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ADVANCED INTELLIGENT SYSTEMS 2025年第5期7卷

作者： Im, Jiseong Ko, Jonghyun Hwang, Joon Kim, Jangsaeng Shin, Wonjun Koo, Ryun-Han Park, Minkyu Park, Sung-Ho Choi, Woo Young Kim, Jae-Joon Lee, Jong-Ho Seoul Natl Univ Coll Engn Interuniv Semicond Res Ctr Dept Elect & Comp Engn Seoul 08826 South Korea Sungkyunkwan Univ Dept Semicond Convergence Engn Suwon 16419 South Korea

Compute-in-memory (CIM) technology based on emerging nonvolatile memories (NVMs) has shown promise in enhancing artificial intelligence applications by integrating computation directly within NVM arrays. However, the efficiency of CIM systems is often curtailed by the substantial overhead that is caused by traditional complementary metal-oxide-semiconductor (CMOS)-based analog-to-digital converters (ADCs). Here, we report an in-memory ADC (IMADC) that leverages NVMs to perform the dual functionalities of reference generation and voltage comparison, effectively minimizing the area occupancy and energy consumption, is reported. The IMADC not only significantly outperforms traditional ADCs but also enables the inherent processing of nonlinear activation functions such as the sigmoid function, which is required for neural networks. The IMADC-based CIM system achieves software-comparable accuracy in CIFAR-10 image classification on the VGG-9 network. The IMADC exhibits significantly reduced area occupancy (45 mu m2) and energy consumption (29.6 fJ) compared to conventional CMOS-based ADCs. The IMADC, compatible with various types of NVMs, demonstrates significant potential for enhancing the efficiency of CIM systems in terms of area occupancy and energy consumption.

关键词： analog-to-digital converter compute-in-memory flash thin-film-transistor hardware-based artificial intelligence neuromorphic computing

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Engineering of A-site cations in APbI3 (A = Cs, Rb, K) perovskites for resistive switching control and self-rectifying memristors for next-generation computing applications

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NANO ENERGY 2025年 138卷

作者： Jain, Muskan Fasnick, C. K. Bismiya Khemnani, Manish Kortstee, Lotte Andola, Bhawana Patel, Mayur Jagdishbhai Guerrero, Antonio Srivastava, Yogesh Kumar Castelli, Ivano E. Solanki, Ankur Pandit Deendayal Energy Univ Flextron Lab Gandhinagar 382426 Gujarat India Pandit Deendayal Energy Univ Sch Energy Technol Dept Phys Gandhinagar 382426 India Tech Univ Denmark Dept Energy Convers & Storage Anker Engelundsvej 411 DK-2800 Lyngby Denmark Indian Inst Technol Hyderabad Dept Phys Ultrafast Terahertz Photon Lab Sangareddy 502284 Telangana India Indian Inst Technol Guwahati Dept Chem Gauhati 781039 Assam India Univ Jaume 1 Inst Adv Mat INAM Castellon de La Plana 12006 Spain

The resistive switching in memristor devices is highly influenced by structural and electronic properties, which can be tailored through material composition. Unlike optoelectronic devices, lattice distortion driven by A-site cation variation (A = Cs, Rb, K) is a key mechanism for enhancing the switching properties of APbI(3)-based flexible memristor devices. This distortion, particularly noticeable in KPbI3's orthorhombic phase, alters crystal structure, electronic as well as charge transport properties. Density functional theory (DFT) calculations are performed to calculate ionic migration barriers in the orthorhombic structures, while X-ray photoelectron spectroscopy (XPS) analysis provides insights into the chemical environment and bonding states. Leveraging this effect, KPbI3 exhibits exceptional non-volatile data storage performance, with a high ON/OFF ratio (similar to 10(3)) and self-rectification (similar to 10(3)). Conversely, CsPbI3 with subtle lattice distortion, displays superior synaptic behavior and efficient spike-timing-dependent plasticity (STDP), ideal for neuromorphic computing. CsPbI3 demonstrates remarkable potential for artificial neural networks (ANNs), achieving similar to 97 % accuracy in Modified National Institute of Standards and Technology (MNIST) dataset, image classification with minimal training, and 85 % accuracy in convolutional neural networks (CNNs) on the Canadian Institute for Advanced Research (CIFAR-10) dataset. Our findings highlight the critical role of small cation size in modulating Schottky barriers, morphology, and ionic migration, and their direct impact on resistive switching properties. These results underscore the potential of cation size modulation as an effective strategy for designing memristors for a wide range of applications, including neuromorphic computing, signal processing, logic gates, and quantum computing.

关键词： Memristor KPbI3 Self-rectifying Perovskites artificial Synapse Neuromorphic Computation Resistive Switching

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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine

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IEEE ACCESS 2025年 13卷 43289-43315页

作者： Guarda, Andre F. R. Rodrigues, Nuno M. M. Pereira, Fernando Inst Telecomunicacoes P-1049001 Lisbon Portugal Politecn Leiria ESTG P-2411901 Leiria Portugal Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal

Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.

关键词： Transform coding Point cloud compression Encoding Standards image coding Three-dimensional displays Geometry image color analysis artificial intelligence Codecs JPEG Pleno standard learning-based coding man and machine point cloud coding

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Comparative analysis of deep learning models for crack detection in buildings

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SCIENTIFIC REPORTS 2025年第1期15卷 1-33页

作者： Krishnan, S. Siva Rama Karuppan, M. K. Nalla Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Tandon, Saarthak Balusamy, Balamurugan Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamilnadu India Sri Balaji Univ Balaji Inst Modern Management Pune 411033 India King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia Kebri Dehar Univ Dept Comp Sci Kebri Dehar Ethiopia Shiv Nadar Univ Delhi Ncr 201314 Delhi India Chennai Inst Technol Dept Comp Sci & Engn Chennai India Chitkara Univ Ctr Res Impact & Outcome Rajpura Punjab India King Abdulaziz Univ Ctr Res Excellence Artificial Intelligence & Data Jeddah Saudi Arabia

Life-time of the buildings is generally challenged by the act of nature. In-spite of the fact that the constructions provide minimum guarantee on quality and durability, certain mismatch in the composition of the materials, stress on the building, and chemical or physical imbalance of the materials, lead to surface crack. Cracks are also generated due to the shuffle of climatic conditions, which leads to the contraction and expansion of the building surfaces, and other damages. The guarantee on building safety and serviceability depends on how these buildings are successfully assessed and maintained. The development of artificial Intelligence (AI) techniques, provide favourable solutions in-order to handle, manage and solve building cracks, through analysis using deep image neural network models, that perform classification of the building with crack images. As a result, a critical challenge for many civil engineering applications is the precise, quick, and automated identification of cracks on structural surfaces is addressed with the solutions provided by the deep image neural networks. In this research, we tackle the research gap and data scarcity by developing and curating a novel deep learning image processing for detecting cracks in brickwork. We also train and validate several deep learning models to classify brickwork images as either cracked or normal. The dataset of the proposed work contains 24,000 images which are classified through binary classes. These classes are generated for crack and non-crack images. The various parameters such as Batch size, Pooling, Activation functions Learning-rate, Kernel-Size, Normalization, and Optimizers are used for the evaluation of the model. The proposed work performs a comparative analysis of four deep image models such as Inception V3, VGG-16, RESNET-50 VGG-19, Inception ResNetV2 and CNN-RES MLP. With the analysis of all these models, the Inception V3 provides the best of all with the accuracy value of 99.98%. The In

关键词： Building cracks Inception V3 VGG-16 ResNet-50 InceptionV2ResNet CNN

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Convolutional neural network is used in target contour extraction

Convolutional neural network is used in target contour extra...

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2024 International Conference on Mechatronic Engineering and artificial Intelligence, MEAI 2024

作者： Bi, Mingxue Lu, Na College of Equipment Engineerings Shenyang Ligong University Liaoning Shenyang110168 China

ISBN: (纸本)9781510689176

Target contour extraction is a key task in the field of image processing, which is of great significance for applications such as image segmentation, object detection, and scene understanding. Traditional methods are often limited by complex scenarios and changing target shapes, and their effects may be unstable. In recent years, with the vigorous development of deep learning technology, Convolutional neural Network (CNN) has shown significant advantages in target contour extraction tasks due to its powerful feature representation and learning *** this paper, the loss function is used, and the loss function mechanism adopted is Softmax, which is especially suitable for multi-classification problems and is widely used in supervised and unsupervised learning scenarios. © 2025 SPIE.

关键词： Convolutional neural networks

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Involution fused convolution for classifying eye-tracking patterns of children with Autism Spectrum Disorder

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ENGINEERING applications OF artificial INTELLIGENCE 2025年 139卷

作者： Islam, Md. Farhadul Manab, Meem Arafat Mondal, Joyanta Jyoti Zabeen, Sarah Bin Rahman, Fardin Hasan, Md. Zahidul Sadeque, Farig Noor, Jannatun BRAC Univ Sch Data & Sci Dept Comp Sci & Engn Comp Sustainabil & Social Good Res Grp C2SG Dhaka 1212 Bangladesh BRAC Univ Sch Data & Sci Dept Comp Sci & Engn Dhaka 1212 Bangladesh BRAC Univ Sch Data & Sci Dept Math & Nat Sci Dhaka 1212 Bangladesh Dublin City Univ Sch Law & Govt Dublin D09 V209 Ireland Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA Concordia Univ Concordia Inst Informat Syst Engn CIISE Gina Cody Sch Engn & Comp Sci Montreal PQ H3G 1M8 Canada

Autism Spectrum Disorder (ASD) is a neurological condition that is challenging to diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with maintaining attention spans and have less focused vision. The eye-tracking technology has drawn special attention in the context of ASD since anomalies in gaze have long been acknowledged as a defining feature of autism. Deep Learning (DL) approaches coupled with eye-tracking sensors are exploiting additional capabilities to advance the diagnostic and its applications. DL architectures like convolution have been dominating this domain. However, convolutions alone maybe insufficient to capture the important spatial information in eye-tracking patterns, as these patterns are more likely to be localized. The dynamic kernel-based process known as involutions can improve the classification efficiency. In this study, we utilize two image-processing operations to see how these processes learn eye-tracking patterns. Since these patterns are primarily based on spatial information, we employ a hybrid of involution and convolution. Our study shows that adding a few involution layers reduces size and computational cost while enhancing location-specific capabilities, maintaining performance comparable to pure convolutional models. However, excessive involution layers lead to weaker performance. For comparisons, we experiment with two datasets and a combined version of both in ablation studies. Our proposed model, featuring three involution layers and three convolution layers, achieved 99.43% accuracy on the first dataset and 96.78% on the second, with a size of only 1.36 megabytes. These results showcase the effectiveness of combining involution and convolution layers which outperforms previous literature.

关键词： Autism Spectrum Disorder Involutional neural networks Convolutional neural networks Eye-tracking Deep learning

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Deep Learning-Driven Analysis of Tumor Regions in Magnetic Resonance Imaging 24

Deep Learning-Driven Analysis of Tumor Regions in Magnetic R...

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5th International Symposium on artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Cao, Bingyu School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400717826

The early diagnosis of tumor is very important to improve the survival rate of patients. Magnetic resonance imaging (MRI), as a non-invasive advanced imaging technology, has a wide range of potential applications in tumor detection and evaluation. However, MRI images are often complex and susceptible to noise, and traditional manual analysis methods have limitations in accuracy and efficiency. In recent years, deep learning technology has made a breakthrough in the field of image recognition, which brings a new method and perspective for the intelligent analysis of MRI images. This paper studied the application of several classical deep learning models in MRI image tumor region recognition, including Convolutional neural Network (CNN) models such as VGG, GoogLeNet, ResNet, EfficientNet and Vision Transformer model, combined with data processing technology and parameter adjustment technology. The performance of these models on data sets is discussed, and a high classification accuracy is finally achieved. This study not only provides an effective solution for automated tumor region detection, but also provides important technical support and reference for clinical tumor diagnosis, and further promotes the intelligent development of imaging diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Transforming urban waste collection inventory: AI-Based container classification and Re-Identification

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WASTE MANAGEMENT 2025年 199卷 25-35页

作者： Galan, Javier Gonzalez, Miguel Moral, Paula Garcia-Martin, Alvaro lvaro Martinez, Jose M. Univ Autonoma Madrid Video Proc & Understanding Lab Madrid 28049 Spain

This work lays the groundwork for creating an automated system for the inventory of urban waste elements. Our primary contribution is the development of, to the best of our knowledge, the first re-identification system for urban waste elements that uses artificial Intelligence and Computer Vision, incorporating information from a classification module and geolocation context to enhance post-processing performance. This re-identification system helps to create and update inventories by determining if a new image matches an existing element in the inventory based on visual similarity or, if not, by adding it as a new identity (new class or new identity of an existing class). Such a system could be highly valuable to local authorities and waste management companies, offering improved facility maintenance, geolocation, and additional applications. This work also addresses the dynamic nature of urban environments and waste management elements by exploring Continual Learning strategies to adapt pretrained systems to new settings with different urban elements. Experimental results show that the proposed system operates effectively across various container types and city layouts. These findings were validated through testing in two different Spanish locations, a "City" and a "Campus", differing in size, illumination conditions, seasons, urban design and container appearance. For the final re-identification system, the baseline system achieves 53.18 mAP (mean Average Precision) in the simple scenario, compared to 21.54 mAP in the complex scenario, with additional challenging unseen variability. Incorporating the proposed post-processing techniques significantly improved results, reaching 74.14 mAP and 71.75 mAP in the simple and complex scenario respectively.

关键词： Waste Container Classification Deep Learning Convolutional neural networks Object Detection Continual Learning Re-identification

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neural networks and Their applications in Smart Systems 9th

Neural Networks and Their Applications in Smart Systems

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9th International Conference on Smart City applications, SCA 2024

作者： Atmani, Aziz Boumhamdi, Mounir Laboratoire de Recherche Business Intelligence Gouvernance des Organisations Finance et Criminalité Financière Université Hassan II Faculté des Sciences Juridiques Économiques et Sociales de Casablanca No8110 Oasis Casablanca Morocco

ISBN: (纸本)9783031886522

This work investigates the potential of neural networks in the processing of artificial intelligence, especially in smart systems, and explores their applications regarding advancing automation, user experience, and resource management. The methodology will include collecting data from public datasets and international organizations, preprocessing or cleaning, normalization and augmentation of data, followed by the development of different neural network models: Convolutional neural networks for image recognition;Recurrent neural networks and Long Short-Term Memory for speech recognition and time series prediction;hybrids for such complicated tasks as energy consumption forecasting andhealth data analysis. Results are very high for a wide variety of datasets, with CNNs attaining state-of-the-art in image recognition, LSTMs doing the same in speech recognition with very low word error rates, and the hybrids quite effective in predictive analytics and health data analysis. The discussion has been about how neural networks are impacting smart systems in a big way while also presenting the challenges regarding the requirements around data and computational resources and model interpretability. Future directions in this regard may include transfer learning, explainable AI, edge computing, and quantum computing to further enhance neural network capabilities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data Preprocessing image Recognition Intelligent Systems neural networks Predictive Analytics Speech Recognition

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ClusVPR: Efficient Visual Place Recognition With Clustering-Based Weighted Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial Intelligence 2025年第4期6卷 1038-1049页

作者： Xu, Yifan Shamsolmoali, Pourya Zareapoor, Masoume Yang, Jie Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China East China Normal University School of Communication Shanghai200240 China University of York YorkYO10 5DD United Kingdom

Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficient attention to small objects in complex scenes, resulting in recognition deviations. In this article, we present ClusVPR, a novel approach that tackles the specific issues of redundant information in duplicate regions and representations of small objects. Different from existing methods that rely on convolutional neural networks (CNNs) for feature map generation, ClusVPR introduces a unique paradigm called clustering-based weighted transformer network (CWTNet). CWTNet uses the power of clustering-based weighted feature maps and integrates global dependencies to effectively address visual deviations encountered in large-scale VPR problems. We also introduce the optimized-VLAD (OptLAD) layer, which significantly reduces the number of parameters and enhances model efficiency. This layer is specifically designed to aggregate the information obtained from scale-wise image patches. Additionally, our pyramid self-supervised strategy focuses on extracting representative and diverse features from scale-wise image patches rather than from entire images. This approach is essential for capturing a broader range of information required for robust VPR. Extensive experiments on four VPR datasets show our model's superior performance compared to existing models while being less complex. © 2024 IEEE.

关键词： Self-supervised learning

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