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Biofunctionalization-Optimized Mos2-Based Fet Biosensors for the Detection of Tau Protein

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SSRN 2024年

作者： Wen, Xiaohong Zhao, Xuefeng Shan, Xinzhi Lu, Hongliang Gao, Xiumin Zhuang, Songlin Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of High-Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai200050 China State Key Laboratory of ASIC System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Fudan University Shanghai200433 China

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2-based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

Biofunctionalization-Optimized MoS2-Based Fet Biosensors for the Detection of Tau Protein

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SSRN 2024年

Field effect transistor (FET)-based biosensors can accurately quantify changes in weak signals and show excellent performance advantages in the detection of trace biological disease markers. Biological functionalization (biofunctionalization) is an important factor in the fabrication of FET-based biosensors, which directly affects the performance of the biosensor. However, little attention from researchers has been paid to biofunctionalization, which results in uneven performance of biosensors. Herein, MoS2-based FET biosensors are designed and fabricated using four different biofunctionalization methods. The most suitable biofunctionalization method is obtained by comparing the sensing performances of different functionalized MoS2based FET biosensors. Moreover, the working mechanism of the optimized biofunctionalization method is explored. Besides, the biofunctionalization-optimized MoS2-based FET biosensor exhibits excellent sensing performance, achieving sensitive detection of Tau protein in the concentration range of 10-2~103 pg/mL. This study provides a universal and effective strategy for the biofunctionalization optimization of two-dimensional materials-based FET biosensors. © 2024, The Authors. All rights reserved.

关键词： Molybdenum compounds

VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection

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arXiv

arXiv 2024年

作者： Song, Ziying Zhang, Guoxin Xie, Jun Liu, Lin Jia, Caiyan Xu, Shaoqing Wang, Zhepeng School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang050018 China Lenovo Research Beijing100085 China State Key Laboratory of Internet of Things for Smart City Department of Electrome chanical Engineering University of Macau 999078 China

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

An Adaptive Bionic Sensor: Enhancing Ankle Joint Tracking with High Sensitivity and Superior Cushioning Performance

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SSRN 2024年

作者： Jin, Jianqiao Zhang, Chen Zhao, Jianyuan Yu, Minghan Lei, Ming Jin, Chun Yin, Rui Zhao, Weiwei Sauvage Laboratory for Smart Materials The School of Integrated Circuit Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding & Joining Harbin Institute of Technology Harbin150001 China Human-Computer Interaction Design Lab School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Flexible sensors are renowned for their rapid responsiveness, high flexibility, and outstanding mechanical properties, making them ideal for applications in wearable devices, sports and health monitoring, and human-machine interfaces. To enhance the sensing performance of these flexible sensors, researchers have drawn inspiration from nature, creating various bionic microstructures. However, these structures often manifest in the macroscopic form of thin films, which do little to improve impact resistance and joint protection. In response, this work achieves a highly sensitive and impact-resistant pressure sensor by integrating bionic principles at both micro and macro levels. Specifically, at the micro level, a muscle-like hydrogel system consisting of MXene nanosheets incorporated into the double network of polyvinyl alcohol and sodium alginate matrix is selected. At the macro level, the bionic prototypes of mimosa leaves and durian spikes with good energy absorption structure, together with octopus’ sucker with good adsorption capacity are selected, and the synergistic construction of the three structures is realized through 3D printing methods. The obtained sensor has an appropriate response range, sensitivity and stability. More importantly, the displacement deformation of the hydrogel can be reduced by 91.22%, while its adhesion strength can be increased by 57.5% compared to the unstructured hydrogel. As a proof-of-concept, the proposed bionic flexible sensor can be used to monitor the motion status of the human ankle joint in real-time, thereby assisting users in making real-time judgments on gait health. © 2024, The Authors. All rights reserved.

关键词： Sodium alginate

SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

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arXiv

arXiv 2024年

作者： Mao, Qianchen Li, Qiang Wang, Bingshu Zhang, Yongjun Dai, Tao Chen, C.L. Philip The School of Software Northwestern Polytechnical University Taicang Campus Suzhou215400 China The School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 China

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves MIoU by 4.7 and has a 7× FPS acceleration compared to the previous state-of-the-art model. The code will be open to the public. © 2024, CC BY.

关键词： Deep learning

Hfcyclegan: High-Frequency Information Guided Cycle-Consistent Adversarial Network for Fundus Image Enhancement

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SSRN 2025年

作者： Liu, Shaopeng Wang, Kai Wu, Xiaohang Xu, Fabao Zhou, Ying Wang, Xinpeng Wang, Qinkang Cen, Haoran Guo, Jianhua Lu, Xu School of Computer Science Guangdong Polytechnic Normal University Guangzhou510665 China Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology Sun Yat-Sen University Guangdong Guangzhou China Qilu Hospital Shandong University Jinan China Shenzhen Institute of Information Technology Shenzhen China Guangdong Provincial Key Laboratory of Intellectual Property & Big Data Guangzhou510665 China Pazhou Lab Guangzhou510330 China

The development of fundus imaging techniques has greatly supported the diagnosis of ophthalmic *** advancements, poor imaging quality, such as artifacts or insufficient illumination, poses challenges for disease *** clinical, it is often difficult to obtain paired data for fundus image enhancement. For unpaired data, CycleGAN-based methods are commonly used to learn the style transfer from low-quality images to high-quality images for enhancement, improving the aforementioned transfer process through brightness loss or regularization ***, fundus images are quite complex, and high-quality images can also exhibit uneven *** methods are not efficient in modeling the characteristics of fundus *** wavelet analysis, we found that the texture information represented by vessels is expressed in the high-frequency components of the ***, we designed a high-frequency information guided cycle-consistent generative adversarial network for unpaired fundus image enhancement, termed *** introduces a novel high-frequency guided generator (HFGen) and a corresponding frequency-aware discriminator (FADis).Specifically, HG uses the proposed high-frequency feature extraction module (HFM) to extract high-frequency features and inject them into the decoding process, outside of the main branches of the *** concatenate the input image with the frequency components obtained from wavelet transformation and input them into *** embedded channel-wise attention blocks of discriminator enable HFCycleGAN to focus on the frequency information differences between low-quality and high-quality fundus *** experiments on two real-world datasets and one synthetic dataset verify that HFcycleGAN outperforms existing *** conclusion, the HFcycleGAN effectively enhances important structures such as vessels in fundus images, significantly impacting fundus image analysis. © 2025, The Author

关键词： Image segmentation

Learning Fair and Efficient Multiple Access Schemes with Decomposed MADDPG

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Learning Fair and Efficient Multiple Access Schemes with Dec...

Future Communications and Networks (FCN), International Conference on

作者： Zhaoyang Liu Haoxin Lin Xijun Wang Jie Gong Xiang Chen School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China State Key Lab of Novel Software Technology Nanjing University Nanjing China Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homogeneous wireless networks, focusing on both fairness and efficiency. We introduce a media access control protocol, named Fair and Efficient Multiple Access (FEMA), by leveraging decomposed Multi-Agent Deep Deterministic Policy Gradient (MADDPG). FEMA decouples the dual objectives into self-oriented efficiency reward and team-oriented fairness reward. Correspondingly, local critic networks and global critic networks target self-oriented reward and team-oriented reward, respectively. Extensive simulations demonstrate FEMA’s superiority over conventional MAC protocols and other multiple access techniques based on deep reinforcement learning, showcasing its ability to simultaneously enhance fairness and efficiency.

关键词： Protocols Wireless networks Focusing Media Access Protocol Dynamic scheduling Deep reinforcement learning Resource management

Parameterization-driven Neural Surface Reconstruction for Object-oriented Editing in Neural Rendering

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arXiv

arXiv 2023年

作者： Xu, Baixin Hu, Jiangbei Hou, Fei Lin, Kwan-Yee Wu, Wayne Qian, Chen He, Ying S-Lab Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Dalian University of Technology China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shanghai AI Lab China SenseTime Research China

The advancements in neural rendering have increased the need for techniques that enable intuitive editing of 3D objects represented as neural implicit surfaces. This paper introduces a novel neural algorithm for parameterizing neural implicit surfaces to simple parametric domains like spheres and polycubes. Our method allows users to specify the number of cubes in the parametric domain, learning a configuration that closely resembles the target 3D object’s geometry. It computes bidirectional deformation between the object and the domain using a forward mapping from the object’s zero level set and an inverse deformation for backward mapping. We ensure nearly bijective mapping with a cycle loss and optimize deformation smoothness. The parameterization quality, assessed by angle and area distortions, is guaranteed using a Laplacian regularizer and an optimized learned parametric domain. Our framework integrates with existing neural rendering pipelines, using multi-view images of a single object or multiple objects of similar geometries to reconstruct 3D geometry and compute texture maps automatically, eliminating the need for any prior information. We demonstrate the method’s effectiveness on images of human heads and man-made objects. The source code is available at https://***/neuparam. Copyright © 2023, The Authors. All rights reserved.

关键词： Parameterization

GMap Results for OAEI 2021 16

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GMap Results for OAEI 2021

16th International Workshop on Ontology Matching, OM 2021

作者： Li, Weizhuo Zhou, Shiqi Ji, Qiu Lu, Bingjie School Of Modern Posts Nanjing University Of Posts And Telecommunications China School Of Computer Science And Engineering Southeast University Nanjing China Zhejiang Lab HangZhou China State Key Laboratory For Novel Software Technology Nanjing University China

GMap is an alternative probabilistic scheme for ontology matching, which combines the sum-product network and the noisy-or model. More precisely, we employ the sum-product network to encode the similarities based on the set of individuals and disjointness axioms. The noisy-or model is utilized to encode the probabilistic matching rules, which describe the influences among entity pairs across ontologies. This paper briefly introduces GMap and its results of two tracks (i.e., Conference, Anatomy) on OAEI 2021. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology