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In this paper, we propose a cross-modal deep variational hashing (CMDVH) method for cross-modality multimedia retrieval. Unlike existing cross-modal hashing methods which learn a single pair of projections to map each example as a binary vector, we design a couple of deep neural network to learn non-linear transformations from image-text input pairs, so that unified binary codes can be obtained. We...
With the large amount of distributed generation and diversity load access to the power grid, the changes of distribution network structure and operating characteristics put forward new requirements for its control. Improving the safety and stability of the distribution network has been the important goal of the current distribution network construction. In order to realize the interaction between...
The authors propose a L3 defect photonic crystal nanolaser embedded in flexible medium for nanoscale strain detections. A theoretical optical strain sensitivity of ∼4 nm per ε (1% strain) in the x-direction and ∼3 nm per ε (1% strain) in the y-direction is predicted.
In this paper, we propose a discriminative aggregation network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently. Unlike existing aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn...
In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning...
In order to solve the problem that the accuracy of flaws identification in ultrasonic testing is not high enough due to the error in sensor information acquisition and the noise interference in the detection environment, a method of flaws identification in ultrasonic testing based on evidential reasoning rule (ER rule) is studied. Firstly, ER rule is proposed to consider the reliability and weights...
Large-scale wireless sensor networks (LSWSNs) consist of a large number of wireless sensors that have processing, wireless communication and information acquisition abilities. LSWSNs are promising techniques in many areas such as target detection and tracking, commercial management, intelligent family, military use, preventing forest fire loss, medical diagnostic, etc. In LSWSNs, maximizing the network...
Self-organizing wireless sensor networks (SOWSNs) are emerging as promising techniques in a variety of fields such as traffic control, military purposes, environmental monitoring, industrial process control, automotive, medical diagnostic, etc. The target scheduling problem in SOWSNs attracts attention of many academic researchers. Micro nodes are self-organized small devices with limited monitoring...
High density wireless sensor networks (HDWSNs) made up of a group of sensing units with limited computation, communications, sensing, and self-adaptation abilities. In HDWSNs, minimizing the power consumption of each cluster separately will not lead to optimum power consumption for the whole networks. The sensing units clustering problem is combinatorial in nature and consists of many hard restrictions...
High density wireless sensor networks (HDWSNs) are emerging as promising techniques in a variety of fields such as target detection and tracking, military surveillance, intelligent family, preventing forest fire loss, building monitoring and control, medical diagnostic, etc. HDWSNs composed of a large number of sensors with wireless communication, computation, information acquisition, and self-adaptation...
For metro applications, direct detection offers the advantages of low cost and low complexity. Discrete multi-tone (DMT) is a promising format due to its high spectral efficiency, flexibility and tolerance to chromatic dispersion (CD). In this work, single-side-band-DMT (SSB-DMT) and tuneable electrical dispersion compensation double side-band-DMT (TEDC-DSB-DMT) are summarized and compared.
In order to solve the problem of low accuracy of flaws detection in non-destructive testing, a data-fusion method based on RBF neural network and evidential reasoning (ER) rule is studied. Firstly, a data-fusion model is proposed which contains two parts. Secondly, the first part RBF neural network is used to fuse the feature layer, and its output is normalized as the basic probability assignment...
In this paper, an improved LDPMOS_SCR without a LDPMOS structure (NonLDPMOS_SCR) is discussed, which is realized in 0.5-μm 5V/18V/24V CDMOS process. The theoretical analysis and transmission line pulse (TLP) testing system are used to predict and characterize the proposed ESD protection devices. According to the measurement results, compared with the normal LDPMOS_SCR, NonLDPMOS_SCR elevates the second...
As the device size shrinks beyond 45nm technology node, logic BEOL (back end of line) started adopting Cu/Ultra low k (ULK) to reduce RC delay. With the introduction of low k material, IMD TDDB is notably degraded as numerous publications reported. The impact of ULK material deposition process, barrier layer deposition process on TDDB performance were investigated and discussed in-depth in this paper...
In this paper, we propose a consistent-aware deep learning (CADL) framework for person re-identification in a camera network. Unlike most existing person re-identification methods which identify whether two body images are from the same person, our approach aims to obtain the maximal correct matches for the whole camera network. Different from recently proposed camera network based re-identification...
In this paper, we propose an unsupervised feature learning method called deep binary descriptor with multi-quantization (DBD-MQ) for visual matching. Existing learning-based binary descriptors such as compact binary face descriptor (CBFD) and DeepBit utilize the rigid sign function for binarization despite of data distributions, thereby suffering from severe quantization loss. In order to address...
Current reliability assessment approaches targeted at dynamic systems suffer from the challenge of uncertainties. This paper, we proposed a new online reliability evaluation method which based on evidential reasoning, which can integrate both historic and present status information to online assess the reliability of space relay under uncertainties. Firstly, the space relay is analyzed and the characteristic...
We present studies of two kinds of hybrid plasmonic annular resonators with different cross-sectional shapes of circle and square. Their performance as defined by the Q/V ratio is enhanced considerably with a reduction in their physical dimensions. There exists critical annular radii below which “circle ring” outperforms “square ring”.
We proposed a tunable planar chiral metamaterial (PCMM) which can achieve a giant and photoexcited tunable circular dichroism (CD) effect. The tunable PCMM is composed of an array of conjugated bilayer wheel structures integrated photoconductive silicon (Si). The conductivity of photoconductive Si pads filled in the gap of wheel structures can be tuned efficiently through illumination with different...
In this paper, we propose a topology preserving graph matching (TPGM) method for partial face recognition. Most existing face recognition methods extract features from holistic face images, yet faces in real-world unconstrained environments are usually occluded by objects or other faces, which cannot provide the whole face images for recognition. Latest keypoint-based partial face recognition methods...
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