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deep learning Based Proactive Optimization for Mobile LiFi Systems With Channel Aging

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第6期72卷 3543-3557页

作者： Arfaoui, Mohamed Amine Ghrayeb, Ali Assi, Chadi Qaraqe, Marwa InterDigital Canada Ltee Montreal PQ H3A 3G4 Canada Texas A&M Univ Qatar Elect & Comp Engn ECE Dept Doha Qatar Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ H3G 1M8 Canada Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Doha Qatar

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often cause a high processing delay at the physical layer. Hence, the obtained solutions are rendered sub-optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and inference complexity, as well as its application in a practical LiFi optimization problem.

关键词： Light fidelity Optimization Three-dimensional displays Estimation Wireless communication Aging Channel estimation LiFi LSTM mobile users prediction proactive optimization random orientation

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Improved Wild Horse Optimizer with deep learning Model for Skin Lesion Detection and Classification on Dermoscopic images 23th

Improved Wild Horse Optimizer with Deep Learning Model for S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Maranco, M. Tyagi, Amit Kumar Sivakumar, M. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu 603203 India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031648120

Melanoma is a deadly kind of skin cancer which can spread to other parts of the body. Therefore, it is necessary to identify melanoma at the beginning level. Visual examinationat the time of medical examination of skin lesion is a challenging process as there exist high resemblance among the lesions. Besides, dermoscopy is a non-invasive imaging tool which enables visualizationof the skin surface by light magnifying device and immersion fluid. Traditional image processing models such as histogram thresholding, clustering, or active contours are employed to segment skin lesions. This motivates the study of how to best employ AI, and deep learning (DL) algorithms in particular, to identify skin lesions in dermoscopic pictures. By using these images, current research refines the existing wild horse optimizer (IWHO) by using a deep learning driven skin lesion detection and classification (IWHODL-SLDC) *** proposed model utilizes optimal Tsallis entropy based image segmentation. Besides, Squeeze Netapproach can be employed to derive feature vectors. Also, IWHO with deep wavelet neural network (DWNN) technique is used to classify the dermoscopic images. Numerous tests are carried out to guarantee the enhancement of the IWHODL-SLDC method on the reference dataset. It was found via experimentation that the IWHODL-SLDC model outperformed the other approaches in a wide range of ways. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image classification

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A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets

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INVERSE PROBLEMS 2024年第4期40卷 045001-045001页

作者： Ruiz, Alvaro Yago Stevanovic, Maria Nikolic Cavagnaro, Marta Crocco, Lorenzo Inst Electromagnet Sensing Environm Natl Res Council Italy CNR IREA Naples Italy Singidunum Univ Belgrade Serbia Sapienza Univ Rome DIET Rome Italy

In this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, raw-data are processed via orthogonality sampling method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation.

关键词： inverse scattering microwave imaging deep learning qualitative methods orthogonality sampling method

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RHNet: A real-time High-Resolution Network for Surface Defect Detection 43

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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43rd Chinese Control Conference, CCC 2024

作者： Wang, Yijie Ren, Zhigang Cai, Jianpu Wu, Zongze Xie, Shengli Shenzhen518060 China Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Guangzhou510006 China

ISBN: (纸本)9789887581581

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network (RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small defects. Therefore, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module (SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Semantic Segmentation

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Autonomous Robot Navigation: deep learning Approaches for Line Following and Obstacle Avoidance 20

Autonomous Robot Navigation: Deep Learning Approaches for Li...

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20th CSI International Symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Alitappeh, Reza Javanmard Mahmoudi, Nima Jafari, Mohammad Reza Foladi, Ali University of Science and Technology of Mazandaran Department of Electrical and Computer Engineering Behshahr Iran

ISBN: (纸本)9798350383942

This research presents a navigation robotic system designed for the concurrent tasks of line following and obstacle avoidance in partially-known environments with presence of obstacles. By applying a strategically positioned camera for precise line following with a LSTM model and distance sensors guided by a CNN model for obstacle avoidance, our system exhibits robust performance. The seamless transition between these modes, driven by real-time environmental inputs, underscores the adaptability and autonomousness of the platform. Experimental results indicate a notable enhancement in performance, demonstrating the efficacy of the proposed approach in achieving superior outcomes in challenging robotic navigation scenarios. © 2024 IEEE.

关键词： Long short-term memory

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deep learning Based High-Resolution Frequency Estimation for Sparse Radar Range Profiles

Deep Learning Based High-Resolution Frequency Estimation for...

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IEEE Radar Conference (RadarConf)

作者： Biswas, Sabyasachi Gurbuz, Ali C. Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

ISBN: (纸本)9798350329216;9798350329209

Frequency estimation is a pivotal process in many signal-processing applications. Generating radar range profiles for linear frequency modulated radar systems is such a case where spectral analysis is used to estimate target ranges. Conventional methods like fast Fourier transform (FFT) are the golden standard in frequency estimation, despite its Rayleigh resolution limit and high sidelobe levels. To address such limitations this paper introduces HRFreqNet;a deep neural network (DNN) architecture for high-resolution frequency estimation from 1D complex time domain data consisting of multiple frequency components. Our deep learning (DL) architecture consists of an auto-encoder block to improve signal-to-noise ratio (SNR), a frequency estimation block to learn frequency transformations to generate pseudo frequency representations(FR), and finally, a 1D-UNET block to reconstruct high-resolution FR. Experimental results on synthetically generated data show enhanced performance in terms of resolution, estimation accuracy, and ability to suppress noise. Achieved range profiles are also sparser with lower sidelobe levels. The proposed HRFreqNet is evaluated over both synthetic and experimental real-world radar data and it is observed that accurate, sparse, high-resolution range profiles are obtained compared to existing approaches.

关键词： Stretch processing deep learning Frequency Estimation Auto-encoder Unet Radar Range profile Sparsity

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Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning

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ENERGY SCIENCE & ENGINEERING 2022年第3期10卷 800-813页

作者： Meng, Ziyao Xu, Shengzhi Wang, Lichao Gong, Youkang Zhang, Xiaodan Zhao, Ying Minist Educ Engn Res Ctr Thin Film Photoelect Technol Tianjin 300350 Peoples R China Nankai Univ Inst Photoelect Thin Film Devices & Technol Tianjin Peoples R China Nankai Univ Key Lab Photoelect Thin Film Devices & Technol Ti Tianjin Peoples R China

Visual inspection of photovoltaic modules using electroluminescence (EL) images is a common method of quality inspection. Because human inspection requires a lot of time, object detection algorithm to replace human inspection is a popular research direction in recent years. To solve the problem of low accuracy and slow speed in EL image detection, we propose a YOLO-based object detection algorithm YOLO-PV, which achieves 94.55% of AP (average precision) on the photovoltaic module EL image data set, and the interference speed exceeds 35 fps. The improvement of speed and accuracy benefits from the targeted design of the network architecture according to the characteristics of EL image. First, we weaken the backbone's ability to extract deep-level information so that it can focus on extracting the low-level defect information. Second, the PAN network is used for feature fusion in the Neck part. But, only the single-size feature map output is retained, which significantly reduces the amount of calculation. Also, we analyze the impact of data enhancement methods on model overfitting and performance. Finally, we give effective data enhancement methods. The results show that the object detection algorithm in this paper can meet the requirements for high-precision and real-time processing on the PV module production line.

关键词： deep learning electroluminescence image object detection photovoltaic modules YOLO algorithm

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An Improved Framework for Precision Grading of Renal Cell Carcinoma using Histopathological images 24

An Improved Framework for Precision Grading of Renal Cell Ca...

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15th Indian Conference on Computer Vision Graphics and image processing

作者： Bagri, Rashika Rajpal, Ankit Kumar, Naveen Univ Delhi Fac Math Sci Dept Comp Sci Delhi India

ISBN: (纸本)9798400710759

The progressive increase in the number of renal cancer cases worldwide indicates a need for computer-aided diagnostic systems to meet future challenges. Out of all the types of renal cancer, renal cell carcinoma (RCC) accounts for approximately 85% of cases. The prognosis of RCC is important to determine patients' effective treatment plans. In this context, histopathological grading helps to provide critical information on tumour aggressiveness by examining cell abnormalities compared to normal cells. However, manual grading is subjective, leading to potential errors and making diagnosis time-consuming. This paper introduces a new, computationally efficient approach for precision grading of RCC using histological images, specifically utilizing the KMC kidney dataset, which contains histopathological images sorted into five grades ranging from 0 to 4. The proposed framework comprises two modules: texture features extraction and grade classification. The first module extracts texture features using first-order statistics, GLCM, GLRLM, GLDM, LBP, and Gabor filters, which are combined to yield a feature pool. The second module trains multiple machine learning classifiers on the extracted pool of features via 10-fold cross-validation. The proposed framework utilizing the textural features outperformed the frameworks based on deep learning-driven features, with an accuracy of 0.9752 +/- 0.0036 at a 95% confidence level.

关键词： Renal cancer grading Histopathology images Feature extraction Machine learning Texture Features

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deep learning based Dysgraphia Symptom Prediction 15

Deep Learning based Dysgraphia Symptom Prediction

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Vanjari, Nisha Shete, Prasanna K. J. Somaiya Institute of Technology Mumbai India K. J. Somaiya College of Engineering Mumbai India

ISBN: (纸本)9798331300579

People who have learning disabilities frequently struggle in the areas of reading and writing. The main effects of learning disabilities are bad grades and a lack of motivation that lasts a lifetime. Most of the time, it starts in childhood and has a big effect on academics. The early recognition by using technological prediction will assist the academicians to devise special learning strategy to accommodate such children in the teaching learning and hence perform well in academics. Specific learning disability is a complex neurodevelopmental/neurological disorder that affects many children. learning Disability have two major categories like Specific learning Disability in reading (dyslexia) and Specific learning Disability in writing (dysgraphia). Our primary focus is to predict the symptoms of dysgraphia using deep learning algorithms. deep learning algorithms are general-purpose methods of artificial intelligence that can learn patterns from data without the need to define them apriori. Convolutional Neural Network is a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. To generate automated and trained models that reduce the amount of human intervention by significant margins is the primary goal of deep learning. For experimentation purpose, we have used MNIST alphabets dataset to analyse the problem. For this research, we have reviewed several methods like handwriting analysis, eye tracking, Tests, questionnaire, games;etc but we choose handwriting modality for our research. In this paper, experimental study done on convolutional neural network and transfer learning models for predict the dysgraphia symptoms and implemented using publicly available dataset of MNIST alphabet dataset. We compared the accuracy of the VGG16, VGG19, and ResNet50 architectures. Based on the analysis, we have arrived at the conclusion that the VGG16 architecture is the most superior option in training and validation phase. © Gr

关键词： Convolution

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LeNet - IoT Security Aware Intrusion Detection Framework Using deep learning

LeNet - IoT Security Aware Intrusion Detection Framework Usi...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ananthajothi, K. Adharshini, C. Amirdha, M. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

The rising complexity of cyberthreats created the demand for compelling cybersecurity solutions that along with expanding Internet of Things (IoT) devices has made them indispensable. In the work our proposal is a deep learning-Assisted Intrusion Detection System (IDS), based on the LeNet architecture, that can be used for the identification and prevention of potential intrusions in Internet of Things environments. To increase the detection accuracy, we developed our method based on a properly structured preprocessing step comprising normalisation and data augmentation using image-based and QR-based datasets. We take a look at the architecture of LeNet including the pooling and convolutional layers that help to identify important features of the input dataset. The final parts of the model are fully linked layers and a Softmax classifier that predicts the type of anomaly. Performance metrics measure the efficacy of the IDS including precision, recall, accuracy, and F1 score that reveal our method surpass the classical methods with a high margin. We shall describe in this paper how one can train the LeNet model using IoT-specific attack data so that it could be used to detect intrusion in surrounding network space and inject the walled-off attack, thereby facilitating threat prevention. Since the model is GPU accelerated, it converged close to what was able to protect the IoT network effectively from the evolving cyber-attacks and was quite close to its high accuracy and real time efficient performance in terms of time. © 2024 IEEE.

关键词： Network intrusion

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