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Proton-Gated Synaptic Transistors, Based on an Electron-Beam Patterned Nafion Electrolyte

ACS APPLIED MATERIALS & INTERFACES 2023年第15期15卷 19279-19289页

作者： Mohanty, Himadri Nandan Tsuruoka, Tohru Mohanty, Jyoti Ranjan Terabe, Kazuya Indian Inst Technol Hyderabad Dept Phys Nanomagnetism & Microscopy Lab Sangareddy 502285 Telangana India Natl Inst Mat Sci Res Ctr Mat Nanoarchitecton Tsukuba Ibaraki 305004 Japan

Neuromorphic processors using artificial neural networks are the center of attention for energy-efficient analog computing. artificial synapses act as building blocks in such neural networks for parallel information processing and data storage. Herein we describe the fabrication of a proton-gated synaptic transistor using a Nafion electrolyte thin film, which is patterned by electron-beam lithography (EBL). The device has an active channel of indium???zinc-oxide (IZO) between the source and drain electrodes, which shows Ohmic behavior with a conductance level on the order of 100 ??S. Under voltage applications to the gate electrode, the channel conductance is changed due to the injection and extraction of protons between the IZO channel and the Nafion electrolyte, emulating various synaptic functions with short-term and long-term plasticity. When positive (negative) gate voltage pulses are consecutively applied, the device exhibits long-term potentiation (depression) at the same number of steps as the number of input pulses. Based on these characteristics, an artificial neural network using this transistor shows ???84% image recognition accuracy for handwritten digits. The subject transistor also successfully mimics paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. Finally, dynamical pattern image memorization is demonstrated in a 5 ?? 5 array of these synaptic transistors. The results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.

关键词： protonic transistor Nafion electron-beam patterning synaptic plasticity Hebbian and Pavlovian learning artificial neural network

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Patch- and Class-Wise Hyperspectral Knowledge Learning: A Composite Consistency-Constrained Self-Ensemble Framework for Change Detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhao, Xiaoyang Li, Siyao Liu, Xinyue Song, Chuanming Wang, Xianghai Liaoning Normal Univ Sch Geog Sci Dalian 116029 Peoples R China Liaoning Normal Univ Sch Comp & Artificial Intelligence Dalian 116029 Peoples R China Dalian Univ Sch Informat Engn Dalian 116622 Peoples R China

Obtaining fine land surface change information from multitemporal hyperspectral images (HSIs) is a key goal pursued in remote sensing image processing. Recently, HSI change detection (HSI-CD) methods based on convolutional neural networks (CNNs) have achieved surprising detection results. One of the reasons is the support of large-scale labeled samples for network learning. However, the existence of mixed pixels greatly increases the difficulty of HSI interpretation, resulting in accurate pixel-level labeling work with a heavy burden and unable to meet the needs of time-sensitive applications. For this reason, achieving stable and high-precision CD with fewer samples is a difficult issue in this field. To address the above problems, a composite consistency-constrained self-ensemble framework (C3SelF) for HSI-CD is proposed, to alleviate the problems of low detection accuracy and instability caused by small samples. The framework mainly comprises two lightweight networks with the same structure aiming at accelerating the model inference process and thus improving the processing timeliness. The composite learning mode implements patch-wise classification loss, class-wise consistency loss on labeled samples, and patch-wise consistency loss on unlabeled samples under a multilevel noise perturbation strategy, which improves the classification results and reduces the labeling cost. Moreover, to exploit the multidimensional features contained in HSIs, a lightweight selective spatial-spectral feature joint network (S3Net) is designed to overcome over-fitting, and to deeply mine the discriminative information in unlabeled samples, a new sample screening strategy is designed to ensure the stability of the network during training unlabeled samples. Extensive experiments prove that the proposed C3SelF outperforms the state-of-the-art (SOTA) methods at a sampling rate of 0.1%, reaching 93.44% Kappa and 97.26% overall accuracy (OA) on the Farmland dataset. The source code of the

关键词： Accuracy Training Feature extraction Hyperspectral imaging Transformers Data mining Vectors Semisupervised learning Convolutional neural networks Unsupervised learning Change detection (CD) convolutional neural networks (CNNs) deep learning hyperspectral image (HSI) processing semi-supervised learning

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DLSIA: Deep Learning for Scientific image Analysis

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2024年第2期57卷 392-402页

作者： Roberts, Eric J. Chavez, Tanny Hexemer, Alexander Zwart, Petrus H. Lawrence Berkeley Natl Lab Ctr Adv Math Energy Res Applicat Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Mol Biophys & Integrated Bioimaging Div Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Adv Light Source Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Berkeley Synchrotron Infrared Struct Biol Program Berkeley CA 94720 USA

DLSIA (Deep Learning for Scientific image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

关键词： deep learning convolutional neural networks X-ray scattering tomography data compression

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AutoFace: How to Obtain Mobile neural Network-Based Facial Feature Extractor in Less Than 10 Minutes?

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IEEE ACCESS 2024年 12卷 25106-25118页

作者： Savchenko, Andrey V. Sber AI Lab Moscow 117312 Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod 603155 Russia

Various mobile and edge devices have significantly different processing capabilities, making it challenging to develop a single universal architecture of a neural network to extract facial embeddings. In this paper, we study the automated machine learning techniques to design a neural network with the best performance on a concrete device. The novel procedure is proposed to choose the better subnetwork of the Supernet based on a genetic algorithm with a surrogate binary classifier to compare the expected accuracy of two subnetworks. The latter uses only encoding of a candidate subnetwork and does not require directly estimating its accuracy on a validation set. As a result, the most computationally efficient and accurate model in TensorFlow Lite format is obtained in less than 10 minutes for a specific device and latency constraint. An Android demo application has been developed to demonstrate the potential of designed neural networks. It is experimentally shown that the proposed approach is universal: it can extract deep embeddings for tasks such as face verification and facial expression recognition and for various types of devices, including smartphones and Raspberry Pi single-board mini-computers. Our models process one facial image in real-time and achieve much higher accuracy when compared to the best-known lightweight networks.

关键词： Face recognition Task analysis Training Computer architecture Feature extraction artificial neural networks Performance evaluation Mobile communication Genetic algorithms Search methods Evolutionary computation Emotion recognition Mobile applications edge devices genetic algorithms evolutionary search face verification facial expression recognition

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Topological Signal processing Over Generalized Cell Complexes

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IEEE TRANSACTIONS ON SIGNAL processing 2024年 72卷 687-700页

作者： Sardellitti, Stefania Barbarossa, Sergio Sapienza Univ Rome Dept Informat Engn Elect & Telecommun I-00184 Rome Italy

Topological Signal processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes are required to satisfy the so-called inclusion property, according to which, if a set belongs to the complex, then all its subsets must also belong to the complex. In some applications, this is a severe limitation. To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells. We show that, even if the algebraic formulation does not change significantly, the extension to the generalized cell complexes considerably broadens the number of applications. Most important, the new representation provides a much better trade-off between the complexity of the representation and its accuracy. In addition, we propose a method to infer the structure of the cell complex from data and we propose distributed filtering strategies, including a method to retrieve the sparsest representation of the harmonic component. We quantify the advantages of using cell complexes instead of simplicial complexes, in terms of the complexity/accuracy trade-off, for different applications such image segmentation and recovering of real flows measured on data traffic and transportation networks.

关键词： Signal processing Data mining Topology Finite impulse response filters artificial neural networks Transportation Partitioning algorithms Topological signal processing algebraic topology cell complexes simplicial complexes FIR filters topology inference

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image Classification with Regularized Likelihood Estimation and Large Interval Discriminant Dimensionality Reduction

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2025年第2期41卷 385-400页

作者： Shen, Ying Zhu, Wei-hua Fu, Si-yong Wu, Guang-sheng Xinyu Univ Zhongxing Commun & Informat Coll Xinyu 338004 Peoples R China Xinyu Univ Sch Math & Comp Sci Xinyu 338004 Peoples R China

With the expansion of image application scale and the development of artificial intelligence industry, the demand for high precision and high robustness of image feature expression is gradually increasing. Convolutional neural networks (CNNS) have attracted much attention in the field of computer vision in recent years because of their excellent image feature learning ability. In order to further improve the accuracy and robustness of image classification, a second-order statistical convolutional neural network with multilayer fusion and cross-convolutional layer pooling are proposed in this study to obtain feature information of different dimensions in different convolutional layers. Due to the increase of computing and storage costs, an image classification method based on robust covariance of deep convolution features is proposed, and two regularized maximum likelihood estimators are introduced. Finally, the proposed method is tested by fine-grained experiments, image retrieval experiments and image classification experiments. The results show that MFBP (4,5) structure achieves 92.4%, 74.2% and 72.9% accuracy in finegrained image classification. Compared with MFBP (3,4,5), despite a slight decrease in accuracy, FPS performance is improved by more than 40%, demonstrating the advantages of this method in real-time applications. In addition, RMIE-VND and RMIE-IM methods improved by 4.1%, 8.4%, 7.8% and 7.1%, 12%, 9.8%, respectively, compared with LW methods on different data sets. The experimental results of image classification show that MFBP+RLME (VND+IM) has better performance and applicability than the current mainstream classification methods. This research is of great value for improving the efficiency and accuracy of image processing in big data environment, and provides a new research direction and practical tool for future real-time image application and computer vision system development.

关键词： image classification image dimensionality reduction convolutional neural net- work bilinear pooling regularized likelihood estimation

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From methods to datasets: A survey on image-Caption Generators

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MULTIMEDIA TOOLS AND applications 2024年第9期83卷 28077-28123页

作者： Agarwal, Lakshita Verma, Bindu Delhi Technol Univ Dept Informat Technol Delhi 110042 India

image - Caption Generator is a popular artificial Intelligence research tool that works with image comprehension and language definition. Creating well-structured sentences requires a thorough understanding of language in a systematic and semantic way. Being able to describe the substance of an image using well-structured phrases is a difficult undertaking, but it can have a significant impact in terms of assisting visually impaired people in better understanding the images' content. image captions has gained a lot of attention as a study subject for various computer vision and natural language processing (NLP) applications. The goal of image captions is to create logical and accurate natural language phrases that describes an image. It relies on the caption model to see items and appropriately characterise their relationships. Intuitively, it is also difficult for a machine to see a typical image in the same way that humans do. It does, however, provide the foundation for intelligent exploration in deep learning. In this review paper, we will focus on the latest in-depth advanced captions techniques for image captioning. This paper highlights related methodologies and focuses on aspects that are crucial in computer recognition, as well as on the numerous strategies and procedures being developed for the development of image captions. It was also observed that Recurrent neural networks (RNNs) are used in the bulk of research works (45%), followed by attention-based models (30%), transformer-based models (15%) and other methods (10%). An overview of the approaches utilised in image captioning research is discussed in this paper. Furthermore, the benefits and drawbacks of these methodologies are explored, as well as the most regularly used data sets and evaluation processes in this sector are being studied.

关键词： image- Caption Generator Natural language processing Computer vision Intelligent exploration Deep learning

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Role of Convolutional neural networks in Hyperspectral Imaging applications: A Review 1

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4th Congress on Intelligent Systems (CIS)

作者： Lanke, Neha P. Chandak, M. B. Shri Ramdeobaba Coll Engn & Management Nagpur Maharashtra India

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

In last few years, considerable improvements have been made in digital image acquisition and processing, improving image quality and results. However, hyperspectral imaging techniques evolved to gain more valuable information from images that integrate digital spectroscopy and imaging to generate information in special and spectral dimensions. HSI is a powerful tool for image analysis in potential application areas, including remote sensing, agriculture, horticulture, food quality assessment, medical imaging, forensic science, etc. artificial intelligence technologies are used as an important tool in many research areas. As a result, image analysis and classification studies are carried out on HSI with various ML techniques. Lately, deep learning with convolutional neural networks has emerged as an important tool for dealing with hyperspectral images' high dimensionality. In this paper, we review work done on different hyperspectral imaging applications using convolutional neural networks, which shows that deep learning using convolutional neural networks can be a powerful tool for hyperspectral image analysis and classification.

关键词： Hyperspectral imaging (HSI) Deep learning (DL) Convolutional neural network (CNN)

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The use of physics-informed neural network approach to image restoration via nonlinear PDE tools

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COMPUTERS & MATHEMATICS WITH applications 2023年 152卷 355-363页

作者： Namaki, Neda Eslahchi, M. R. Salehi, Rezvan Tarbiat Modares Univ Fac Math Sci Dept Appl Math POB 14115134 Tehran Iran

In this work, we propose a physics-informed solution for a blending PDE model with application in image denoising and edge-preserving. The blending model contains isotropic diffusion (ID) model and the modified Perona-Malik model introduced by Catte et al. Then we solve this model applying a physics-informed approach. Solving the new blending model is done using DeepXDE, which is a deep learning library for solving differential equations, by considering the image as input. Error analysis for neural network approximations of the new PDE model is shown. Applying PSNR criteria for some images, it can be seen that the proposed method can compete with solving denoising PDE models using finite difference method (FDM).

关键词： Isotropic diffusion model Perona-Malik model Physics-informed Deep learning artificial neural networks image processing

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RCT: Resource Constrained Training for Edge AI

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IEEE TRANSACTIONS ON neural networks AND LEARNING SYSTEMS 2024年第2期35卷 2575-2587页

作者： Huang, Tian Luo, Tao Yan, Ming Zhou, Joey Tianyi Goh, Rick ASTAR Inst High Performance Comp Singapore 138632 Singapore ASTAR Ctr Frontier AI Res Singapore 138632 Singapore

Efficient neural network training is essential for in situ training of edge artificial intelligence (AI) and carbon footprint reduction in general. Train neural network on the edge is challenging because there is a large gap between limited resources on edge and the resource requirement of current training methods. Existing training methods are based on the assumption that the underlying computing infrastructure has sufficient memory and energy supplies. These methods involve two copies of the model parameters, which is usually beyond the capacity of on-chip memory in processors. The data movement between off-chip and on-chip memory incurs large amounts of energy. We propose resource constrained training (RCT) to realize resource-efficient training for edge devices and servers. RCT only keeps a quantized model throughout the training so that the memory requirement for model parameters in training is reduced. It adjusts per-layer bitwidth dynamically to save energy when a model can learn effectively with lower precision. We carry out experiments with representative models and tasks in image classification, natural language processing, and crowd counting applications. Experiments show that on average, 8-15-bit weight update is sufficient for achieving SOTA performance in these applications. RCT saves 63.5%-80% memory for model parameters and saves more energy for communications. Through experiments, we observe that the common practice on the first/last layer in model compression does not apply to efficient training. Also, interestingly, the more challenging a dataset is, the lower bitwidth is required for efficient training.

关键词： Training Quantization (signal) Memory management neural networks Computational modeling System-on-chip Program processors Efficient training memory efficient neural network quantization weight update

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