- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Ultrafast Epoxy–Amine Photopolyaddition
摘要： A new generation of light-induced production of polymeric materials is presented here. In detail, we propose to use photoacidic catalysis during the well-known epoxy?amine polyaddition reaction: it is now referred to as “epoxy?amine photopolyaddition”. Soft irradiation (405 nm visible light, 150?450 mW/cm2) of a photosensitizer/iodonium salt system leads to the production of superacids (e.g., H+, PF6?) that spectacularly enhance state-of-the-art epoxy?amine polyaddition kinetics: <3 min is necessary to obtain full conversion when >3 h is required to complete the reaction without light. Also, photoactivation greatly enhances final epoxy and amine conversions which resulted in increases (+15 °C) of the glass transition temperature of the final 3D polymer networks. This work clearly shows the extremely versatile applications for epoxy?amine photopolyaddition: thin layers (40 μm), thick layers (up to 2.5 cm), and composites (45 wt % fillers). This work paves the path toward ultrafast production of epoxy?amine composites and adhesives.
关键词： composites,photoacidic catalysis,visible light,superacids,polymer networks,epoxy?amine photopolyaddition,adhesives
更新于2025-09-09 09:28:46
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[IEEE 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) - Enschede (2018.8.26-2018.8.29)] 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) - Application of Convolutional Neural Networks to Femur Tracking in a Sequence of X-Ray Images
摘要： A path along which the human knee joint moves can be estimated from real-time moving images or a sequence of static images. In case of many algorithms solving this problem, it is essential to locate the characteristic points (i.e., key-points) on each image and find the correspondence between them in the image sequence. In this paper we present an algorithm, which detects such key-points facilitating effective femur tracking in a sequence of X-ray images. We use a set of X-ray images manually labeled with the key-point positions, to train a Convolutional Neural Network (CNN) for the purposes of solving a regression task corresponding to finding key-point positions in previously unknown images. CNN hyper-parameters such as number of convolutions and layers, learning rate, regularization parameters, and activation functions were optimized using a tree of Parzen estimators guiding the process of training multiple models. Results for models with the best mean-square estimation error computed for a validation set and lowest structural complexity are presented. Key-point positions predicted by the CNN are on par with human predictions, even though the actual key-point position is ambiguous in some cases. The feasibility of detected key-points for femur tracking has been verified by several case studies.
关键词： key-point detection,femur tracking,medical image analysis,X-ray images,Convolutional Neural Networks
更新于2025-09-09 09:28:46
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FSCOI: A High Fan-out, Scalable, and Cluster-based Optical Interconnect for Data Center Networks
摘要： In this letter, a high fan-out, scalable, and cluster-based optical interconnect (FSCOI) for data center networks is proposed. FSCOI is consisted of an optical switch and clusters. Compared to OSA, the improved fan-out of FSCOI is achieved by two steps. The first is a coupler for sending can multiplex the optical signals from all top-of-rack (ToR) switches that are within a cluster. The second is a wavelength selective switch for receiving can talk to all ToR switches within the cluster. FSCOI is based on cluster, and it is suited to intra-cluster traffic. In addition, the high scalability is achieved by increasing the port usage efficiency of the optical switch. Its performance is evaluated by simulations. Extensive results demonstrate FSCOI has better performance in comparison to OSA.
关键词： cluster,data center networks,scalability,optical interconnect,fan-out
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments
摘要： Robust real-time object detection and tracking are challenging problems in autonomous transportation systems due to operation of algorithms in inherently uncertain and dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with each other to achieve smooth tracking of detected objects that later can be used by the navigation system. In this paper, we first present an efficient multi-sensor fusion approach based on the probabilistic data association method in order to achieve accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from four main sensors: radar, LiDAR, RGB camera and infrared camera. It generates object region proposals based on the fused detection result. Then, a Convolutional Neural Network (CNN) approach is used to identify the object categories within these regions. The CNN is trained on a real dataset from different ferry driving scenarios. The experimental results of tracking and classification on real datasets show that the proposed approach provides reliable object detection and classification results in maritime environments.
关键词： maritime environment,object detection,convolutional neural networks,region proposals,autonomous vessel,multi-sensor fusion
更新于2025-09-09 09:28:46
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Measuring Oxygen Saturation with Smartphone Cameras using Convolutional Neural Networks
摘要： Arterial oxygen saturation (SaO2) is an indicator of how much oxygen is carried by hemoglobin in the blood. Having enough oxygen is vital for the functioning of cells in the human body. Measurement of SaO2 is typically estimated with a pulse oximeter, but recent works have investigated how smartphone cameras can be used to infer SaO2. In this paper, we propose methods for the measurement of SaO2 with a smartphone using convolutional neural networks and preprocessing steps to better guard against motion artifacts. To evaluate this methodology, we conducted a breath-holding study involving 39 participants. We compare the results using two different mobile phones. We compare our model with the ratio-of-ratios model that is widely used in pulse oximeter applications, showing that our system has significantly lower mean absolute error (2.02%) from a medical pulse oximeter.
关键词： Mobile Sensing,Convolutional Neural Networks,Oxygen Saturation
更新于2025-09-09 09:28:46
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Retinal Vessel Detection in Wide-Field Fluorescein Angiography with Deep Neural Networks: A Novel Training Data Generation Approach
摘要： Retinal blood vessel detection is a crucial step in automatic retinal image analysis. Recently, deep neural networks have significantly advanced the state of the art for retinal blood vessel detection in color fundus (CF) images. Thus far, similar gains have not been seen in fluorescein angiography (FA) because the FA modality is entirely different from CF and annotated training data has not been available for FA imagery. We address retinal vessel detection in wide-field FA images with generative adversarial networks (GAN) via a novel approach for generating training data. Using a publicly available dataset that contains concurrently acquired pairs of CF and fundus FA images, vessel maps are detected in CF images via a pre-trained neural network and registered with fundus FA images via parametric chamfer matching to a preliminary FA vessel detection map. The co-aligned pairs of vessel maps (detected from CF images) and fundus FA images are used as ground truth labeled data for de novo training of a deep neural network for FA vessel detection. Specifically, we utilize adversarial learning to train a GAN where the generator learns to map FA images to binary vessel maps and the discriminator attempts to distinguish generated vs. ground-truth vessel maps. We highlight several important considerations for the proposed data generation methodology. The proposed method is validated on VAMPIRE dataset that contains high-resolution wide-field FA images and manual annotation of vessel segments. Experimental results demonstrate that the proposed method achieves an estimated ROC AUC of 0.9758.
关键词： retinal image analysis,Fluorescein angiography,deep learning,vessel detection,generative adversarial networks
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Single Shot Feature Aggregation Network for Underwater Object Detection
摘要： The rapidly developing ocean exploration and observation make the demand for underwater object detection become increasingly urgent. Recently, deep convolutional neural networks (CNN) have shown strong ability in feature representation and CNN-based detectors also achieve remarkable performance, but still facing the big challenge when detecting multi-scale objects in a complex underwater environment. To address this challenge, we propose a novel underwater object detector, introducing multi-scale features and complementary context information for better classification and location ability. In the auto-grabbing contest of 2017 Underwater Robot Picking Contest sponsored by National Natural Science Foundation of China (NSFC), we won the 1-st place by using proposed method for real coastal underwater object detection.
关键词： context information,multi-scale features,underwater object detection,deep convolutional neural networks
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
摘要： We propose a deep bilinear model for blind image quality assessment (BIQA) that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNN), specializing in the two distortion scenarios separately. For synthetic distortions, we first pre-train a CNN to classify the distortion type and level of an input image, whose ground truth label is readily available at a large scale. For authentic distortions, we make use of a pre-train CNN (VGG-16) for the image classification task. The two feature sets are bilinearly pooled into one representation for a final quality prediction. We fine-tune the whole network on target databases using a variant of stochastic gradient descent. Extensive experimental results show that the proposed model achieves state-of-the-art performance on both synthetic and authentic IQA databases. Furthermore, we verify the generalizability of our method on the large-scale Waterloo Exploration Database, and demonstrate its competitiveness using the group maximum differentiation competition methodology.
关键词： Blind image quality assessment,convolutional neural networks,bilinear pooling,perceptual image processing,gMAD competition
更新于2025-09-09 09:28:46
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[IEEE 2018 International Joint Conference on Neural Networks (IJCNN) - Rio de Janeiro (2018.7.8-2018.7.13)] 2018 International Joint Conference on Neural Networks (IJCNN) - STDP Learning of Image Patches with Convolutional Spiking Neural Networks
摘要： Spiking neural networks are motivated from principles of neural systems and may possess unexplored advantages in the context of machine learning. A class of convolutional spiking neural networks is introduced, trained to detect image features with an unsupervised, competitive learning mechanism. Image features can be shared within subpopulations of neurons, or each may evolve independently to capture different features in different regions of input space. We analyze the time and memory requirements of learning with and operating such networks. The MNIST dataset is used as an experimental testbed, and comparisons are made between the performance and convergence speed of a baseline spiking neural network.
关键词： Spiking Neural Networks,Unsupervised Learning,Convolution,STDP,Machine Learning
更新于2025-09-09 09:28:46
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[IEEE 2017 International Conference on Optical Network Design and Modeling (ONDM) - Budapest (2017.5.15-2017.5.18)] 2017 International Conference on Optical Network Design and Modeling (ONDM) - Simultaneous connections routing in W-S-W elastic optical switches with limited number of connection rates
摘要： The three-stage switching fabric of wavelength-space-wavelength architecture for elastic optical switches is considered in the paper. It serves connections which can occupy different spectrum width. The upper bound for rearrangeable condition for such switching fabric which serves a limited number of connection rates is derived and proved. The control algorithm based on matrix decomposition is also proposed. For the switching fabric of capacity 2×2 serving only two connection rates, necessary and sufficient conditions are derived and proved. The required number of frequency slot units in interstage links is much lower than in the strict-sense nonblocking switching fabrics.
关键词： rearrangeable nonblocking conditions,interconnection networks,Elastic optical networks,elastic optical switching nodes
更新于2025-09-04 15:30:14