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< Page ,Total 17 >
Effective haze removal under mixed domain and retract neighborhood EI SCIE Scopus
期刊论文 | 2018 , 293 , 29-40 | NEUROCOMPUTING
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Abstract :

The quality of images would exhibit degraded visibility during inclement weather conditions. We proposed a novel method for estimating an optimal transmission map and recovering the real scene. Under HSI color model, saturation layer and intensity layer are mixed together for obtaining the rough transmission. To avoid halos and artifacts, proposed approach employs edge preserving constraint of shrinkage neighborhood on the color line model, which can maintain maximum smoothness and sharp edges in the refined transmission map. A comparative experiment with a few previous methods shows improvement visual results. (c) 2018 Elsevier B.V. All rights reserved.

Keyword :

Visibility restoration Single image dehazing Color line model

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GB/T 7714 He, Lin-Yuan , Zhao, Ji-Zhong , Bi, Du-Yan . Effective haze removal under mixed domain and retract neighborhood [J]. | NEUROCOMPUTING , 2018 , 293 : 29-40 .
MLA He, Lin-Yuan 等. "Effective haze removal under mixed domain and retract neighborhood" . | NEUROCOMPUTING 293 (2018) : 29-40 .
APA He, Lin-Yuan , Zhao, Ji-Zhong , Bi, Du-Yan . Effective haze removal under mixed domain and retract neighborhood . | NEUROCOMPUTING , 2018 , 293 , 29-40 .
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Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing EI SCIE Scopus
期刊论文 | 2018 , 28 (6) , 1434-1444 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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Abstract :

Object recognition is widely used in vision computing for various applications. Traditional CPU and application specific integrated circuit for vision computing cannot provide high performance and enough flexibility, which limit the use of vision systems. In this paper, a hierarchical and parallel pipelined heterogeneous chip for object recognition is proposed to achieve high flexibility, high performance, and area efficiency. In addition, a reformulation of 3D position estimation is proposed. The method uses single precision to achieve the short computing time and accuracy requirement. The hardware resource is small. Application-specific components, such as connected component information extractor and information extraction accelerator, are designed for high performance. Reconfiguration processors and application-specific instruction set processor are introduced to improve flexibility. These components are connected to hierarchical parallel buses. The chip is fabricated in 180-nm CMOS technology and occupies 72.25 mm(2) with 1.09M bits on-chip memory. It delivers 204 GOPS + 665M FLOPS operations. The results show that this hierarchical and parallel pipelined heterogeneous chip is suitable for embedded vision systems.

Keyword :

3D position estimation application-specific instruction set processor (ASIP) system on chip (SoC) reconfigurable embedded vision system

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GB/T 7714 Zhang, Bin , Zhao, Chen , Mei, Kuizhi et al. Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (6) : 1434-1444 .
MLA Zhang, Bin et al. "Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28 . 6 (2018) : 1434-1444 .
APA Zhang, Bin , Zhao, Chen , Mei, Kuizhi , Zhao, Jizhong , Zheng, Nanning . Hierarchical and Parallel Pipelined Heterogeneous SoC for Embedded Vision Processing . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (6) , 1434-1444 .
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Hardware Implementation of Reconfigurable Separable Convolution EI CPCI-S Scopus
会议论文 | 2018 , 232-237 | 17th IEEE-Computer-Society Annual Symposium on VLSI (ISVLSI)
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Abstract :

Convolution operations occupy large amounts of computation resource in convolutional neural networks (CNNs). Separable convolution can greatly reduce computational complexity. Unfortunately, most trained kernels in CNNs are not separable. In this paper, least squares approach is applied to decompose a non-separable 2D kernel into two 1D kernels. A reconfigurable convolutional architecture is proposed to convert a 2D convolution into 1D convolution in convolutional layers. Moreover, a denoising CNN is mapped to the proposed convolution architecture. Experimental results show that the hardware architecture can restore a 1280x 720 image in 0.83s, which achieves an 8.4x speed-up over GPU implementation. Verification experiments demonstrate that our approach and hardware architecture can drastically reduce the computational complexity in convolution operations without sacrificing the performance.

Keyword :

hardware implementation reconfigurable architecture separable convolution convolutional neural networks

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GB/T 7714 Rao, Lei , Zhang, Bin , Zhao, Jizhong . Hardware Implementation of Reconfigurable Separable Convolution [C] . 2018 : 232-237 .
MLA Rao, Lei et al. "Hardware Implementation of Reconfigurable Separable Convolution" . (2018) : 232-237 .
APA Rao, Lei , Zhang, Bin , Zhao, Jizhong . Hardware Implementation of Reconfigurable Separable Convolution . (2018) : 232-237 .
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Secure Device Pairing via Handshake Detection EI SCIE Scopus CSCD
期刊论文 | 2018 , 23 (5) , 621-633 | TSINGHUA SCIENCE AND TECHNOLOGY
SCOPUS Cited Count: 1
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Abstract :

Multi-party applications are becoming popular due to the development of mobile smart devices. In this work, we explore Secure Device Pairing (SDP), a novel pairing mechanism, which allows users to use smart watches to detect the handshake between users, and use the shaking information to create security keys that are highly random. Thus, we perform device pairing without complicated operations. SDP dynamically adjusts the sensor's sampling frequency and uses different classifiers at varying stages to save the energy. A multi-level quantization algorithm is used to maximize the mutual information between two communicating entities without information leakage. We evaluate the main modules of SDP with 1800 sets of handshake data. Results show that the recognition accuracy of the handshake detection algorithm is 98.2%, and the power consumption is only 1/3 of that of the single sampling frequency classifier.

Keyword :

device pairing handshake detection key extraction

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GB/T 7714 Guo, Zhenge , Gao, Xueguang , Ma, Qiang et al. Secure Device Pairing via Handshake Detection [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2018 , 23 (5) : 621-633 .
MLA Guo, Zhenge et al. "Secure Device Pairing via Handshake Detection" . | TSINGHUA SCIENCE AND TECHNOLOGY 23 . 5 (2018) : 621-633 .
APA Guo, Zhenge , Gao, Xueguang , Ma, Qiang , Zhao, Jizhong . Secure Device Pairing via Handshake Detection . | TSINGHUA SCIENCE AND TECHNOLOGY , 2018 , 23 (5) , 621-633 .
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Verifiable Smart Packaging with Passive RFID EI Scopus
期刊论文 | 2018 | IEEE Transactions on Mobile Computing
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Abstract :

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes. IEEE

Keyword :

Backscatter signals Design and implements Internal changes Non destructive Nondestructive testing method RF signal Sensing abilities Verification systems

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GB/T 7714 WANG, GE , Han, Jinsong , Qian, Chen et al. Verifiable Smart Packaging with Passive RFID [J]. | IEEE Transactions on Mobile Computing , 2018 .
MLA WANG, GE et al. "Verifiable Smart Packaging with Passive RFID" . | IEEE Transactions on Mobile Computing (2018) .
APA WANG, GE , Han, Jinsong , Qian, Chen , Xi, Wei , Ding, Han , Jiang, Zhiping et al. Verifiable Smart Packaging with Passive RFID . | IEEE Transactions on Mobile Computing , 2018 .
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SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring SCIE PubMed
期刊论文 | 2018 , 18 (11) | SENSORS
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Abstract :

Estimation of regional soil carbon flux is very important for the study of the global carbon cycle. The spatial heterogeneity of soil respiration prevents the actual status of regional soil carbon flux from being revealed by measurements of only one or a few spatial sampling positions, which are usually used by traditional studies for the limitation of measurement instruments, so measuring in many spatial positions is very necessary. However, the existing instruments are expensive and cannot communicate with each other, which prevents them from meeting the requirement of synchronous measurements in multiple positions. Therefore, we designed and implemented an instrument for soil carbon flux measuring based on dynamic chamber method, SCFSen, which can measure soil carbon flux and communicate with each other to construct a sensor network. In its working stage, a SCFSen node measures the concentration of carbon in the chamber with an infrared carbon dioxide sensor for certain times periodically, and then the changing rate of the measurements is calculated, which can be converted to the corresponding value of soil carbon flux in the position during the short period. A wireless sensor network system using SCFSens as soil carbon flux sensing nodes can carry out multi-position measurements synchronously, so as to obtain the spatial heterogeneity of soil respiration. Furthermore, the sustainability of such a wireless sensor network system makes the temporal variability of regional soil carbon flux can also be obtained. So SCFSen makes thorough monitoring and accurate estimation of regional soil carbon flux become more feasible.

Keyword :

spatial and temporal heterogeneity soil carbon flux measurement wireless sensor networks dynamic chamber method

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GB/T 7714 Wang, Guoying , Wu, Xiaoping , Mo, Lufeng et al. SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring [J]. | SENSORS , 2018 , 18 (11) .
MLA Wang, Guoying et al. "SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring" . | SENSORS 18 . 11 (2018) .
APA Wang, Guoying , Wu, Xiaoping , Mo, Lufeng , Zhao, Jizhong . SCFSen: A Sensor Node for Regional Soil Carbon Flux Monitoring . | SENSORS , 2018 , 18 (11) .
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Haze Removal Using the Difference-Structure-Preservation Prior EI SCIE Scopus
期刊论文 | 2017 , 26 (3) , 1063-1075 | IEEE TRANSACTIONS ON IMAGE PROCESSING | IF: 5.071
WoS CC Cited Count: 5 SCOPUS Cited Count: 8
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Abstract :

Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned, such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage. Experimental results show that the method preserves the natural appearance of the image.

Keyword :

local depth consistency difference-structure-preservation Visibility restoration contrast restoration single image dehazing

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GB/T 7714 He, Linyuan , Zhao, Jizhong , Zheng, Nanning et al. Haze Removal Using the Difference-Structure-Preservation Prior [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2017 , 26 (3) : 1063-1075 .
MLA He, Linyuan et al. "Haze Removal Using the Difference-Structure-Preservation Prior" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 26 . 3 (2017) : 1063-1075 .
APA He, Linyuan , Zhao, Jizhong , Zheng, Nanning , Bi, Duyan . Haze Removal Using the Difference-Structure-Preservation Prior . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2017 , 26 (3) , 1063-1075 .
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Hardware Implementation for Real-Time Haze Removal EI SCIE Scopus
期刊论文 | 2017 , 25 (3) , 1188-1192 | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS | IF: 1.744
SCOPUS Cited Count: 2
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Abstract :

Haze removal is useful in computational photography and computer vision applications. Although many haze removal algorithms have been proposed, their computational efficiency requires improvement. A real-time haze removal method is presented in this paper. The method is based on the concept of a dark channel prior. To enhance the haze removal performance, an approximate method to estimate the atmospheric light and transmission is employed. For embedded system applications, a hardware architecture to perform real-time haze removal is proposed. The hardware can achieve 116 MHz on Stratix FPGA. The simulation results indicate that the hardware is highly efficient and performs well. It obtains good image recovery results and satisfies the real-time requirement even for large images.

Keyword :

image processing Haze removal real-time

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GB/T 7714 Zhang, Bin , Zhao, Jizhong . Hardware Implementation for Real-Time Haze Removal [J]. | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS , 2017 , 25 (3) : 1188-1192 .
MLA Zhang, Bin et al. "Hardware Implementation for Real-Time Haze Removal" . | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 25 . 3 (2017) : 1188-1192 .
APA Zhang, Bin , Zhao, Jizhong . Hardware Implementation for Real-Time Haze Removal . | IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS , 2017 , 25 (3) , 1188-1192 .
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Combining local and global hypotheses in deep neural network for multi-label image classification EI SCIE Scopus
期刊论文 | 2017 , 235 , 38-45 | NEUROCOMPUTING | IF: 3.241
WoS CC Cited Count: 6 SCOPUS Cited Count: 8
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Abstract :

Multi-label image classification is a challenging problem in computer vision. Motivated by the recent development in image classification performance using Deep Neural Networks, in this work, we propose a flexible deep Convolutional Neural Network (CNN) framework, called Local-Global-CNN (LGC), to improve multi-label image classification performance. LGC consists of firstly a local level multi-label classifier which takes object segment hypotheses as inputs to a local CNN. The output results of these local hypotheses are aggregated together with max-pooling and then re-weighted to consider the label co-occurrence or inter-dependencies information by using a graphical model in the label space. LGC also utilizes a global CNN that is trained by multi-label images to directly predict the multiple labels from the input. The predictions of local and global level classifiers are finally fused together to obtain MAP estimation of the final multi-label prediction. The above LGC framework could benefit from a pre-train process with a large-scale single-label image dataset, e.g., ImageNet. Experimental results have shown that the proposed framework could achieve promising performance on Pascal VOC2007 and VOC2012 multi-label image dataset.

Keyword :

Multi-label classification Deep learning Convolutional neural network

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GB/T 7714 Yu, Qinghua , Wang, Jinjun , Zhang, Shizhou et al. Combining local and global hypotheses in deep neural network for multi-label image classification [J]. | NEUROCOMPUTING , 2017 , 235 : 38-45 .
MLA Yu, Qinghua et al. "Combining local and global hypotheses in deep neural network for multi-label image classification" . | NEUROCOMPUTING 235 (2017) : 38-45 .
APA Yu, Qinghua , Wang, Jinjun , Zhang, Shizhou , Gong, Yihong , Zhao, Jizhong . Combining local and global hypotheses in deep neural network for multi-label image classification . | NEUROCOMPUTING , 2017 , 235 , 38-45 .
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A Platform for Free-Weight Exercise Monitoring with Passive Tags EI SCIE Scopus
期刊论文 | 2017 , 16 (12) , 3279-3293 | IEEE TRANSACTIONS ON MOBILE COMPUTING | IF: 4.098
SCOPUS Cited Count: 6
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Abstract :

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Keyword :

RFID Activity recognition and assessment

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GB/T 7714 Ding, Han , Han, Jinsong , Shangguan, Longfei et al. A Platform for Free-Weight Exercise Monitoring with Passive Tags [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2017 , 16 (12) : 3279-3293 .
MLA Ding, Han et al. "A Platform for Free-Weight Exercise Monitoring with Passive Tags" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 16 . 12 (2017) : 3279-3293 .
APA Ding, Han , Han, Jinsong , Shangguan, Longfei , Xi, Wei , Jiang, Zhiping , Yang, Zheng et al. A Platform for Free-Weight Exercise Monitoring with Passive Tags . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2017 , 16 (12) , 3279-3293 .
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