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2024 International conference on artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Einarson, Daniel Mengistu, Dawit Kristianstad University Department of Computer Science Kristianstad Sweden

ISBN: (纸本)9798350394528

image processing tasks have benefited from deep learning models based on convolutional neural networks. However, the success of image classification models is dependent on several factors such image quality, dataset size and class distribution. Achieving acceptable accuracies with datasets not meeting these requirements is challenging. Domain specific dataset augmentation techniques have been proposed to mitigate the problem. This paper investigates adaptation of multi-source datasets as an augmentation approach to improve accuracy of crack detection in bridge concrete structures from low quality images in limited and imbalanced datasets. While experimental results show that data augmentation can improve accuracy of detection, we anticipate achieving even better results by combining this approach with generative machine learning models in future research. © 2024 IEEE.

关键词： Convolutional neural networks

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Reconfigurable parallel photonic matrix-vector multiplication processor based on multi-dimensional multiplexing

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OPTICS EXPRESS 2025年第9期33卷 19837-19850页

作者： Bi, Yanfeng Wu, Xingyu Fan, Chenrui Zhang, Lufan Wang, Chuan Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Appl Opt Beijing Area Major Lab Beijing 100875 Peoples R China

Matrix-vector multiplication (MVM) operations play an important role in applications such as data processing and artificial neural networks. To meet the growing demand for computing power, the photonic MVM processor provides what we believe to be a new computing architecture. In this paper, we propose a reconfigurable parallel MVM (RP-MVM) processor. To further improve the parallel computing dimension, wavelength division multiplexing (WDM) and digital subcarrier multiplexing (DSM) technologies were first incorporated into the photonic MVM. Compared with the traditional WDM-MVM architecture, the parallelism of RP-MVM scheme is increased by N times, where N is the carrier number of DSM signal. Moreover, the input data channel can be dynamically adjusted without changing the hardware scale, which improves the flexibility of computing system. The simulation results show that the RP-MVM scheme can achieve parallel computing operations of eight MVMs, with a computing speed of 128 GOPs. For a random 6-bit resolution data sequence, the root mean square error (RMSE) of calculation results is on the order of 1E-3. In addition, for the image edge extraction task based on Roberts operator, this scheme can realize the parallel processing of four grayscale images. Therefore, the proposed scheme provides an alternative approach for realizing a highly parallel and reconfigurable large-scale photonic MVM architecture.

关键词： Machine vision Mode division multiplexing neural networks Optical computing Variable optical attenuators Wavelength division multiplexing

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Test Automation for Symbol Recognition on the Map 31

Test Automation for Symbol Recognition on the Map

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31st IEEE conference on Signal processing and Communications applications (SIU)

作者： Turhan, Fatmanur Carkacioglu, Levent Toreyin, Behcet Ugur Aselsan AS Ankara Turkiye Istanbul Tech Univ Bilisim Enstitusu Istanbul Turkiye

ISBN: (纸本)9798350343557

In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

关键词： Symbol Recognition Feature Extraction Support Vector Machines Template Matching Convolutional neural Network Object Detection Software Testing

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networks of random lasers: current perspective and future challenges [Invited]

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OPTICAL MATERIALS EXPRESS 2023年第4期13卷 1060-1076页

作者： Consoli, Antonio Caselli, Niccolo Lopez, Cefe CSIC Inst Ciencia Mat Madrid ICMM Sor Juana Ines Cruz 3 Madrid 28049 Spain Univ Rey Juan Carlos ETSI Telecomunicac Camino Molino 5 Madrid 28942 Spain Univ Complutense Madrid Dept Quim Fis Ciudad Univ S-N Madrid Spain

artificial neural networks are widely used in many different applications because of their ability to deal with a range of complex problems generally involving massive data sets. These networks are made up of nodes, connections, and nonlinear response connections, which are typically implemented as software code running on ordinary electronic computers. In such systems, electrons, with their advantages and drawbacks, are in charge of storing, processing, and transmitting information. Signal processing in the optical domain can provide ultrafast, parallel operation, nonlinear dynamics, and high energy efficiency, making photonics a suitable technology for the realization of neuroinspired computing platforms. This advantage stimulated the development of photonics neural networks based on single and multiple lasers with classical optical cavities. Recently, networks made of random lasers emerged as a novel concept that uses randomly placed scattering elements to create nonlinearity and complexity in photonics neural networks. In this review paper, we present the general framework for networks of coupled lasers, discuss recent advances in networks of random lasers, and outline future directions in this area. We also examine the challenges and limitations of using random lasers in photonic networks, as well as potential solutions. By harnessing the properties of random lasers, such as their unique spectral characteristics in pulsed emission mode and their robustness against noise, networks of interacting random lasers can explore new and exciting possibilities for photonics technology that could find applications in a variety of fields, including image recognition and encryption.(c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Diode laser arrays Fiber lasers image encryption neural networks Optical feedback Random lasers

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The Utilization of artificial Intelligence and Deep Learning for Identifying Vehicle Wheels 4th

The Utilization of Artificial Intelligence and Deep Learning...

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4th International conference on Digital Technologies and applications (ICDTA)

作者： Maaroufi, Mohammed Mouhcine Stour, Laila Agoumi, Ali Demori, Julien Hassan II Univ Casablanca Fac Sci & Technol Mohammedia Proc & Environm Engn Lab BP 146 Mohammadia Morocco Hassania Sch Publ Works Civil Engn Hydraul Environm & Climate Lab Casablanca Morocco Macq Mobil Evere Belgium

ISBN: (纸本)9783031686498;9783031686504

The technology using artificial intelligence (AI) allows, through the preprocessing of video and image data, their transformation into traffic information, the recognition in abusive conditions of elements, the specification of the different characteristics of the vehicle (type, brand, model, color), object detection and tracking, automatic calibration and positioning. Algorithms for performance evaluation can evaluate recognition quality and detect malfunctions due to external factors, reduced lighting conditions, or incorrect camera installation. This article discusses the application of AI and deep learning techniques to identify vehicle wheels. It highlights the preprocessing of image and video data to extract traffic information, detect vehicle attributes, and perform object tracking. The study focuses on understanding AI and convolutional neural networks (CNNs) for image processing, emphasizing the significance of feature extraction algorithms. The research outlines the methodology for creating a dataset of annotated wheel images, selecting the YOLO detection algorithm, and using the Darknet Github framework for training and testing CNNs. It discusses the training process, including configuration, data augmentation, and evaluation metrics like Mean Average Precision (MAP) and Intersection Over Union (IOU). Finally, the article concludes with insights into the practical applications of the developed wheel detection system, suggesting future improvements and potential integrations with other projects.

关键词： Smart mobility artificial intelligence deep learning convolutional neural networks wheel

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Dual-function switchable terahertz metamaterial device assisted by a deep neural network

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2025年第4期42卷 529-541页

作者： Ren, Jiali Zhang, Ting Li, Jing Shanxi Vocat Univ Engn Sci & Technol Sch Comp Engn Jinzhong 030619 Peoples R China Shanxi Vocat Univ Engn Sci & Technol Sch Informat Engn Jinzhong 030619 Peoples R China North Univ China Sch Instrument & Elect Taiyuan 030051 Peoples R China

A dual-functional switchable terahertz metamaterial (THz MM) device with broadband absorption and polarization conversion capabilities is proposed, leveraging the insulator-to-metal phase transition of vanadium dioxide (VO2). In its metallic state, the device functions as an absorber, achieving a bandwidth of 3.93 THz within the frequency range of 2.53-6.46 THz and maintaining an absorption rate of >= 90%. In its insulating state, the device operates as a polarization converter, providing two conversion modes: linear-to-cross-polarization (LTX) over the range of 1.17-3.63 THz, and linear-to-circular polarization (LTC) within the ranges of 1.04-1.14 THz and 3.71-3.87 THz. This study presents a significant advancement over conventional THz devices by overcoming the limitations of single-functionality through innovative design strategies and offering what we believe is a novel approach to multifunctional integration. Leveraging the switchable properties of VO2, the device achieves outstanding performance in both broadband absorption and polarization conversion. Furthermore, deep neural networks (DNNs) significantly reduce the computational time required by traditional optimization methods by efficiently navigating multi-dimensional parameter spaces. DNNs can also capture complex nonlinear relationships between parameters that are often difficult to model with conventional techniques. This highlights the potential of artificial intelligence in the design of functional materials. With these capabilities, the device shows promising applications in fields such as THz stealth technology, energy harvesting, THz communication, polarization imaging, and optical information processing. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Anderson localization Information processing Metamaterial devices Mode conversion neural networks Polarimetric imaging

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A Quality-Aware Voltage Overscaling Framework to Improve the Energy Efficiency and Lifetime of TPUs Based on Statistical Error Modeling

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IEEE ACCESS 2024年 12卷 92181-92197页

作者： Senobari, Alireza Vafaei, Jafar Akbari, Omid Hochberger, Christian Shafique, Muhammad Tarbiat Modares Univ Dept Elect & Comp Engn Tehran 14115111 Iran Univ Tehran Sch Elect & Comp Engn Tehran 14395515 Iran Tech Univ Darmstadt Dept Elect Engn D-64289 Darmstadt Germany NYU Abu Dhabi NYU AD Div Engn Abu Dhabi U Arab Emirates

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by the structure and function of the human brain, designed to process and learn from large amounts of data, making them particularly well-suited for tasks such as image and speech recognition. However, applications of DNNs are experiencing emerging growth due to the deployment of specialized accelerators such as the Google's Tensor processing Units (TPUs). In large-scale deployments, the energy efficiency of such accelerators may become a critical concern. In the voltage overscaling (VOS) technique, the operating voltage of the system is scaled down beyond the nominal operating voltage, which increases the energy efficiency and lifetime of digital circuits. The VOS technique is usually performed without changing the frequency resulting in timing errors. However, some applications such as multimedia processing, including DNNs, have intrinsic resilience against errors and noise. In this paper, we exploit the inherent resilience of DNNs to propose a quality-aware voltage overscaling framework for TPUs, named X-TPU, which offers higher energy efficiency and lifetime compared to conventional TPUs. The X-TPU framework is composed of two main parts, a modified TPU architecture that supports a runtime voltage overscaling, and a statistical error modeling-based algorithm to determine the voltage of neurons such that the output quality is retained above a given user-defined quality threshold. We synthesized a single-neuron architecture using a 15-nm FinFET technology under various operating voltage levels. Then, we extracted different statistical error models for a neuron corresponding to those voltage levels. Using these models and the proposed algorithm, we determined the appropriate voltage of each neuron (the voltage level of each column of the X-TPU). Results show that running a DNN on X-TPU can achieve 32% energy saving for only 0.6% accuracy loss.

关键词： Voltage control Accuracy artificial neural networks Energy efficiency Approximate computing Power demand Computer architecture Statistical analysis Error analysis Voltage overscaling deep neural networks approximate computing accuracy TPU energy efficiency statistical error analysis

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Heart Signal processing Using Wavelet Analysis and Deep Learning Algorithms Based on artificial Intelligence

Heart Signal Processing Using Wavelet Analysis and Deep Lear...

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International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Zilaie, Morteza Mohammadkhani, Zohreh Asrari, Keyvan Azimi Talebiyan, Seyed Reza Islamic Azad University Science and Research Branch Khorasan Razavi Mashhad Iran The University of Pavia Pavia Italy Imam Reza International University Khorasan Razavi Mashhad Iran

ISBN: (纸本)9783031840999

The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different heart failures produces different types of this signal. In this research, the cardiac signals of the patients are analyzed by an artificial intelligence neural network, and the type of the person’s disease is diagnosed. The artificial intelligence in question can be learned and can be used as an assistant ECG signal is calculated using continuous wavelet transform and then it is subjected to learning and evaluation using a deep convolutional neural network. In the architecture of artificial intelligence, two well-known neural networks GoogLeNet and SqueezeNet are used, which have been sufficiently trained in similar applications such as image processing and machine vision. In the end, the type of heart failure is diagnosed and classified. The cardiac signals of the patients used in the simulation were extracted from the PhysioNet medical engineering standard dataset of MIT University. In the end, the simulation results of the research are given. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Tchebichef Transform Domain-Based Deep Learning Architecture for image Super-Resolution

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2024年第2期35卷 2182-2193页

作者： Kumar, Ahlad Singh, Harsh Vardhan Khare, Vijeta Dhirubhai Ambani Inst Informat & Commun Technol D Dept Informat & Commun Technol Gandhinagar 382007 Gujarat India Dhirubhai Ambani Inst Informat & Commun Technol D Gandhinagar 382007 Gujarat India Adani Inst Infrastruct Engn Adalaj 382421 Gujarat India

Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low-frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.

关键词： Transforms Deep learning Computer architecture Convolutional neural networks COVID-19 Training Kernel Convolutional neural network (CNN) COVID-19 deep learning image super-resolution (SR) Tchebichef moments

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Out-of-distribution Data Detection using Bayesian Convolutional neural Network with Variational Inference

Out-of-distribution Data Detection using Bayesian Convolutio...

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International Joint conference on neural networks (IJCNN)

作者： Minagawa, Koki Saito, Taisei Kojima, Sena Ando, Kota Asai, Tetsuya Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Japan

ISBN: (纸本)9798350359329;9798350359312

Out-of-distribution (OOD) data detection is critical when using machine learning models for practical applications. Many methods have been proposed to estimate the uncertainty of machine learning models for input data. Therefore, the purpose of this paper is to propose and verify toward elucidation of the nature and the practical use. We proposes data preprocessing to improve OOD image data detection performance using Bayesian neural networks (BNNs) to estimate two types of uncertainties. The aim of the proposed preprocessing is to increase the usefulness of the two uncertainties by providing more information to the BNN. The proposed preprocessing extracts high-frequency components of the input image by wavelet transform and adds the channel direction to create new input data. The contributions of this paper are as follows. First, we show that in training FashionMNIST, the BNN has a high performance in detecting OOD data by epistemic uncertainty that can be estimated. Second, we infer the relationship between the degree of BNN learning and OOD data detection performance. Finally, the proposed pre-processing is demonstrated to improve the OOD data detection performance. The validation results show that the average area under the receiver operating characteristic (AUROC) increases from 0.936 to 0.970 with the proposed preprocessing for BNNs trained in 10 epochs. These contributions reveal some aspects of the nature of uncertainty and provide clues to approaches for practical applications.

关键词： bayesian neural network out-of-distribution detection wavelet transform uncertainty

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