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Novel Dictionary Pair Learning Through Locality-Constrained Profiles for the Identification of Chili Pepper Varieties via E-Noses

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IEEE Sensors Journal 2025年第10期25卷 17530-17540页

作者： Chen, Liang Peng, Guihua Zhang, Changfu Wang, Xueya Chen, Ju Yin, Yong Yan, Jia Southwest University College of Artificial Intelligence Chongqing400715 China Chili Pepper Research Institute Guizhou Academy of Agricultural Sciences Guiyang550006 China Chongqing Key Laboratory of Brain-Inspired Computing and Intelligent Chips Chongqing400715 China

In modern society, to make it easier for customers to assess product quality and regularize distribution and supply, almost all crops are sorted and labeled. This sorting and labeling process also facilitates packaging and transportation of products and benefits farmers. As an emerging technology, electronic noses (E-noses) can be applied to food quality control. In this study, we proposed a novel dictionary pair learning (DPL) model named DPL based on locality constraint of profiles (DPL-LCPs), which achieved better performance in the identification of chili pepper varieties using E-noses. This model incorporates profile vectors to define a profile locality constraint term and integrates it with a dictionary reconstruction error term into a unified dictionary learning (DL) model to fully learn the characteristics of samples from various aspects. This approach not only reduces the similarity between dictionary atoms but also avoids the problem of weakening the connections between characteristics of different classes of samples caused by using the block diagonal constraint. In addition, the model utilizes the l2,1 norm to regularize the analysis dictionary matrix, enhancing the sparsity of features and thereby enhancing the flexibility and robustness of the model. The experimental results show that the proposed model can achieve better classification results and higher stability than other advanced DL models can. With this method, it is possible to reliably identify 13 varieties of chili peppers based on flavor assessment. © 2001-2012 IEEE.

关键词： Contrastive Learning

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Research on Double Attention Mechanism High-resolution Network for Human Pose Estimation

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Engineering Letters 2025年第2期33卷 338-347页

作者： Li, Haisheng Yuan, Rongrong Chen, Jingyin Li, Qiuyi Hu, Cong Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guangxi Guilin541004 China School of Electronic and Information Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guangxi Guilin541004 China

Recently, high-resolution networks combining different resolution features have achieved good results in human pose estimation tasks. However, there are still some problems for high-resolution networks, such as insufficient use of important feature information and lost information in the sampling process. This paper proposes a double attention mechanism high-resolution network based on human pose estimation for these problems. The double attention mechanism consists of coordinate and downsampling attention modules. The coordinate attention module can enhance the feature representation of the network by extracting more useful node information. The downsampling attention module reuses the initial feature information to predict the occluded parts better and accurately estimate the corresponding point positions of human joints. Experimental results on MPII and COCO datasets show that the proposed network model effectively improves the accuracy of keypoint estimation in human pose estimation tasks. Especially, our network model achieves 78.1% average precision (AP) on the COCO dataset. © 2025, International Association of Engineers. All rights reserved.

关键词： Importance sampling

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A Secure Storage and Verification Framework Based on Consortium Blockchain for Engineering Education Accreditation Data

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Computers, Materials and Continua 2025年第3期83卷 5323-5343页

作者： Luo, Yuling Lin, Xiaoguang Liu, Junxiu Fu, Qiang Qin, Sheng Min, Zhen Hu, Tinghua Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin 541004 China Key Laboratory of Nonlinear Circuits and Optical Communications Guangxi Normal University Education Department of Guangxi Zhuang Autonomous Region Guilin 541004 China

The majors accredited by the Engineering Education Accreditation (EEA) reflect the accreditation agency’s recognition of the school’s engineering programs. Excellent accreditation management holds significant importance for the advancement of engineering education programs. However, the traditional engineering education system framework suffers from the opacity of raw education data and the difficulty for accreditation bodies to forensically examine the self-assessment reports. To solve these issues, an EEA framework based on Hyperledger Fabric blockchain technology is proposed in this work. Firstly, all relevant stakeholders and information interactions occur within the blockchain network, ensuring the authenticity of educational data and enhancing the transparency of accreditation processes. Secondly, multiple roles are abstracted into a single organization to optimize the network topology. The original data is stored off-chain, and the hash values of the data are stored on-chain to reduce on-chain storage costs. The experimental results show that the proposed framework has a high throughput and the network latency of writing data to the blockchain is reduced by at least 0.04 s. It effectively improves network performance and security, which provides new insights for EEA management. Copyright © 2025 The Authors.

关键词： Blockchain distributed store engineering education accreditation hyperledger fabric

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High-Performance Thin Film Lithium Niobate Electro-optic Modulator By Embedding Structure

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IEEE Photonics Technology Letters 2025年第13期37卷 701-704页

作者： Song, Shuxiang Li, Haisheng Yang, Jiayu Yin, Yihui Yang, Wanli Li, Yue Li, Hangyu Cen, Mingcan Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guilin China Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology 541004 China 34th Research Institute of China Electronics Technology Group Corporation Guilin541004 China

Electro-optic modulators with low driving voltage and wide bandwidth are critical for advanced analog and digital communication systems. Improving the modulation efficiency of thin-film lithium niobate (TFLN) modulators is essential to reducing drive voltage and device length, thereby enabling compact chip integration and enhanced modulation bandwidth. This work introduces a novel TFLN modulator featuring an embedded dual capacitance-loaded traveling-wave electrode (EDC-TWE) to achieve superior electro-optic modulation efficiency and minimized optical loss. The fabricated modulator, with a 10-mm-long modulation region, achieves a half-wave voltage of 1.45 V and an electro-optic modulation bandwidth exceeding 40 GHz, demonstrating its potential for high-performance optical communication applications. © 1989-2012 IEEE.

关键词： Digital communication systems

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A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Lesion Segmentation Network Based on U-shaped Network 8

A Multi-Location Enhanced Multi-Scale Feature Fusion Skin Le...

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Wu, Rongqing Song, Haixing Zeng, Xuqing Li, Guanglian Zhang, Junyu He, Fuyun Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Key Laboratory of Integrated Circuits and Microsystems Guangxi Normal University Education Department of Guangxi Zhuang Autonomous Region Guilin China

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

With the continuous development of deep learning, computer-aided diagnosis reduces the pressure of dermatologists. Although many U-Net-based convolution neural networks can achieve effective segmentation, they still need to be improved when faced with challenging skin lesion segmentation tasks such as fuzzy and irregular lesions. To this end, we propose a skin lesion segmentation algorithm (MSMF-Net) based on U-shaped network architecture, which mainly includes the following aspects: the feature distribution of different encoding stages are fused and fully utilised by the EFM module. To better handle high-level semantic features, multiscale and attention parallel module (MS-AM) is introduced at the bottleneck of the network. Attention bridging module (ABM) is used at skip connection, which enables effective local and global information fusion and provides rich feature information for decoding. The decoding fusion module (DFM) processes the decoded information by flexibly embedding channel and spatial attention into the fusion process, so that the information is more concentrated in the lesion area. Our network is compared with other state-of-the-art methods on two public datasets and obtains the best results. © 2025 SPIE.

关键词： Convolutional neural networks

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Application of the CNN-GRU-MHA Model in Partial Discharge Recognition 24

Application of the CNN-GRU-MHA Model in Partial Discharge Re...

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2nd International Conference on Advances in Artificial Intelligence and Applications, AAIA 2024

作者： Bi, Jinjie Chen, Guofu Li, Tinghui Huang, Shoulin Shen, Biquan Yang, Rixing Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Guangxi Normal University Guilin China

ISBN: (纸本)9798400712883

Partial discharge (PD) is one of the most important factors affecting the stable operation of high-voltage equipment and poses a great threat to the reliability and safety of power systems. Although some deep learning-based methods, such as Convolutional Neural Networks (CNN), have been proposed for PD detection, there are still some shortcomings in terms of global dependencies. In this paper, it is proposed to develop a model of Convolutional Neural Networks-Gated Recurrent Unit-Multi-Head Attention (CNN-GRU-MHA). Wavelet transformation is adopted for noise suppression. reduction, together with statistical feature downsampling for reducing data dimensionality. In the model, CNN extract localized temporal features, GRU capture sequential temporal patterns, and MHA focuses on modeling global dependencies while highlighting critical features. Experimental evaluations reveal that the model reaches a Matthews correlation coefficient of 74.78% and a recall rate of 74.61%, performing better than some conventional methods in terms of both accuracy and robustness. This model securely and effectively provided a method of enhancing power system safety and stability. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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MA-MNN: Multi-flow attentive memristive neural network for multi-task image restoration

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Signal Processing: Image Communication 2025年 138卷

作者： He, Peng Zhang, Lin Yang, Yu Zhou, Yue Duan, Shukai Hu, Xiaofang College of Artificial Intelligence Southwest University Chongqing400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing400715 China Ministry of Education China

Images taken in rainy, hazy, and low-light environments severely hinder the performance of outdoor computer vision systems. Most data-driven image restoration methods are task-specific and computationally intensive, whereas the capture and processing of degraded images occur largely in end-side devices with limited computing resources. Motivated by addressing the above issues, a novel software and hardware co-designed image restoration method named multi-flow attentive memristive neural network (MA-MNN) is proposed in this paper, which combines a deep learning algorithm and the nanoscale device memristor. The multi-level complementary spatial contextual information is exploited by the multi-flow aggregation block. The dense connection design is adopted to provide smooth transportation across units and alleviate the vanishing-gradient. The supervised calibration block is designed to facilitate achieving the dual-attention mechanism that helps the model identify and re-calibrate the transformed features. Besides, a hardware implementation scheme based on memristors is designed to provide low energy consumption solutions for embedded applications. Extensive experiments in image deraining, image dehazing and low-light image enhancement have shown that the proposed method is highly competitive with over 20 state-of-the-art methods. © 2025 Elsevier B.V.

关键词： Optical flows

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Improving E-Nose Performance: A Novel ELM-Based Dual-Level Joint Domain Adaptation Method for Sensor Drift Data

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Zijian Zhang, Linxia Li, Zhe Duan, Shukai Yan, Jia Southwest University College of Artificial Intelligence Chongqing400715 China Southwest University Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips College of Artificial Intelligence Chongqing400715 China

The theory and technology of electronic nose (E-nose) systems have been vigorously developed, and these systems have achieved success in many practical applications, such as medical diagnosis, food quality inspection, and environmental detection. However, the drift problem of a sensor array affects the industrialization and commercialization of E-nose systems. In this article, a novel ELM-based dual-level joint domain adaptation method (JDAELM) is proposed to effectively suppress drift and address the distribution discrepancy issue. Specifically, the proposed method implements joint domain adaptation (DA) at the feature level and label level. For source domain data without drift, the information of the data could be preserved as much as possible. Considering the inconsistent distribution caused by drift, the marginal and conditional distribution discrepancies are reduced to a minimum at the feature level to achieve domain alignment. To reduce the impact of pseudolabels on the model, we align the label space to achieve DA at the label level. By maximizing the Hilbert-Schmidt independence criterion, the relationship between the feature projection space and label projection space is strengthened in this model. The joint learning model is effectively solved by an efficient alternative optimization strategy. The average accuracy of the proposed method is 88.30% and 87.21% under long-term drift and short-term drift, respectively, and 96.41% on the instrument variation dataset, which is superior to that of other comparison methods in terms of accuracy. This proves that the JDAELM can be well adapted to long-term and short-term drift scenarios, and can effectively compensate for instrument variation drift caused by inherent differences. © 1963-2012 IEEE.

关键词： Adversarial machine learning

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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems

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Journal of King Saud University - Computer and Information Sciences 2025年第4期37卷 1-22页

作者： Zhang, Ruoxin An, Angela Cen, Mingcan Zhang, Chong Huangfu, Aimee Vinnakota, Sunny Song, Shuxiang Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin 541004 China Key Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University) Education Department of Guangxi Zhuang Autonomous Region Guilin 541004 China Faculty of Science Engineering and Built Environment Deakin University Geelong VIC Australia Academies Australasia Polytechnic 628 Bourke Street Melbourne 3000 Australia

Monitoring fragmented mangrove ecosystems presents significant challenges due to their sparse distribution and the limitations of traditional detection methods, which often suffer from poor convergence and high rates of false positives and false negatives. To address these issues, we propose FragMangro, a cross-domain zero-shot detection model designed to enhance the accuracy and generalization of mangrove detection. FragMangro integrates parallel multi-scale convolution for progressive dimensional upscaling, improving spectral feature extraction. Additionally, it employs a dynamic learning rate and iterative control algorithm, which adaptively adjusts learning rates and training epochs based on loss monitoring to prevent suboptimal convergence. We conducted extensive experiments in three major mangrove reserves in Guangxi, China. The results demonstrate that FragMangro achieves over 98% accuracy, an average F1 Score exceeding 85%, and the Intersection over Union(IoU) surpassing 74%, significantly outperforming conventional methods such as support vector machines (SVM), random forests (RF), and k-nearest neighbors (KNN). Specifically, in the Beilun estuary, FragMangro exhibits a 0.96% improvement in accuracy over RF, a 17.24% higher F1 Score than SVM, and an 18.08% higher IoU than KNN. Furthermore, the model’s adaptive learning rate and dynamic adjustment strategy enhance convergence efficiency and feature representation. FragMangro serves as an effective tool for ecological monitoring and mangrove conservation, with broader implications for environmental protection and sustainable ecosystem management. © The Author(s) 2025.

关键词： Adaptive learning rate and iteration rounds Attention fully convolutional network Deep learning Scattered mangroves Zero-shot detection

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The parameter-feature-based transfer learning for mixed gas monitoring under small datasets

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Sensors and Actuators B: Chemical 2025年 441卷

作者： Xia, Xingyue Chen, Xingyu Zhuo, Junwei Wang, Xue Peng, Xiaoyan Wu, Pengcheng Chu, Jin College of Artificial Intelligence Southwest University Chongqing400715 China Chongqing Key Laboratory of Brain-Inspired Computing and Intelligent Chips Chongqing400715 China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of Education Chongqing400715 China

Gas monitoring is an important application in the field of gas sensor systems. Currently, gas monitoring methods primarily rely on processing large amounts of high-quality data using machine learning or deep learning algorithms. While this high quality and large quantity of data are sometimes hard to achieve due to the high costs associated with the data collection stage. To this end, a novel gas monitoring method developed for small datasets using parameter-feature-based transfer learning is proposed in this paper. First, a deep learning-based source model will be developed, which will use a large dataset that has undergone data augmentation for parameter training. Then, a parameter-feature transfer learning method will be used to transfer the model parameters to the target domain of the small dataset for mixed gas detection and recognition. Experiments were conducted on a convolutional neural network combined with a long and short-term memory network, and the results showed that the amount of data required was reduced by 96.5 % and 76.1 %, respectively, compared to the traditional experimental procedure. In addition, the developed model was able to achieve robust concentration prediction with accuracies of 96.3 % and 99.6 % in different gas monitoring tasks. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

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