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Vector-Valued Hopfield neural networks and Distributed Synapse Based Convolutional and Linear Time-Variant Associative Memories

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neural processing LETTERS 2023年第4期55卷 4163-4182页

作者： Garimella, Rama Murthy Valle, Marcos Eduardo Vieira, Guilherme Rayala, Anil Munugoti, Dileep Mahindra Univ Ecole Cent Sch Engn Dept Comp Sci Hyderabad India Univ Estadual Campinas Campinas SP Brazil Int Inst Informat Technol Hyderabad India Indian Inst Technol Gauhati India

The Hopfield network is an example of an artificial neural network used to implement associative memories. A binary digit represents the neuron's state of a traditional Hopfield neural network. Inspired by the human brain's ability to cope simultaneously with multiple sensorial inputs, this paper presents three multi-modal Hopfield-type neural networks that treat multi-dimensional data as a single entity. In the first model, called the vector-valued Hopfield neural network, the neuron's state is a vector of binary digits. Synaptic weights are modeled as finite impulse response (FIR) filters in the second model, yielding the so-called convolutional associative memory. Finally, the synaptic weights are modeled by linear time-varying (LTV) filters in the third model. Besides their potential applications for multi-modal intelligence, the new associative memories may also be used for signal and image processing and solve optimization and classification tasks.

关键词： Vector-valued Hopfield neural network Finite impulse response filter Convolution Sub-sampling matrix Linear time-varying filter Multimodal intelligence

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A survey of the vision transformers and their CNN-transformer based variants

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artificial INTELLIGENCE REVIEW 2023年第SUPPL3期56卷 S2917-S2970页

作者： Khan, Asifullah Raufu, Zunaira Sohail, Anabia Khan, Abdul Rehman Asif, Hifsa Asif, Aqsa Farooq, Umair Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Pattern Recognit Lab Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Artificial Intelligence Ctr PAIC Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci Ctr Math Sci Islamabad 45650 Pakistan Khalifa Univ Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Air Univ E-9 Islamabad 44230 Pakistan

Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correlation in images. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations. These hybrid vision transformers, also referred to as CNN-Transformer architectures, have demonstrated remarkable results in vision applications. Given the rapidly growing number of hybrid vision transformers, it has become necessary to provide a taxonomy and explanation of these hybrid architectures. This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers. Additionally, the key features of these architectures such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. In contrast to the previous survey papers that are primarily focused on individual vision transformer architectures or CNNs, this survey uniquely emphasizes the emerging trend of hybrid vision transformers. By showcasing the potential of hybrid vision transformers to deliver exceptional performance across a range of computer vision tasks, this survey sheds light on the future directions of this rapidly evolving architecture.

关键词： Auto encoder Channel boosting Computer vision Convolutional neural networks Deep learning Hybrid vision transformers image processing Self-attention Transformer

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Research on image and text in visual communication design under artificial intelligence

Research on image and text in visual communication design un...

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2024 International Conference on image, Signal processing, and Pattern Recognition, ISPP 2024

作者： Yuan, Yuanyi Wu, Longjun Zou, Yong Chengdu Technological University Chengdu China

ISBN: (纸本)9781510680425

The paper examines the integration of AI in visual communication design, particularly focusing on the enhanced impact of images when combined with text. It explores the evolution of visual communication design in the context of AI advancements, emphasizing the increasing significance of this discipline in today's technology-driven society. Key areas of discussion include the role of artificial intelligence, specifically deep learning, in image recognition and natural language processing, and its application in visual communication design. The paper details the development and training of an image recognition and text matching model, showcasing the effective application of convolutional neural networks (CNNs) and Word2Vec in visual design tasks. The research concludes by highlighting the successful translation of deep learning theoretical achievements into practical visual communication design applications, demonstrating the model's proficiency in image recognition and text matching within a business and engineering framework. © 2024 SPIE.

关键词： Convolutional neural networks

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Scale-preserving automatic concept extraction (SPACE)

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MACHINE LEARNING 2023年第11期112卷 4495-4525页

作者： Posada-Moreno, Andres Felipe Kreiskoether, Lukas Glander, Tassilo Trimpe, Sebastian Rhein Westfal TH Aachen Inst Data Sci Mech Engn DSME Aachen Germany Deevio GmbH Berlin Germany

Convolutional neural networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave in ways unexpected to human users or developers, severe consequences can arise, such as economic losses or an increased risk to human life. Concept extraction techniques can be applied to increase the reliability and transparency of CNNs through generating global explanations for trained neural network models. The decisive features of image datasets in quality control often depend on the feature's scale;for example, the size of a hole or an edge. However, existing concept extraction methods do not correctly represent scale, which leads to problems interpreting these models as we show herein. To address this issue, we introduce the Scale-Preserving Automatic Concept Extraction (SPACE) algorithm, as a state-of-the-art alternative concept extraction technique for CNNs, focused on industrial applications. SPACE is specifically designed to overcome the aforementioned problems by avoiding scale changes throughout the concept extraction process. SPACE proposes an approach based on square slices of input images, which are selected and then tiled before being clustered into concepts. Our method provides explanations of the models' decision-making process in the form of human-understandable concepts. We evaluate SPACE on three image classification datasets in the context of industrial quality control. Through experimental results, we illustrate how SPACE outperforms other methods and provides actionable insights on the decision mechanisms of CNNs. Finally, code for the implementation of SPACE is provided.

关键词： Explainable artificial intelligence image processing neural networks Industry

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An image style transfer model based on convolutional neural network (CNN)

An image style transfer model based on convolutional neural ...

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2023 International Conference on Computer Graphics, artificial Intelligence, and Data processing, ICCAID 2023

作者： Li, Shan Li, Min Li, Xiaoying Xiamen Institute of Technology Fujian Province Xiamen China Chengyi College Jimei University Fujian Province Xiamen China College of Marine Engineering Jimei University Fujian Province Xiamen China

ISBN: (纸本)9781510677685

With the rise of deep learning, the cross collision between artificial intelligence and art, represented by image style transfer, has attracted high attention in the fields of graphic and image technology and art. Based on Convolutional neural networks(CNN), this article discusses an image style transfer model. Firstly, define the content loss function and style loss function,weigh them to calculate the total loss function, incorporate texture features extracted by the Canny operator into the content image;secondly, use the cost function to calculate the values representing content and style for comparison between the initial style and the target style, and then generate a new target image through deformation and combination;finally, train the VGG model to generate the output of the content and style layers of the image, and calculated the corresponding layer parameters applied to the original image for retraining to achieve the minimum loss value. The verification results indicate that adding the Canny operator makes the edges of the style transfer graph more delicate and the contours clearer. This model has a very wide range of applications, such as video processing, photo beautification, social communication, etc. It is currently one of the popular technologies in artificial intelligence. © 2024 SPIE.

关键词： Convolutional neural networks

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SAM: cross-modal semantic alignments module for image-text retrieval

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MULTIMEDIA TOOLS AND applications 2024年第4期83卷 12363-12377页

作者： Park, Pilseo Jang, Soojin Cho, Yunsung Kim, Youngbin Chung Ang Univ Dept Artificial Intelligence Seoul 06974 South Korea Chung Ang Univ Dept Image Sci & Arts Seoul 06974 South Korea

Cross-modal image-text retrieval has gained increasing attention due to its ability to combine computer vision with natural language processing. Previously, image and text features were extracted and concatenated to feed the transformer-based retrieval network. However, these approaches implicitly aligned the image and text modalities since the self-attention mechanism computes attention coefficients for all input features. In this paper, we propose cross-modal Semantic Alignments Module (SAM) to establish an explicit alignment through enhancing an inter-modal relationship. Firstly, visual and textual representations were extracted from an image and text pair. Secondly, we constructed a bipartite graph by representing the image regions and words in the sentence as nodes, and the relationship between them as edges. Then our proposed SAM allows the model to compute attention coefficients based on the edges in the graph. This process helps explicitly align the two modalities. Finally, a binary classifier was used to determine whether the given image-text pair is aligned. We reported extensive experiments on MS-COCO and Flickr30K test sets, showing that SAM could capture the joint representation between the two modalities and could be applied to the existing retrieval networks.

关键词： image-text retrieval Cross-modal Vision-language Graph neural networks

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artificial intelligence (AI) in textile industry operational modernization

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RESEARCH JOURNAL OF TEXTILE AND APPAREL 2024年第1期28卷 67-83页

作者： Sikka, Monica Puri Sarkar, Alok Garg, Samridhi Dr BR Ambedkar Natl Inst Technol Dept Text Technol Jalandhar Punjab India

Purpose With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing. Design/methodology/approach The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration. Findings AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances. Originality/value This research conducts a thorough analysis of artificial neural network applications in the textile sector.

关键词： artificial intelligence neural networking Machine learning Deep learning AI in dyeing processing

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Optoelectronic Synapse Enabled by Defect Engineering of Tellurene for Neuromorphic Computing

IEEE ELECTRON DEVICE LETTERS

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IEEE ELECTRON DEVICE LETTERS 2025年第1期46卷 68-71页

作者： Guo, Junxiong Huang, Junyan Gu, Shuyi Lin, Lin Zhang, Yafei Wang, Xiang Liu, Yu Gong, Tianxun Lin, Yuan Yu, Bin Huang, Wen Zhang, Xiaosheng Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn Chengdu 611731 Peoples R China Sch Elect Informat & Elect Engn Chengdu 610106 Peoples R China Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Univ Elect Sci & Technol China Sch Mat & Energy Chengdu 610054 Peoples R China Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China

Emerging optoelectronic synapses hold immense potential for advancing neuromorphic computing systems. However, achieving precise control over selective responses in optoelectronic memory and clarifying tunable synaptic weights has remained challenging. This study reports an optoelectronic synapse utilizing oxygen plasma-assisted defect engineering in tellurene for artificial neural networks. Through DFT calculations and experimental analyses, we demonstrate that tellurene conductance can be modulated by controlling plasma-defined defect engineering, allowing a transition from short-term to long-term synaptic plasticity, largely determined by intrinsic large-lattice-relaxation effects. Our artificial synapses exhibit high linearity, a broad dynamic range, and tunable synaptic weights. Additionally, our optoelectronic synapses display selective sensitivity to multi-spectral light and achieve a pattern recognition accuracy of up to 96.7% across five typical datasets, surpassing even the ideal synapse. These tunable spectral responses, combined with high-performance neuromorphic applications using spike coding, establish a foundation for developments in brain-inspired machine learning, robotics, and real-time data processing.

关键词： Optoelectronic synapse defect engineering image recognition neural network Optoelectronic synapse defect engineering image recognition neural network

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An Instrument Tone Recognition System Based on artificial Intelligence and Feature Recognition 2

An Instrument Tone Recognition System Based on Artificial In...

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2nd IEEE International Conference on image processing and Computer applications, ICIPCA 2024

作者： Hu, Zheng School of Music Zhaoqing University Guangdong Zhaoqing City526061 China

ISBN: (纸本)9798350360240

Instrument tone recognition systems have over time had the highest application value and significance in information retrieval. Notably, the traditional systems and methods often rely on convolutional neural networks (CNN) that serve as fundamental through reportedly, have a poor performance. To this effect, this work seeks to examine a resent artificial intelligence (AI) and a feature recognition system in instrument tone recognition. A proposed model in this case is integrated in an interactive system of humanoid robot. The findings of this study reveal that the proposed resent artificial intelligence (AI) and a feature recognition system in the design of an instrument tone recognition system outperforms the traditional model in terms of recognition accuracy and tonal loss rate. The findings of the research therefore providing methods and ideas that may be used in instrument tonal recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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To Spike or Not to Spike: A Digital Hardware Perspective on Deep Learning Acceleration

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2023年第4期13卷 1015-1025页

作者： Ottati, Fabrizio Gao, Chang Chen, Qinyu Brignone, Giovanni Casu, Mario R. Eshraghian, Jason K. Lavagno, Luciano Politecn Torino Dept Elect & Telecommun Engn I-10129 Turin Italy Delft Univ Technol Dept Microelect NL-2628 CD Delft Netherlands Univ Zurich Inst Neuroinformat CH-8006 Zurich Switzerland Swiss Fed Inst Technol CH-8092 Zurich Switzerland Univ Calif Santa Cruz Dept Elect & Comp Engn Santa Cruz CA 95064 USA

As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing;however, this happens at the expense of efficiency since they require increasingly more memory and computing power. The power efficiency of the biological brain outperforms any large-scale deep learning (DL) model;thus, neuromorphic computing tries to mimic the brain operations, such as spike-based information processing, to improve the efficiency of DL models. Despite the benefits of the brain, such as efficient information transmission, dense neuronal interconnects, and the co-location of computation and memory, the available biological substrate has severely constrained the evolution of biological brains. Electronic hardware does not have the same constraints;therefore, while modeling spiking neural networks (SNNs) might uncover one piece of the puzzle, the design of efficient hardware backends for SNNs needs further investigation, potentially taking inspiration from the available work done on the artificial neural networks (ANNs) side. As such, when is it wise to look at the brain while designing new hardware, and when should it be ignored? To answer this question, we quantitatively compare the digital hardware acceleration techniques and platforms of ANNs and SNNs. As a result, we provide the following insights: (i) ANNs currently process static data more efficiently, (ii) applications targeting data produced by neuromorphic sensors, such as event-based cameras and silicon cochleas, need more investigation since the behavior of these sensors might naturally fit the SNN paradigm, and (iii) hybrid approaches combining SNNs and ANNs might lead to the best solutions and should be investigated further at the hardware level, accounting for both efficiency and loss optimization.

关键词： artificial neural networks deep learning digital hardware neuromorphic computing spiking neural networks

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