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A Deep High-Order Tensor Sparse Representation for Hyperspectral Image Classification Journal article
Cheng, Chunbo, Zhang, Liming, Li, Hong, Cui, Wenjing, Gao, Junbin, Cun, Yuxiao. A Deep High-Order Tensor Sparse Representation for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62, 5521416.
Authors:  Cheng, Chunbo;  Zhang, Liming;  Li, Hong;  Cui, Wenjing;  Gao, Junbin; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:7.5/7.6 | Submit date：2024/08/05
Convolutional Neural Network (Cnn)  Deep High-order Tensor Sparse Representation (Sr)  Deep Learning  Graph-based Learning (Gsl)  Hyperspectral Image (Hsi) Classification  
FLEX-CIM: A Flexible Kernel Size 1-GHz 181.6-TOPS/W 25.63-TOPS/mm2 Analog Compute-in-Memory Macro Journal article
Fu, Yuzhao, Yu, Wei Han, Un, Ka Fai, Chan, Chi Hang, Zhu, Yan, Zhang, Minglei, Martins, Rui P., Mak, Pui In. FLEX-CIM: A Flexible Kernel Size 1-GHz 181.6-TOPS/W 25.63-TOPS/mm2 Analog Compute-in-Memory Macro[J]. IEEE Journal of Solid-State Circuits, 2024.
Authors:  Fu, Yuzhao;  Yu, Wei Han;  Un, Ka Fai;  Chan, Chi Hang;  Zhu, Yan; et al.
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:4.6/5.6 | Submit date：2024/05/16
Analog Partial Sum (Aps)  Compute-in-memory (Cim)  Convolutional Neural Network (Cnn)  Flexible Kernel Size  Utilization  
Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition Journal article
Li, Qilin, Zhang, Tong, Chen, C. L.P., Yi, Ke, Chen, Long. Residual GCB-Net: Residual Graph Convolutional Broad Network on Emotion Recognition[J]. IEEE Transactions on Cognitive and Developmental Systems, 2023, 15(4), 1673 - 1685.
Authors:  Li, Qilin;  Zhang, Tong;  Chen, C. L.P.;  Yi, Ke;  Chen, Long
Favorite | TC[WOS]:25 TC[Scopus]:26  IF:5.0/4.6 | Submit date：2022/05/17
Broad Learning System (Bls)  Emotion Recognition  Graph Convolutional Broad Network (Gcb-net)  Graph Convolutional Neural Network (Cnn)  Residual Graph Convolutional Broad Network (Residual Gcb-net)  
A 0.05-mm2 2.91-nJ/Decision Keyword-Spotting (KWS) Chip Featuring an Always-Retention 5T-SRAM in 28-nm CMOS Journal article
Tan,Fei, Yu,Wei Han, Un,Ka Fai, Martins,Rui P., Mak,Pui In. A 0.05-mm2 2.91-nJ/Decision Keyword-Spotting (KWS) Chip Featuring an Always-Retention 5T-SRAM in 28-nm CMOS[J]. IEEE Journal of Solid-State Circuits, 2023, 59(2), 626-635.
Authors:  Tan,Fei;  Yu,Wei Han;  Un,Ka Fai;  Martins,Rui P.;  Mak,Pui In
Favorite | TC[WOS]:8 TC[Scopus]:6  IF:4.6/5.6 | Submit date：2023/08/03
5t-sram  Convolutional Neural Network (Cnn)  Input Stationery  Keyword Spotting (Kws)  Low-leakage Memory  Quantization  Switched-capacitor Circuits  
An FPGA-Based Energy-Efficient Reconfigurable Depthwise Separable Convolution Accelerator for Image Recognition Journal article
Lei Xuan, Ka-Fai Un, Chi-Seng Lam, Rui P. Martins. An FPGA-Based Energy-Efficient Reconfigurable Depthwise Separable Convolution Accelerator for Image Recognition[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(10), 4003-4007.
Authors:  Lei Xuan;  Ka-Fai Un;  Chi-Seng Lam;  Rui P. Martins
Favorite | TC[WOS]:26 TC[Scopus]:26  IF:4.0/3.7 | Submit date：2022/06/14
Frequency Modulation  Field Programmable Gate Arrays  Energy Efficiency  Memory Management  Random Access Memory  Arrays  Computational Cost  Convolutional Neural Network (Cnn)  Field-programmable Gate Array (Fpga)  Mobilenetv2  Neural Network  Quantization  
A 108-nW 0.8-mm 2 Analog Voice Activity Detector Featuring a Time-Domain CNN With Sparsity-Aware Computation and Sparsified Quantization in 28-nm CMOS Journal article
Chen, Feifei, Un, Ka Fai, Yu, Wei Han, Mak, Pui In, Martins, Rui P.. A 108-nW 0.8-mm 2 Analog Voice Activity Detector Featuring a Time-Domain CNN With Sparsity-Aware Computation and Sparsified Quantization in 28-nm CMOS[J]. IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2022, 57(11), 3288 - 3297.
Authors:  Chen, Feifei;  Un, Ka Fai;  Yu, Wei Han;  Mak, Pui In;  Martins, Rui P.
Adobe PDF | Favorite | TC[WOS]:8 TC[Scopus]:8  IF:4.6/5.6 | Submit date：2022/07/22
Approximate Computing  Convolutional Neural Network (Cnn)  Feature Extraction  Keyword Spotting (Kws)  Quantization  Reconfigurable  Sparsity  Switched-capacitor Circuits  Voice Activity Detection (Vad)  
NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote-Sensing Imagery Journal article
Lu, Ming, Fang, Leyuan, Li, Muxing, Zhang, Bob, Zhang, Yi, Ghamisi, Pedram. NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote-Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60.
Authors:  Lu, Ming;  Fang, Leyuan;  Li, Muxing;  Zhang, Bob;  Zhang, Yi; et al.
Favorite | TC[WOS]:25 TC[Scopus]:36  IF:7.5/7.6 | Submit date：2022/05/17
Convolutional Neural Network (Cnn)  Deep Learning  Semantic Segmentation  Water Extraction  Weak Supervision  
Innovative Contactless Palmprint Recognition System Based on Dual-Camera Alignment Journal article
Liang, Xu, Li, Zhaoqun, Fan, Dandan, Zhang, Bob, Lu, Guangming, Zhang, David. Innovative Contactless Palmprint Recognition System Based on Dual-Camera Alignment[J]. IEEE Transactions on Systems Man Cybernetics-Systems, 2022, 52(10), 6464-6476.
Authors:  Liang, Xu;  Li, Zhaoqun;  Fan, Dandan;  Zhang, Bob;  Lu, Guangming; et al.
Favorite | TC[WOS]:13 TC[Scopus]:17  IF:8.6/8.7 | Submit date：2022/05/17
Bimodal Palm Alignment  Contactless Biometrics  Palmprint Recognition  Convolutional Neural Network (Cnn)  Palmprint Sensor  Ranging Sensor Calibration  
An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications Journal article
Li, Jixuan, Un, Ka Fai, Yu, Wei Han, Mak, Pui In, Martins, Rui P.. An FPGA-Based Energy-Efficient Reconfigurable Convolutional Neural Network Accelerator for Object Recognition Applications[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68(9), 3143-3147.
Authors:  Li, Jixuan;  Un, Ka Fai;  Yu, Wei Han;  Mak, Pui In;  Martins, Rui P.
Favorite | TC[WOS]:40 TC[Scopus]:51  IF:4.0/3.7 | Submit date：2021/09/20
Computation Efficiency  Convolutional Neural Network (Cnn)  Fpga  Object Recognition  Reconfigurability  
A 50.4 GOPs/W FPGA-Based MobileNetV2 Accelerator using the Double-Layer MAC and DSP Efficiency Enhancemen Conference paper
Li, J., Chen, J., Un, K. F., Yu, W. H., Mak, P. I., Martins, R. P.. A 50.4 GOPs/W FPGA-Based MobileNetV2 Accelerator using the Double-Layer MAC and DSP Efficiency Enhancemen[C], IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE, 2021.
Authors:  Li, J.;  Chen, J.;  Un, K. F.;  Yu, W. H.;  Mak, P. I.; et al.
Favorite | TC[WOS]:2  | Submit date：2022/01/25
Computation Efficiency  Convolutional Neural Network (Cnn)  Fpga  Object Recognition  Reconfigurability