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ADVANCED INTELLIGENT SYSTEMS 2022年第7期4卷

作者： Poddar, Swapnadeep Chen, Zhesi Ma, Zichao Zhang, Yuting Chan, Chak Lam Jonathan Ren, Beitao Zhang, Qianpeng Zhang, Daquan Shen, Guozhen Zeng, Haibo Fan, Zhiyong Department of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China State Key Laboratory for Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences & Center of Materials Science and Optoelectronic Engineering University of Chinese Academy of Sciences Beijing China MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics & Nanomaterials School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing China

The Gestalt principles of perceptual learning elucidate how the human brain categorizes and comprehends a set of visual elements grouped together. One of the principles of Gestalt perceptual learning is the law of closure which propounds that human perception has the proclivity to visualize a fragmented object as a preknown whole by bridging the missing gaps. Herein, a letter recognition scheme emulating the Gestalt closure principle is demonstrated, utilizing artificial synapses made of 3D integrated MA(3)Bi(2)I(9) (MBI) perovskite nanowire (NW) array. The artificial synapses exhibit short-term plasticity (STP) and long-term potentiation (LTP) and a transition from STP to LTP with increasing number of input electrical pulses. Initiatory ab initio molecular dynamics (AIMD) simulations attribute the conductance change in the MBI NW artificial synapses to the rotation of MA(+) clusters, culminating in charge exchange between MA(+) and Bi2I93-. Each device yields 40 conductance states with excellent retention >10(5) s, minimal variation (2 sigma/mean) <10%, and endurance of approximate to 10(5) cycles. MBI NW-based artificial neural network (ANN) is constructed to recognize fragmented letters alike their distinction in unabridged form and also the gradual withering of synaptic connectivity with engendered missing fragments is demonstrated, thereby successfully implementing Gestalt closure principle.

关键词： artificial synapses Gestalt principle of closure letter recognition perovskite nanowires

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Optical Analysis of the UPQC using PI Controller in Power flow System 7

Optical Analysis of the UPQC using PI Controller in Power fl...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Karthik, Mamidala Vijay Chakravarthi, M.Kalyan Yapanto, Lis M Selvapandian, D. Harish, R. Subramani, Karthick Noida International University Department of Electrical Engineering Uttar Pradesh Greater Noida India VIT-AP University School of Electronics Engineering Amaravathi India Universitas Negeri Gorontalo Faculty of Fishery and Marine Science Indonesia Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu India Sns College of Engineering Department of Electrical and Electronics and Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh 201204 India

ISBN: (纸本)9781665405201

This examination depends on a three-phase unified control quality conditioner (UPQC) to extend control quality in a three-phase distribution framework. It is nice for day-to-day trade and domestic sectors. Researchers have endeavored and executed different advantageous advances to induce freed of both voltage and current consonant issues, making strides the quality of control given by the control framework. UPQC was made up of a arrangement and a shunt converter that were associated back-to-back employing a common dc-link capacitor. This paper works by actualizing UPQC control quality to compensate for voltage and current sounds issues. UPQC uses the PI controller, which calculates error value differently between the measured and desired set points. The conventional UPQC tool is limited to the PI controller's DC link voltage law. Also, it presents a three-phase transformation using a special multi-winding transformer connection scheme. A MATLAB simulation is utilized in this paper to demonstrate how the UPQC demonstrate can decrease the percent THD in source voltage, source current, and stack voltage waveforms actuated by nonlinear loads. The execution of UPQC is analyzed with a nonlinear load, and simulation studies utilizing MATLAB/Simulink prove the suitable execution of UPQC. © 2021 IEEE.

关键词： Power quality

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Forgery Detection Scheme of Deep Video Frame-rate Up-conversion Based on Dual-stream Multi-scale Spatial-temporal Representation

Forgery Detection Scheme of Deep Video Frame-rate Up-convers...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qing Gu Xiangling Ding Dengyong Zhang Ce Yang The School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan P. R. China Hunan University of Science and Technology The State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Zhengzhou Xinda Institute of Advanced Technology Guangdong Key Laboratory of Information Security Technology The School of Computer and Communication Engineering Changsha University of Science and Technology Changsha P. R. China

Video Frame-Rate Up-Conversion (FRUC) is originally designed to produce the high frame-rate video by periodically inserting new frames between two adjacent frames. However, it can also be utilized to synthesize the faked high frame-rate videos or spliced videos for malicious intents. The existing FRUC detection methods can efficiently identify its occurrence by exploring the blurring effects or deformed structures left over from the traditional FRUC methods. But the video FRUC has been substantially improved in the past years, especially in this deep learning era. Deep learning-based video FRUC (Deep FRUC) weakens the visual traces of the traditional ones such that it is challenging for the current FRUC detectors. In this paper, we propose a forensics algorithm based on dual-stream multi-scale spatial-temporal representation for Deep FRUC. Specifically, we develop a multi-scale receptive field strategy, and attention scheme to learn hidden tampered trace and spatial-temporal representation, respectively. Besides, frame residual and noise residual attention streams are complementary learning from the spatial-temporal dimension, in which the former can capture contrast differences and tampered abnormal boundaries, while the latter can explode noise inconsistencies between original and tampered frames. Experimental results show that the proposed algorithm can effectively achieve the best detection accuracy compared with the existing FRUC forensics works under the Deep FRUC video datasets.

关键词： Location awareness Deep learning Visualization Privacy Forensics Computational modeling Detectors

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LSTM-Based Jamming Diagnosis and Prediction Model Utilizing Transport in Wi-Fi-Enabled IoT Systems

LSTM-Based Jamming Diagnosis and Prediction Model Utilizing ...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Wamika Goyal Vikrant Sharma K Karthika Raghu G Anand Y.V. Dongre K. Ranjith Singh Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management -UG Management JAIN Deemed to be University Bangalore Karnataka Comp Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Every industry is seeing a rise in the use of the Internet of Things (IoT). Because of its widespread availability, high bandwidth, low cost, and adaptability, Wi-Fi-based communication is favored by wireless IoT devices. However, serious dangers exist due to security flaws in IoT devices, particularly in mission-critical and industrial settings. With an emphasis on industrial communication and environmental dynamics, this article introduces a new method for detecting jamming in the Internet of Things (IoT). Using production lines that include wireless IoT devices with a server-client architecture, it first focuses on collecting connection parameters and quality of service (QoS) during normal communication and jamming attacks so that it can more effectively investigate the communication conditions along with jamming in the industrial production environment. Results from experiments show that spot jamming has a devastating effect on higher-level protocol layers, showing that physical-layer jamming assaults may cause transport and application-layer failures to cascade. This research introduces a novel multi-layer jamming detection model based on stacked Long Short-Term Memory (LSTM). With its strong performance in real-time applications for IoT and impressive accuracy in anticipating jamming attempts, the suggested LSTM model is a clear winner. The model undergoes testing on a real-time experimental testbed that simulates different sorts of jamming assaults after being trained on a dataset that has been meticulously selected. A 99.5% accuracy rate for jamming detection and a 99.4% precision rate were produced by the findings. This study provides new insight into the effects of jamming assaults on Internet of Things (IoT) devices and introduces a sophisticated detection model that has the potential to reduce the severity of these attacks.

关键词： Wireless communication Accuracy Quality of service Production Real-time systems Data models Internet of Things Jamming Long short term memory Wireless fidelity

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Cross-Image Distillation for Semi-Supervised Semantic Segmentation

Cross-Image Distillation for Semi-Supervised Semantic Segmen...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nan Zhang Fan Xiao Junlin Hou Ruiwei Zhao Xiaobo Zhang Rui Feng Academy for Engineering and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

Semi-supervised semantic segmentation approaches have drawn much more attention in recent years, which aim to exploit a large amount of unlabeled data together with a small number of labeled data. However, existing models usually regarded segmentation as pixel-wise classification, neglecting global semantic relations among pixels across various images. Moreover, scarce annotated data usually exhibits a biased distribution against the desired one, hindering performance improvement. To address these challenging problems, we propose a novel cross-image distillation framework for semi-supervised semantic segmentation. Specifically, we introduce a relation distillation module to model inter-channel correlations between features of labeled samples and unlabeled samples. In addition, we propose a style distillation strategy to explicitly calibrate the learned feature distributions of labeled and unlabeled data to be aligned. Experimental results on two popular benchmarks demonstrate that our proposed approach achieves superior performance over other state-of-the-art methods. We will release the code soon.

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ADAPT: Action-aware Driving Caption Transformer

ADAPT: Action-aware Driving Caption Transformer

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bu Jin Xinyu Liu Yupeng Zheng Pengfei Li Hao Zhao Tong Zhang Yuhang Zheng Guyue Zhou Jingjing Liu Chinese Academy of Sciences Institute of Automation China Institute for AI Industry Research (AIR) Tsinghua University China Xidian University China Department of Computer Science and Technology Tsinghua University China Southern University of Science and Technology China School of Mechanical Engineering and Automation Beihang University China

End-to-end autonomous driving has great potential in the transportation industry. However, the lack of transparency and interpretability of the automatic decision-making process hinders its industrial adoption in practice. There have been some early attempts to use attention maps or cost volume for better model explainability which is difficult for ordinary passengers to understand. To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action. ADAPT jointly trains both the driving caption task and the vehicular control prediction task, through a shared video representation. Experiments on BDD-X (Berkeley DeepDrive eXplanation) dataset demonstrate state-of-the-art performance of the ADAPT framework on both automatic metrics and human evaluation. To illustrate the feasibility of the proposed framework in real-world applications, we build a novel deployable system that takes raw car videos as input and outputs the action narrations and reasoning in real time. The code, models and data are available at https://***/jxbbb/ADAPT.

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Improved Denoising for Multi-frame Image Restoration with Convolutional Neural Networks

Improved Denoising for Multi-frame Image Restoration with Co...

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International Conference on Computing and Networking technology (ICCNT)

作者： Hitesh Kalra Kiran Lokesh Maney Omkaresh S. Kulkarni K. Ranjith Singh Gunveen Ahluwalia T. Surulivel Rajan Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Mangement JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multi-body photograph restoration is a key application of state-of-the-art image processing. With the improvement of cutting-edge convolutional neural networks (CNNs), it has come to be modern-day viable to repair photos with improved denoising performance. CNNs allow the present-day modern-day complicated non-linear features with high accuracy and fast inference. A CNN-based totally multi-body image healing (MFIR) technique is proposed that is able to seize the spatial correlations between noise and texture in multi-body snapshots. Experiments on benchmark datasets show that our technique achieves overall performance, demonstrating the effectiveness of modern-day CNNs in this challenge. Our approach is powerful to extraordinary multi-frame noise levels and works higher than CNNs with the best unmarried-body entry. Additionally, our proposed technique will be extended to different picture healing responsibilities with diverse today’s corpora.

关键词： Correlation Accuracy Noise reduction Noise Maintenance engineering Benchmark testing Image restoration Convolutional neural networks Noise measurement Noise level

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Vivim: a Video Vision Mamba for Ultrasound Video Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yang, Yijun Xing, Zhaohu Yu, Lequan Fu, Huazhu Huang, Chunwang Zhu, Lei Nansha Guangdong Guangzhou511400 China School of Computing and Data Science University of Hong Kong Hong Kong 138632 Singapore Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Department of Ultrasound China Robotics and Autonomous Systems Thrust China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Ultrasound video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are unsatisfactory in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited linear complexity and impressive achievements in efficient long sequence modeling, which have developed deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for ultrasound video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation in ultrasound videos, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. © 1991-2012 IEEE.

关键词： Image segmentation

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