Research Accomplishments of Prof. Chin-Teng Lin |
Topic 1. Pioneered the Development of a Series of Fuzzy Neural Network (FNN) Models with On-Line Learning Capability for High-IQ Machine Learning Topic 2. Developed Structured Neural Networks with Self-Constructive/Learning Capability for Autonomous Machine Learning in Changing Environments Topic 3. Derived Fundamental Theories of Fuzzy Control Systems Topic 4. Solved Real-World Human-in-Loop Problems in Robotics, Automation, and Control Topic 5. Developed Bio-inspired Intelligent Multimedia Information Processing and Recognition Techniques and Their VLSI Implementation Topic 6. Invented Hierarchical Learning Architecture (HLA) with Automatic Feature Selection for High-Accurate Bioinformatics/Bioengineering Applications Topic 7. Pioneered the Study of Brain Dynamics for Driver’s Drowsiness Detection, Arousal Feedback, and Distraction in a Realistic Driving Environment Topic 8. Discovered Spatial and Temporal EEG Dynamics of Motion Sickness and Spatial Navigation in Driving Cognition Topic 9. Developed Novel Tools and Pioneering Studies in Rehabilitation Engineering Topic 10. Developed High-Density Mobile and Wireless EEG Devices with Dry Sensors Topic 11. Developed Bio-inspired Expert Systems based on Computational Intelligence for Daily Healthcare and Homecare Applications Topic 12. Designed Advanced Optical Sensing Devices for Functional Brain Imaging
Description of Contribution
Since 1991, Prof. Lin proposed the world first fuzzy neural network (FNN) at the most authoritative journal in the area of computer science; IEEE Transactions on Computers, the FNN has attracted great attention globally and become one major area in the computational intelligence realm. The FNN integrates the high-level reasoning mechanism of fuzzy logic and low-level learning ability of neural networks into a functional unit. FNN is a complementarily cooperative model of fuzzy logic and neural networks; it brings learning ability into fuzzy systems, and brings human-like thinking structure into neural networks. Before the invention of FNN, fuzzy systems can be only "designed" by experts, and neural networks can be only trained like a black box. FNN can also house expert IF-THEN rules as background knowledge before adaptive learning. FNN has the major advantages of high learning speed, smaller network size, and human understandable network structure. The total citations for this pioneering FNN paper are 1105 (average 60 citations per year) up to August 22th, 2012. Since the publication of this generic FNN model, he has developed a series of FNNs with various learning capabilities suitable for different learning environments such as supervised/reinforcement/unsupervised learning, structure/parameter learning, learning with crisp/fuzzy data, etc. In [10], he further invented the first recurrent FNN (RFNNN) containing recursive fuzzy rules with dynamic reasoning. RFNN expands the learning ability of FNN to deal with temporal learning problems and can perform concurrent structure and parameter learning on the fly. Each invented FNN or RFNN model formed a milestone in the FNN development history and induced new enhanced models and applications by many researchers in the world. Prof. Lin is also the co-Author of the first FNN textbook ([1] below), Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall, 1996, 797 pages (ISBN 0-13-261413-8), whose total citations are 1356 up to December 22th, 2012.
Significance of Contribution
Prof. Lin was one prominent pioneer to introduce the human-like reasoning mechanism to incorporate adaptive learning ability. The resultant novel model, FNN, had become one major research domain in Computational Intelligence since his invention 20 years ago. More than 1 million items can be found when we search "FNN" in Google. The FNN has very high impact to the intelligent systems and machine learning societies nowadays.
This work resulted in the following publications: 1. (Textbook) C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (with disk), Prentice Hall, 1996, 797 pages (ISBN 0-13-261413-8). 2. (Textbook) C. T. Lin, Neural Fuzzy Control Systems with Structure and Parameter Learning, World Scientific, 1994, 127 pages (ISBN 981-02-1613-0). 3. C. T. Lin and C. S. G. Lee, “Neural-Network-Based Fuzzy Logic Control and Decision System,” IEEE Transactions on Computers, Vol. 40, No. 12, pp. 1320-1336, Dec. 1991. (IF: 1.822, 55/245 or 22.24% of ENGINEERING, ELECTRICAL & ELECTRONIC) 4. C. T. Lin and C. S. G. Lee, “Reinforcement Structure/Parameter Learning for Neural-Network-Based Fuzzy Logic Control Systems,” IEEE Transactions on Fuzzy Systems, Vol. 2, No. 1, pp. 46-63, Feb. 1994. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC 5. C. T. Lin, C. J. Lin, and C. S. G. Lee, “Fuzzy Adaptive Learning Control Network with On-line Neural Learning,” Fuzzy Sets and Systems, Vol. 71, pp. 25-45, April 1995. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 6. C. T. Lin and Y. C. Lu, “A Neural Fuzzy System with Linguistic Teaching Signals,” IEEE Transactions on Fuzzy Systems, Vol. 3, No. 2, pp. 169-189, May 1995. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 7. C. J. Lin and C. T. Lin, “Reinforcement Learning for an ART-based Fuzzy Adaptive Learning Control Network,” IEEE Transactions on Neural Networks, Vol. 7, No. 3, pp. 709-731, May 1996. (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 8. C. T. Lin and Y. C. Lu, “A Neural Fuzzy System with Fuzzy Supervised Learning,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 26, No. 5, pp. 744-763, Oct. 1996. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 9. C. F. Juang and C. T. Lin, “An On-line Self-constructing Neural Fuzzy Inference Network and Its Applications,” IEEE Transactions on Fuzzy Systems, Vol. 6, No. 1, pp. 12-32, Feb. 1998. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 10. C. F. Juang and C. T. Lin, “A Recurrent Self-Organizing Neural Fuzzy Inference Network,” IEEE Transactions on Neural Networks, Vol. 10, No. 4, pp. 828-845, July 1999. (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 11. C. T. Lin and I. F. Chung, “A Reinforcement Neuro-fuzzy Combiner for Multiobjective Control,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 29, No. 6, pp. 726-744, Dec.1999. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 12. C. F. Juang, J. Y. Lin, and C. T. Lin, “Genetic Reinforcement Learning Through Symbiotic Evolution for Fuzzy Controller Design,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 30, No. 2, pp. 290-302, April 2000. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 13. I. F. Chung, C. J. Lin, and C. T. Lin, “A GA-based Fuzzy Adaptive Learning Control Network,” Fuzzy Sets and Systems, Vol. 112, No. 1, pp. 65-84, May 2000. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 14. C. T. Lin, W. C. Cheng, and S. F. Liang, “An On-line ICA-mixture-model-based Self-constructing Fuzzy Neural Network,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 52, No. 1, pp. 207-221, Jan. 2005. (EI and SCI) (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 15. C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and Nimit Kumar, “Support-vector-based Fuzzy Neural Network for Pattern Classification,” IEEE Transactions on Fuzzy Systems, Vol. 14, No. 1, pp. 31-41, Feb. 2006. (EI and SCI) (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 16. C. H. Chen, C. J. Lin, and C. T. Lin, “A Functional-link-based NeuroFuzzy Network for Nonlinear System Control,” IEEE Transactions on Fuzzy Systems, Vol. 16, No. 5, pp. 1362-1378, Oct. 2008. (EI and SCI) (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 17. C. J. Lin, C. H. Chen, and C. T. Lin, “A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications,” IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Vol. 39, No. 1, pp. 55-68, Jan. 2009. (EI and SCI) (IF: 2.016, 22/95 or 23.1% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 18. C. H. Chen, C. J. Lin, and C. T. Lin, “Using an Efficient Immune Symbiotic Evolution Learning for Compensatory Neuro-fuzzy Controller,” IEEE Transactions on Fuzzy Systems, Vol. 17, No. 3, pp. 668-682, June 2009. (EI and SCI) (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 19. Y. Y. Lin, J. Y. Chang, and C. T. Lin*, “Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-evolving Fuzzy Neural Network (IRSFNN)”, IEEE Transactions on Neural Networks and Leaning Systems (TNNLS), Accepted in 2012 (IF: 2.952, 1/50 or 2% of COMPUTER SCIENCE, HARDWARE & ARCHITECTURE) 20. M. F. Han, C. T. Lin, and J. Y. Chang, “Efficient Differential Evolution Algorithm based Optimization of Fuzzy Prediction Model for Time Series Forecasting,” International Journal of Intelligent Information and Database Systems, Vol 7, Issue 3, pp. 225-241, May 2013 21. Y. Y. Lin, S. H. Liao, J. Y. Chang, and C. T. Lin, “Simplified Interval Type-2 Fuzzy Neural Networks,” accepted to appear in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2013. (EI and SCI) (IF: 3.766, 1/50 or 2.0% of COMPUTER SCIENCE, HARDWARE & ARCHITECTURE) 22. Y. Y. Lin, J. Y. Chang, N. R. Pal and C. T. Lin, “A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) with Self-evolving Structure and Parameters,” IEEE Transactions on Fuzzy Systems, Vol. 21,Issue 3 , June 2013(SCI/EI) (IF: 5.484, 1/114 or 0.87% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 23. M. F. Han, C. T. Lin, and J. Y. Chang, “Differential Evolution with Local Information for Neuro-Fuzzy Systems Optimisation,” accepted by Knowledge Based Syst., Vol. 44, pp. 78-89, May 2013. (EI and SCI) (IF: 4.104, 6/114 or 5.26% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 24. Y. Y. Lin, S. H. Liao, J. Y. Chang, and C. T. Lin, “Simplified Interval Type-2 Fuzzy Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Vol. 25, No. 5, pp. 85-93, May 2014. (EI and SCI) (IF: 3.766, 1/50 or 2.0% of COMPUTER SCIENCE, HARDWARE & ARCHITECTURE) 25. Y. Y. Lin, N.R. Pal, and C. T. Lin, “An Interval Type-2 Neural Fuzzy System for online system identification and Feature Elimination (IT2NFS-SIFE)” accepted by IEEE Transactions on Neural Networks and Learning Systems, July 2014. (SCI/EI) (IF: 3.766, 1/50 or 2% of COMPUTER SCIENCE, HARDWARE & ARCHITECTUR)
Description of Contribution
Our fundamental contributions add to the key foundation of structured neural networks research. This work in novel learning methods provides new scientific tools for studying spatial/temporal learning environments of the complex world. Prior to Prof. Lin's research, the neural network systems with embedded if-then rules could only handle static mapping. Our breakthrough research with recurrent approach and also with structure learning enables dynamic mapping and temporal learning of networks in dynamically changing environments. His innovative research in cellular neural networks provides a systematic way to learn fuzzy rules and network templates automatically, which makes possible the scientific design of an integrated system for mimicking high-level bio-visual functions. His contributions in reinforcement learning allow the network to learn by rough critic-type linguistic teaching signals. This achievement can be directly applied to many real-world problems, where the supervised training data are typically unavailable. Prof. Lin also designed the advanced recurrent fuzzy cellular neural networks (CNN) to achieve high-level information processing by mimicking the local function of biological neural circuits, especially the human visual pathway system. Furthermore, he proposed a novel framework for automatically constructing a multiple-CNN integrated neural system. It provides a systematic way to fuzzily integrate a set of CNNs to achieve real-time high-level information processing/reasoning in applications or biologics studies,
Significance of Contribution
Prof. Lin's contributions on the Structured Neural Networks are of significance to neural network field because they enable the neural networks to learn in various learning environments with high complexity due to the innovative bio-inspired or modularized neural network structures.
This work resulted in the following publications: 1. C. T. Lin, “A Neural Fuzzy Control System with Structure and Parameter Learning,” Fuzzy Sets and Systems, Vol. 70, pp. 183-212, March 1995. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 2. C. T. Lin and C. S. G. Lee, “A Multi-valued Boltzmann Machine,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 25, No. 4, pp. 660-669, April 1995. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 3. C. J. Lin and C. T. Lin, “An ART-based Fuzzy Adaptive Learning Control Network,” IEEE Transactions on Fuzzy Systems, Vol. 5, No. 4, pp. 477-496, Nov. 1997. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 4. Y. J. Wang and C. T. Lin, “Runge Kutta Neural Network for Identification of Dynamical Systems in High Accuracy,” IEEE Transactions on Neural Networks, Vol. 9, No. 2, pp. 294-307, March 1998. (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 5. Y. J. Wang and C. T. Lin, “A Second-order Learning Algorithm for Multilayer Networks Based on Block Hessian Matrix,” Neural Networks, Vol. 11, pp. 1607-1622, Dec. 1998. (IF: 2.656, 15/94 or 16% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 6. C. T. Lin, H. W. Nein, and W. C. Lin, “A Space-time Delay Neural Network for Motion Recognition and Its Application to Lipreading,” International Journal of Neural Systems, Vol. 9, No. 4, pp. 311-334, Aug. 1999. (IF: 2.988, 12/102 or 11.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 7. Y. J. Wang and C. T. Lin, “Recurrent Learning Algorithms for Designing Optimal Controllers of Continuous Systems,” IEEE Transactions on Systems Man and Cybernetics Part A-Systems And Humans, Vol. 30, No. 5, pp. 580-588, Sep. 2000. (IF: 2.033, 16/91 or 17.5% of COMPUTER SCIENCE, THEORY & METHODS) 8. C. S. Shieh and C. T. Lin, “A Vector Neural Network for Emitter Identification,” IEEE Transactions on Antennas and Propagation, Vol. 50, No. 8, pp. 1120-1127, Aug. 2002. (IF: 2.011, 10/76 or 13.1% of TELECOMMUNICATIONS) 9. C. T. Lin, C. L. Chang, and W. C. Cheng, “A Recurrent Fuzzy Cellular Neural Network System with Automatic Structure and Template Learning,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 51, No, 5, pp.1024-1035, May 2004. (EI and SCI) (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 10. C. L. Chang, K. W. Fan, I. F. Chung, and C. T. Lin, “A Recurrent Fuzzy Coupled Cellular Neural Network System with Automatic Structure and Template Learning,” IEEE Transactions on Circuits and Systems II-Express Briefs, Vol. 53, No. 8, pp. 602-606, Aug. 2006 . (EI and SCI) (IF: 1.320, 85/245 or 34.1% of ENGINEERING, ELECTRICAL & ELECTRONIC) 11. C. H. Huang and C. T. Lin, “Bio-inspired Computer Fovea Model Based on Hexagonal-type Cellular Neural Network,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 54, No. 1, pp. 35-47, Jan. 2007. (EI and SCI) (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 12. C.H. Li, B.C. Kuo, and C.T. Lin, “LDA-Based Clustering Algorithm And Its Application To An Unsupervised Feature Extraction,” IEEE Transactions on Fuzzy Systems, Vol.19, No.1, pp.152-163, Feb. 2011. (EI and SCI) (IF: 2.695, 17/247 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 13. M. T. Su, C. H. Chen, C. J. Lin and C. T. Lin, “A Rule-based Symbiotic Modified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems,” Applied Soft Computing, Vol. 11, No. 8, pp. 4847-4858, June 2011. (EI and SCI) (IF: 2.097, 19/97 or 19.59% of COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS) 14. M. F. Han, S. H. Liao, J. Y. Chang, and C. T. Lin, “Dynamic Group-Based Differential Evolution Using a Self-Adaptive Strategy for Global Optimization Problems,” Applied Intelligence, Vol. 39, Issue 1, pp 41-56, July 2013. (SCI/EI) (IF: 0.849, 111/72 or 64.8% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 15. C. T. Lin*, M. F. Han, J. Y. Chang, and D. L. Li, “Differential Evolution Using Neighbourhood-Based Mutation Strategy for Fuzzy System Design,” accepted by International Journal of Innovative Computing, Information and Control, Vol. 4, pp.78-89, May 2013. (SCI/EI) (IF: 1.667, 12/60 or 20% of AUTOMATION & CONTROL SYSTEMS)
Description of Contribution
Although fuzzy logic control has been applied to many real-life systems and products, the evidence of its theoretic soundness was usually a pushing issue to pursue. Especially, people were usually questioning the stability, robustness, and optimality of a fuzzy control system embedded with expert knowledge. To attack this problem, Prof. Lin presented a global optimal and stable fuzzy controller design method. He first proposed a theory indicating that the global optimal effect can be achieved by the fuzzily combined local optimal controllers. Based on this foundation, he derived a local concept approach to designing the optimal fuzzy controller. The stability of the entire closed-loop fuzzy system can then be ensured; the optimal feedback fuzzy system can not only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Also, the total energy of system output is absolutely finite. These techniques can be used in both continuous- and discrete-time fuzzy systems under both finite and infinite horizons.
Significance of Contribution
This work uniquely proposed the design method to ensure the stability, robustness, and optimality simultaneously of a fuzzy control system. Before Prof. Lin's work, only part of these 3 indexes were targeted for the theoretic study of fuzzy logic control. His work set up a new milestone on the fuzzy control theory development.
This work resulted in the following publications: 1. S. J. Wu and C. T. Lin, “Optimal Fuzzy Controller Design: Local Concept Approach,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 2, pp. 171-185, April 2000. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 2. S. J. Wu and C. T. Lin, “Optimal Fuzzy Controller Design in Continuous Fuzzy System: Global Concept Approach,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 6, pp. 713-729, Dec. 2000. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 3. S. J. Wu and C. T. Lin, “Discrete-time Optimal Fuzzy Controller Design: Global Concept Approach,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 1, pp. 21-38, Feb. 2002. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 4. C. H. Wang, H. L. Liu, and C. T. Lin, “Dynamic Optimal Learning Rates of a Certain Class of Fuzzy Neural Networks and its Applications with Genetic Algorithm,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 31, No. 3, pp. 467-475, June 2001. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 5. M. F. Han, C. T. Lin, and J. Y. Chang, " Group-Based Differential Evolution for Numerical Optimization Problems," accepted by Int. J. of Innovative Computing Information and Control, May 2012. (IF: 1.667, 12/60 or 20% of AUTOMATION & CONTROL SYSTEMS) 6. S. Y. Li, C. H. Yang, S. A. Chen, L. W. Ko, and C. T. Lin*, “Fuzzy Adaptive Synchronization of Time-Reversed Chaotic Systems via a New Adaptive Control Strategy,” Information Sciences, Vol. 222, Issue 10, pp. 486-500, Nov. 2012. (EI and SCI) (IF= 2.833, 9/133 or 6.76% of COMPUTER SCIENCE, INFORMATION SYSTEMS) 7. S. Y. Li, C. H. Yang, C. T. Lin, L. W. Ko, and T. T. Chiu, ”Adaptive Synchronization of Chaotic Systems with Unknown Parameters via New Backstepping Strategy”, Nonlinear Dynamics, Vol. 70, Issue 3, pp. 2129–2143, Dec. 2012. (EI and SCI) (IF: 1.741, 17/116 or 14.65% of ENGINEERING, MECHANICAL) 8. S. Y. Li, C. H. Yang, L. W. Ko, C. T. Lin, and Z. M. Ge, “Implementation on Electronic Circuits and RTR Pragmatical Adaptive Synchronization: Time-Reversed Uncertain Dynamical Systems’ Analysis and Applications,” Abstract and Applied Analysis, pp.1-10, June 2013. (EI and SCI) (IF: 1.102, 30/295 or 10.17% of MATHEMATICS) 9. S. Y. Li, C. H. Yang, C. T. Lin, L. W. Ko and T. T. Chiu, “Chaotic Motions in the Real Fuzzy Electronic Circuits,” Abstract and Applied Analysis, 8 pages, May 2013. (EI and SCI) (IF: 1.102, 30/295 or 10.17% of MATHEMATICS) 10. R. Chakraborty, C. T. Lin, and N.R. Pal, “Sensor (Group Feature) Selection with Controlled Redundancy in a Connectionist Framework ,” accepted by International Journal of Neural Systems, May 2014. (IF: 5.054, 2/115 or 1.739% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE)
Description of Contribution
Prof. Lin and his team had contributed widely in applying the aforementioned computational intelligence technology to real-world systems, covering the domains of robotics, automation, and control. The main challenges in these real-world applications are unknown exact system characteristics; various uncertainties and noise from systems, environments, and human operators; large-scale, time various, and high complexities; imprecise sensing devices, etc. He developed fuzzy neural network (FNN)-based intelligent technology with self-construction, on-line learning, and real-time adaptation capabilities is a key to attack these challenges. His created applications include Fault-Tolerant Reconfigurable Architecture for Robot Control, Automatic Freeway Traffic Incidents Detection, Road Tunnel Ventilation System Control, Pyrometer Correction and Temperature Control in Rapid Thermal Processing, On-line Diagnosis System for Rotor Vibration, Magnetic Bearing System Control, Machining Control at Corner Parts for Wire-EDM, Mixed Scheduling in ATM (Asynchronous Transfer Mode) Communication Network, Maneuvering Target Tracking, Automatic PCB (printed circuited board) Solder Joints Locating, etc.
Significance of Contribution
Prof. Lin advanced the applications of computational intelligence technology to solve the real-world human-in-loop problems efficiently. Many of these techniques have been successfully transferred to the industry. For example, His work to predict the emissivity changes for compensating the temperature reading of the pyrometer in rapid thermal processing has been transferred to nanometer silicon semiconductor foundries including Taiwan Semiconductor Manufacturing Company (TSMC). These achievements directly impact the development of human-centered intelligent systems.
This work resulted in the following publications: 1. C. T. Lin and C. S. G. Lee, “Fault-Tolerant Reconfigurable Architecture for Robot Kinematics and Dynamics Computations,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-21, No. 5, pp. 983-999, Sep./Oct. 1991. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 2. C. H. Hsiao, C. T. Lin, and M. Cassidy, “Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents,” Journal of Transportation Engineering, American Society of Civil Engineers (ASCE), Vol. 120, No. 5, pp. 753-772, Sep./Oct. 1994. 3. C. J. Lin and C. T. Lin, “Adaptive Fuzzy Control of Unstable Nonlinear Systems,” International Journal of Neural Systems, Vol. 6, No. 3, pp. 283-298, 1995. (IF: 2.988, 12/102 or 11.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 4. P. H. Chen, J. H. Lai, and C. T. Lin, “Application of Fuzzy Control to a Road Tunnel Ventilation System,” Fuzzy Sets and Systems, Vol. 100, pp. 9-28, Nov. 1998. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 5. C. F. Juang, C. T. Lin, and J. C. Huang, “Temperature Control of Rapid Thermal Process Using Neural Fuzzy Network,” Fuzzy Sets and Systems, Vol. 103, pp. 49-65, April 1999. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 6. J. H. Lai and C. T. Lin, “Application of Neural Fuzzy Network to Pyrometer Correction and Temperature Control in Rapid Thermal Processing,” IEEE Transactions on Fuzzy Systems, Vol. 7, No. 2, pp. 160-175, April 1999. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 7. C. T. Lin and C. P. Jou, “Controlling Chaos by GA-based Reinforcement Learning Neural Network,” IEEE Transactions on Neural Networks, Vol. 10, No. 4, pp. 846-859, July 1999. (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 8. C. T. Lin, C. F. Juang, and C. P. Li, “Temperature Control with a Neural Fuzzy Inference Network,” IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Vol. 29, No. 3, pp. 440-451, Aug. 1999. (IF: 2.016, 22/95 or 23.1% of COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS) 9. M. S. Bai, I L. Hsiao, H. M. Tsai, and C. T. Lin, “Development of an On-line Diagnosis System for Rotor Vibration via Model-based Intelligent Inference,” Journal of Acoustical Society of America, Vol. 107, No. 1, pp. 315-323, Jan. 2000. (IF: 1.717, 7/26 or 26.92% of ACOUSTICS) 10. C. T. Lin and C. P. Jou, “GA-based Fuzzy Reinforcement Learning for Control of a Magnetic Bearing System,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 30, No. 2, pp. 276-289, April 2000. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 11. C. T. Lin, C. F. Juang, and C. P. Li, “Water Bath Temperature Control with a Neural Fuzzy Inference Network,” Fuzzy Sets and Systems, Vol. 111, No. 2, pp. 285-306, April 2000. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED)
12.
C. T. Lin, I F. Chung, and S. Y.
Huang, “Improvement
of Machining Accuracy at Corner Parts for Wire-EDM,” Fuzzy Sets
and Systems, Vol. 122, No. 3, pp. 499-511, Sep. 2001.
13.
C. T. Lin, I. F. Chung, H. C.
Pu, T. H. Lee, and J. Y. Chang, “Genetic
Algorithm-Based Neural Fuzzy Decision Tree for Mixed Scheduling in ATM
Networks,” IEEE Transactions on Systems Man and Cybernetics Part
B-Cybernetics, Vol. 32, No. 6, pp. 832-845, Dec. 2002. 14. F. B. Duh and C. T. Lin, “Tracking a Maneuvering Target Using Neural Fuzzy Network,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 34, No. 1, pp. 16-33, Feb. 2004. (EI and SCI) (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 15. M. T. Su, C. T. Lin and K. W. Hsu, “A Novel Method for Locating Solder Joints Based on Modified Binary Potential Function,” International Journal of Innovative Computing Information and Control, Vol.8, No.1(B) , pp. 911-932, Feb 2012. (EI and SCI) (IF: 1.667, 12/60 or 22.95% of AUTOMATION & CONTROL SYSTEMS)
Description of Contribution
Multimedia information technology (IT) is the kernel in this 3C (Computer, Communications, and Consumer) electronics era. Its main challenge is to meet the increasing high demanding of acute human perceptual systems in real time. Prof. Lin's major contributions in this area is to adopt the biological models and features of human perceptual systems into the multimedia information processing techniques to process the multimedia signals in the way of well fitting human perceptual characteristics in reasonable time and space bandwidth. This so-called Bio-inspired Intelligent Multimedia Information Processing Technology can highly increases the signal perceptual quality of advanced 3C products/applications in reasonable hardware and software cost. For example ([8] in [Speech (1D Signal) Signals] below), in auditory signal processing, in order to make better use of the perceptual property of human ear, he proposed the weighted LSD (log-spectral distortion) measure instead of the original (unweighted) LSD measure to calculate the distortion between the unquantized speech spectrum and the quantized one. In this way, the error distortion distribution of digitalized speech signals can be shaped into the curve which matches human perceptual characteristics. This is a fundamental technique applicable to any speech and audio signal processing in various multimedia applications. Another example ([8] in [Image/Video (2D Signal) Processing] below), in image processing, he proposed a novel human visual system (HVS)-directed adaptive interpolation scheme for natural image resizing. A fuzzy classifier built from the characteristics of the HVS is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. Two interpolation schemes with different computation complexity were then used to process different image regions. This resulted in high-quality resized images with low computation cost. Again, the HVS model is a fundamental tool applicable to any image and video processing in various multimedia applications.
Significance of Contribution
The Bio-inspired Intelligent Multimedia Information Processing Technology that Prof. Lin proposed can effectively increase the signal perceptual quality of various multimedia products and applications in IT industries. In addition to his many published papers and patents on this topic, these techniques had been transferred to several major IT companies such as high-speed NN chips in speech signal processing for United Microelectronics Co. (UMC), a human-perception-based error-shaping speech coding IC for UMC, a fuzzy-based digital stabilization technique for Aiptek Co., a network filter to mimic the human visual system fort Hewlett-Packard Co., a series of image-based intelligent traffic detection systems for V5 Technology Co., etc.
This work resulted in the following publications: [Speech (1D Signal) Signals] 1. C. T. Lin and C. F. Juang, “An Adaptive Neural Fuzzy Filter and Its Applications,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 27, No. 4, pp. 635-656, Aug. 1997. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
2.
C. T. Lin and M. C. Kan,
“Adaptive Fuzzy Command Acquisition with Reinforcement Learning,” IEEE
Transactions on Fuzzy Systems, Vol. 6, No. 1, pp. 102-121, Feb. 1998. 3. S. F. Liang, W. Y. Su, and C. T. Lin, “Model-based Synthesis of Plucked String Instruments by Using a Class of Scattering Recurrent Networks,” IEEE Transactions on Neural Networks, Vol. 11, No. 1, pp. 171-185, Jan. 2000. (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 4. C. S. Shieh and C. T. Lin, “Direction of Arrival Estimation Based on Phase Difference Using Neural Fuzzy Network,” IEEE Transactions on Antennas and Propagation, Vol. 48, No. 7, pp. 1115-1124, July 2000. (IF: 2.011, 10/76 or 13.1% of TELECOMMUNICATIONS) 5. G. D. Wu and C. T. Lin, “Word Boundary Detection with Mel-scale Frequency Bank in Noisy Environment,” IEEE Transactions on Speech and Audio Processing, Vol. 8, No. 5, pp. 541-554, Sep. 2000. (IF: 1.668, 5/30 or 16.7% of ACOUSTICS) 6. C. T. Lin, H. W. Nein, and J. Y. Hwu, “GA-based Noisy Speech Recognition Using Two-Dimensional Cepstrum,” IEEE Transactions on Speech and Audio Processing, Vol. 8, No. 6, pp. 664-675, Nov. 2000. (IF: 1.668, 5/30 or 16.7% of ACOUSTICS) 7. C. F. Juang and C. T. Lin, “Noisy Speech Processing by Recurrently Adaptive Fuzzy Filters,” IEEE Transactions on Fuzzy Systems, Vol. 9, No. 1, pp. 139-152, Feb. 2001. (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 8. H. W. Nein and C. T. Lin, “Incorporating Error Shaping Techniques into LSF Vector Quantization,” IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 2, pp. 73-86, Feb. 2001. (IF: 1.668, 5/30 or 16.7% of ACOUSTICS) 9. G. D. Wu and C. T. Lin, “A Recurrent Neural Fuzzy Network for Word Boundary Detection in Variable Noise-level Environments,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 31, No. 1, pp. 84-97, Feb. 2001. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 10. D. J. Liu and C. T. Lin, “Fundamental Frequency Estimation Based on the Joint Time-frequency Analysis of Harmonic Spectral Structure,” IEEE Transactions on Speech and Audio Processing, Vol. 9, No. 6, pp. 609-621, Sep. 2001. (IF: 1.668, 5/30 or 16.7% of ACOUSTICS) 11. C. T. Lin, J. Y. Lin, and G. D. Wu, “A Robust Word Boundary Detection Algorithm for Variable Noise-level Environment in Cars,” IEEE Transactions on Intelligent Transportation Systems, Vol. 3, No. 1, pp. 89-101, March 2002. (IF: 2.092, 7/106 or 6.6% of ENGINEERING, CIVIL) 12. G. D. Wu and C. T. Lin, “Single-channel Speech Enhancement in Variable Noise-level Environment,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 33, No.1, pp. 137-143, Jan. 2003. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 13. F. B. Duh, C. F. Juang, and C. T. Lin, “A Neural Fuzzy Network Approach to Radar Pulse Compression,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 1, No. 1, pp. 15-20, Jan. 2004. (EI and SCI) (IF: 2.234, 34/245 or 13.8% of ENGINEERING, ELECTRICAL & ELECTRONIC) 14. C. T. Lin, R. C. Wu, J. Y. Chang, and S. F. Liang, “A Novel Prosodic-information Synthesizer Based on Recurrent Fuzzy Neural Network for the Chinese TTS System,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 34, pp. 309-324, Feb. 2004. (EI and SCI) (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 15. S. F. Liang, S. M. Lu, J. Y. Chang and C. T. Lin, “A Novel Two-stage Impulse Noise Removal Technique Based on Neural Networks and Fuzzy Decision,” IEEE Transactions on Fuzzy Systems, Vol. 16, No. 4, pp. 863-873, Aug. 2008. (EI and SCI) (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC)
[Image/Video (2D Signal) Processing] 1. C. T. Lin, S. C. Hsiao, and G. D. Wu, “New Techniques on Deformed Image Motion Estimation and Compensation,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 29, No. 6, pp. 846-859, Dec. 1999. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 2. C. T. Lin, Y. C. Lee, and H. C. Pu, “Satellite Sensor Image Classification Using Cascaded Architecture of Neural Fuzzy Network,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, pp. 1033-1043, March 2000. (IF: 2.234, 34/245 or 13.8% of ENGINEERING, ELECTRICAL & ELECTRONIC) 3. C. T. Lin, I. F. Chung, and L. K. Sheu, “A Neural Fuzzy System for Image Motion Estimation,” Fuzzy Sets and Systems, Vol. 114, No. 2, pp. 281-304, Sep. 2000. (IF: 2.138, 8/202 or 3.9% of MATHEMATICS, APPLIED) 4. Y. W. Shou and C. T. Lin, “Image Descreening by GA-CNN-based Texture Classification,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 51, pp. 2287- 2299, Nov. 2004. (EI and SCI) (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 5. S. C. Hsu, S. F. Liang, and C. T. Lin, “A Robust Digital Image Stabilization Technique Based on Inverse Triangle Method and Background Detection,” IEEE Transactions on Consumer Electronics, Vol. 51, No. 2, pp. 335-345, May 2005. (EI and SCI) (IF: 0.942, 121/229 or 44.7% of TELECOMMUNICATIONS) 6. C. T. Lin, W. C. Cheng, and S. F. Liang, “A 3-D Surface Reconstruction Approach Based on Postnonlinear ICA Model,” IEEE Transactions on Neural Networks, Vol. 16, No. 6, pp. 1638-1650, Nov. 2005. (EI and SCI) (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 7. C. T. Lin, W. C. Cheng, and S. F. Liang, “Neural-network-based Adaptive Hybrid-reflectance Model for 3-D Surface Reconstruction,” IEEE Transactions on Neural Networks, Vol. 16, No. 6, pp. 1601-1615, Nov. 2005. (EI and SCI) (IF: 2.889, 17/245 or 6.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 8. C. T. Lin, H. C. Pu, K. W. Fan, S. M. Lu, and S. F. Liang, “An HVS-directed Neural-network-based Image Resolution Enhancement Scheme for Image Resizing,” IEEE Transactions on Fuzzy Systems, Vol.15, No. 4, pp.605-615, Aug. 2007. (EI and SCI) (IF: 3.343, 10/245 or 4% of ENGINEERING, ELECTRICAL & ELECTRONIC) 9. S. C. Hsu, S. F. Liang, K. W. Fan, and C. T. Lin, “A Robust In-car Digital Image Stabilization Technique,” IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, Vol. 37, No. 2, pp. 234-247, March 2007. (EI and SCI) (IF: 2.016, 22/95 or 23.1% of COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS) 10. C. T. Lin, C. H. Huang, and S. A. Chen, “CNN-based Hybrid-order Texture Segregation as Early Vision Processing and its Implementation on CNN-UM,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 54, No.10, pp. 2277-2287, Oct. 2007. (EI and SCI) (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 11. C. T. Lin, Y. C. Yu, and L. D. Van, “Cost-effective Triple-mode Reconfigurable Pipeline FFT/IFFT/2-D DCT Processor,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 16, No. 8, pp. 1058-1071, Aug. 2008. (EI and SCI) (IF:1.010, 121/245 or 49.3% of ENGINEERING, ELECTRICAL & ELECTRONIC) 12. C. T. Lin, C. T. Hong, and C. T. Yang, “Real-time Digital Image Stabilization System Using Modified Proportional Integrated Controller,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 19, No. 3, pp. 427-431, March 2009. (EI and SCI) (IF: 2.548, 24/245 or 9.7% of ENGINEERING, ELECTRICAL & ELECTRONIC) 13. P. Y. Chen, L. D. Van, I. H. Khoo, H.C. Reddy, and C. T. Lin, “Power-efficient and Cost-effective 2-D Symmetry Filter Architectures,” IEEE Transactions on Circuits and Systems I-Regular Papers, Vol. 58, No. 1, pp. 112-125, Jan. 2011. (EI and SCI) (IF: 1.580, 64/247 or 25.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 14. C. H. Li, B.C. Kuo, C.-T. Lin, and C.-S. Huang, “A Spatial-Contextual Support Vector Machine For Remotely Sensed Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 3, pp. 784-799, March 2012. (EI and SCI) (IF: 2.485, 27/247 or 10.93% of ENGINEERING) 15. C. Y. Lee, C. T. Lin, C. T. Hong, and M. T. Su, “Smoke Detection Using Spatial and Temporal Analyses,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 7, July 2012. (SCI) (IF: 1.667, 12/60 or 20% of AUTOMATION & CONTROL SYSTEMS) 16. C. T. Lin, S. C. Hsu, K. P. Chou, L. Siana and C. T. Yang, “Real-Time Boosted Vehicle Detection Deal with High Detection Rate Using False Alarm Eliminating Method,” International Journal of Innovative Computing, Information and Control, Vol.9, No.7, pp. 3039-3052, July. 2013. (IF: 1.667, 12/60 or 20% of ENGINEERING, ELECTRICAL & ELECTRONIC)
Description of Contribution
The present era is an era of biological science and engineering which has been generating a huge amount of data. Those data usually requires specialized modeling and analysis tools. Furthermore, for such biological/medical data there are always too many indifferent and derogatory features which may lead to enhanced data acquisition time and cost, more design time, more decision making time, and other increased expenditures in cost, time and effort. Hence reducing the dimensionality, if possible, is always desirable through feature selection. For the bioinformatics and bioengineering applications, the role of appropriate features has not been paid adequate importance. With Prof. Lin's in-depth background in computational intelligence, he has adopted various strategies and machine learning techniques in conjunction with novel feature selection methods to attack several bioinformatics problems, such as protein fold recognition, protein metal binding residue prediction, and subcellular protein localization classification, etc. In addition, through the use of clustering approaches, he developed systematic pipelines that can be used to re-construct protein 3-D structures. In bioengineering, he also attacked the problem of uncertain coupling between the sensor and the artery in noninvasively continuous blood pressure waveform detection by developing a model-based hierarchical learning architecture.
Significance of Contribution
The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. Prof. Lin's invented HLA with automatic feature selection had provided a general tool in bioinformatics to effectively predicts protein structures in high accuracy. It had also been adopted to do biomarker discovery and cancer diagnostic system construction. Such a technique may be further applied to drug discovery. In bioengineering, Prof. Lin had designed a micro syringe device with tonometer chamber based on a model-based HLA for noninvasively continuous blood pressure waveform detection accurately without distortion. This was the first of such devices in the world. This technology was transferred to Microlife Co., which is an international well known healthcare-devices company located in over 50 countries in the world.
This work resulted in the following publications: [Bioinformatics] 1. C. D. Huang, C. T. Lin, and N. R. Pal, “Hierarchical Learning Architecture with Automatic Feature Selection for MultiClass Protein Fold Classification,” IEEE Transactions on Nanobioscience, Vol. 2, No. 4, pp. 221-232, Dec. 2003. (IF: 1.705, 31/59 or 52.5% of NANOSCIENCE & NANOTECHNOLOGY) 2. C. T. Lin, K. L. Lin, C. H. Yang, I F. Chung, C. D. Huang, and Y. S. Yang, “Protein Metal Binding Residue Prediction Based on Neural Networks,” International Journal of Neural Systems, Vol. 15, No. 1&2, pp. 71-84, Feb/April 2005. (EI and SCI) (IF: 2.988, 12/102 or 11.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 3. K. L. Lin, C. Y. Lin, C. D. Huang, H. M. Chang, C. Y. Yang, C. T. Lin, C. Y. Tang, and D. F. Hsu, “Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction,” IEEE Transactions on NanoBioscience, Vol. 6, No.2, pp.186-196, June 2007. (EI and SCI) (IF: 1.705, 31/59 or 52.5% of NANOSCIENCE & NANOTECHNOLOGY) 4. Y. S. Tsai, I. F. Chung, J. C. Simpson, M. I. Lee, C. C. Hsiung, T. Y. Chiu, L. S. Kao, T. C. Chiu, C. T. Lin, W. C. Lin, S. F. Liang, and C. C. Lin, “Automated Recognition System to Classify Subcellular Protein Localizations in Images of Different Cell Lines Acquired by Different Imaging Systems,” Microscopy Research and Technique, Vol. 71, No.4, pp. 305-314, Apr. 2008. (EI and SCI) (IF: 1.850, 6/16 or 37.5% of ANATOMY & MORPHOLOGY) 5. Y. S. Tsai, C. T. Lin, G. C. Tseng, I. F. Chung, and N. R. Pal "Discovery of Dominant and Dormant Genes from Expression Data Using a Novel Generalization of SNR for Multi-class Problems,” BMC Bioinformatics, Vol. 9: 425, Oct. 2008. (EI and SCI) (IF: 3.428, 4/29 or 13.7% of MATHEMATICAL & COMPUTATIONAL BIOLOGY ) 6. K. L. Lin, C. T. Lin, N. R. Pal, and S. Ojha, "Structural Building Blocks: Construction of Protein 3-D Structures Using a Structural Variant of Mountain Clustering Method,” IEEE Engineering in Medicine and Biology Magazine, Vol. 28, No. 4, pp. 38-44, July 2009. (EI and SCI) (IF: 1.081, 16/23 or 69.5% of MEDICAL INFORMATICS) 7. K. L. Lin, C. T. Lin and N. R. Pal, “Incremental Mountain Clustering Method to Find Building Blocks For Constructing Structures of Proteins,” IEEE Transactions on NanoBioscience, Vol. 9, No. 4 ,pp.278-288, Dec. 2010. (EI and SCI) (IF: 1.712, 39/64 or 60.9% of NANOSCIENCE & NANOTECHNOLOGY)
[Bioengineering] 1. S. H. Liu and C. T. Lin, “A Model-based Fuzzy Logic Controller with Kalman Filtering for Tracking Mean Arterial Pressure,” IEEE Transactions on Systems Man and Cybernetics Part A-Systems And Humans, Vol. 31, No. 6, pp. 676-686, Nov. 2001. (IF: 2.033, 16/91 or 17.5% of COMPUTER SCIENCE, THEORY & METHODS) 2. J. J. Wang, C. T. Lin, S. H. Liu, and Z. C. Wen, “Model-based Synthetic Fuzzy Logic Controller for Indirect Blood Pressure Measurement,” IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Vol. 32, No. 3, pp. 306-315, June 2002. (IF: 3.007, 11/102 or 10.7% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 3. B. C. P. Hu, Y. C. Shih, Y. S. Yang, C. Y. Wu, C. J. Yuan, M. D. Ker, T. K. Wu, Y. K. Li, Y. Z. Hsieh, W. S. Hsu, and C. T. Lin, “CMOS Chip As Luminescent Sensor For Biochemical Reactions,” IEEE Sensors Journal, Vol. 3, pp. 310-316, June 2003. (IF: 1.581, 67/229 or 29.3% of ENGINEERING, ELECTRICAL & ELECTRONIC) 4. C. T. Lin, S. H. Liu, J. J. Wang, and Z. C. Wen, “Reduction of Interference in Oscillometric Arterial Blood Pressure Measurement Using Fuzzy Logic,’’ IEEE Transactions on Biomedical Engineering, Vol. 50, No. 4, pp. 432-441, April 2003. (IF: 2.154, 22/59 or 37.2% of ENGINEERING, BIOMEDICAL) 5. C. S. Huang, C. L. Lin, L. W. Ko, S. Y. Liu, T. P. Su, and C. T. Lin, “Knowledge-based Identification of Sleep Stages based on Two Forehead Electroencephalogram Channels,” Frontiers in Neuroscience, Vol. 8, Issue 263, doi:10.3389, Aug. 2014.
Description of Contribution
The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver’s inattention such as fatigue, drowsiness or distraction behind the steering wheel have a high fatality rate because of the marked decline in the driver’s abilities of perception, recognition, and vehicle control abilities while being sleepy. Prof. Lin's contributions to this area came by his work is constructing the virtual-reality (VR) based driving environment to finish a series of brain dynamic studies in the realistic driving cognitive tasks including drowsiness, arousing feedback and distraction, where the Electroencephalographic (EEG) and behavioral data were simultaneously collected. In drowsiness related studies, experimental results indicated that monitoring the power spectra in theta (4-7 Hz) and alpha (8-12 Hz) range over the frontal, occipital, and parietal regions could assess subtle EEG dynamics in response to driver’s drowsiness level, which could lead to practical applications in noninvasive monitoring of the cognitive state of human operators in attention-critical settings. In arousing feedback study, he find that the occipital area is highly correlated with driver’s drowsiness. Observing the changes of the theta power activities with different drowsiness level, the effective warning of the arousing feedback can be reached around 35 seconds. After this period, driver will easily fall asleep into the drowsiness stage. Regarding driver’s distraction study, he find the power increases in the theta and beta bands in relation with distraction effects in the frontal cortex. Brain dynamics of handling the wheel are also observed in the motor area, especially in alpha and beta power suppressions.
Significance of Contribution
Prof. Lin's work demonstrated the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges. This research is the first to study the real-time cognitive state monitoring of drivers in realistic VR-based traffic situations. These findings can now be applied in the developments of the real-time driver’s cognitive state monitoring system and also be integrated with the car system in warning feedback. Also, the realistic driving environment was the first to construct for the driving cognitive tasks close to the real world applications.
This work resulted in the following publications:
1. C. T. Lin, S. A. Chen, T. T. Chiu, H. Z. Lin, and L. W. Ko, “Spatial and Temporal EEG Dynamics of Dual-Task Driving Performance,” Journal of NeuroEngineering and Rehabilitation (JNER), Vol. 8, No. 11, pp. 11-23, 2011. (IF: 2.638, 4/44 or 9.09% of Rehabilitation) 2. C. T. Lin, K. C. Huang, C. F. Chao, J. A. Chen, T. W. Chiu, L. W. Ko, and T. P. Jung, “Tonic and Phasic EEG and Behavioral Changes Induced by Arousing Feedback,” NeuroImage, Vol. 52, No. 2, pp. 633-642, August 2010. (IF: 5.937, 3/113 or 2.7% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 3. C. T. Lin, L. W. Ko, and T. K. Shen, “Computational Intelligent Brain Computer Interaction and Its Applications on Driving Cognition,” IEEE Computational Intelligence Magazine, Vol. 4, No. 4, pp. 32-46, November 2009. (IF: 2.622, 19/102 or 18.6% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 4. C. T. Lin, T. T. Chiu, T. Y. Huang, C. F. Chao, W. C. Liang, S. H. Hsu, and L. W. Ko, “Assessing Effectiveness of Various Auditory Warning Signals in Maintaining Drivers” Attention in Virtual Reality-based Driving Environments,” Perceptual and Motor Skills, Vol. 108, pp. 825-835, June 2009. (IF: 0.492, 77/81, Top 95.06%) 5. C. T. Lin, Y. C. Chen, T. Y. Huang, T. T. Chiu, L. W. Ko, S. F. Liang, H. Y. Hsieh, S. H. Hsu, and J. R. Duann, “Development of Wireless Brain Computer Interface with Embedded Multi-task Scheduling and its Application on Real-time Driver’s Drowsiness Detection and Warning,” IEEE Transactions on Biomedical Engineering, Vol. 55, No. 5, pp.1582-1591, May 2008. (EI and SCI) (IF: 2.154, 22/59 or 37.2% of ENGINEERING, BIOMEDICAL) 6. C. T. Lin, K. L. Lin, L. W. Ko, S. F. Liang, B. C. Kuo, and I. F. Chung, “Nonparametric Single-trial EEG Feature Extraction and Classification of Driver's Cognitive Responses,” EURASIP Journal on Advances in Signal Processing, Vol. 2008, Article ID 849040, 10 pages, DOI: 10.1155/2008/849040, March 2008. (IF: 0.885, 132/245 or 53.8% of ENGINEERING, ELECTRICAL & ELECTRONIC) 7. C. T. Lin, I. F. Chung, L. W. Ko, Y. C. Chen, S. F. Liang, and J. R. Duann, “EEG-based Assessment of Driver Cognitive Responses in a Dynamic Virtual-Reality Driving Environment,” IEEE Transactions on Biomedical Engineering, Vol. 54, No. 7, pp. 1349-1352, July 2007. (IF: 2.154, 22/59 or 37.2% of ENGINEERING, BIOMEDICAL) 8. C. T. Lin, L. W. Ko, I. F. Chung, T. Y. Huang, Y. C. Chen, T. P. Jung, and S. F. Liang, “Adaptive EEG-based Alertness Estimation System by Using ICA-based Fuzzy Neural Networks,” IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 53, No. 11, pp. 2469-2476, November 2006. (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 9. C. T. Lin, R. C. Wu, S. F. Liang, W. H. Chao, Y. J. Chen, and T. P. Jung, "EEG-based Drowsiness Estimation For Safety Driving Using Independent Component Analysis," IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 52, pp. 2726-2738, 2005. (IF: 1.420, 80/245 or 32.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) 10. Y. K. Wang, S. A. Chen, and C. T. Lin*, “An EEG-Based Brain-Computer Interface for Dual Task Driving Detection,” Neurocomputing, Accepted in 2012. (IF: 1.580, 39/11 or 35.1% of COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) 11. C. T. Lin, K. C. Huang, C. H. Chuang, L. W. Ko and T. P. Jung, “Can arousing feedback rectify lapses in driving? Prediction from EEG power spectra,” Journal of Neural Engineering, Vol.10, No. 5, pp. 1-10, 2013. (EI and SCI) (IF: 3.282, 12/79 or 15.19% of ENGINEERING, BIOMEDICAL) 12. C. H. Chuang, L. W. Ko, Y. P. Lin, C. T. Lin, T. P. Jung, “Independent component ensemble for brain-computer interface,” accepted by IEEE Transactions on Neural Systems and Rehabilitation Engineering, Aug. 2013. (IF: 3.255, 3/64 or 4.69% of REHABILITATION) 13. C. H Chuang, L. W Ko, T. P. Jung, and C. T. Lin, “Kinesthesia in a Sustained-Attention Driving Task,” NeuroImage, Vol. 91, pp. 187-202, May 2014. (IF: 5.895, 3/116 or 2.59% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 14. Y. T. Wang, K. C. Huang, C. S. Wei, T. Y. Huang, L. W. Ko, C. T. Lin, C. K. Cheng, and T. P. Jung, “Developing An EEG-Based On-Line Closed-Loop Lapse Detection and Mitigation System,” Frontiers in Neuroscience, Vol. 8, Issue 321, doi:10.3389, October, 2014.
Description of Contribution
Motion sickness (MS) and spatial navigation are the common experience of numerous people and has motivated extensive physiological, neurophysiological and psychophysiological research. The main challenge to study such driving related cognitive tasks in the real operational environment is to develop an easy-to-operate online rating mechanism sought to record continuously the level of motion sickness in subjects. Prof. Lin's work demonstrates a VR-based driving environment that comprises a 32-channel EEG system and a joystick with a continuous scale, by which subjects can continuously report their behavior responses such as level of motion sickness and spatial navigation performance during experiments. This research showed that five MS-related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta (4-7 Hz) and delta (1-3 Hz) bands; the occipital midline components exhibited a broadband power increase. These findings can be seen as the main indicators of the brain dynamics in motion sickness in the realistic environment. In spatial navigation study, participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. This research found that parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. The different phenomenon between egocentric and allocentric subject can be determinated as the main indicator for the applications.
Significance of Contribution
This work is the first to establish the bridge between the basic neuroscience studies of driving motion sickness/spatial navigation and their real-life applications. Prof. Lin's work demonstrated (1) utilized both visual and vestibular stimuli to induce realistic motion sickness, (2) proposed a continuous rating mechanism using which subjects can report their MS level without interrupting the experiment, and (3) evaluated reproducible spectral changes in multiple brain areas that accompany fluctuations in the severity of motion sickness. In spatial navigation study, the main findings support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior.
This work resulted in the following publications:
1. Y. C. Chen, J. R. Duann, S. W. Chuang, C. L. Lin, L. W. Ko, T. P. Jung, and C. T. Lin, “Spatial and Temporal EEG Dynamics of Motion Sickness,” NeuroImage, Vol. 49, No. 3, pp. 2862-2870, February 2010. (IF: 5.937, 3/113 or 2.7% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 2. S. W. Chuang, L. W. Ko, Y. P. Lin, R. S. Huang, T. P. Jung, and C. T. Lin, "Co-modulatory Spectral Changes in Independent Brain Processes Are Correlated with Task Performance," accepted by NeuroImage, May 2012. (IF: 5.937, 3/113 or 2.7% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 3. T. C. Chiu, K. Gramann, L. W. Ko, J. R. Duann, T. P. Jung, and C. T. Lin, “Alpha Modulation in Parietal and Retrosplenial Cortex correlates with Navigation Performance,” Psychophysiology, Vol. 49, No. 1, pp. 43-55, 2012. (IF: 3.263, 14/73, 19.18% of Psychology) 4. C. T. Lin, T. Y. Huang, W. J. Lin, S. Y. Chang, Y. H. Lin, L. W. Ko, D. L. Hung, and E. C. Chang, “Gender Differences in Wayfinding in Virtual Environments with Global or Local Landmarks”, accepted by Journal of Environmental Psychology, 2012. (SSCI) (IF: 1.449, 39/120 or 32.5% of PSYCHOLOGY, MULTIDISCIPLINARY) 5. K. Gramann, J. T. Gwin, D. P. Ferris, K. Oie, T. P. Jung, C. T. Lin, L. D. Liao, and S. Makeig, “Cognition in Action: Imaging Brain/Body Dynamics in Mobile Humans”, Neurosciences, Vol. 22, No.6, pp. 593–608, 2011. (IF: 0.102, 182/185 or 98.37% of Clinical Neurology) 6. C. S. Wei, L. W. Ko, S. W. Chuang, T. P. Jung, and C. T. Lin, “EEG-based Evaluation System for Motion Sickness Estimation,” Proceedings of the 5th International IEEE EMBS Conference on Neural Engineering, Cancun, Mexico, April 27 – May 1, 2011. 7. L. W. Ko, C. S. Wei, S. W. Chuang, T. P. Jung, and C. T. Lin, “Development of A Motion Sickness Evaluation System Based on EEG Spectrum Analysis,” Proceedings of the 2011 IEEE International Symposium on Circuits and Systems (ISCAS 2011), Rio de Janeiro, Brazil, May 15–18, 2011. 8. L. W. Ko, C. S. Wei, T. P. Jung, and C. T. Lin, “Estimating The Level of Motion Sickness Based on EEG Spectra,” Proceedings of the 14th International Conference on Human-Computer Interaction (HCI 2011), Orlando, Florida, USA, July 9-14, 2011. 9. C. T. Lin, F. S. Yang, T. C. Chiou, L. W. Ko, J. R. Duann, and K. Gramann, “EEG-Based Spatial Navigation Estimation in a Virtual Reality Driving Environment,” Proceedings of the 9th IEEE International Conference on Bioinformatics and Bioengineering (BIBE2009), Taichung, Taiwan, June 22-24, 2009. 10. C. L. Lin, T. P. Jung, S. W. Chuang, J. R. Duann, C. T. Lin*, T. W. Chiu*, “Self-adjustments May Account for the Contradictory Correlations between HRV and Motion-sickness Severity,” International Journal of Psychophysiology, Accepted in 2012 (IF: 2.144, 34/75 or 45.3% PSYCHOLOGY) 11. C. T. Lin, S. F. Tsai and L. W. Ko, “EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment,” accepted by IEEE Transactions on Neural Networks and Learning Systems, April 2013. (IF: 3.766, 1/50 or 2% of COMPUTER SCIENCE, HARDWARE & ARCHITECTUR)
Description of Contribution
Learning a new movement or rehabilitation engineering often guided by external stimuli and feedback. In recent years, EEGs have increasingly been utilized as a means to study the neuronal mechanisms underlying behavioral changes associated with the acquisition of motor skills. Haptic feedback is now receiving increasing attention related to motor-skill learning due to its novelty and potential for real world applications. There was no EEG study reported to date explored the temporal brain dynamics and interactions between different brain regions during motor skill learning in conjunction with haptic feedback. Prof. Lin's work therefore investigates the temporal brain dynamics associated with haptic feedback in a visuomotor tracking task. The results of his work showed that in epochs with haptic feedback, components with equivalent dipoles in or near the right motor region exhibited greater alpha band power suppression. Components with equivalent dipoles in or near the left frontal, central, left motor, right motor, and parietal regions exhibited greater beta-band power suppression, while components with equivalent dipoles in or near the left frontal, left motor, and right motor regions showed greater gamma-band power suppression relative to non-haptic conditions. In contrast, the right occipital component cluster exhibited less beta-band power suppression in epochs with haptic feedback compared to non-haptic conditions. In summary, the results showed the six component clusters were significant increases in coherence between different brain networks in response to haptic feedback relative to the coherence observed when haptic feedback was not present. In addition, rehabilitation engineering in clinical studies, neural prosthetic technologies have helped many patients by restoring vision, hearing, or movement and relieving chronic pain or neurological disorders. While most neural prosthetic systems to date have used invasive or implantable devices for patients with inoperative or malfunctioning external body parts or internal organs, a much larger population of healthy people who suffer episodic or progressive cognitive impairments in daily life can benefit from noninvasive neural prostheses such as the mobile and wireless brain computer interfaces Prof. Lin developed.
Significance of Contribution
Prof. Lin's work demonstrates that haptic feedback improves the behavioral performance and associates haptic feedback with behavioral improvement by describing a variety of spectral changes in different brain processes and the interactions between them. This is the first work never showed in the rehabilitation engineering field and provides the novel insight into the effects of haptic feedback on the brain and may aid the development of new tools to facilitate the learning of motor skills and rehabilitation engineering. In addition, he also demonstrated the use of mobile and wireless EEG BCI systems during gaming control to monitor the attention level. His developed system can be reliably used to control outside-world applications for general purposes. This device complements other existing BCI approaches for investigating the human cognitive states of neuronal activation and behavioral responses in daily life.
This work resulted in the following publications:
1. C. L. Lin, F. Z. Shaw, K. Y. Young, C. T. Lin, and, T. P. Jung, “EEG Correlates of Haptic Feedback in A Visuomotor Tracking Task,” NeuroImage, Vol. 60, No. 4, pp. 2258-2273, 2012. (IF: 5.937, 3/113 or 2.7% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 2. L. D. Liao, C. Y. Chen, I. J. Wang, S. F. Chen, S. Y. Li, B. W. Chen, J. Y. Chang, and C. T. Lin, “Gaming Control Using A Wearable and Wireless EEG-Based Brain-Computer Interface Device with Novel Dry Foam-Based Sensors,” Journal of NeuroEngineering and Rehabilitation (JNER), Vol. 9, No. 5, 2012. (IF: 2.638, 4/44 or 9.09% of REHABILITATION) 3. T. T. Chiu, C. L. Lin, K. Y. Young, C. T. Lin, S. H. Hsu, B. S. Yang, and, Z. R. Huang, “A Study of Fitts' Law on Goal-Directed Aiming Task with Moving Targets. Perceptual and Motor Skills,” Vol. 113, pp. 339-352, 2011. (IF: 0.492, 77/81 or 95% of PSYCHOLOGY, EXPERIMENTAL) 4. C. T. Lin, C. L. Lin, K. C. Huang, S. A. Chen, and J. H. Tung, “The Performance of Visuo-Motor Coordination Changes Under Force Feedback Assistance System,” Proceedings of the 2010 IEEE International Symposium on Circuits and Systems (ISCAS 2010), pp. 1376-1379, Paris, France, May 30 – June 2, 2010. 5. S. L. Wu, L. D. Liao, S. W. Lu, W. L. Jiang, S. A. Chen, and C. T. Lin* “Controlling a Human-Computer Interface System with a Novel Classification Method that uses Electrooculography Signals,” IEEE Transactions on Biomedical Engineering (TBME) , Vol. 60, No. 8, pp. 2133-2141, Aug. 2013. (IF: 2.278, 22/72 or 30% of ENGINEERING, BIOMEDICAL)
Description of Contribution
EEG is the only brain imaging modality with high temporal and fine (potentially cm2-scale) spatial resolution that is lightweight enough to be worn in operational settings. The lack of availability of EEG monitoring system capable of high-definition recording, online signal processing and artifact cancellation, without use of conductive gels applied to the scalp, has long thwarted the applications of EEG monitoring in the workplace. Because of current limitations in size, weight, and cost of available EEG systems, early brain-computer interface (BCI) systems have been designed to use a minimal number of recording channels. To attacked this problem, Prof. Lin has designed, built and tested ultra-lightweight, wearable, wireless, low-cost, whole-head, microelectronic EEG systems with successively higher sensor densities, called Mindo systems. (Please see the website of Mindo: http://www.mindo.com.tw/en/index.php) He had developed Mindo-2, Mindo-4, Mindo-16, and Mindo-32 systems in which the number means the number of EEG channels. Mindo-2 and 4 systems have been successfully applied to attention monitoring tasks and sleep quality scoring, and delivered to several collaborators and technical transferred to the industries. Mindo-16 and 32 systems are delivered to the academic research organization for performing the neuroergonomic research in the real world environment. Results of these studies provide many new insights into the understanding of complex brain functions of participants performing ordinary/routine tasks in a minimum constrained environment. These results also allow a better appreciation of the limitations of normal human performance in repetitive task environments, and may allow more detailed study of changes in cognitive dynamics in brain-damaged, diseased, or genetically abnormal individuals. This work will open a new chapter in neuro-cognitive human-machine interface/interaction.
Significance of Contribution
Data collection in most EEG studies requires skin preparation and gel application to ensure good electrical conductivity between sensor and skin. These procedures are time consuming, uncomfortable, and even painful for participants since skin preparation usually involves abrasion of the outer skin layer. Further, the signal quality may degrade over time as the skin regenerates and the conductive gel becomes dry. This work demonstrates that the developed dry EEG sensors do not require the skin preparation and conductive gel. The signal qualities of the dry EEG sensors are highly consistent with the commercial wet EEG sensors both in temporal and frequency domain. The developed mobile and wireless EEG systems are miniaturized, non-invasive and easily donned and doffed with implementing online signal-processing techniques to continuously and accurately extract the meaningful physiological information. This work is the first innovative and world leading technique, and can also be employed in clinical trials to monitor/detect patients' EEG brain signals at home. Now, the Mindo systems are well known in this world: Mindo users are around the world! http://mindo.com.tw/en/info.php?act=view&no=13.
This work resulted in the following publications:
1. L. D. Liao, C. T. Lin, K. McDowell, A. Wickenden, K. Gramann, T. P. Jung, L.W. Ko, and J. Y. Chang, “Biosensor Technologies for the Augmented Brain-Computer Interface in the Next Decades," Proceedings of the IEEE, Vol. 100, 100th Anniversary Issue, pp. 1553-1566, May 2012, 2012. (IF: 5.151, 4/247, Top 1.6% of Engineering, Electrical & Electronic) 2. C. T. Lin, L. D. Liao, Y. H. Liu, I. J. Wang, B. S. Lin, and J. Y. Chang, “Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement,” IEEE Transactions on Biomedical Engineering, Vol.58, No.5, pp. 1200-1207, May 2011. (IF: 1.790, 32/70 of Engineering, Biomedical) 3. L. D. Liao, I J. Wang, S. F Chen, J. Y. Chang, and C. T. Lin, “Design, Fabrication and Experimental Validation of a Novel Dry Contact Sensor for Measuring Electroencephalography Signals without Skin Preparation,” Sensors, Vol. 11, No. 6, pp. 5819-5834, June 2011. (IF: 1.774, 14/61 or 22.95% of Instruments & Instrumentation) 4. C. T. Lin, C. J. Chang, B. S. Lin, S. H. Hung, C. F. Chao, and I. J. Wang, “A Real-time Wireless Brain Computer Interface System for Drowsiness Detection,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 4, pp. 214-222, Aug. 2010. (EI and SCI) (IF: 1.689,61/247 or 24.7% of ENGINEERING, ELECTRICAL & ELECTRONIC) 5. C. T. Lin, L. W. Ko, M. H. Chang, J. R. Duann, J. Y. Chen, T. P. Su, and T. P. Jung, “Review of Wireless and Wearable Electroencephalogram Systems and Brain-Computer Interfaces - A Mini-Review,” Gerontology, Vol. 56, pp. 112-119, DOI: 10.1159/000230807, 2010. (IF: 1.661, 20/44 or 45.4% of GERIATRICS & GERONTOLOGY) 6. C. T. Lin, L. W. Ko, J. C. Chiou, J. R. Duann, R. S. Huang, T. W. Chiu, S. F. Liang, and T. P. Jung, “Noninvasive Neural Prostheses Using Mobile & Wireless EEG,” Proceedings of the IEEE, vol. 96, no. 7, pp. 1167-1183, July 2008. (IF: 4.878, 2/245 or 0.8% of ENGINEERING, ELECTRICAL & ELECTRONIC) 7. C. T. Lin, Y. C. Chen, T. Y. Huang, T. T. Chiu, L. W. Ko, S. F. Liang, H. Y. Hsieh, S. H. Hsu, and J. R. Duann, “Development of Wireless Brain Computer Interface with Embedded Multitask Scheduling and its Application on Real-time Driver’s Drowsiness Detection and Warning,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 5, pp. 1582-1591, May 2008. (IF: 2.154, 22/59 or 37.2% of ENGINEERING, BIOMEDICAL) 8. C. T. Lin, W. R. Wang, I. J. Wang, L. D. Liao, S. F. Chen, K. C. Tseng, and L. W. Ko, “A New Design of the Multi-channels Mobile and Wireless EEG System,” Proceedings of the 14th International Conference on Human-Computer Interaction (HCI 2011), Orlando, Florida, USA, July 9-14, 2011. 9. C. T. Lin, L. W. Ko, C. J. Chang, Y. T. Wang, C. H. Chung, F. S. Yang, J. R. Duann, T. P. Jung, and J. C. Chiou, “Wearable and Wireless Brain-Computer Interface and Its Applications,” Proceedings of the 13th International Conference on Human-Computer Interaction (HCI 2009), San Diego, CA, USA, July 19-24, 2009. 10. L. D. Liao, H. Y. Lai, C. T. Lin, and Y. Y. Chen, “Design and Experimental Verification of a Novel Light-addressable Multi-electrode Arrays Chip for Multi-channel Neural Signal Recording,” Journal of Neuroscience and Neuroengineering, Accepted in 2012. 11. L. D. Liao and C. T. Lin*, “Development Trends of the Biosensors for Electroencephalography Measurements in Cognitive Neuroscience Applications,” Journal of Neuroscience and Neuroengineering, Accepted in 2012. 12. K. Mcdowell, C. T. Lin, K. S. Oie, T. P. Jung, S. Gordon, K. W. Whitaker, S. Y. Li, S. W. Lu, W. D. Hairston, "Real-World Neuroimaging Technologies," Access, IEEE , vol.1, no., pp.131,149, 2013. 13. L. D. Liao, S. L. Wu, C. H. Liou, S. W. Lu, S. A. Chen, L. W. Ko, S. F. Chen and C. T. Lin, “A Novel 16-Channel Wireless System for Electroencephalography Measurements with Dry Spring-Loaded Sensors,” accepted by IEEE Transactions on Instrumentation and Measurement, 2013. (EI and SCI) (IF: 1.357, 94/242 or 38.84% of ENGINEERING, ELECTRICAL & ELECTRONIC) 14. L. D. Liao, B. W. Chen, K. C. Tseng, L. W. Ko, I. J. Wang, J. Y. Chang, and C. T. Lin, “Design and Implementation of a Wearable and Wireless Multi-channel Electroencephalography (EEG)-based Brain-Computer Interface Device with the Novel Dry Sensors,” accepted by Sensor Letters, 2012. (EI and SCI) (IF: 0.819, 23/27 or 85.18% of ELECTROCHEMISTRY)
Description of Contribution
Physiological signals (like EEG and ECG) detection is the powerful non-invasive tool widely used for both medical diagnosis and neurobiological research because it can provide high temporal resolution in milliseconds which directly reflects the dynamics of the generating cell assemblies. EEG is also the only brain imaging modality that can be performed without fixing the head/body. Heart rate variability (HRV) through common ECG signal process can reflect the sympathetic and parasympathetic changes. Both physiological signals can be easily applied to daily life healthcare applications. After understanding the brain dynamic changes on several cognitive studies and clinical research, Prof. Lin further developed the real-time bio-inspired expert systems based on the computational intelligent technologies for daily healthcare and homecare applications such as sleepy quality monitoring and classification, fatigue prediction, cardiological arrhythmia classification, and smart living environment control. The expertise knowledge inside the expert system learned from his developed algorithm is highly consistent with the clinical medical doctors’ expertise domain knowledge. Furthermore, the expert systems could learn and discover the knowledge which was not aware to the medical doctors. The developed expert system can also be generalized to apply to a wide range of individual subjects. For instance, the developed mobile wireless ECG system to detect atrial fibrillation has been promoted and applied to chronic cardiology patients by many cardiologists in Taiwan hospitals. Nissan Company of Japan is also highly interested in my designed EEG-based drowsiness monitoring system and is discussing with him the technology transfer now.
Significance of Contribution
Prof. Lin developed EEG and ECG-based expert systems imbedded with doctors' knowledge and self-learning ability had demonstrated their high accuracy and reliability in clinics applications. The developed expert systems were also integrated with his invented mobile wireless EEG and ECG systems mentioned in the above for daily homecare applications. Extensive clinical studies demonstrated that the developed novel wireless, ambulatory, real-time, and auto-alarm intelligent telecardiology system can efficiently improve the healthcare of cardiovascular disease, which is one of the most prevalent and costly health problems in the world. The acquired ECG signals are instantaneously transmitted to mobile devices, such as notebooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel who is tracking cardiac-rhythm disorders.
This work resulted in the following publications:
1. F. C. Lin, L. W. Ko, C. H. Chuang, and C. T. Lin, “Generalized EEG-based Drowsiness Prediction System by Using a Self-Organizing Neural Fuzzy System”, accepted by IEEE Transactions on Circuits and Systems I-REGULAR PAPERS, December 2011. (IF: 1.580, Top 25.91%, 64/247 of Engineering, Electrical & Electronic) 2. C. T. Lin, K. C. Chang, C. L. Lin, C. C. Chiang, S. W. Lu, S. S. Chang, B. S. Lin, H. Y. Liang, R. J. Chen, Y. T. Lee, and L. W. Ko, “An Intelligent Telecardiology System Using a Wearable and Wireless ECG to Detect Atrial Fibrillation,” IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 3, pp. 726-733, May 2010. (IF: 1.707, 37/128, Top 28.9% of Computer Science, Information Systems) 3. C. T. Lin, F. C. Lin, S. A. Chen, S. W. Lu, T. C. Chen, and L. W. Ko, “EEG-based Brain-Computer Interface for Smart Living Environmental Auto-adjustment,” Journal of Medical and Biological Engineering, vol. 30, No. 4, pp. 237-245, 2010. (Elected as the Best Annual Paper Award in the Taiwanese Society of Biomedical Engineers 2010) (IF: 0.420, 63/70 or 90% of ENGINEERING, BIOMEDICAL) 4. C. T. Lin, Y. C. Chen, T. Y. Huang, T. T. Chiu, L. W. Ko, S. F. Liang, H. Y. Hsieh, S. H. Hsu, and J. R. Duann, “Development of Wireless Brain Computer Interface with Embedded Multi-task Scheduling and its Application on Real-time Driver’s Drowsiness Detection and Warning,” IEEE Transactions on Biomedical Engineering, Vol. 55, No. 5, pp.1582-1591, May 2008. (IF: 1.790, 32/70 of Engineering, Biomedical) 5. N. R. Pal, C. Y. Chuang, L. W. Ko, C. F. Chao, T. P. Jung, S. F. Liang, and C. T. Lin, “EEG-based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach,” EURASIP Journal on Advances in Signal Processing, Vol. 2008, Article ID 519480, 11 pages, DOI: 10.1155/2008/519480, July 2008. (IF: 0.885, 132/245 or 53.8% of ENGINEERING, ELECTRICAL & ELECTRONIC) 6. C. T. Lin, S. W. Lu, S. A. Chen, H. S. Pan, C. L. Lin, T. C. Chiu, Y. H. Yu, and L. W. Ko, “A Novel Telehealthcare Service System with Personal Health Record Management,” Proceedings of the 2010 International Symposium on Biomedical Engineering Society, I-Shou University, Kaohsiung, Taiwan, December 10-11, 2010. 7. C. T. Lin, K. C. Chang, S. W. Lu, C. L. Lin, B. S. Lin, and L. W. Ko, “A Wearable and Wireless ECG Monitoring System and Its Clinical Applications,” Proceedings of the IEEE Biomedical Circuits and Systems Conference (BioCAS 2009), Beijing, China, November 26-28, 2009. 8. Y. Y. Lin, J. Y. Chang, and C. T. Lin, “A TSK-type-based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications,” IEEE Transactions on Industrial Electronics, Vol. 61, No. 1, JAN. 2014. (IF: 5.160, 4/245 or 16.326% of ENGINEERING, ELECTRICAL & ELECTRONIC)
Description of Contribution
Speeding up the functional brain imaging is the ‘holy technology’ of cognitive neurosciences because of its compelling potential to localize and investigate functional or cognitive brain responses to realistic, rapidly presented stimuli. Advances in functional magnetic resonance imaging continue to result in improved spatial resolution, but temporal resolution—imaging the early response to any stimulus—remains a challenge. Prof. Lin developed a novel functional photoacoustic microscopy technique for detecting multiparameter functional responses evoked by peripheral stimuli. This technique is able to measure the total hemoglobin concentration (HbT), cerebral blood volume, hemoglobin oxygen saturation (SO2), and the transient hemodynamic response in single cortical vessels of mice with intact skulls. This transcranial imaging technique complements other existing neuroimaging approaches for longitudinal investigation of the hemodynamic response with high temporal resolution. It uses the optimal balance between spatial resolution, temporal resolution, and depth of imaging that is possible with ultrasonic imaging to achieve high intrinsic optical contrast while imaging through an intact skull. Thus, the method shows tremendous potential for small animal imaging for basic science research. One major publication about this innovative work ([5] below) was selected as a Feature Article highlighted with a commentary and Candidate Cover Figure of the Journal of Cerebral Blood Flow & Metabolism. This paper [5] was also commented by Dr. Nitish V Thakor in his Research Highlight article published in Journal of Cerebral Blood Flow & Metabolism, April 4, 2012.
Significance of Contribution
Prof. Lin's innovative work serves two purposes. First, it establishes the capabilities and utilities of the noninvasive photoacoustic microscopy system for brain function studies. Second, it contributes to the understanding of vascular response to functional stimuli. The technology shows compelling applications that evidently complements the other methodologies ranging from optical intrinsic signal imaging to blood oxygen level dependent functional magnetic resonance imaging. It can boost our understanding on the relationship between cognitive reactions and brain neuro-dynamics for solving related clinics as well as daily-life problems.
This work resulted in the following publications: 1. L. D. Liao, Paul C. P. Chao, J. T. Chen, W. D. Chen, W. H. Hsu, C. W. Chiu, and C. T. Lin, “A Miniaturized Electromagnetic Generator With Planar Coils and Its Energy Harvest Circuit,” IEEE Transactions on Magnetics, Vol. 45, No. 10, pp. 40216-4027, Oct. 2009. (EI and SCI) (IF: 1.061, 115/245 or 46.9% of ENGINEERING, ELECTRICAL & ELECTRONIC) 2. C. H. Tsai, L. D. Liao, Y. S. Luoa, Paul C.-P. Chao, E. C. Chen, H. F. Meng, W. D. Chen, S. K. Lin, and C. T. Lin, “Optimal Design and Fabrication of ITO/Organic Photonic Crystals in Polymer Light Emitting Diodes using a Focused Ion Beam,” Microelectronic Engineering, Vol. 87, Issues 5-8, pp. 1331-1335, May-Aug. 2010. (EI and SCI) (IF: 1.575, 66/247 or 26.7% of ENGINEERING, ELECTRICAL & ELECTRONIC) 3. L. D. Liao, M. L. Li, H. Y. Lai, Y. Y. Shih, Y. C. Lo, S. Tsang, Paul C.-P. Chao, C. T. Lin, F. S. Jaw, and Y. Y. Chen, “Imaging Brain Hemodynamic Changes During Rat Forepaw Electrical Stimulation Using Functional Photoacoustic Microscopy,” NeuroImage, Vol. 52, No. 2, pp. 562-570, Aug. 2010. (EI and SCI) (IF: 5.937, 3/113 or 2.7% of RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING) 4. H. Y. Lai, L. D. Liao, C. T. Lin, Y.Y. I. Shih, Y. Y. Chen, J. H. Hsu, H. F. Chen, S. Tsang, and J. Y. Chang, "Design, Simulation and Experimental Validations of a Novel Flexible Neural Probe for Deep Brain Stimulation and Multichannel Recording," Journal of Neural Engineering, Vol. 9, No. 15, 2012. (IF: 2.628, 16/70 of Engineering, Biomedical) 5. L. D. Liao, C. T. Lin, Y. Y. I. Shih, Timothy Q, Duong, M. L. Li, and Y. Y. Chen, “Transcranial Imaging of Functional Cerebral Hemodynamic Changes in Single Blood Vessels Using In Vivo Photoacoustic Microscopy,” accepted by Journal of Cerebral Blood Flow & Metabolism, 2012. (SCI) (IF: 4.522, 27/116 or 16.27% of ENDOCRINOLOGY & METABOLISM) [Selected as a Feature Article highlighted with a commentary and Candidate Cover Figure of this Journal][See also Research Highlight in the “Nitish V Thakor, April 4, Journal of Cerebral Blood Flow & Metabolism, 2012”] 6. L. D. Liao, C. T. Lin, Y. Y. I. Shih, H. Y. Lai, W. T. Zhao, T. Q. Duong, J. Y. Chang, Y. Y. Chen, and M. L. Li, "Investigation of the Cerebral Hemodynamic Response Function in Single Blood Vessels by Functional Photoacoustic Microscopy," Vol. 17(6), Journal of Biomedical Optics, June 2012. (SCI ) (IF: 3.188, 8/78 or 10.27% of OPTICS) 7. T. Y. Tu, Paul C. P. Chao, and C. T. Lin, "A New Liquid Crystal Lens with Axis-Tunability via Three Sector Electrodes," accepted by Journal of Microsystem Technologies, May 2012. (IF: 1.071, 115/247 or 46.6% of ENGINEERING, ELECTRICAL & ELECTRONIC) |