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Intelligent Surface Plasmon Resonance Sensor for Refractive Index Substance Identification With Convolutional neural Network image-Based Model

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Oliveira, Leiva C. Sales, Adna Q. Lopes, Mateus V. L. Lima, Antonio M. N. Univ Fed Rural Semi Arido Dept Comp Elect Engn Grad Program BR-59625900 Mossoro RN Brazil Univ Fed Rural Semi Arido Dept Comp Comp Sci Grad Program BR-59625900 Mossoro RN Brazil Univ Fed Rural Semiaido UFERSA Elect Engn Grad Program Mossoro RN Brazil Univ Fed Rural Semi Arido Comp Sci Grad Program Mossoro RN Brazil Univ Fed Campina Grande UFCG Dept Elect Engn Campina Grande PB Brazil

The surface plasmon resonance (SPR) sensors are technologically attractive for applications that demand quick and accurate biological substance monitoring. Through its typical SPR image response, the resonance condition indicated by the minimum reflectivity values works like an optical signature for changes in the refractive index (RI) of the substance under analysis. Recently, the incorporation of machine and deep learning methods (MDLMs) on SPR sensors to create intelligent tasks along the signal processing chain employed in SPR biosensing was witnessed. One possible intelligent application is substance identification based on the analysis of SPR responses. Occasionally, this problem is addressed with data from SPR curves, requiring a prior SPR image manipulation for the respective curve generation, leading to extra process steps and time consumption. This article presents the design of an intelligent SPR sensor with analyte identification capabilities directly from its SPR image, offering guidance on the precise moment for substance switching during injection routines. An image-based prediction model with convolutional neural networks (CNNs) was fine-tuned to directly identify individually aqueous solutions with different refractive indices. A new approach was described to generate SPR images (Fresnel images) from calculation with the Fresnel analysis (FA) framework. The proposed CNN architecture was evaluated and compared with seven state-of-the-art CNN architectures. The models were integrated into the experimental setup for real-time identification. The experimental tests demonstrate the viability of the overall pipeline for the model conception, being able to reach more than 96% accuracy in performing the identification task.

关键词： Surface plasmon resonance Convolutional neural networks Feature extraction Training Sensors Resonance Optical imaging Optical reflection Refractive index Predictive models Convolutional neural network (CNN) intelligent sensor surface plasmon resonance (SPR) image substance identification

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Deep unfolding model-based for hybrid robust wide band adaptive beamforming

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IET MICROWAVES ANTENNAS & PROPAGATION 2024年第7期18卷 480-493页

作者： Janani, Reza Fatemi Mofrad, Reza Malek Ashtar Univ Technol Fac Elect & Comp Engn Tehran Iran

The design of arrays capable of receiving wideband signals differs from arrays that can only receive narrowband signals. These arrays must be able to receive signals with an instant bandwidth of several GHz across the entire operating frequency, such as High-Resolution Radars or Terahertz in 6G communication systems. In these arrays, using a time delay line structure leads to an increase in beamformer coefficients, resulting in high computational complexity. This poses a challenge for beamforming in wideband systems. Additionally, classic Wideband beamformers face other factors, such as poor performance in the presence of input direction of arrival error, array calibration error, and requiring too many snapshots to reach the steady state of the beamformer. Therefore, the robustness of wideband adaptive beamforming using deep unfolding model-based technique is focused on, which has not been discussed before. The advent of deep unfolding, an innovative technique, amalgamates iterative optimization approaches with elements of neural networks. The aim is to deftly maneuver through various tasks across disciplines such as machine learning, signal and image processing, and telecommunication systems. Also, the network training method is done to become more robust against the mentioned factors. In the proposed structure, the constraints of the previous methods have been evaluated. It is observed to have better performance compared to other classic algorithms. Also, with the investigations of the proposed method with other conventional deep learning methods, it was observed that in some cases the proposed structure performance is equal to the conventional deep learning method and sometimes better. This article introduces a novel approach to estimating robust adaptive wideband beamforming coefficients called the deep unfolding model-based method. The method transforms an iterative algorithm with a fixed number of iterations into a layer-wise structure resembling a neural netw

关键词： adaptive antenna arrays beam steering signal processing

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Surface defect identification method for hot-rolled steel plates based on random data balancing and lightweight convolutional neural network

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signal image AND VIDEO processing 2024年第8-9期18卷 5775-5786页

作者： Zeng, Weihui Wang, Junyan Chen, Peng Zhong, Zhimin Hu, Gensheng Bao, Wenxia Anhui Univ Sch Internet Hefei 230039 Peoples R China AIMS Technol Co Ltd Hefei 230088 Peoples R China Anhui Univ Natl Engn Res Ctr Agroecol Big Data Anal & Applica Hefei 230601 Peoples R China

Hot-rolled strip steel is an extremely important industrial foundational material. The rapid and precise identification of surface defects in hot-rolled strip steel is beneficial for enhancing the quality of steel materials and reducing economic losses. Current research primarily focuses on using convolutional neural networks (CNNs) for strip steel surface defect identification. Although the accuracy of identification has remarkably improved in comparison with traditional machine learning methods, it has overlooked issues related to dataset preprocessing and the problem of nonlightweight CNN models with large model parameters and high computational complexity. To address the abovementioned issues, this study proposes a hot-rolled steel strip surface defect identification method based on random data balancing and the lightweight CNN MobileNet-Pro. Random data balancing employs image augmentation to eliminate the differences in the quantity of categories between the hot-rolled strip steel surface defect data, providing diverse images to alleviate overfitting during model training. MobileNet-Pro is used to increase the model's effective receptive field. Building upon MobileNetV1, it introduces large convolutional kernels and improves depth-wise separable convolution. Experiments show that the new MobileNet-Pro, after random data balancing on the X-SDD dataset, achieves an accuracy of 96.47%, surpassing RepVGG + SA (95.10% accuracy, nonlightweight) and ResNet50 (93.86% accuracy, nonlightweight). Additionally, MobileNet-Pro outperforms mainstream lightweight networks from the MobileNet series, ShuffleNetV2, and GhostnetV2 in terms of performance on the CIFAR-100 and PASCAL VOC 2007 datasets, demonstrating excellent generalization capabilities. All our code and models are available on GitHub: https://***/OnlyForWW/MobileNet-Pro.

关键词： Hot-rolled strip steel surface defect identification Random data balancing Lightweight convolutional neural network Effective receptive field Large convolutional kernel

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AN INTERPRETABLE DEEP GRAPH neural NETWORK BASED ON ATTENTIONAL MULTI-SCALE FEATURE FUSION FOR FMRI ANALYSIS 31

AN INTERPRETABLE DEEP GRAPH NEURAL NETWORK BASED ON ATTENTIO...

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2024 International Conference on image processing

作者： Wang, Likai Zhu, Tao Zhang, Yipu Changan Univ Sch Elect & Control Engn Xian Peoples R China Changan Univ Sch Energy & Elect Engn Xian Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Understanding which brain regions are associated with specific neurological disorders has been an important area of neuroimaging research, which has important implications for biomarker and diagnostic studies. In this paper, we propose an interpretable deep graph neural network ( GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers, namely IDGNN. Specifically, we design a novel deep graph convolutional layer to better utilize the spatial and functional information of fMRI, and aggregate feature information of multi-hop neighbor regions of interest (ROIs) more efficiently. Considering the need for interpretability in brain image analysis, we improve the Gradient Class Activation Mapping (Grad-CAM) technique for fMRI brain graphs as a way to discover the most significant ROIs identified by IDGNN from brain connectivity patterns. Furthermore, we employ an attentional feature fusion mechanism to better perform fusion of multi-scale features. We apply our IDGNN on ABIDE fMRI dataset. Results show that our method outperforms several competing methods, and successfully identifies biomarkers of autism spectrum disorders (ASD).

关键词： Multi-scale Interpretability Graph neural network

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Molecular Noise in Synaptic Communication

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第2期22卷 268-283页

作者： Lotter, Sebastian Schafer, Maximilian Schober, Robert Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91058 Erlangen Germany

In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitter (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.

关键词： Monte Carlo methods Degradation Computational modeling Biological system modeling Ions Adaptation models Synapses Molecular communication (MC) diffusion synaptic transmission ligand receptors statistical signal model chemical master equation (CME)

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Open space radar specific emitter identification using MSAK-CNN-LSTM network

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IET RADAR SONAR AND NAVIGATION 2024年第7期18卷 1080-1093页

作者： Zheng, Yuanhao Wang, Jiantao Huang, Jie Informat Engn Univ Zhengzhou Peoples R China China Classificat Soc 40 Dong Huang Cheng Gen Nan Jie Beijing 100006 Peoples R China

To enhance the capability of identifying unknown emitters in open spaces, an open-multiscale attention kernel (MSAK)-convolutional neural network-long short-term memory (CNN-LSTM) structure is proposed. To this end, first, a MSAK module and CNN-LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK-CNN-LSTM model is improved to obtain an open-MSAK-CNN-LSTM model with open-set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open-MSAK-CNN-LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal-to-noise ratio (SNR) scenarios. The challenge of enhancing the capability of identifying unknown emitters in open spaces is addressed. The authors believe that their study significantly contributes to the literature because it introduces an innovative approach to radar emitter identification. By combining the MSAK-CNNLSTM model with open-set recognition capabilities, the authors extend the boundaries of existing methods, enabling accurate classification of unknown emitters in open environments. image

关键词： radar radar emitter recognition radar signal processing signal classification signal processing

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STCNet: Alternating CNN and improved transformer network for COVID-19 CT image segmentation

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BIOMEDICAL signal processing AND CONTROL 2024年 93卷

作者： Geng, Peng Tan, Ziye Wang, Yimeng Jia, Wenran Zhang, Ying Yan, Hongjiang Shijiazhuang Tiedao Univ Sch Informat & Sci Technol Shijiazhuang 050043 Peoples R China Xingtai Univ Coll Resources & Environm Xingtai 054001 Peoples R China Hebei Med Univ Dept Thorac Surg Hosp 2 Shijiazhuang 050004 Peoples R China

Since the emergence of the Corona Virus Disease in 2019 (COVID-19), it has become a serious health problem affecting the human respiratory system. At present, automatic segmentation of lung infection areas from Computed Tomography has been playing a crucial role in the diagnosis of this disease because of its ability to perform pathological studies based on the lung infection areas. However, due to the lung infection areas scattered distribution, the existing segmentation methods generally have the problems of missing and incomplete segmentation. The Convolutional neural Network (CNN)-based approaches generally lack the ability to model explicit long-range relation, and the transformer-based methods are not conducive to capturing the detailed boundaries of infected areas. Whereas the infected regions of the coronavirus images are scattered and boundary information plays an important role, both the boundaries and the global infected areas need to be taken into account. Therefore, we propose a novel coronavirus image segmentation network alternately using Swin transformer and CNN (STCNet). Firstly, to enable network to capture richer features, the ReSwin transformer block is proposed and added after each level of convolution block in the encoder-decoder. Secondly, to effectively retain the infected areas boundary information, the skip connection cross attention module is used to provide spatial information to each decoder. And through the fine-tuned scale-aware pyramid fusion module to fuse multi-scale context information. Experimental results show that STCNet at can achieve state-of-the-art performance on two coronavirus segmentation datasets, with Dice achieves 79.92 % and 82.78 %, respectively. Our code is available at https://***/sineagles/STCNet.

关键词： COVID-19 Convolutional neural network Swin transformer image segmentation

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UNROLLING DECENTRALIZED stochastic FRANK WOLFE ALGORITHM 13

UNROLLING DECENTRALIZED STOCHASTIC FRANK WOLFE ALGORITHM

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13rd IEEE Sensor Array and Multichannel signal processing Workshop (SAM)

作者： Francis, Robin Ramakrishnan, Sai Rajaji Chepuri, Sundeep Prabhakar Indian Inst Sci Bengaluru India

ISBN: (纸本)9798350344820;9798350344813

Decentralized Frank-Wolfe methods are suitable for solving the decentralized constrained optimization with clients having computational limitations, as they solve a linear program to find the descent direction instead of the computationally intense projection operation. However, traditional decentralized Frank-Wolfe approaches may exhibit limitations in scenarios where convergence speed is critical. To address this challenge, we propose an unrolled decentralized Frank-Wolfe method to improve convergence. In particular, each layer learns the best descent direction by training a neural network, facilitating faster convergence through layer-wise updates. Furthermore, we introduce two weight sharing techniques, namely intra-client and inter-client weight sharing mechanisms, that can reduce the training complexity associated with a growing number of network layers and clients, respectively. These techniques ensure scalability of the model and improves its performance. We demonstrate the efficacy of the proposed unrolled model on various machine-learning tasks for both synthetic and real-world datasets.

关键词： Constrained learning decentralized optimization Frank-Wolfe algorithm unrolled methods

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HorSR: High-order spatial interactions and residual global filter for efficient image super-resolution

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signal processing-image COMMUNICATION 2024年 127卷

作者： Wang, Fengsui Chu, Xi Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Peoples R China Anhui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China Minist Educ Key Lab Adv Percept & Intelligent Controlof High e Wuhu 241000 Peoples R China

Recent advances in efficient image super-resolution (EISR) include convolutional neural networks, which exploit distillation and aggregation strategies with copious channel split and concatenation operations to fully exploit limited hierarchical features. In contrast, the Transformer network presents a challenge for EISR because multiheaded self-attention is a computationally demanding process. To respond to this challenge, this paper proposes replacing multiheaded self-attention in the Transformer network with global filtering and recursive gated convolution. This strategy allows us to design a high-order spatial interaction and residual global filter network for efficient image super-resolution (HorSR), which comprises three components: a shallow feature extraction module, a deep feature extraction module, and a high-quality image-reconstruction module. In particular, the deep feature extraction module comprises residual global filtering and recursive gated convolution blocks. The experimental results show that the HorSR network provides state-of-the-art performance with the lowest FLOPs of existing EISR methods.

关键词： Super-resolution Lightweight Recursive gated convolution Residual global filtering

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BIOMEDICAL signal processing AND CONTROL 2023年 81卷

作者： Zhu, Fei Wang, Sheng Li, Dun Li, Qiang Huazhong Univ Sci & Technol Britton Chance Ctr Biomed Photon Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Collaborat Innovat Ctr Biomed Engn Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Hubei Peoples R China United Imaging Surg Healthcare Co Ltd Shanghai Peoples R China

In recent years, deep learning (DL)-based registration technology has significantly improved the calculation speed of medical image registration. Existing DL-based registration methods generally use raw data features to predict the deformation field. However, this strategy may not be very effective for difficult registration tasks. Hence, in this study, we propose a similarity attention-based convolutional neural network (CNN) for accurate and robust three-dimensional medical image registration. We first introduce a similarity-based local attention model as an auxiliary module for building a displacement searching space, instead of a direct displacement prediction based on raw data. The proposed model can help the network focus on spatial correspondences with high similarities and ignore those with low similarities. A multi-scale CNN is then integrated with the similaritybased local attention for providing non-local attention, lightweight network, and coarse-to-fine registration. We evaluated the proposed method for various applications, such as the registration of large-scope abdominal computerized tomography (CT) images and chest CT images acquired at different respiratory phases, and atlas registration in magnetic resonance imaging. The experimental results demonstrate that the proposed method can provide a more accurate and robust registration performance than state-of-the-art registration methods.

关键词： Convolutional neural network Medical image registration Similarity Attention Multi -scale

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