Browsing by Author "Lin, CT"

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1.3 mu m quantum dot vertical-cavity surface-emitting laser with external light injection
(2005-10-27)
Peng, PC
;
Chang, YH
;
Kuo, HC
;
Tsai, WK
;
Lin, G
A 1.3 mu m quantum dot vertical-cavity surface-emitting laser (QD VCSEL) with external light injection is presented, having been experimentally demonstrated. The QD VCSEL is fabricated on GaAs substrate. The 3 dB frequency response of the QD VCSEL based on the TO-Can package is enhanced from the free-running 1.75 to 7.44 GHz with the light injection technique.
A 3-D surface reconstruction approach based on postnonlinear ICA model
(2005-11-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
生物科技學系
;
資訊工程學系
Photometric stereo technique deals with the reconstruction of three-dimensional (3-D) shape of an object by using several images of the same surface taken from the same viewpoint but under illuminations from different directions. In this paper, we propose a new photometric stereo scheme based on a new reflectance model and the postnonlinear (PNL) independent components analysis (ICA) method. The proposed nonlinear reflectance model consists of diffuse components and specular components for modeling the surface reflectance of a stereo object in an image. Unlike the previous approaches, these two components are not separated and processed individually in the proposed model. An unsupervised learning adaptation algorithm is developed to estimate the reflectance model based on image intensities. In this algorithm, the PNL ICA method is used to obtain the surface normal on each point of an image. Then, the 3-D surface model is reconstructed based on the estimated surface normal on each point of image by using the enforcing integrability method. Two experiments are performed to assess the performance of the proposed approach. We test our algorithm on synthetically generated images for the reconstruction of surface of objects and on a number of real images captured from the Yale Face Database B. These testing images contain variability due to illumination and varying albedo in each point of surface of human faces. All the experimental results are compared to those of the existing photometric stereo approaches tested on the same images. The results clearly indicate the superiority of the proposed nonlinear reflectance model over the conventional Lambertian model and the other linear hybrid reflectance model.
A complex texture classification algorithm based on Gabor-type filtering cellular neural networks and self-organized fuzzy inference neural networks
(2005)
Lin, CT
;
Huang, CH
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a bio-inspired complex texture classification algorithm has been introduced. This algorithm included two neural networks; One is Gabor-type Filtering Cellular Neural Networks which used to simulate human retina, the other is Self-Organized Fuzzy Inference Neural Networks which used to simulate the brain. Both the neural networks can be considered as feed-forward neural networks. Thus, we can say that the whole system which has been introduced in this paper is also a feed-forward system and which contains the ability of parallel processing.
A fast lane and vehicle detection approach for autonomous vehicles
(2005)
Wu, BF
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Lin, CT
;
Chen, CJ
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Lai, TC
;
Liao, HL
This paper proposes a lane detection algorithm, a fuzzy-based vehicle detection approach and a distance measurement method for intelligent autonomous vehicles. The lane detection algorithm reduces computational load and has proven successful on highway and in urban settings at different velocities (110km/h and 50km/h, respectively). The proposed fuzzy-based vehicle detection approach named Contour Size Similarity (CSS), compares the contour size of objects by fuzzy rules. CSS efficiently distinguishes target obstacles, namely vehicles, from other objects detected and differentiates between obstacles and pattern on the road surface. After the nearest vehicle in the lane is found, we estimate its distance to the intelligent autonomous vehicle according to its position in the image. Then the information obtained would sever as aids for automatic driving of the intelligent autonomous vehicles.
A fuzzy vehicle detection based on contour size similarity
(2005)
Wu, BF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, the obstacle detection for autonomous vehicle is carried out by a monocular camera, which is capable of detecting multiple obstacles, identifying the difference between obstacles and text or shadows on the road surface The task of detection can be fulfilled under the circumstances of general brightness or in the presence of strong sunshine. An obstacle detection approach named Contour Size.Similarity (CSS) is developed in this paper. CSS is a way to make use of, the contour size of objects, and compare the similarity between objects and obstacles detected by means of fuzzy so as to judge whether the objects in the images are the obstacles we want to detect. The I.obstacles in this paper mainly refer to vehicles. Vehicles and obstacles with similar contour of vehicles can be detected by CSS. Then, we use section division to find out the nearest obstacle in separate section to provide necessary information for autonomous vehicles.
A GA-based fuzzy adaptive learning control network
(2000-05-16)
Chung, IF
;
Lin, CJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. A structure/parameter learning algorithm, called FALCON-GA, is proposed for constructing the FALCON automatically. The FALCON-GA is a three-phase hybrid learning algorithm. In the first phase, the fuzzy ART algorithm is used to do fuzzy clustering in the input/output spaces according to the supervised training data. In the second phase, the genetic algorithm (GA) is used to find proper fuzzy logic rules by associating input clusters and output clusters. Finally, in the third phase, the backpropagation algorithm is used for tuning input:output membership functions. Hence, the FALCON GA combines the backpropagation algorithm for parameter learning and both the fuzzy ART and GAs for structure learning. It can partition the input/output spaces, tune membership functions and find proper fuzzy logic rules automatically. The proposed FALCON has two important features. First, it reduces the combinatorial demands placed by the standard methods for adaptive linearization of a system. Second, the FALCON is a highly autonomous system. In its learning scheme, only the training data need to be provided from the outside world. The users need not give the initial fuzzy partitions, membership functions and fuzzy logic rules. Computer simulations have been conducted to illustrate the performance and applicability of the proposed system. (C) 2000 Elsevier Science B.V. All rights reserved.
A HVS-directed neural-network-based approach for impulse-noise removal from highly corrupted images
(2003)
Lu, SM
;
Pu, HC
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper a novel two-stage noise removal algorithm to deal with fixed-value impulse noise is proposed In the first stage, the decision-based recursive adaptive median filter is applied to remove the noise cleanly and keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify pixels of the image into human perception sensitive class and non-sensitive class. A neural network is proposed to enhance the sensitive regions to perform better visual quality. According to the experiment results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions.
A low-cost and application-driven digital signal processor for speech/audio processing
(2004)
Chung, JF
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Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a new application-driven digital signal processor, called LASP24 (Low-cost Application-driven Speech Processor, 24-bit data width), for speech and audio signal processing is designed. Two applications, MELP and reverberation algorithms, are performed on LASP24. The developed emulator can quickly verify these two algorithms. The design has been integrated in the total area of 6.5 mm(2) by using UMC 0.18 mu m 1P6M technology and fabricated. The maximum clock frequency is about 100 MHz with a single 1.8V supply.
A model-based fuzzy logic controller with Kalman filtering for tracking mean arterial pressure
(2001-11-01)
Liu, SH
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a new noninvasive measurement method for tracking the tendency of mean arterial pressure (MAP) in the radial artery. The designed system consists of a tonometer, a microsyringe device, and a model-based fuzzy logic controller. The modified flexible diaphragm tonometer is to detect the continuous blood pressure waveform and vessel volume pulse. A precise mathematical model describing the interaction between the tonometer and artery is derived. To reach accurate measurement without distortion, a model-based fuzzy logic control system is designed to compensate the change of MAP by applying a counter pressure on the tonometer chamber through the microsyringe device. The proposed control system consists of a linear predictor, a Kalman filter, and a synthetic fuzzy logic controller (SFLC). The linear predictor is to estimate the MAPs changing tendency based on the identified arterial pressure-volume model and then to beat-to-beat adjust the function of SFLC. The Kalman filter is to reduce the physiologic and measurement disturbance of the vessel volume oscillation amplitude (VOA). The SFLC is composed of three parallel subcontrollers, each of which is a simple fuzzy logic controller, for processing the three changing states of the MAP: ascending, descending, and stabilizing states, respectively. The design of the fuzzy rules in each subcontroller is based on the oscillometric principle saying that the arterial vessel has the maximum compliance when the detected vessel volume pulse reaches its maximum amplitude. Simulation results show that, for the real physiologic MAP with changing rates up to 20 or -20 mm-Hg/minute, the model-based SFLC can beat-to-beat adjust the tonometer's chamber pressure to follow the tendency of MAP accurately.
A neural fuzzy network for word information processing
(2002-04-01)
Lin, CT
;
Duh, FB
;
Liu, DJ
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電控工程研究所
;
Institute of Electrical and Control Engineering
A neural fuzzy system learning with fuzzy training data is proposed in this study. The system is able to process and learn numerical information as well as word information. At first, we propose a basic structure of five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Also they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only word teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs. An experimental system is constructed to illustrate the performance and applicability of the proposed scheme. (C) 2002 Elsevier Science B.V. All rights reserved.
A neural fuzzy system for image motion estimation
(2000-09-01)
Lin, CT
;
Chung, IF
;
Sheu, LKM
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電控工程研究所
;
Institute of Electrical and Control Engineering
Many methods for computing optical flow (image motion vector) have been proposed while others continue to appear. Block-matching methods are widely used because of their simplicity and easy implementation. The motion vector is uniquely defined, in block-matching methods, by the best fit of a small reference subblock from a previous image frame in a larger, search region from the present image frame. Hence, this method is very sensitive to the real environments (involving occlusion, specularity, shadowing, transparency, etc.). In this paper, a neural fuzzy system with robust characteristics and learning ability is incorporated with the block-matching method to make a system adaptive for different circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing different degrees to the motion vector. This system is more reliable, robust, and accurate in motion estimation than many other methods including Horn and Schunck's optical flow, fuzzy logic motion estimator (FME), best block matching, NR, and fast block matching. Since fast block-matching algorithms can be used to reduce search time, a three-step fast search method is employed to find the motion vector in our system. However, the candidate motion vector is often trapped by the local minimum, which makes the motion vector undesirable. An improved three-step fast search method is tested to reduce the effect from local minimum and some comparisons about fast search algorithms are made. In addition, a Quarter Compensation Algorithm for compensating the interframe image to tackle the problem that the motion vector is not an integer but rather a floating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many different applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper. (C) 2000 Elsevier Science B.V. All rights reserved.
A neural fuzzy system with fuzzy supervised learning
(1996-10-01)
Lin, CT
;
Lu, YC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper, This system is able to process and learn numerical information as well as linguistic information, At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system, The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference, We use a-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape, Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons, Based on interval arithmetics, a fuzzy supervised learning algorithm; is developed for the proposed system, It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available, The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values, With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base, Simulation results are presented to illustrate the performance and applicability of the proposed system.
A neural fuzzy system with fuzzy supervised learning
(1996-10-01)
Lin, CT
;
Lu, YC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper, This system is able to process and learn numerical information as well as linguistic information, At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system, The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference, We use a-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape, Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons, Based on interval arithmetics, a fuzzy supervised learning algorithm; is developed for the proposed system, It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available, The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values, With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base, Simulation results are presented to illustrate the performance and applicability of the proposed system.
A neural network that learns from fuzzy data for language acquisition
(1996-12-01)
Lin, CT
;
Kan, MC
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Chung, IF
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper(a) proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for example, the phrase ''move forward'' represents the meaningful semantic action and the phrase ''very high speed'' represents the linguistic information in the fuzzy command ''Move forward in a very high speed.''). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on alpha-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.
A neuro-fuzzy combiner for multiobjective control
(2001)
Chung, IF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a neuro-fuzzy combiner (NFC) with supervised learning capability for solving multiobjective control problems, The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. Here a NFC can be designed by proposed architecture and supervised learning scheme. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.
A novel neural-network-based image resolution enhancement
(2003)
Pu, HC
;
Lin, CT
;
Liang, SF
;
Kumar, N
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電控工程研究所
In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.
A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system
(2004-02-01)
Lin, CT
;
Wu, RC
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Chang, JY
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Liang, SF
;
電控工程研究所
In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-constructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two,parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are on-line available for demonstration.
A recurrent fuzzy cellular neural network system with automatic structure and template learning
(2004-05-01)
Lin, CT
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Chang, CL
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Cheng, WC
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電控工程研究所
;
Institute of Electrical and Control Engineering
It is widely accepted that using a set of cellular neural networks (CNNs) in parallel can achieve higher level information processing and reasoning functions either from application or biologics points of views. Such an integrated CNN system can solve more complex intelligent problems. In this paper, we propose a novel framework for automatically constructing a multiple-CNN integrated neural system in the form of a recurrent fuzzy neural network. This system, called recurrent fuzzy CNN (RFCNN), can automatically learn its proper network structure and parameters simultaneously. The structure learning includes the fuzzy division of the problem domain and the creation of fuzzy rules and CNNs. The parameter learning includes the tuning of fuzzy membership functions and CNN templates. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. A new online adaptive independent component analysis mixture-model technique is proposed for the structure learning of RFCNN, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. The proposed RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the proposed RFCNN is demonstrated on the real-world defect inspection problems. Experimental results show that the proposed scheme is effective and promising.
A recurrent neural fuzzy network for word boundary detection in variable noise-level environments
(2001-02-01)
Wu, GD
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Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level mag vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level, In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al, from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information, Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent sell-organizing neural fuzzy inference network (RSONFIN). Since RSONFIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed, Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition. It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.
A recurrent self-organizing neural fuzzy inference network
(1997)
Juang, CF
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Lin, CT
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
A Recurrent Self-Organizing Neural Fuzzy Inference Network (RSONFIN) is proposed in this paper. The RSONFIN is constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Simulations on temporal problems are done finally.