English | 繁體

Search in the results

UM

Browse/Search Results:  1-8 of 8 Help

Selected(0)Clear Items/Page:    Sort:
Tensorial Global-Local Graph Self-Representation for Hyperspectral Band Selection Journal article
Zhang, Yongshan, Qi, Jianwen, Wang, Xinxin, Cai, Zhihua, Peng, Jiangtao, Zhou, Yicong. Tensorial Global-Local Graph Self-Representation for Hyperspectral Band Selection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024.
Authors:  Zhang, Yongshan;  Qi, Jianwen;  Wang, Xinxin;  Cai, Zhihua;  Peng, Jiangtao; et al.
Favorite | TC[Scopus]:2  IF:8.3/7.1 | Submit date：2024/09/03
Hyperspectral Band Selection  Graph Self-representation  High-order Tensor  Global-local Information  
A Fully-Integrated CMOS Dual-Band RF Energy Harvesting Front-End Employing Adaptive Frequency Selection Journal article
Lian,Wen Xun, Ramiah,Harikrishnan, Chong,Gabriel, Churchill,Kishore Kumar Pakkirisami, Lai,Nai Shyan, Mekhilef,Saad, Chen,Yong, Mak,Pui In, Martins,Rui P.. A Fully-Integrated CMOS Dual-Band RF Energy Harvesting Front-End Employing Adaptive Frequency Selection[J]. IEEE Access, 2023, 11, 74121-74135.
Authors:  Lian,Wen Xun;  Ramiah,Harikrishnan;  Chong,Gabriel;  Churchill,Kishore Kumar Pakkirisami;  Lai,Nai Shyan; et al.
Favorite | TC[WOS]:9 TC[Scopus]:10  IF:3.4/3.7 | Submit date：2023/08/03
Adaptive Frequency Selection  Cmos  Dickson  Dual-band  Impedance Matching Network (Imn)  Multiband  Rf Energy Harvesting (Rfeh)  
Tensor Decomposition Based Latent Feature Clustering for Hyperspectral Band Selection Conference paper
Qi, Jianwen, Zhang, Jie, Zhang, Yongshan, Jiang, Xinwei, Cai, Zhihua. Tensor Decomposition Based Latent Feature Clustering for Hyperspectral Band Selection[C], 2023.
Authors:  Qi, Jianwen;  Zhang, Jie;  Zhang, Yongshan;  Jiang, Xinwei;  Cai, Zhihua
Favorite | TC[Scopus]:0 | Submit date：2024/02/22
Band Selection  Hyperspectral Image  Latent Feature Clustering  Tensor Decomposition  
GRAPH LEARNING BASED AUTOENCODER FOR HYPERSPECTRAL BAND SELECTION Conference paper
Zhang, Yongshan, Wang, Xinxin, Wang, Zhenyu, Jiang, Xinwei, Zhou, Yicong. GRAPH LEARNING BASED AUTOENCODER FOR HYPERSPECTRAL BAND SELECTION[C], 2022, 2794-2798.
Authors:  Zhang, Yongshan;  Wang, Xinxin;  Wang, Zhenyu;  Jiang, Xinwei;  Zhou, Yicong
Favorite | TC[WOS]:4 TC[Scopus]:5 | Submit date：2022/08/05
Autoencoder  Band Selection  Graph Learning  Hyperspectral Image  
Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection Journal article
Zhang, Yongshan, Wang, Xinxin, Jiang, Xinwei, Zhou, Yicong. Marginalized Graph Self-Representation for Unsupervised Hyperspectral Band Selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 5516712.
Authors:  Zhang, Yongshan;  Wang, Xinxin;  Jiang, Xinwei;  Zhou, Yicong
Favorite | TC[WOS]:23 TC[Scopus]:57  IF:7.5/7.6 | Submit date：2022/05/17
Graph Convolution  Hyperspectral Band Selection  Marginalized Corruption  Self-representation (Sr)  Unsupervised Learning  
A 1.7–3.6 GHz 20 MHz-Bandwidth Channel-Selection N-Path Passive-LNA Using a Switched-Capacitor-Transformer Network Achieving 23.5 dBm OB-IIP₃ and 3.4–4.8 dB NF Journal article
Shao, Haijun, Qi, Gengzhen, Mak, Pui In, Martins, Rui P.. A 1.7–3.6 GHz 20 MHz-Bandwidth Channel-Selection N-Path Passive-LNA Using a Switched-Capacitor-Transformer Network Achieving 23.5 dBm OB-IIP₃ and 3.4–4.8 dB NF[J]. IEEE Journal of Solid-State Circuits, 2022, 57(2), 413-422.
Authors:  Shao, Haijun;  Qi, Gengzhen;  Mak, Pui In;  Martins, Rui P.
Favorite | TC[WOS]:15 TC[Scopus]:16  IF:4.6/5.6 | Submit date：2022/03/04
Bandpass Filtering  Channel-selection  Cmos  Harmonic-folding Rejection Ratio (Hfrr)  Linearity  Local Oscillator (Lo)  Low-noise Amplifier (Lna)  N-path Filter  Noise Figure (Nf)  Out-of-band (Ob) Linearity  Passive Lna (pLna)  Radio Frequency (Rf)  Receiver (Rx)  Switched-capacitor-transformer (Sct) Network  Transformer  
Robust Dual Graph Self-Representation for Unsupervised Hyperspectral Band Selection Journal article
Zhang, Yongshan, Wang, Xinxin, Jiang, Xinwei, Zhou, Yicong. Robust Dual Graph Self-Representation for Unsupervised Hyperspectral Band Selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60.
Authors:  Zhang, Yongshan;  Wang, Xinxin;  Jiang, Xinwei;  Zhou, Yicong
Favorite | TC[WOS]:5 TC[Scopus]:3  IF:7.5/7.6 | Submit date：2023/01/30
Band Selection  Graph Convolution  Hyperspectral Imagery  Self-representation  Unsupervised Learning  
A Fourier-LDA approach for image recognition Journal article
Jing X.-Y., Tang Y.-Y., Zhang D.. A Fourier-LDA approach for image recognition[J]. PATTERN RECOGNITION, 2005, 38(3), 453-457.
Authors:  Jing X.-Y.;  Tang Y.-Y.;  Zhang D.
Favorite | TC[WOS]:37 TC[Scopus]:53  IF:7.5/7.6 | Submit date：2019/02/11
Fourier Transform  Fourier-lda Approach (Fla)  Frequency-band Selection  Linear Discrimination Analysis (Lda)  Two-dimensional Separability Judgment