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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Multi-object Crowd Real-time Tracking in Dynamic Environment Based on Neural Network

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第2期7卷 386-394页

作者： Zhang, Fu-Quan Ma, Lin-Juan College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In this paper, it proposes a new lightweight neural network detection model and tracking algorithm that enables us to perform multi-object crowd tracking. The technique applied in our study is known as object detection and tracking which is based on neural network and can be calculated and reproduced intelligently according to the movement and behavior of the tracked crowd. Also, a number of experiments have been carried out to test the validity of the accuracy in the algorithm. And it is indicated that the results of the experiments have better performance and can contribute to multi-object crowd real-time tracking with high accuracy, which is similar to the state-of-the-art. All our preliminary results throw light on the nature of detection and tracking and result in a solution of multi-object crowd tracking, and it proves to be fruitful and encouraging. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Object detection

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Image Dehazing Network Based on Multi-scale Feature Extraction 4th

Image Dehazing Network Based on Multi-scale Feature Extracti...

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4th International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2021

作者： Feng, Ting Zhang, Fuquan Yu, Zhaochai Li, Zuoyong Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789811640384

To remove image haze and make haze image scene clear, we proposed an image dehazing network based on multi-scale feature extraction (MSFNet) in this paper. The MSFNet first directly performs feature extraction on hazy images with three different resolutions to obtain fine feature maps and concatenates them with the rough feature maps extracted in the downsampling process for fusing and obtaining richer image information. Then, the fused feature maps are put into a network module composed of ResNeXt building blocks for network learning. Next, the feature maps extracted by upsampling are sequentially concatenated with the feature maps learned by the ResNeXt module for obtaining the residual image. Finally, the learned residual image is added to the input hazy image to obtain the image dehazing result. The experimental results on the SOTS dataset show that the MSFNet improves effectiveness of image dehazing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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A Mean Pyramid Strategy for Binary Pattern Family

A Mean Pyramid Strategy for Binary Pattern Family

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Xiaochun Xu Yifan Shen Jiaxing Lei Xin Wang Ente Guo College of Computer and Control Engineering Minjiang University Fuzhou China International Digital Economy College Minjiang University Fuzhou China

This paper proposes a novel mean pyramid strategy for binary pattern family. The mean pyramid strategy can help the binary pattern family to capture robust multilayer local texture structure instead of the traditional single pixel value. To validate the effectiveness of the proposed strategy, we apply the mean pyramid strategy to the classical completed local binary pattern and present the completed mean pyramid local binary pattern for texture classification tasks. The experimental results on Outex database show that the proposed mean pyramid strategy can significantly enhances the classification performance of binary pattern family.

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An adaptive enhanced windowed correlation filter for visual tracking

An adaptive enhanced windowed correlation filter for visual ...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Xiaochun Xu Huibin Feng Qihang Wu Ente Guo Xinxin Wang College of Computer and Control Engineering Minjiang University Fuzhou China International Digital Economy College Minjiang University Fuzhou China

Boundary effect, as an inherent drawback of discriminative correlation filter (DCF) trackers, cannot be handled well in most existing studies. This paper proposes an adaptive enhanced windowed correlation filter tracker (AEWCF), which can alleviate boundary effect adaptively and capture more reliable target information effectively. Firstly, a target enhanced likelihood map is introduced to extract reliable target information from searching window. Then, this paper further proposes an adaptive parted searching window including two sub-windows: the object region window depended on object likelihood maximization strategy and the background window generated by background suppression scheme. The extensive evaluation on OTB2015 benchmarks demonstrate that the AWCF tracker performs favorably against some popular trackers and keeps real-time speed.

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High-Frequency Resonance Suppression Based on Active and Passive Joint Damping for MMC-HVDC 2

High-Frequency Resonance Suppression Based on Active and Pas...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Liu, Yiqi Liu, Jiayi Liu, Wei Wu, Yucheng Yin, Laicheng Ban, Mingfei College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350313765

High-frequency resonance (HFR) incidents have occurred in several Modular Multilevel Converter-based high voltage direct current (MMC-HVDC) projects recently. The interaction between the impedance characteristic of MMC and the grid is assumed as the main factor of HFR, caused by a large time delay. Complex operating conditions arouse the issue of resonance frequency shifts, making the conventional suppression method ineffective. Thus, an active and passive joint damping suppression method is proposed based on the cross-coupled filter (CCF) and improved C-type filter damper. This method can remove the risk of HFR and provide a sufficient phase margin, enhancing the stability and robustness of the MMC-HVDC system. The effectiveness of the proposed method is verified through simulations. © 2023 IEEE.

关键词： C-type filter damper Cross-couped Filter High-frequency resonance Modular Multilevel Converter

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Research on Magnetic Circuit and Electromagnetic Performance of Combined-Pole Less-Rare-Earth Permanent-Magnet Synchronous Machine Used for Fully Electric Unmanned Aerial Vehicle

Research on Magnetic Circuit and Electromagnetic Performance...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Wang, Weinan Fu, Lingfang Wang, Shuo Zhu, Liangkuan Liu, Yiqi Wei, Jian Northeast Forestry University College of Computer and Control Engineering Harbin China

ISBN: (纸本)9798350314274

The combined-pole less-rare-earth permanent-magnet synchronous machine (CP-LRE-PMSM) has a series of advantages, such as better sinusoidal air-gap field, reduced reliance on rare-earth permanent-magnet materials. and lower cost. This paper firstly founds CP-LRE-PMSM's equivalent magnetic circuit model, then the calculation formulas of air-gap field are deduced, meanwhile the model accuracy is examined by finite element analysis (FEA). After that, based on machine performance requirements used for fully electric unmanned aerial vehicle (FE-UAV), the electromagnetic performance of CP-LRE-PMSM is analyzed. The total harmonics distortion (THD) and fundamental component of air-gap field versus permanent magnet width, permanent magnet thickness and flux-barrier position angle are studied, respectively. The torque characteristics and the machine efficiency map are investigated, which provides the reference for theoretical analysis and design of the CP-LRE-PMSM © 2023 IEEE.

关键词： Rare earths

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Frequency Attacks Based on Invertible Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 292-300页

作者： Shao, Ming-Wen Yang, Jian-Xin Meng, Ling-Zhuang Hu, Zhi-Yong China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China China University of Petroleum College of Control Science and Engineering East China Qingdao266580 China

Adversarial attacks reveal the vulnerability of classifiers based on deep neural networks to well-designed perturbations. Most existing attack methods focus on adding perturbations directly to the pixel space. However, the perturbations generated by these methods may be easily perceived by humans. To alleviate the aforementioned problem, we propose a novel high-frequency attack based on invertible neural networks (HA-INN) that relies on INNs to adds perturbations to the high-frequency space of the image instead of the pixel space. In this way, we can fool the classifier while the perturbations are not easily detected by humans. Specifically, we introduce INNs to separate the high-frequency and low-frequency components of the image. And the low-frequency components are guaranteed to be reconstructed as the original image, while the high-frequency components are replaced with resampled high-frequency latent variables with additional adversarial information. Then, the low-frequency components and the high-frequency components with adversarial information are inversely fed into the INN to generate effective adversarial examples. Extensive experiments on two datasets (CIFAR-10 and CIFAR-100) show that our method can generate misleading and transferable cross-architectural adversarial examples with greatly reduced computational resource requirements. Under the white-box setting, the attack success rate of CIFAR10 and CIFAR100 is 99.8% and 99.74%, respectively. Further, under the black-box setting, the adversarial examples generated by our method are more effective than the other methods. © 2020 IEEE.

关键词： Deep neural networks

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Fourier Series Fitting-Based Intrusion Detection for Real-Time Industrial control Systems

Fourier Series Fitting-Based Intrusion Detection for Real-Ti...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhang, Heng Wang, Yinchu Xue, Yawen Deng, Ruilong Li, Hongran Zhang, Jian School of Computer Engineering Jiangsu Ocean University Lianyungang China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331507886

Recent incidents have shown the attacks against Industrial control System (ICS), which may result in failure or even catastrophe and prompt the studies of defense. Existing security systems are mostly based on network perimeter defenses which lacking effective countermeasures in the event that such defenses are bypassed and attackers gain access to the system. In this paper, we propose a Fourier series fitting based intrusion detection method, which enables a detection and an alarm when ICS is under attack. Specifically, we first divide the data into three parts based on time with a sliding window, namely real-Time data, sliding window data to be predicted, and historical data. Then we fit the data within the prediction window to the data around the window, together with the root mean squared error (RMSE) calculated for the data within the window and the historical data. The intrusion detection system (IDS) will alarm when the difference between the above RMSE and the reference one is greater than a threshold value. We test two types of data injection attacks, extensive simulations illustrate the effectiveness of our method in detecting both of these attacks. © 2024 IEEE.

关键词： Network intrusion

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