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2D nanomaterials for realization of flexible and wearable gas sensors: A review

Chemosphere 2024年 352卷 141234页

作者： Pawar, Krishna Kiran Kumar, Ashok Mirzaei, Ali Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea The Research Institute of Industrial Science Hanyang University Seoul 04763 South Korea School of Nanoscience and Technology Shivaji University Kolhapur 416004 India Department of Electrical Engineering Indian Institute of Technology Jodhpur 342030 India Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Gas sensors are extensively employed for monitoring and detection of hazardous gases and vapors. Many of them are produced on rigid substrates, but flexible and wearable gas sensors are needed for intriguing usage including the internet of things (IoT) and medical devices. The materials with the greatest potential for the fabrication of flexible and wearable gas sensing devices are two-dimensional (2D) semiconducting nanomaterials, which consist of graphene and its substitutes, transition metal dichalcogenides, and MXenes. These types of materials have good mechanical flexibility, high charge carrier mobility, a large area of surface, an abundance of defects and dangling bonds, and, in certain instances adequate transparency and ease of synthesis. In this review, we have addressed the different 2D nonmaterial properties for gas sensing in the context of fabrication of flexible/wearable gas sensors. We have discussed the sensing performance of flexible/wearable gas sensors in various forms such as pristine, composite and noble metal decorated. We believe that content of this review paper is greatly useful for the researchers working in the research area of fabrication of flexible/wearable gas sensors. © 2024 Elsevier Ltd

关键词： 2D nanomaterials Flexible/wearable gas sensor Graphene MXenes Sensing mechanism TMDs Toxic gas

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Sample hardness based gradient loss for long-tailed cervical cell detection

arXiv

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arXiv 2022年

作者： Liu, Minmin Li, Xuechen Gao, Xiangbo Chen, Junliang Shen, Linlin Wu, Huisi Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China University of California Irvine United States

Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the cancer cells in a WSI (Whole Slice Image) image captured from the TCT (Thinprep Cytology Test) specimen, head categories (e.g. normal cells and inflammatory cells) typically have a much larger number of samples than tail categories (e.g. cancer cells). Most existing state-of-the-art long-tailed learning methods in object detection focus on category distribution statistics to solve the problem in the long-tailed scenario, without considering the "hardness" of each sample. To address this problem, in this work we propose a Grad-Libra Loss that leverages the gradients to dynamically calibrate the degree of hardness of each sample for different categories, and re-balance the gradients of positive and negative samples. Our loss can thus help the detector to put more emphasis on those hard samples in both head and tail categories. Extensive experiments on a long-tailed TCT WSI image dataset show that the mainstream detectors, e.g. RepPoints, FCOS, ATSS, YOLOF, etc. trained using our proposed Gradient-Libra Loss, achieved much higher (7.8%) mAP than that trained using cross-entropy classification loss. Copyright © 2022, The Authors. All rights reserved.

关键词： Cytology

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ECO v1: Towards Event-Centric Opinion Mining

arXiv

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arXiv 2022年

作者： Xu, Ruoxi Lin, Hongyu Liao, Meng Han, Xianpei Xu, Jin Tan, Wei Sun, Yingfei Sun, Le Chinese Information Processing Laborator Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies. © 2022, CC BY.

关键词： data mining

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COLLABORATIVE AUTO-ENCODING FOR BLIND IMAGE QUALITY ASSESSMENT

arXiv

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arXiv 2023年

作者： Zhou, Zehong Zhou, Fei Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham Nottingham United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE’s decoder for reconstructing distorted images, thus effectively forcing DAE’s encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. The codes are available at: https://***/Macro-Zhou/NRIQA-VISOR/. © 2023, CC BY.

关键词： Supervised learning

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Collaborative Auto-encoding for Blind Image Quality Assessment

Collaborative Auto-encoding for Blind Image Quality Assessme...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Zehong Zhou Fei Zhou Guoping Qiu College of Electronic and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China Peng Cheng Laboratory Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen China School of Computer Science University of Nottingham Nottingham U.K

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE's decoder for reconstructing distorted images, thus effectively forcing DAE's encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. All the related codes, trained models, and the supplementary material are available at: https://***/Macro-Zhou/NRIQA-VISOR/.

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Learning Dynamic Local Context Representations for Infrared Small Target Detection

arXiv

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arXiv 2024年

作者： Zhang, Guoyi Xu, Guangsheng Wang, Han Chen, Siyang Shan, Yunxiao Zhang, Xiaohu The School of Aeronautics and Astronautics Sun Yat-sen University Guangdong Shenzhen518107 China The School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai519082 China The Southern Marine Science and Engineering Guangdong Laboratory Sun Yat-sen University Guangdong Zhuhai519082 China Shenzhen Institute Sun Yat-sen University Shenzhen510275 China The Guangdong Key Laboratory of Big Data Analysis and Processing SunYat-sen University Guangzhou510006 China

Infrared small target detection (ISTD) is challenging due to complex backgrounds, low signal-to-clutter ratios, and varying target sizes and shapes. Effective detection relies on capturing local contextual information at the appropriate scale. However, small-kernel CNNs have limited receptive fields, leading to false alarms, while transformer models, with global receptive fields, often treat small targets as noise, resulting in miss-detections. Hybrid models struggle to bridge the semantic gap between CNNs and transformers, causing high complexity. To address these challenges, we propose LCRNet, a novel method that learns dynamic local context representations for ISTD. The model consists of three components: (1) C2FBlock, inspired by PDE solvers, for efficient small target information capture;(2) DLC-Attention, a large-kernel attention mechanism that dynamically builds context and reduces feature redundancy;and (3) HLKConv, a hierarchical convolution operator based on large-kernel decomposition that preserves sparsity and mitigates the drawbacks of dilated convolutions. Despite its simplicity, with only 1.65M parameters, LCRNet achieves state-of-the-art (SOTA) performance. Experiments on multiple datasets, comparing LCRNet with 33 SOTA methods, demonstrate its superior performance and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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AADNet: An End-to-End Deep Learning Model for Auditory Attention Decoding

arXiv

引用

arXiv 2024年

作者： Nguyen, Nhan Duc Thanh Phan, Huy Geirnaert, Simon Mikkelsen, Kaare Kidmose, Preben Center for Ear-EEG Department of Electrical and Computer Engineering Aarhus University Aarhus N8200 Denmark Amazon CambridgeMA02142 United States Stadius Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven and Leuven.AI - KU Leuven institute for AI Kasteelpark Arenberg 10 LeuvenB-3001 Belgium The Department of Neurosciences Research Group ExpORL Herestraat 49 box 721 LeuvenB-3000 Belgium

Auditory attention decoding (AAD) is the process of identifying the attended speech in a multi-talker environment using brain signals, typically recorded through electroencephalography (EEG). Over the past decade, AAD has undergone continuous development, driven by its promising application in neuro-steered hearing devices. Most AAD algorithms are relying on the increase in neural entrainment to the envelope of attended speech, as compared to unattended speech, typically using a two-step approach. First, the algorithm predicts representations of the attended speech signal envelopes;second, it identifies the attended speech by finding the highest correlation between the predictions and the representations of the actual speech signals. In this study, we proposed a novel end-to-end neural network architecture, named AADNet, which combines these two stages into a direct approach to address the AAD problem. We compare the proposed network against the traditional approaches, including linear stimulus reconstruction, canonical correlation analysis, and an alternative non-linear stimulus reconstruction using two different datasets. AADNet shows a significant performance improvement for both subject-specific and subject-independent models. Notably, the average subject-independent classification accuracies from 56.1% to 82.7% with analysis window lengths ranging from 1 to 40 seconds, respectively, show a significantly improved ability to generalize to data from unseen subjects. These results highlight the potential of deep learning models for advancing AAD, with promising implications for future hearing aids, assistive devices, and clinical assessments. © 2024, CC BY-NC-SA.

关键词： Hearing aids

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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An Efficient Multi-Order Cascade Distillation Model for the Detection of Small Targets and Occluded Objects in Transmission Line Inspection

SSRN

引用

SSRN 2023年

作者： Qu, Zhaoyang Zang, Jiye Dong, Yunchang Qu, Nan Song, Siqi Jiang, Tao Li, Min Liang, Feng Li, Lingcong School of Computer Northeast Electric Power University Jilin132012 China Jilin Electric Power Big Data Intelligent Processing Engineering Technology Research Center Jilin132012 China State Grid Jilin Electric Power Research Institute Changchun130012 China State Grid Jiangsu Electric Power Co. Ltd. Nanjing Power Supply Company 210000 China Northeast Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Changchun130021 China

The proposed cascade distillation detection model improves the accuracy of small target defect detection and robustness in complex environments during UAV inspection of transmission lines. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. A high-order aggregated feature extraction network is introduced to enhance feature extraction of small and obscured targets. Lastly, a spatially deformable attention mechanism optimizes sampling point selection and attention weight calculation by aggregating nearby contextual information, improving bounding box adjustment. © 2023, The Authors. All rights reserved.

关键词： Electric lines

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Chao He Nan Yang Kwok Wa Leung School of Electronics and Information Technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Liaoning Technical University Huludao China Department of Electrical Engineering State Key Laboratory of Terahertz and Millimeter Waves City University of Hong Kong Kowloon China Information and Communication Technology Centre CityU Shenzhen Research Institute Shenzhen China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB.

关键词： Antenna measurements Impedance measurement Shape Prototypes Glass Bandwidth Gain measurement Impedance Microstrip Gain

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