C.-Y. Chi, W.-C. Li, and Chia-Hsiang Lin, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, Boca Raton, FL, Feb. 2017. (Available in CRC Press; also available in Taiwan SCI-TECH.)

电子工业出版社，「信号处理与通信中的凸优化：从基础到应用」（作者：祁忠勇、李威錆、林家祥）（译者：陈翔、沈超），2020年12月出版。(Available in PHEI.)

Chia-Hsiang Lin, C.-Y. Hsieh, and J.-T. Lin, “CODE-IF: A convex/deep image fusion algorithm for efficient hyperspectral super-resolution,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2024.

Chia-Hsiang Lin, and S.-S. Young, “Signal subspace identification for incomplete hyperspectral image with applications to various inverse problems,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2024.

(Invited Paper) S.-M. Hsu, T.-H. Lin, and Chia-Hsiang Lin, “HyperQUEEN-MF: Hyperspectral quantum deep network with multi-scale feature fusion for quantum image super-resolution,” accepted by IEEE SAM, Corvallis, OR, USA, July 8-11, 2024.

Chia-Hsiang Lin, S.-S. Young, L.-Y. Chang, and Cynthia S.J. Liu, “Synthesis of high-resolution FORMOSAT-8 satellite image using fast convex deep learning algorithm,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024.

Chia-Hsiang Lin, C.-Y. Kuo, and S.-S. Young, “Quantum adversarial learning for hyperspectral remote sensing,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024.

Chia-Hsiang Lin, and S.-S. Young, “HyperKING: Hyperspectral knot-like intelligent quantum discriminator and generator,” submitted to IEEE Transactions on Neural Networks and Learning Systems, 2024.

Chia-Hsiang Lin, C.-C. Hsu, S.-S. Young, C.-Y. Hsieh, and S.-C. Tai, "QRCODE: Quasiresidual convex deep network for fusing misaligned hyperspectral and multispectral images," accepted by IEEE Transactions on Geoscience and Remote Sensing, 2024. 

Y. Liu, and Chia-Hsiang Lin, “Hyperspectral anomaly detection using morphological component analysis empowered convex self-similarity regularization,” submitted to IEEE Transactions on Neural Networks and Learning Systems, 2023.

Chia-Hsiang Lin, Z.-C. Leng, C.-H. Yu, Y. Liu, C.-L. Lin, B.-H. Mao, and T.-Y. Tu, “On-chip Analysis of Cell Nuclei in Spheroids with SENSE: An Approach Integrating Fluorescence Calibration and Supervoxel Segmentation,” submitted to Science Advances, 2023.

J.-T. Lin, and Chia-Hsiang Lin, “SuperRPCA: A collaborative superpixel representation prior-aided RPCA for hyperspectral anomaly detection,” submitted to IEEE Transactions on Geoscience and Remote Sensing, 2023.

S.-S. Young, Chia-Hsiang Lin, and Z.-C. Leng, “Unsupervised abundance matrix reconstruction transformer guided fractional attention mechanism for hyperspectral anomaly detection,” submitted to IEEE Transactions on Neural Networks and Learning Systems, 2023.

Chia-Hsiang Lin, S.-H. Huang, T.-H. Lin, and P.-C. Wu, “Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory,” Nature Communications, 2023.

Chia-Hsiang Lin, M.-C. Chu, and P.-W. Tang,“CODE-MM: Convex deep mangrove mapping algorithm based on optical satellite images,” IEEE Transactions on Geoscience and Remote Sensing, 2023. 

P.-W. Tang, Chia-Hsiang Lin, and Y.-R. Liu,“Transformer-driven inverse problem transform for fast blind hyperspectral image dehazing,” IEEE Transactions on Geoscience and Remote Sensing, 2023. 

Chia-Hsiang Lin, and T.-H. Lin, "Hyperspectral change detection using semi-supervised graph neural network and convex deep learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023.

Chia-Hsiang Lin, and Y.-Y. Chen, “HyperQUEEN: Hyperspectral quantum deep network for image restoration,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-20, 2023.

Chia-Hsiang Lin, Y. Liu, C.-Y. Chi, C.-C. Hsu, H. Ren, and T. Q. S. Quek, “Hyperspectral tensor completion using low-rank modeling and convex functional analysis,” IEEE Transactions on Neural Networks and Learning Systems, 2023.

C.-H. Lee, R. Chang, S.-M. Cheng, Chia-Hsiang Lin, and C.-H. Hsiao, “Joint beamforming and power allocation for M2M/H2H co-existence in green dynamic TDD networks: Low-complexity optimal designs,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4799-4815, 2022.

L. Chen, C.-T. Wu, Chia-Hsiang Lin, R. Dai, C. Liu, R. Clarke, G. Yu, J. E. Van Eyk, D. M. Herrington, and Y. Wang, “swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution,” Bioinformatics, vol. 38, no, 5, pp. 1403--1410, 2022.

P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “Optimization-based hyperspectral spatiotemporal super-resolution,” IEEE Access, vol. 10, pp. 37477-37494, 2022.

Chia-Hsiang Lin, Y.-C. Lin, and P.-W. Tang, “ADMM-ADAM: A new inverse imaging framework blending the advantages of convex optimization and deep learning,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2021.

Chia-Hsiang Lin and T.-H. Lin, “All-addition hyperspectral compressed sensing for metasurface-driven miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021.

Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, H.-C. Wu, H.-T. Kuo, C.-F. Lin, P. Chen, and P.-C. Wu, “Automatic inverse design of high-performance beam-steering metasurfaces via genetic-type tree optimization,” Nano Letters, vol. 21, no. 12, pp. 4981-4989, 2021.

Chia-Hsiang Lin and J. M. Bioucas-Dias, “Nonnegative blind source separation for ill-conditioned mixtures via John ellipsoid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2209-2223, May 2021.

C.-C. Hsu, Chia-Hsiang Lin, C.-H. Kao, and Y.-C. Lin, “DCSN: Deep compressed sensing network for efficient hyperspectral data transmission of miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7773-7789, 2020.

Chia-Hsiang Lin and J. M. Bioucas-Dias, “An explicit and scene-adapted definition of convex self-similarity prior with application to unsupervised Sentinel-2 super-resolution,” IEEE Transactions on Geoscience and Remote Sensing,” vol. 58, no. 5, pp. 3352-3365, May 2020.

L. Zhuang, Chia-Hsiang Lin, M. A. T. Figueiredo, and J. M. Bioucas-Dias, “Regularization parameter selection in minimum volume hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 9858-9877, Dec. 2019.

Y.-R. Syu, Chia-Hsiang Lin, C.-Y. Chi, “An outlier-insensitive unmixing algorithm with spatially varying hyperspectral signatures,” IEEE Access, vol. 7, pp. 15086-15101, Jan. 2019.

Chia-Hsiang Lin, C.-Y. Chi, L. Chen, D. J. Miller, and Y. Wang, “Detection of sources in non-negative blind source separation by minimum description length criterion,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4022-4037, Sep. 2018.

Chia-Hsiang Lin, R. Wu, W.-K. Ma, C.-Y. Chi, and Y. Wang, “Maximum volume inscribed ellipsoid: A new simplex-structured matrix factorization framework via facet enumeration and convex optimization,” SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1651-1679, 2018.

Chia-Hsiang Lin, F. Ma, C.-Y. Chi, and C.-H. Hsieh, “A convex optimization based coupled non-negative matrix factorization algorithm for hyperspectral and multispectral data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1652-1667, Mar. 2018.

G. Xu, Chia-Hsiang Lin, W. Ma, S. Chen, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” IEEE Access, vol. 5, pp. 13601-13616, Mar. 2017.

Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1946-1961, Apr. 2016.

A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, F.-S. Yang, C.-Y. Chi, and Y. Wang, “Convex optimization-based compartmental pharmacokinetic analysis for prostate tumor characterization using DCE-MRI,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 707-720, Apr. 2016.

Chia-Hsiang Lin, W.-K. Ma, W.-C. Li, C.-Y. Chi, and A. Ambikapathi, “Identifiability of the simplex volume minimization criterion for blind hyperspectral unmixing: The no pure-pixel case,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5530-5546, Oct. 2015.

S.-S. Young, Chia-Hsiang Lin, and J.-T. Lin, “CiDAR-Former: Cosine-weighting deep abundance reconstruction transformer for fast unsupervised hyperspectral anomaly detection,” IEEE WHISPERS, Athens, Greece, Oct. 31-Nov. 2, 2023.

(Invited Paper) T.-H. Lin, and Chia-Hsiang Lin, and S.-S. Young, “GNN-based small-data learning with area-control mechanism for hyperspectral satellite change detection,” Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Taipei, Taiwan, Oct. 31-Nov. 3, 2023.

Chia-Hsiang Lin, and Y.-Y. Chen, “Quantum deep hyperspectral satellite remote sensing,” IEEE IGARSS, Pasadena, California, July 16-21, 2023.

Chia-Hsiang Lin, M.-C. Chu, and H.-J. Chu, “High-dimensional multiresolution satellite image classification: An approach blending the advantages of convex optimization and deep learning,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.

Chia-Hsiang Lin, T.-H. Lin, T.-H. Lin, and T.-H. Lin, “Fast reconstruction of hyperspectral image from its RGB counterpart using ADMM-Adam theory,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.

Y. Liu, Chia-Hsiang Lin, and Y.-C. Kuo, “Low-rank representation with morphological-attribute-filter based regularization for hyperspectral anomaly detection,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.

P.-W. Tang, and Chia-Hsiang Lin, “Hyperspectral dehazing using ADMM-Adam theory,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.

P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “A fast multidimensional data fusion algorithm for hyperspectral spatiotemporal super-resolution,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.

Chia-Hsiang Lin, M.-C. Chu, and T.-H. Lin, “Convex optimization aided deep classification of ESA's Sentinel-2 satellite image,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20,  2022.

Chia-Hsiang Lin, T.-H. Lin, Y.-Y. Lai, K.-H. Yu, and T.-H. Lin, “Reconstructing hyperspectral image from its RGB counterpart: An approach blending deep learning and convex optimization,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20,  2022.

Chia-Hsiang Lin, C.-C. Hsu, S.-W. Jian, and Y.-C. Kuo, “Convex feature-aligned quasiresidual network for alignment-free deep high-dimensional image fusion,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20,  2022.

Y. Liu, Chia-Hsiang Lin, Z.-C. Leng, and C.-Y. Hsieh, “Convex hyperspectral anomaly detection algorithm using random dictionary without column-sparsity-promoting regularization,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Nantou, Taiwan, Aug. 18-20,  2022.

T.-H. Lin, and Chia-Hsiang Lin, “Single hyperspectral image super-resolution using ADMM-Adam theory,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.

J.-T. Lin, and Chia-Hsiang Lin, “Real-time hyperspectral anomaly detection using collaborative superpixel representation with boundary refinement,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.

C.-H. Yu, Z.-C. Leng, Y. Liu, J.-Y. Huang, Chia-Hsiang Lin, and T.-Y. Tu, “A total solutioning workflow for sample processing and precise nuclei quantification in 3D tumor spheroids using unsupervised algorithm,” World Congress of Biomechanics, Taipei, Taiwan, Jul. 10-14, 2022.

A. Hassanfiroozi, Chia-Hsiang Lin, J.-T. Lin, and P.-C. Wu, “High-performance metasurfaces for wavefront engineering,” Materials Research Society Fall Meeting & Exhibit, Boston, MA, USA, Nov. 28 - Dec. 3, 2021.

C.-H. Kao, Chia-Hsiang Lin, S.-W. Jian, and P.-Y. Lin, “Solving hyperspectral single-image super-resolution via fusion-based inverse problem transform,” The 34th IPPR Conference on Computer Vision, Graphics, and Image Processing, Taipei, Taiwan, Aug. 22-24, 2021.

Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, Y.-C. Cheng, A. Hassanfiroozi, H.-C. Wu, H.-T. Kuo, and P.-C. Wu, “Toward high-performance plasmonic metasurfaces: From forward to inverse design approach,” SPIE Optics and Photonics, San Diego, CA, USA, Aug. 1-5, 2021.

Chia-Hsiang Lin, C.-Y. Sie, P.-Y. Lin, and J.-T. Lin, “Fast unsupervised spatiotemporal super-resolution for multispectral satellite imaging using plug-and-play machinery strategy,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.

Chia-Hsiang Lin, Y.-C. Lin, P.-W. Tang, and M.-C. Chu, “Deep hyperspectral tensor completion just using small data,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.

Chia-Hsiang Lin and P.-W. Tang, “Inverse problem transform: Solving hyperspectral inpainting via deterministic compressed sensing,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.

Chia-Hsiang Lin and Y. Liu, “Blind hyperspectral inpainting via John ellipsoid,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.

Chia-Hsiang Lin,  Y.-S. Chen, J.-T. Lin, and P.-C. Wu, “Inverse design of non-periodical metasurfaces via high-performance automatic optimization,” accepted by Optics & Photonics Taiwan International Conference (OPTIC), Taipei, Taiwan, Dec. 3-5, 2020.

(Invited Paper) (Top Performance Award) C.-C. Hsu, W.-H. Zheng, H.-T. Yang, Chia-Hsiang Lin, and C.-H. Kao, “Rethinking relation between model stacking and recurrent neural networks for social media prediction,” ACM Multimedia, Seattle, WA, USA, Oct. 12-16,  2020.

Y.-C. Hung*, Chia-Hsiang Lin*, F.-Y. Wang, and S.-H. Yang, “Penetrating terahertz hyperspectral unmixing via Löwner-John ellipsoid: An unsupervised algorithm,” International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), Buffalo, NY, USA, Sep. 13-18, 2020. (*Contributed Equally)

(Invited Paper) C.-C. Hsu, Y.-C. Lin, C.-H. Kao, and Chia-Hsiang Lin, “Deep joint compression and super-resolution low-rank network for fast hyperspectral data transmission,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18,  2020.

(Invited Paper) T.-H. Lin, Chia-Hsiang Lin, Y. Liu, and C.-H. Kao, “A simple spatial-spectral proximal compression method for high-dimensional imagery with proximal computing based blind reconstruction,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18,  2020.

(Invited Paper) (Outstanding Paper Award) C.-Y. Sie, Chia-Hsiang Lin, P.-W. Tang, and Y.-C. Lin, “Solving the algebraic hyperspectral inpainting problem: A fast hyperplane geometry based approach,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18,  2020.

(Invited Paper) Chia-Hsiang Lin, J. M. Bioucas-Dias, T.-H. Lin, Y.-C. Lin, and C.-H. Kao, “A new hyperspectral compressed sensing method for efficient satellite communications,” IEEE SAM, Hangzhou, China, June 8-11, 2020.

W.-C. Zheng, K.-H. Tseng, and Chia-Hsiang Lin, “Unsupervised change detection using convex relaxation and dynamic threshold selection in remotely sensed images ,” American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA, Dec. 9-13, 2019.

(Invited Paper) C.-C. Hsu, and Chia-Hsiang Lin, “Dual reconstruction with densely connected residual network for single image super-resolution,” IEEE International Conference on Computer Vision (ICCV), Seoul, Korea, Oct. 27 - Nov. 2, 2019.

T.-Y. Lin, H. Ren, and Chia-Hsiang Lin, “Bathymetry estimation via convex geometry in multispectral satellite imagery: A case study in Dongsha Atoll,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.

C.-H. Wang, K.-H. Tseng, and Chia-Hsiang Lin, “Waterline detection using fusion based super-resolution of multispectral satellite image with self-similarity,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.

W.-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, T.-H. Lin, C.-H. Wang, and C.-Y. Chi, “Unsupervised change detection in multitemporal multispectral satellite images: A convex relaxation approach,” IEEE IGARSS, Yokohama, Japan, July 28 - Aug. 2, 2019.

(Interactive Session Prize Paper Award) C.-H. Wang, Chia-Hsiang Lin, J. M. Bioucas-Dias, W.-C. Zheng, K.-H. Tseng, “Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization” IEEE IGARSS, Yokohama, Japan, July 28 - Aug. 2, 2019.

W-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, and T.-H. Lin, “Criterion design and large-scale optimization algorithm for blind change detection in multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.

C.-H. Wang, Chia-Hsiang Lin, and K.-H. Tseng, “Patch similarity guided super-resolution algorithm for fusing panchromatic and multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.

Chia-Hsiang Lin and J. M. Bioucas-Dias, “Linear spectral unmixing via matrix factorization: Identifiability criteria for sparse abundances,” in Proc. IEEE IGARSS, Valencia, Spain, July 23-27, 2018.

(Invited Paper) Chia-Hsiang Lin and J. M. Bioucas-Dias, “New theory for unmixing ill-conditioned hyperspectral mixtures,” in Proc. IEEE SAM, Sheffield, UK, July 8-11, 2018.

Chia-Hsiang Lin and J. M. Bioucas-Dias, “Provably and robust blind source separation of ill-conditioned hyperspectral mixtures,” in Proc. IEEE SSP, Freiburg, Germany, June 10-13, 2018.

G. Xu, Chia-Hsiang Lin, W. Ma, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” in Proc. IEEE ICC, Paris, France, May 21-25, 2017.

W.-K. Ma, Chia-Hsiang Lin, W.-C. Li, and C.-Y. Chi, “When can the minimum volume enclosing simplex identify the endmembers correctly when there is no pure pixel?,” in Proc. IEEE WHISPERS, Tokyo, Japan, June 2-5, 2015.

Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based MVES algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Brisbane, Australia, Apr. 19-24, 2015.

(Invited Paper) A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, and C.-Y. Chi, “Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images,” in Proc. IEEE WHISPERS, Gainesville, Florida, USA, June 25-28, 2013.

Chia-Hsiang Lin, A. Ambikapathi, W.-C. Li, and C.-Y. Chi, “On the endmember identifiability of Craig’s criterion for hyperspectral unmixing: A statistical analysis for three-source case,” in Proc. IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.

Chia-Hsiang Lin, Simplex Geometry Based Non-negative Blind Source Separation, ICE, NTHU, July 2016.

Chia-Hsiang Lin, “Super-resolution for ESA's Sentinel-2 multi-resolution satellite images,” Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung, Taiwan, Nov. 4, 2020.

Chia-Hsiang Lin, “Mathematical principles behind fast unsupervised image super-resolution,” Department of Statistics & Institute of Data Science, National Cheng Kung University, Tainan, Taiwan, Sep. 10, 2020.

Chia-Hsiang Lin, “[UR] Intelligent Hyperspectral Computing Laboratory,” The Office of Research and Development, National Cheng Kung University, Tainan, Taiwan, Aug. 31, 2020.

Chia-Hsiang Lin, “A comparison between deep learning (supervised) and convex optimization (unsupervised) approaches for hyperspectral compressed sensing on miniaturized satellite,” AI Center, Formosa Plastics Group, Kaohsiung, Taiwan, Aug. 28, 2020.

Chia-Hsiang Lin, “An explicit and scene-adapted denition of convex self-similarity prior with application to unsupervised Sentinel-2 super-resolution,” Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan, June 5, 2020.

Chia-Hsiang Lin, “Super-resolution of optical satellite imagery,” Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei, Taiwan, May 8, 2020.

Chia-Hsiang Lin, “A convex geometry perspective on hyperspectral unmixing,” Institute of Communications Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, Dec. 10, 2019.

Chia-Hsiang Lin, “Non-negative blind source separation based on information theory and convex geometry,” Taiwan Telecommunications Annual Meeting (ITCOM), Chiayi, Taiwan, Aug. 22-23, 2019.

Chia-Hsiang Lin, “Advanced hyperspectral super-resolution theory,” National Space Organization (NSPO), Taipei, Taiwan, Apr. 26, 2019.

Chia-Hsiang Lin, “Unsupervised source detection in non-negative blind source separation by information theoretic criterion,” Institute of Information Science, Academia Sinica, Taipei, Taiwan, Apr. 24, 2019.

Chia-Hsiang Lin, “Advanced blind source separation and hyperspectral superresolution imaging via convex geometry and big data optimization,” Forum of Cross-disciplinary Outstanding Young Scholars (Office of Research and Development & Headquarters of University Advancement), Tainan, Taiwan, Mar. 29, 2019.

Chia-Hsiang Lin, “Hyperspectral unmixing: From Craig simplex to John ellipsoid,” Graduate Institute of Electrical Engineering & Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan, Mar. 25, 2019.

Chia-Hsiang Lin, “Hyperspectral remote sensing: Convex geometry and insights,” Department of Civil Engineering, National Chiao Tung University, Hsinchu, Taiwan, Mar. 12, 2019.

Chia-Hsiang Lin, “A new software for Sentinel-2 super-resolution: Theory and application,” 2018 Taiwan-Japan Workshop on Advanced Remote Sensing Technologies for Environmental Monitoring and Disaster Mitigation, Taipei, Taiwan, Dec. 20-21, 2018.

Chia-Hsiang Lin, “Hyperspectral unmixing: From Craig simplex to John ellipsoid,” Department of Geomatics, National Cheng Kung University, Tainan, Taiwan, Dec. 10, 2018.

Chia-Hsiang Lin, “Provable and robust blind source separation of ill-conditioned hyperspectral mixtures,” Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, Nov. 12, 2018.

Chia-Hsiang Lin, “Hyperspectral unmixing and super-resolution,” Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan, Oct. 19, 2018.

Chia-Hsiang Lin, “Super-resolution imaging and ill-conditioned hyperspectral unmixing for remote sensing,” Center for Space and Remote Sensing Research, National Central University, Taoyuan, Taiwan, Feb. 7, 2018.

Chia-Hsiang Lin, “Detection of sources in non-negative blind source separation by minimum description length criterion,” Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal, Sep. 22, 2017.

Chia-Hsiang Lin, “Detection of sources in non-negative blind source separation by minimum description length criterion,” Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan, June 9, 2017.

Chia-Hsiang Lin, “Simplex geometry based non-negative blind source separation,” The 29th IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), Taipei, Taiwan, Aug. 15-17, 2016.