physics Fujian University of Technology Xueyuan Road Fujian Fuzhou350118 China College of Computer and Control Engineering Minjiang University No. 200 Xiyuangong Road Fujian Fuzhou350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (数字)9789811675027

ISBN: (纸本)9789811675010

Aiming at the problem that commonly used image definition evaluation functions in the focusing process are sensitive to noise, we propose a new image definition evaluation function based on improved maximum local variation and focusing window selection. Firstly, the focusing window is selected by gradient accumulation of a 4-directional Scharr operator in order to reduce the calculation complexity and improve the accuracy of evaluation results. Secondly, an improved 3-neighbors method based on the maximum local variation is proposed to decrease the change in scores for noisy images. Finally, the standard deviation of the improved maximum local variation distribution is used as the measure of clarity. The experimental results show that compared with the method using maximum local variation, the proposed method has better unbiasedness and sensitivity. Compared with the commonly used evaluation functions, the proposed method has better noise immunity and high sensitivity. Compare with other no-reference image quality assessment algorithms, it has better monotonicity, unimodality, unbiasedness, sensitivity and real-time performance as well. The proposed method is suitable for the fine focusing stage with high real-time performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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International Conference on Communications and Signal Processing

作者： Kathari Santosh Timur Kholmukhamedov M Sandeep Kumar Mohd Aarif Iskandar Muda B Kiran Bala Department of MBA CMR Institute of Technology Bengaluru Bengaluru India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4’s robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot’s enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support.

关键词： Ethics Limiting Automation Biological system modeling Customer services Companies Chatbots

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Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

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Security and Communication Networks 2023年第1期2023卷

作者： Shankar, Gauri Ai-Farhani, Liwa H. Anitha Christy Angelin, P. Singh, Parvinder Alqahtani, Abdullah Singh, Abha Kaur, Gaganpreet Samori, Issah Abubakari Chandigarh University Mohali Punjab Mohali140413 India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia PSNA College of Engineering and Technology Tamilnadu Dindigul624622 India Central University of Punjab Bathinda Punjab 151001 India Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Basic Science College of Science and Theoretical Study Dammam-Female Branch Saudi Electronic University Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Engineering Sciences University of Ghana Accra Ghana

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user's biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article's main findings are that the Ed25519 algorithm can be used in blockchain. © 2023 Gauri Shankar et al.

关键词： Digital forensics

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Goods Anomaly Detection Based on Multi-Branch Feature Extraction and Foreground Mining

Goods Anomaly Detection Based on Multi-Branch Feature Extrac...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction Information technology Anomaly detection Biomedical imaging

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Fast Image Processing Approach for the Reliable Quantification of Intracranial Hematoma Volume in Brain CT Images

Fast Image Processing Approach for the Reliable Quantificati...

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control Instrumentation System Conference (CISCON)

作者： Vidhya V. Anjan Gudigar Raghavendra U. Akanksha Shetty Lekha Shree B. T. Parvathy Nair K. Ajay Hegde Girish R. Menon Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Neurosurgery Manipal Hospitals Karnataka India Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Traumatic brain injury (TBI) is a neurological condition that can cause severe physical and mental disabilities or death. One of the serious consequences of TBI is intracranial hematoma that can be diagnosed through early and proper analysis of the brain CT images. Proper estimation of hematoma volume is essential for envisaging the 30-day outcome. The Tada formula is the gold standard used by radiologists to quantify the hematoma volume in CT images which will provide accurate results only for symmetrical hematoma configurations. Manual volume assessment is further subjective and time-consuming, especially in real-time acute clinical care set-up. Hence a computer-aided scheme that uses image processing techniques for rapid, precise, and reliable assessment of hematoma volume is developed. The CT images are pre-processed using histogram equalization and gray matter is further extracted using thresholding and morphological operations. Then the hematoma regions are extracted using thresholding and the 3D hematoma volume for the entire CT scan is computed by multiplying the image width and the total area of hematoma in all CT images. The obtained hematoma volumes are clinically validated by radiologist and the proposed approach achieved an average accuracy of 89.51%. A 3D modeling of the hematoma regions in a CT scan was further generated to provide an extensive visual aid for improved hematoma interpretation and strategic analysis. The entire process of automated hematoma volume computation and generation of 3D volume in a CT scan took around 13 seconds using standalone system. The outcomes of the experiment show that the proposed method can be used as a medical support system for rapid and accurate diagnosis of hematoma.

关键词： Visualization Three-dimensional displays Accuracy Thresholding (Imaging) Computed tomography Estimation Real-time systems Reliability Brain injuries Morphological operations

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EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous Driving

arXiv

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arXiv 2024年

作者： Lin, Jiacheng Chen, Jiajun Peng, Kunyu He, Xuan Li, Zhiyong Stiefelhagen, Rainer Yang, Kailun The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The Institute for Robotics and An-Thropomatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

This paper introduces the task of Auditory Referring Multi-Object Tracking (AR-MOT), which dynamically tracks specific objects in a video sequence based on audio expressions and appears as a challenging problem in autonomous driving. Due to the lack of semantic modeling capacity in audio and video, existing works have mainly focused on text-based multiobject tracking, which often comes at the cost of tracking quality, interaction efficiency, and even the safety of assistance systems, limiting the application of such methods in autonomous driving. In this paper, we delve into the problem of AR-MOT from the perspective of audio-video fusion and audio-video tracking. We put forward EchoTrack, an end-to-end AR-MOT framework with dual-stream vision transformers. The dual streams are intertwined with our Bidirectional Frequency-domain Cross-attention Fusion Module (Bi-FCFM), which bidirectionally fuses audio and video features from both frequency- and spatiotemporal domains. Moreover, we propose the Audio-visual Contrastive Tracking Learning (ACTL) regime to extract homogeneous semantic features between expressions and visual objects by learning homogeneous features between different audio and video objects effectively. Aside from the architectural design, we establish the first set of large-scale AR-MOT benchmarks, including Echo-KITTI, Echo-KITTI+, and Echo-BDD. Extensive experiments on the established benchmarks demonstrate the effectiveness of the proposed EchoTrack and its components. The source code and datasets are available at https://***/lab206/EchoTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Case of Cups and a Ball: Utilizing Generative Artificial Intelligence for Human-robotic Collaboration in Task Execution

A Case of Cups and a Ball: Utilizing Generative Artificial I...

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International Automatic control Conference

作者： Nurdin Khoirurizka Joy Chrissetyo Prajogo Spencer Perkins Chao-Hsiang Kuo Hsien-I Lin College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu R.O.C. National Yang Ming Chiao Tung University Hsinchu R.O.C. Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu R.O.C.

ISBN: (数字)9798350354904

ISBN: (纸本)9798350354911

Within the domain of robotics, effective interaction between a human collaborator and robot is a topic of great importance. With the continued advancements in Generative Artificial Intelligence (GAI), we propose a prototype system that utilizes GAI and seeks to bridge the gap between the human and robot. This prototype utilizes a Large Language Model (LLM) combined with a vision system for detection and tracking, to allow the GAI system to control a robotic arm and manipulate objects within the action space. We model the action space within our experiments on the 3-cup Monte game where the user can prompt the system to manipulate the cups within the action space based on the ball’s location. Experimentation is done on prompts of varying lengths of sequences, and we evaluate our system based on its ability to understand the prompt and correctly execute the desired task. With this collaboration of techniques and technologies, we hope to open the possibilities and extend human and robotic collaboration into new areas. The results of our experiments show promising results to this end.

关键词： Visualization Accuracy Service robots Large language models Machine vision Prototypes Aerospace electronics Robot sensing systems Manipulators Robots

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Image Object Background Classification with Jensen-Shannon Divergence

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Nie, Fangyan Li, Jianqi College of Computer and Electrical Engineering Hunan University of Arts and Science Hunan 415000 China Hunan Provincial Key Laboratory of Distributed Electric Propulsion Vehicle Control Technology Hunan 415000 China

The classification of image object and background by thresholding is a technique widely used in the field of image processing. In this paper, a new image thresholding method for object and background classification is proposed based on Jensen-Shannon divergence and Poisson distribution. The Jensen-Shannon divergence is a measure that has excellent performance in measuring the information difference between different information systems. Firstly, the Poisson distribution is used to fit the grayscale frequency distribution of original image in the proposed method. And then, the optimal fitting distribution is searched by minimizing the Jensen-Shannon divergence of original and segmented images. At the same time, the optimal threshold is calculated for image thresholding. Finally, the proposed method is extended to the problem of multi-class classification, and the particle swarm optimization algorithm is adopted to reduce the computing time. Compared with some well-known and prevailing methods, the performance and effectiveness of the proposed method are analyzed qualitatively and quantitatively. The experimental results demonstrate the validity and popularization value of the proposed method. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Poisson distribution

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