中国科学院机构知识库网格系统: 检索

Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing 期刊论文 OAI收割
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2023, 卷号: 42, 期号: 5, 页码: 1518-1531
作者: Cui, Yangguang; Cao, Kun; Zhou, Junlong; Wei, Tongquan

|

收藏 | 浏览/下载：16/0 | 提交时间：2023/12/04

Training Servers Cloud computing Delays Costs Computational modeling Prototypes Device frequency determination federated learning (FL) high efficiency low cost mobile-edge cloud computing (MECC) user selection

On the System Performance of Mobile Edge Computing in an Uplink NOMA WSN With a Multiantenna Access Point Over Nakagami-m Fading 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 4, 页码: 668-685
作者: Van-Truong Truong, Van Nhan Vo, Dac-Binh Ha, Chakchai So-In

|

收藏 | 浏览/下载：28/0 | 提交时间：2022/03/09

Mobile edge computing (MEC),Nakagami-m fading,offloading,selection combining (SC)/maximal ratio combining (MRC),successful computation probability (SCP),uplink nonorthogonal multiple access (NOMA),wireless sensor networks (WSNs)

Research on edge enhancement of optical image based on acousto-optic filtering 会议论文 OAI收割
Xi'an, China, 2021-07-23
作者: Chu, Yukun; Chen, Liqun; Wang, Hao; Zhang, Chunguang; Liu, Wenyao

|

收藏 | 浏览/下载：50/0 | 提交时间：2022/01/21

AOTF angular spectrum selection spatial filter edge enhancement

Ancient Environmental Preference and the Site Selection Pattern Based on the Edge Effect and Network Structure in An Ecosystem 期刊论文 OAI收割
SUSTAINABILITY, 2020, 卷号: 12, 期号: 1, 页码: 23
作者: Zhu, Jianfeng; Yu, Lijun; Nie, Yueping; Liu, Fang; Sun, Yu

|

收藏 | 浏览/下载：15/0 | 提交时间：2021/12/06

site selection archaeological sites edge effect ecological network geomantic environment

Robust Image Restoration for Motion Blur of Image Sensors 期刊论文 OAI收割
SENSORS, 2016, 卷号: 16, 期号: 6
作者: Yang, Fasheng; Huang, Yongmei; Luo, Yihan; Li, Lixing; Li, Hongwei

收藏 | 浏览/下载：32/0 | 提交时间：2016/10/27

blind image restoration blur kernel estimation edge selection image sensor signal processing motion deblurring

An auto-focus algorithm of fast search based on combining rough and fine adjustment (EI CONFERENCE) 会议论文 OAI收割
3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012, March 27, 2012 - March 29, 2012, Xiamen, China
作者: Zhang S.; Zhang Y.

收藏 | 浏览/下载：26/0 | 提交时间：2013/03/25

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing and then the wavelet transform based image definition evaluation function which is sensitivity to the variation in image definition was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing. (2012) Trans Tech Publications.

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：77/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Edge direction dispersion and its application in automatic suitable-matching area selection 会议论文 OAI收割
2011 Chinese Control and Decision Conference, CCDC 2011, Mianyang, China, May 23-25, 2011
作者: Ding QH(丁庆海); Yu XR(余新荣); Luo HB(罗海波); Liu HM(刘红梅)

收藏 | 浏览/下载：22/0 | 提交时间：2012/06/06

imaging guidance scene matching local non-texture target tracking automatic suitable-matching area selection edge direction dispersion

Automatic Parameters Selection Method of Edge Detector in the Unstructured Environment 会议论文 OAI收割
IEEE International Conference on Robotics and Biomimetics (ROBIO), Bangkok, THAILAND, February 22-25, 2009
作者: Xia XH(夏兴华); Li B(李斌); Wu CD(吴成东)

收藏 | 浏览/下载：26/0 | 提交时间：2012/06/06

Edge detection Unstructured environment Adaptive parameter selection Chi-square

Autofocusing technique based on image processing for remote-sensing camera (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2007 - Image Processing, September 9, 2007 - September 12, 2007, Beijing, China
作者: Wang X.; Xu S.-Y.; Wang X.; Wang X.

收藏 | 浏览/下载：22/0 | 提交时间：2013/03/25

The key to the auto-focusing technique based on image processing is the selection of focus measure reflecting image definition. Usually the measures derived are on the premise of the images acquired with the same scene. As for the remote-sensing camera working in linear CCD push-broom imaging mode the premise doesn't exist because the scenes shot are different at any moment which brings about difficulties to the selection of the focus measure. To evaluate the image definition the focus measure based on blur estimation for rough adjustment is proposed to estimate the focused position by only two different lens positions which greatly saves the auto-focusing time. Another evaluation function based on edge sharpness is developed to find best imaging position in the narrow range. Simulations show that the combination of the two measures has the advantages of rapid reaction and high accuracy.