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International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Han, Yunpeng Ali Mokhtarzadeh, Amir Xiao, Shaozhang School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu Huai'an223003 China

In recent years, service robots have been widely used in people's daily life, and with the development of more and more intelligence, people put forward higher requirements for autonomous positioning and navigation functions of robots. Like outdoor navigation, indoor navigation also needs the support of navigation data. Although the indoor positioning and navigation scheme based on cameras, lidars and other sensors is gradually developing, due to the complexity of the indoor structure, manual production of indoor navigation data is time-consuming and laborious, and the efficiency is relatively low. In order to solve the problem of low productivity and improve the accuracy of robot automatic navigation, we added a new type of intelligent camera, called OpenCV AI kit or OAK-D, and proposed a method to automatically build data files that can be used for indoor navigation and location services using indoor 3D point cloud data. This intelligent camera performs neural reasoning on chips that do not use GPUs. It can also use stereo drills for depth estimation, and use 4K color camera images as input to run the neural network model. Python API can be called to realize real-time detection of indoor doors, windows and other static objects. The target detection technology uses an artificial intelligence camera, and the robot can well identify and accurately mark on the indoor map. In this paper, a high-performance indoor robot navigation system is developed, and multisensor fusion technology is designed. Environmental information is collected through artificial intelligent camera (OAK-D), laser lidar, and data fusion is carried out. In the experiment part of this paper,The static fusion map module is created based on the laser sensor information and the sensor information of the depth camera, the hierarchical dynamic cost map module is created in the real-time navigation, and the global positioning of the robot is realized by combining the word bag model and the laser point cl

关键词： Cameras

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Bubble-Wave-Mitigation Algorithm and Transformer-Based Neural Network Demodulator for Water-air Optical Camera Communications

IEEE Photonics Journal

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IEEE Photonics Journal 2023年 1-10页

作者： Li, Maolin Ling, Peng Wen, Shangsheng Chen, Xiandong Wen, Fei School of Automation Science and Engineering South China University of Technology Guangzhou Guangdong China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Optical camera communication (OCC) has been widely employed in various applications as a flexible and cost-effective means of communication both on land and underwater. However, the performance of the OCC system through the water-air interface has not been thoroughly investigated. In this paper, we explore the performance of the OCC system in a water-air environment and propose a bubble-wave-mitigation algorithm to pre-process the captured frames of received video. Moreover, we propose a transformer-based neural network to demodulate the transmitted signal, mitigating the deterioration in transmission performance caused by inter-symbol interference (ISI). The experimental results demonstrate that a robust transmission can be achieved in the water-air environment by applying our proposed algorithms and neural network demodulator. Author

关键词： Cost effectiveness

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Three-Port DC-DC Converter with Galvanic Isolation Using Multi-Winding Transformer 3

Three-Port DC-DC Converter with Galvanic Isolation Using Mul...

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3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Paul, Preenu Jose, Babita R. Shahana, T.K. Abraham, Chikku Mathew, Jimson School of Engineering Cochin University of Science and Technology Division of Electronics Engineering Kerala India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Kerala India Indian Institute of Technology Department of Computer Science and Engineering Patna India

ISBN: (纸本)9798350387193

A three port DC-DC converter that employs push-pull topology with isolation is presented. The converter transfers power from two DC input sources using a multi-winding transformer to the load. The high gain of the converter is achieved using quasi - switched boost (qSB) network, switched capacitor (SC) topology, multi winding transformer turn's ratio and voltage doubler circuit (VDR). The converter operation, steady state analysis deriving output voltage and comparison with similar topologies are presented. MATLAB/ Simulink results justify the converter operation and analysis. The underlined features of proposed converter are (i) high voltage conversion ratio (ii) galvanic isolation (iii) reduced utilization of semiconductor devices and (iv) capability to combine multiple input sources. © 2024 IEEE.

关键词： Capacitor bank

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Detecting Adversarial Examples for Hyperspectral Image Classification Via Mutation Testing 13

Detecting Adversarial Examples for Hyperspectral Image Class...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Hao, Yang Chen, Zhifei Xiao, Liang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350395570

With the widespread application of remote sensing technology in various fields, convolutional neural networks (CNNs) have exhibited remarkable performance in the field of hyperspectral image (HSI) classification. However, CNNs also introduce security concerns when demonstrating their accurate classification capabilities. By adding imperceptible and carefully designed adversarial perturbations to input images, CNN models can produce highly confident incorrect results. These images, which have adversarial perturbations added to them, are known as adversarial samples. In this work, we propose a mutation testing-based method for detecting adversarial examples. In our experiments, we employ different attack algorithms to deceive multiple high-accuracy HSI recognition models trained on multiple HSI datasets, and our method aims to determine whether the generated adversarial examples are normal or adversarial. We evaluate our method on the Indian Pines, Salinas, and University of Pavia datasets. The results demonstrate that our method can effectively detect adversarial examples generated by state-of-the-art attack methods. © 2023 IEEE.

关键词： Image classification

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DID-IDS: A Novel Diffusion-based Imbalanced Data Intrusion Detection System 11

DID-IDS: A Novel Diffusion-based Imbalanced Data Intrusion D...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Zhang, Wenxin Chen, Zijie Chen, Dongwei Li, Jianqing Pan, Yuanyuan Macau University of Science and Technology School of Computer Science and Engineering China Zhuhai China

ISBN: (纸本)9798350314014

With the continuous expansion of the volume of Internet, the methods and forms of network attacks have also improved and developed, which requires advanced intrusion detection system. Although deep learning methods achieve remarkable success in the accuracy of intrusion detection over traditional methods, a great number of balanced data are required in the training process. However, in the real-life circumstance, the ratio between benign and abnormal data is not balanced. To address this issue, this paper proposes a novel intrusion detection system, called Diffusion-based Imbalanced Data Intrusion Detection System (DID-IDS). In this system, in order to enhance the correlation between data features, the table data are firstly transformed into the image data. Then, the converted images are sent to the diffusion model to generate high-quality fake images for data enhancement purposes. Finally, the enhanced data set is used to train the Convolutional Neural Network (CNN) model for intrusion detection to improve prediction accuracy. The experimental results show that the proposed system has better performance compared to intrusion detection systems using traditional or Generative Adversarial Networks (GAN) based data augmentation methods. © 2023 IEEE.

关键词： Deep learning

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D3QN-Based Trajectory and Handover Management for UAVs Co-existing with Terrestrial Users 21

D3QN-Based Trajectory and Handover Management for UAVs Co-ex...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Deng, Yuhang Meer, Irshad A. Zhang, Shuai Ozger, Mustafa Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9783903176553

The ubiquitous cellular network is a strong candidate for providing UAVs’ wireless connectivity. Due to the maneuverability advantage and higher altitude, UAVs could have line-of-sight (LoS) connectivity with more base station (BS) candidates than terrestrial users. However, the LoS connectivity could also enhance the propagation of up-link interference caused by UAVs over co-existing terrestrial users. In addition, UAVs would perform more handovers than terrestrial users when moving due to the extensive overlap in the coverage areas of many BS candidates. The solution is to bypass the overlapping coverage areas by designing the UAVs’ trajectory and to reduce interference by optimizing radio resource allocation through handover management. This paper studies the joint optimization of a UAV’s trajectory design and handover management to minimize the weighted sum of three key performance indicators (KPIs): delay, up-link interference, and handover numbers. A dueling double deep Q-network (D3QN) based reinforcement learning algorithm is proposed to solve the optimization problem. Results show that the proposed approach can reduce the handover numbers by 90% and the interference by 18% at the cost of a small increment in transmission delay when compared with the benchmark scheme, which controls the UAV to move along the shortest path and perform handover based on received signal strength. Finally, we verify the advantage of introducing trajectory design, which can reduce the interference by 29% and eliminate the handover numbers by 33% when compared to the D3QN-based policy without trajectory design. © 2023 IFIP.

关键词： Reinforcement learning

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Evaluation of Landslide Susceptibility in Helan Mountain by Fusion of Cluster Analysis and Remote Sensing Images 2

Evaluation of Landslide Susceptibility in Helan Mountain by ...

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2nd International Conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Fang, Tianke Hui, Zhenxing Zhang, Liheng School of Computer and Information Engineering Xiamen University of Technology Xiamen Fujian361024 China

ISBN: (纸本)9798350328530

In order to assess the degree of influence of each landslide influence factor on landslides, this paper takes Helan Mountain as the research object, and collects data from 56 historical landslide sites in Helan Mountain through field surveys and remote sensing images. Through cluster analysis, the landslide influencing factors were categorized into eight main major categories, including slope, slope direction, curvature, planar curvature, profile curvature, terrain undulation, surface roughness, and terrain humidity index. Then, a comprehensive evaluation was carried out using the principal component analysis integrated evaluation method, and the evaluation results showed that terrain undulation and surface roughness had high indices in the evaluation, while the curvature and terrain humidity indices were relatively low. This means that terrain undulation and surface roughness are the main factors leading to landslides, and mountain terrain with high terrain undulation and surface roughness tends to be more likely to cause landslides, while landslides with low terrain undulation and surface roughness have a lower probability of landslide potential. The comprehensive analysis concluded that terrain relief and surface roughness were identified as the main factors in the assessment indicators, which are closely related to the degree of landslide damage. Therefore, special attention needs to be paid to the changes in these geomorphic features. This study provides a scientific basis for landslide prevention and ecological restoration in the Helan Mountain area. © 2023 IEEE.

关键词： Principal component analysis

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Early Fire and Smoke Detection Using Deep Learning - InceptionV3 with Attention Mechanism Through a Real-Time Surveillance

Early Fire and Smoke Detection Using Deep Learning - Incepti...

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2023 International Conference on Computational Intelligence, Networks and Security, ICCINS 2023

作者： Sangaraju, Sai Harsha Varma Jose, Jasmin T. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350313796

Efficient and accurate fire and smoke detection systems are essential to prevent potential catastrophes, save lives, and minimize property damage. This research paper introduces an innovative solution that combines deep learning principles, specifically leveraging the InceptionV3 convolutional neural network architecture, with real-time surveillance for a groundbreaking approach to early fire and smoke detection. The proposed methodology incorporates an attention mechanism to enhance the model's discriminative ability by focusing on salient features within visual data. By harnessing deep learning's capability to extract intricate patterns from extensive datasets, the InceptionV3 model identifies subtle visual cues indicative of fire and smoke instances. Augmented with the attention mechanism, the model effectively prioritizes relevant regions of interest, resulting in elevated detection accuracy. Real-time surveillance further accelerates the system's effectiveness through continuous environmental monitoring, expediting the detection-to-response timeline. In experiments, the proposed architecture achieves remarkable training and validation accuracies of 99.64% and 97.44%, respectively. Comparative assessments against baseline models confirm the superiority of the proposed approach. This research contributes to the advancement of fire safety by integrating cutting-edge technologies, fostering safer environments, and enhancing disaster management strategies. © 2023 IEEE.

关键词： Smoke

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LLM-based Vulnerability Detection

LLM-based Vulnerability Detection

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2023 Human-Centered Cognitive Systems, HCCS 2023

作者： Li, Hongping Shan, Li University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350359183

The emerging Large language models (LLMs) are increasingly used as a revolutionised tools in many industry. In cyber security, the LLMs can offer innovative ways to detect, identify, and mitigate vulnerability through their natural language processing capability. This work aims to fine tune pre-trained LLMs to detect anomalies and vulnerability by analysing vast amounts of data. A ChatGPT 3.5 model based vulnerability detector were developed that can conduct common vulnerability analysis. Experimental results demonstrate the effectiveness of proposed solution. © 2023 IEEE.

关键词： Cybersecurity

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The PCA feature extraction of target based on Carrier-Free UWB Radar 3

The PCA feature extraction of target based on Carrier-Free U...

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3rd International Conference on Digital Signal and computer Communications, DSCC 2023

作者： Liu, Jing Tian, Jin Li, Zhengxiang Engineering School of Networks and Telecommunications Jinling Institute of Technology Nanjing China School of Computer Science and Technology Anhui University of Science and Technology Huainan China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Target recognition of obstacles in the highway is a challenge problem. Most of the abnormal events on the expressway are pigges, traffic cones, stones, etc. This question of radar target recognition includes: echo signals detection and echo signals processing. The echo signals of this paper were produced by impulse UWB radar. And the signals were processed with feature extraction by The PCA (Principal Component Analysis) machine learning algorithm. The PCA is an unsupervised method by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The engineering research in this paper shows that extracting "information principal components" of obstacles can greatly help to identify the target on the expressway. The research results can be widely used in time sensitive target recognition. © 2023 SPIE.

关键词： Principal component analysis

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