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

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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All-In-One Neuromorphic Light-Emitting Memristor for Infrared image Visualization and Recognition

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ADVANCED FUNCTIONAL MATERIALS 2025年

作者： Pan, Youjiang Wu, Chunyan Bu, Guohao Xiang, Chaoyu Wu, Rui Hu, Hailong Guo, Tailiang Li, Fushan Fuzhou Univ Inst Optoelect Technol Fuzhou 350116 Peoples R China Fujian Sci & Technol Innovat Lab Optoelect Informa Fuzhou 350108 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China

Neuromorphic vision systems have been proven to have great potential in simulating the fundamental functions of the human visual system and can even be extended to the infrared (IR) region, which is invisible to the human eye. Here, an IR neuromorphic light-emitting memristor (INLEM) is presented, which achieves full-process functionality from IR image sensing and information processing to visible light output within an all-in-one structure. The INLEM dynamically adjusts its current in response to different IR stimuli, successfully simulating the transition from short-term potentiation (STP) to long-term potentiation (LTP) in biological synapses. More importantly, this synaptic behavior is visualized in real-time through visible light without the need for an additional display module. Leveraging this feature, the process by which INLEM enhances the contrast of target information by suppressing IR noise is successfully demonstrated. Furthermore, it is proved that the intelligent image processing based on INLEM improves the efficiency of artificial neural networks in image recognition and classification tasks (in both energy and speed). Therefore, this intelligent neuromorphic light-emitting hardware, which integrates IR sensing, memory processing, and display functions, contributes to the development of integrated optoelectronic systems and Internet of Things applications.

关键词： all-in-one artificial neuron network image recognition infrared light neuromorphic

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Competitive dual-students using bi-level contrastive learning for semi-supervised medical image segmentation

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 144卷

作者： Hu, Gang Zhao, Feng Houssein, Essam H. Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Minia Univ Fac Comp & Informat Al Minya Egypt

Semi-supervised image segmentation aims to train the neural network with a small number of labeled images and a large number of unlabeled images, which helps to alleviate the burden of having less manually labeled medical data. However, the Mean-Teacher (MT) model, a benchmark method for semi-supervised medical segmentation, leads to a performance bottleneck as its student model eventually converges to the teacher model. In addition, existing segmentation methods treat all pixels equally and underestimate the importance of indistinguishable and underrepresented pixels, failing to mine the potential information in these regions effectively. To address the above issues, this paper proposes a Competitive Dual-Student (CDS) incorporating bi-level contrastive learning. First, an additional competitive dual-student model is added to the MT model and promoting knowledge sharing and complementarity among networks. Competitive instruction by the teacher through feature information exchange and positive comparisons reduces the accumulation of biased knowledge in the model. It stimulates the potential for further optimization of the model as a whole. Furthermore, a bi-level contrastive learning is designed. The high-level contrastive learning encourages competitive dual students to learn high-quality features from each other by constructing reliability constraints. The low-level contrastive achieved deep mining and accurate processing of local edge features by introducing class prototypes of high-quality features for teacher networks. Finally, the comprehensive experimental results on left atrium, brain tumor segmentation 2019 and automated cardiac diagnosis challenge datasets indicate that the segmentation performance of the proposed CDS outperforms the state-of-the-art compared methods. Code is released at https://***/FengZhao2001/CDS.

关键词： neural network Semi-supervised image segmentation Competitive dual-student Bi-level contrastive learning Class prototypes

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Transformer-based image and video inpainting: current challenges and future directions

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artificial INTELLIGENCE REVIEW 2025年第4期58卷 1-45页

作者： Elharrouss, Omar Damseh, Rafat Belkacem, Abdelkader Nasreddine Badidi, Elarbi Lakas, Abderrahmane United Arab Emirates Univ Coll Informat Technol Dept Comp Sci & Software Engn Al Ain U Arab Emirates United Arab Emirates Univ Coll Informat Technol Dept Comp & Network Engn Al Ain U Arab Emirates

image inpainting is currently a hot topic within the field of computer vision. It offers a viable solution for various applications, including photographic restoration, video editing, and medical imaging. Deep learning advancements, notably convolutional neural networks (CNNs) and generative adversarial networks (GANs), have significantly enhanced the inpainting task with an improved capability to fill missing or damaged regions in an image or a video through the incorporation of contextually appropriate details. These advancements have improved other aspects, including efficiency, information preservation, and achieving both realistic textures and structures. Recently, Vision Transformers (ViTs) have been exploited and offer some improvements to image or video inpainting. The advent of transformer-based architectures, which were initially designed for natural language processing, has also been integrated into computer vision tasks. These methods utilize self-attention mechanisms that excel in capturing long-range dependencies within data;therefore, they are particularly effective for tasks requiring a comprehensive understanding of the global context of an image or video. In this paper, we provide a comprehensive review of the current image/video inpainting approaches, with a specific focus on Vision Transformer (ViT) techniques, with the goal to highlight the significant improvements and provide a guideline for new researchers in the field of image/video inpainting using vision transformers. We categorized the transformer-based techniques by their architectural configurations, types of damage, and performance metrics. Furthermore, we present an organized synthesis of the current challenges, and suggest directions for future research in the field of image or video inpainting.

关键词： image inpainting Video inpainting Vision transformers

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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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3D Information in Robot Vision System Based on artificial neural Network 14th

3D Information in Robot Vision System Based on Artificial Ne...

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14th International Conference on Frontier Computing, FC 2024

作者： Liu, Xiaoxiao Department of Mechanical and Electrical Engineering Jinan Engineering Vocational Technical College Shandong Jinan China

ISBN: (纸本)9789819627974

In the exploration of robot vision systems based on artificial neural networks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human visual system, this technology enables robots to more effectively interpret and understand the three-dimensional spatial information of their environment, which has a revolutionary role in robot navigation, object recognition, obstacle avoidance and the execution of complex tasks. And this technology shows great potential in many fields such as industrial automation, autonomous vehicles, drone monitoring, and service robots. Not only that, it also plays a very important role in providing in-depth information, accurate positioning and efficient decision-making applications. According to the experimental data, among the 800 experimental subjects, more than 754 people were satisfied with the recognition accuracy, recognition speed, machine efficiency improvement, image recognition clarity, and overall satisfaction with the system. These findings indicate that with the continuous advancement of technology, 3D vision systems based on artificial neural networks will show more significant performance and value in future applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional neural networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Real-Time Multi-Scale Barcode image Deblurring Based on Edge Feature Guidance

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ELECTRONICS 2025年第7期14卷 1298-1298页

作者： Shi, Chenbo Jiang, Xin Zhang, Xiangyu Zhu, Changsheng Hu, Xiaowei Zhang, Guodong Li, Yuejia Zhang, Chun Shandong Univ Sci & Technol Coll Intelligent Equipment Tai An 271019 Peoples R China Suzhou Lamberv Intelligent Technol Dept Artificial Intelligence Suzhou 215000 Peoples R China

Barcode technology plays a crucial role in automatic identification and data acquisition systems, with extensive applications in retail, warehousing, healthcare, and industrial automation. However, barcode images often suffer from blurriness due to lighting conditions, camera quality, motion blur, and noise, adversely affecting their readability and system performance. This paper proposes a multi-scale real-time deblurring method based on edge feature guidance. Our designed multi-scale deblurring network integrates an edge feature fusion module (EFFM) to restore image edges better. Additionally, we introduce a feature filtering mechanism (FFM), which effectively suppresses noise interference by precisely filtering and enhancing critical signal features. Moreover, by incorporating wavelet reconstruction loss, the method significantly improves the restoration of details and textures. Extensive experiments on various barcode datasets demonstrate that our method significantly enhances barcode clarity and scanning accuracy, especially in noisy environments. Furthermore, our algorithm ensures robustness and real-time performance. The research results indicate that our method holds significant promise for enhancing barcode image processing, with potential applications in retail, logistics, inventory management, and industrial automation.

关键词： barcode deblurring multi-scale strategy neural networks edge guidance

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Radar Signal processing and Its Impact on Deep Learning-Driven Human Activity Recognition

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SENSORS 2025年第3期25卷 724-724页

作者： Ayaz, Fahad Alhumaily, Basim Hussain, Sajjad Imran, Muhammad Ali Arshad, Kamran Assaleh, Khaled Zoha, Ahmed Univ Glasgow James Watt Sch Engn Glasgow City G12 8QQ England Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn POB 346 Ajman U Arab Emirates Ajman Univ Artificial Intelligence Res Ctr POB 346 Ajman 346 U Arab Emirates

Human activity recognition (HAR) using radar technology is becoming increasingly valuable for applications in areas such as smart security systems, healthcare monitoring, and interactive computing. This study investigates the integration of convolutional neural networks (CNNs) with conventional radar signal processing methods to improve the accuracy and efficiency of HAR. Three distinct, two-dimensional radar processing techniques, specifically range-fast Fourier transform (FFT)-based time-range maps, time-Doppler-based short-time Fourier transform (STFT) maps, and smoothed pseudo-Wigner-Ville distribution (SPWVD) maps, are evaluated in combination with four state-of-the-art CNN architectures: VGG-16, VGG-19, ResNet-50, and MobileNetV2. This study positions radar-generated maps as a form of visual data, bridging radar signal processing and image representation domains while ensuring privacy in sensitive applications. In total, twelve CNN and preprocessing configurations are analyzed, focusing on the trade-offs between preprocessing complexity and recognition accuracy, all of which are essential for real-time applications. Among these results, MobileNetV2, combined with STFT preprocessing, showed an ideal balance, achieving high computational efficiency and an accuracy rate of 96.30%, with a spectrogram generation time of 220 ms and an inference time of 2.57 ms per sample. The comprehensive evaluation underscores the importance of interpretable visual features for resource-constrained environments, expanding the applicability of radar-based HAR systems to domains such as augmented reality, autonomous systems, and edge computing.

关键词： human activity classification radar domain representations deep learning computational cost transfer learning

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Classification of Vernacular Landscape Elements and Design applications Based on Deep Learning Fully Convolutional neural networks

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ADVANCES IN CIVIL ENGINEERING 2025年第1期2025卷

作者： Li, Ya Zhang, Yun Zhejiang Ind Polytech Coll Sch Architectural Engn Shaoxing 312000 Zhejiang Peoples R China Yulin Normal Univ Fac Art & Design Yulin 537000 Guangxi Peoples R China

The integration of vernacular landscape elements is a crucial aspect of contemporary garden landscape planning and design, serving as a significant means of expressing traditional culture and evoking folk nostalgia. The external regional, ecological, and symbolic, as well as the internal cultural aspects of vernacular landscape elements, directly influence the overall appearance of residential planning. However, landscape image processing is still largely confined to the designer's professional cognition and experience. Traditional methods for classifying landscape elements primarily rely on manual processing. By integrating an advanced convolutional neural network (CNN) architecture, this study demonstrates its practical utility through rigorous experiments. This paper presents squeeze-and-excitation (SE)-DenseNet, a deep-learning model that uses SE blocks to classify vernacular landscape elements. It achieves 97.3% accuracy, outperforming traditional methods. This reduces manual work and helps integrate cultural elements into modern design.

关键词： artificial intelligence classification deep learning landscape images vernacular landscape

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