- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Any quantum network is structurally controllable by a single driving signal
摘要： Control theory concerns with the questions if and how it is possible to drive the behavior of a complex dynamical system. A system is said to be controllable if we can drive it from any initial state to any desired state in finite time. For many complex networks, the precise knowledge of system parameters lacks. But, it is possible to make a conclusion about network controllability by inspecting its structure. Classical theory of structural controllability is based on the Lin’s structural controllability theorem, which gives necessary and sufficient conditions to conclude whether a network is structurally controllable. Due to this fundamental theorem, we may identify a minimum driver vertex set, whose control with independent driving signals is sufficient to make the whole system controllable. I show that Lin’s theorem does not apply to quantum networks, if local operations and classical communication between vertices are allowed. Any quantum network can be modified to be structurally controllable obeying a single driving vertex.
关键词： Quantum networks,Linear quantum dynamics,Controllability
更新于2025-09-09 09:28:46
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[IEEE 2018 11th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) - Budapest (2018.7.18-2018.7.20)] 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) - Polarization-Insensitive Radio-over-Fibre Receiver Based on a 3× 3 Coupler for C-RAN Back-Hauling in 5G Networks
摘要： The performance of a cost-effective receiver, based on a balanced 3×3 polarization maintaining symmetric coupler, applied to a 15GHz radio-over-fibre back-haul transport network for C-RAN architecture, is experimentally validated. A power sensitivity of -29dBm is demonstrated, with low required local oscillator power (7 dBm) over a long transmission distance (60 km).
关键词： 5G networks,Fiber optics links and subsystems,Radio frequency photonics
更新于2025-09-09 09:28:46
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Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network
摘要： In recent years, deep learning-based object recognition algorithms become emerging in robotic vision applications. This paper addresses the design of a novel deep learning-based visual object recognition and pose estimation system for a robot manipulator to handle random object picking tasks. The proposed visual control system consists of a visual perception module, an object pose estimation module, a data argumentation module, and a robot manipulator controller. The visual perception module combines deep convolution neural networks (CNNs) and a fully connected conditional random field layer to realize an image semantic segmentation function, which can provide stable and accurate object classification results in cluttered environments. The object pose estimation module implements a model-based pose estimation method to estimate the 3D pose of the target for picking control. In addition, the proposed data argumentation module automatically generates training data for training the deep CNN. Experimental results show that the proposed scene segmentation method used in the data argumentation module reaches a high accuracy rate of 97.10% on average, which is higher than other state-of-the-art segment methods. Moreover, with the proposed data argumentation module, the visual perception module reaches an accuracy rate over than 80% and 72% in the case of detecting and recognizing one object and three objects, respectively. In addition, the proposed model-based pose estimation method provides accurate 3D pose estimation results. The average translation and rotation errors in the three axes are all smaller than 0.52 cm and 3.95 degrees, respectively. These advantages make the proposed visual control system suitable for applications of random object picking and manipulation.
关键词： Deep learning,convolution neural networks,pose estimation,semantic segmentation
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Lviv, Ukraine (2018.9.11-2018.9.14)] 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Machine-Learning Identification of Extragalactic Objects in the Optical-Infrared All-Sky Surveys
摘要： We present new fully-automatic classification model to select extragalactic objects within astronomical photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to create feature space; 2) building hypersurface in feature space to limit range of features (outliers detection); 3) building hyperplane separating extragalactic objects from the galactic ones. We trained our model with 1.7 million objects (1.4 million galaxies and quasars, 0.3 million stars). The application of the model is presented as a photometric catalog of 38 million extragalactic objects, identified in the WISE and Pan-STARRS catalogs cross-matched with each other.
关键词： machine learning,classification,data mining,support vector machines,neural networks
更新于2025-09-09 09:28:46
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Pattern recognition of messily grown nanowire morphologies applying multi-layer connected self-organized feature maps
摘要： Multi-layer connected self-organizing feature maps (SOFMs) and the associated learning procedure were proposed to achieve efficient recognition and clustering of messily grown nanowire morphologies. The network is made up by several paratactic 2-D SOFMs with inter-layer connections. By means of Monte Carlo simulations, virtual morphologies were generated to be the training samples. With the unsupervised inner-layer and inter-layer learning, the neural network can cluster different morphologies of messily grown nanowires and build connections between the morphological microstructure and geometrical features of nanowires within. Then, the as-proposed networks were applied on recognitions and quantitative estimations of the experimental morphologies. Results show that the as-trained SOFMs are able to cluster the morphologies and recognize the average length and quantity of the messily grown nanowires within. The inter-layer connections between winning neurons on each competitive layer have significant influence on the relations between the microstructure of the morphology and physical parameters of the nanowires within.
关键词： Messily grown nanowire morphologies,Artificial neural networks,Monte Carlo simulation,Pattern recognition,Self-organizing feature maps
更新于2025-09-09 09:28:46
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[ACM Press the 10th Latin America Networking Conference - São Paulo, Brazil (2018.10.03-2018.10.04)] Proceedings of the 10th Latin America Networking Conference on ZZZ - LANC '18 - Static-Traffic Routing and Wavelength Assignment in Transparent WDM Networks Using Genetic Algorithm
摘要： In order to transmit data efficiently over an optical network, many routing and wavelength assignment (RWA) algorithms have been proposed. This work presents a genetic algorithm that aims at solving the RWA problem, which consists of choosing the most suitable lightpath (i.e., a combination of a route and a wavelength channel) between a source-destination pair of nodes in all-optical networks. A comparison to some already known approaches in terms of blocking probability per load over four network topologies was made. Simulation results show a good performance, since the average blocking probability achieved by the proposed genetic algorithm was relatively equivalent to the values yielded by the standard approaches over two network topologies; and way lower on the other two networks.
关键词： routing and wavelength assignment,general objective function,WDM optical networks,Genetic algorithm
更新于2025-09-09 09:28:46
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[IEEE 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, GA (2017.10.21-2017.10.28)] 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Deep Learning Models for PET Scatter Estimations
摘要： Projection data acquired from a positron emission tomography (PET) scanner consist of true, scattered and random events. Scattered events can cause severe artifacts and quantitation errors in reconstructed PET images unless corrected for properly. A scatter correction algorithm is required to predict scattered events from the measurement. Scatter correction requires estimation of both single scatter and multiple scatter profiles. Usually, single scatter profiles are calculated by model-based simulation and multiple scatter profiles are estimated by a kernel-based convolution method. However, design of the convolution kernels for multiple scatter estimation is sophisticated and requires fine parameter tuning. In this work, we adopt deep learning techniques for scatter estimation. We propose two convolutional neural networks. The first network estimates multiple scatter profiles from single scatter profiles, replacing the kernel-based convolution method. The second network is designed to predict the total scatter profiles (including single and multiple scatters) directly from the input of emission and attenuation sinograms. Initial results from both networks show a promise with the potential for more accurate and faster scatter correction for PET.
关键词： Monte Carlo Simulation,Deep Learning,Scatter Estimation,Convolutional Neural Networks,PET
更新于2025-09-09 09:28:46
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Aerial LaneNet: Lane-Marking Semantic Segmentation in Aerial Imagery Using Wavelet-Enhanced Cost-Sensitive Symmetric Fully Convolutional Neural Networks
摘要： The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today’s transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.
关键词： Aerial imagery,wavelet transform,autonomous driving,traffic monitoring,remote sensing,fully convolutional neural networks (FCNNs),lane-marking segmentation,infrastructure monitoring,mapping
更新于2025-09-09 09:28:46
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Light Field Spatial Super-Resolution Using Deep Efficient Spatial-Angular Separable Convolution
摘要： Light ?eld (LF) photography is an emerging paradigm for capturing more immersive representations of the real-world. However, arising from the inherent trade-off between the angular and spatial dimensions, the spatial resolution of LF images captured by commercial micro-lens based LF cameras are signi?cantly constrained. In this paper, we propose effective and ef?cient end-to-end convolutional neural network models for spatially super-resolving LF images. Speci?cally, the proposed models have an hourglass shape, which allows feature extraction to be performed at the low resolution level to save both computational and memory costs. To fully make use of the four-dimensional (4-D) structure information of LF data in both spatial and angular domains, we propose to use 4-D convolution to characterize the relationship among pixels. Moreover, as an approximation of 4-D convolution, we also propose to use spatial-angular separable (SAS) convolutions for more computationally- and memory-ef?cient extraction of spatial-angular joint features. Extensive experimental results on 57 test LF images with various challenging natural scenes show signi?cant advantages from the proposed models over state-of-the-art methods. That is, an average PSNR gain of more than 3.0 dB and better visual quality are achieved, and our methods preserve the LF structure of the super-resolved LF images better, which is highly desirable for subsequent applications. In addition, the SAS convolution-based model can achieve 3× speed up with only negligible reconstruction quality decrease when compared with the 4-D convolution-based one. The source code of our method is online available at https://github.com/spatialsr/DeepLightFieldSSR.
关键词： Light ?eld,super-resolution,convolutional neural 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) - Background Subtraction via 3D Convolutional Neural Networks
摘要： Background subtraction can be treated as the binary classification problem of highlighting the foreground region in a video whilst masking the background region, and has been broadly applied in various vision tasks such as video surveillance and traffic monitoring. However, it still remains a challenging task due to complex scenes and for lack of the prior knowledge about the temporal information. In this paper, we propose a novel background subtraction model based on 3D convolutional neural networks (3DCNNs) which combines temporal and spatial information to effectively separate the foreground from all the sequences in an end-to-end manner. Different from conventional models, we view background subtraction as three-class classification problem, i.e., the foreground, the background and the boundary. This design can obtain more reasonable results than existing baseline models. Experiments on the Change Detection 2012 dataset verify the potential of our model in both quantity and quality.
关键词： Background Subtraction,Change Detection,3D Convolutional Neural Networks
更新于2025-09-09 09:28:46