Browsing by Author "Chen, Chih-Yu"

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A Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degrees
(2014-01-01)
Wu, Shang-Lin
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Lin, Yang-Yin
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Liu, Yu-Ting
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Chen, Chih-Yu
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Lin, Chin-Teng
Fuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.
A Low-Complexity Joint Angle-of-Arrival and Channel Estimation for Single-Input-Multiple-Output OFDM Systems
(2015)
Chen, Chih-Yu
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Wu, Wen-Rong
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Gau, Chi-Shiang
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資訊工程學系
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電機學院
In future wireless communication system, beam-forming plays an important role in improving link quality. As a result, the angle information is required. In this paper, a low complexity joint angle-of-arrival (AoA) and channel estimation for single-input-multiple-output (SIMO) OFDM system is considered. By exploiting the sparsity of wireless channel, we first propose a compressive-sensing (CS) based channel estimation for pilot-assisted OFDM systems with multiple receive antennas. Then, a low-complexity method is proposed to conduct AoA estimation. Finally, simulations are provided to show the effectiveness of the proposed scheme.
A Novel Classification Method for Motor Imagery Based on Brain-Computer Interface
(2014)
Chen, Chih-Yu
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Wu, Chun-Wei
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Lin, Chin-Teng
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Chen, Shi-An
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電機學院
Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.
Common Spatial Pattern and Linear Discriminant Analysis for Motor Imagery Classification
(2013)
Wu, Shang-Lin
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Wu, Chun-Wei
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Pal, Nikhil R.
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Chen, Chih-Yu
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Chen, Shi-An
A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. We have also experimented with MI data generated in our lab. The proposed system is found to produce good results. In particular, using our EEG data for MI movements, we have obtained an average classification accuracy of 80% for two subjects using only 9 channels, without any feature selection. This proposed MI-based BCI system may be used in real life applications.
Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain-Computer Interface
(2017-02)
Wu, Shang-Lin
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Liu, Yu-Ting
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Hsieh, Tsung-Yu
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Lin, Yang-Yin
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Chen, Chih-Yu
A brain-computer interface (BCI) system using electroencephalography signals provides a convenient means of communication between the human brain and a computer. Motor imagery (MI), in which motor actions are mentally rehearsed without engaging in actual physical execution, has been widely used as a major BCI approach. One robust algorithm that can successfully cope with the individual differences in MI-related rhythmic patterns is to create diverse ensemble classifiers using the subband common spatial pattern (SBCSP) method. To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence levels for classifiers. The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for offline single-trial classification of MI and real-time control of a robotic arm using MI. This paper represents the first attempt to utilize fuzzy fusion technique to attack the individual differences problem of MI applications in real-world noisy environments. The results of this study demonstrate the practical feasibility of implementing the proposed method for real-world applications.
Improvement of bone-tendon fixation by porous titanium interference screw: A rabbit animal model
(2018-10-01)
Tsai, Pei-I
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Chen, Chih-Yu
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Huang, Shu-Wei
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Yang, Kuo-Yi
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Lin, Tzu-Hung
The interference screw is a widely used fixation device in the anterior cruciate ligament (ACL) reconstruction surgeries. Despite the generally satisfactory results, problems of using interference screws were reported. By using additive manufacturing (AM) technology, we developed an innovative titanium alloy (Ti6Al4V) interference screw with rough surface and inter-connected porous structure designs to improve the bone-tendon fixation. An innovative Ti6Al4V interference screws were manufactured by AM technology. In vitro mechanical tests were performed to validate its mechanical properties. Twenty-seven New Zealand white rabbits were randomly divided into control and AM screw groups for biomechanical analyses and histological analysis at 4, 8, and 12 weeks postoperatively; while micro-CT analysis was performed at 12 weeks postoperatively. The biomechanical tests showed that the ultimate failure load in the AM interference screw group was significantly higher than that in the control group at all tested periods. These results were also compatible with the findings of micro-CT and histological analyses. In micro-CT analysis, the bone-screw gap was larger in the control group; while for the additive manufactured screw, the screw and bone growth was in close contact. In histological study, the bone-screw gaps were wider in the control group and were almost invisible in the AM screw group. The innovative AM interference screws with surface roughness and inter-connected porous architectures demonstrated better bone-tendon-implant integration, and resulted in stronger biomechanical characteristics when compared to traditional screws. These advantages can be transferred to future interference screw designs to improve their clinical performance. The AM interference screw could improve graft fixation and eventually result in better biomechanical performance of the bone-tendon-screw construct. The innovative AM interference screws can be transferred to future interference screw designs to improve the performance of implants. (c) 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2633-2640, 2018.
Joint AoA and Channel Estimation for SIMO- OFDM Systems: A Compressive-Sensing Approach
(2016-05)
Chen, Chih-Yu
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Wu, Wen-Rong
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Gau, Chi-Shiang
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資訊工程學系
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電機學院
Millimeter-wave (mmWave) transmission has been considered in future fifth generation (5G) communication systems. Since the pathloss in mmWave is severe, beamforming with antenna arrays, an enabling technology in the 5G era, will become a must. To conduct receive beamforming, however, we need to know the information about the angle-of-arrival (AoA). In this paper, we consider joint AoA and channel estimation for single-input-multiple-output (SIMO) OFDM systems. As known, wireless channels are sparse, and this is particularly true for mmWave environments. Conventional compressive-sensing (CS) based channel estimation methods only consider single-input-single-output systems. We propose new matching-pursuit-based CS methods for channel estimation in SIMO-OFDM systems, using frequency-domain pilots. With the estimated channels, AoA\'s are then estimated by the maximum-likelihood method. Since a hidden parameter is involved in the problem, an expectation-maximization (EM) algorithm is then employed. The Cramer-Rao lower bound (CRLB) is also derived for the AoA estimation. Simulation results show that the proposed channel estimation can significantly outperform existing methods while the proposed AoA estimation attains the CRLB.
Joint AoD, AoA, and Channel Estimation for MIMO-OFDM Systems
(2018-07-01)
Chen, Chih-Yu
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Wu, Wen-Rong
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電機工程學系
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Department of Electrical and Computer Engineering
Beamforming with antenna arrays has been considered as an enabling technology in future wireless communication systems. To conduct beamforming, one has to know the angle-of-departure (AoD) or angle-of-arrival (AoA). For data detection, the receiver also has to know channel response. In this paper, we propose a new joint. AoD, AoA, and channel estimation scheme for pilot-assisted multiple-input-multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) systems. First, a compressive-sensing technique is employed to estimate the channel impulse response, exploiting the sparsity property of wireless channels. Then, AoA and AoD are jointly estimated for each detected path by the maximum likelihood method. The Cramer-Rao lower hound (CRLB) is also derived and a transmit beamforming scheme is proposed accordingly. In the scenario of available prior information, a maximum a posteriori estimation is proposed. The Bayesian CRLB (BCRLB) for the problem is also derived and a transmit beamforming scheme is further proposed. It turns out that only two training OFDM symbols are required for the estimation. Simulation results show that the proposed methods can approach the CRLB/BCRLB in both scenarios and achieve the same spectral efficiency as that obtained with the ideal channel in millimeter-wave communications.
Three-Dimensional Positioning for LTE Systems
(2017-04-01)
Chen, Chih-Yu
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Wu, Wen-Rong
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電機工程學系
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Department of Electrical and Computer Engineering
In the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) specifications, several 2-D positioning methods have been proposed to support location-based services (LBSs). It has been shown that the observed time difference of arrival (OTDOA) method provides the best result. With the advent of the fifth-generation cellular systems, 3-D beamforming will be introduced, and 2-D positioning may not be sufficient for future applications. Although the conventional OTDOA method can be used in 3-D positioning, it requires more base stations (BSs) in cooperation and often performs poorly in typical cellular network geometry. In this paper, we propose a novel OTDOA-based method to solve the problems. First, we exploit the sparsity of the wireless channel and propose to use a compressive-sampling-based channel estimation method, which greatly enhances the precision of time-of-arrival (TOA) estimation. Then, we propose a transmit beamforming scheme such that the elevation angle of departure (AOD) can be accurately estimated. Finally, we combine the estimated TOAs and the AOD in the OTDOA scheme to conduct 3-D positioning. With the proposed scheme, the number of BSs required for 3-D positioning remains the same as that of 2-D, and the positioning accuracy greatly outperforms that of the conventional OTDOA method. Simulations show that 90% of the position error can be less than 7 m.
Three-Dimensional Printed Porous Titanium Screw with Bioactive Surface Modification for Bone-Tendon Healing: A Rabbit Animal Model
(2020-05-01)
Huang, Yu-Min
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Huang, Chih-Chieh
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Tsai, Pei-I
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Yang, Kuo-Yi
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Huang, Shin-I
The interference screw fixation method is used to secure a graft in the tibial tunnel during anterior cruciate ligament reconstruction surgery. However, several complications have been reported, such as biodegradable screw breakage, inflammatory or foreign body reaction, tunnel enlargement, and delayed graft healing. Using additive manufacturing (AM) technology, we developed a titanium alloy (Ti6Al4V) interference screw with chemically calcium phosphate surface modification technology to improve bone integration in the tibial tunnel. After chemical and heat treatment, the titanium screw formed a dense apatite layer on the metal surface in simulated body fluid. Twenty-seven New Zealand white rabbits were randomly divided into control and additive manufactured (AMD) screw groups. The long digital extensor tendon was detached and translated into a tibial plateau tunnel (diameter: 2.0 mm) and transfixed with an interference screw while the paw was in dorsiflexion. Biomechanical analyses, histological analyses, and an imaging study were performed at 1, 3, and 6 months. The biomechanical test showed that the ultimate pull-out load failure was significantly higher in the AMD screw group in all tested periods. Micro-computed tomography analyses revealed early woven bone formation in the AMD screw group at 1 and 3 months. In conclusion, AMD screws with bioactive surface modification improved bone ingrowth and enhanced biomechanical performance in a rabbit model.
Using a Novel LDA-Ensemble Framework to Classification of Motor Imagery Tasks for Brain-Computer Interface Applications
(2015-01-01)
Chiu, Ching-Yu
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Chen, Chih-Yu
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Lin, Yang-Yin
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Chen, Shi-An
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Lin, Chin-Teng
In this paper, we introduce a novel linear discriminate analysis (LDA) ensemble classifier utilizing the Mindo as a brain-computer interface (BCI) device to deal with the problem of motor imagery classification. With regard to the composition of the proposed system, we combine filter bank, sub-band common spatial pattern (SBCSP), LDA together for extracting features of EEG data and classifying the motor imagery with left or right states. In addition, we also employ a gradient descent (GD) algorithm to find the best weight associated with probability fusion function. This novel architecture not only boosts the accuracy of classification but maintains the computational efficiency of the system. Therefore, the proposed LDA-ensemble framework is able to be satisfied with each subject as demonstrated in Section III.
Wing-augmentation reduces femoral head cutting out of dynamic hip screw
(2017-06-01)
Chen, Chih-Yu
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Huang, Shu-Wei
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Sun, Jui-Sheng
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Lin, Shin-Yiing
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Yu, Chih-Sheng
The dynamic hip screw (DHS) is commonly used in the treatment of femoral intertrochanteric fracture with high satisfactory results. However, post-operative failure does occur and result in poor prognosis. The most common failure is femoral head varus collapse, followed by lag screw cut-out through the femoral head. In this study, a novel-designed DHS with two supplemental horizontal blades was used to improve the fixation stability. In this study, nine convention DHS and 9 Orthopaedic Device Research Center (ODRC) DHSS were tested in this study. Each implant was fixed into cellular polyurethane rigid foam as a surrogate of osteoporotic femoral head. Under biaxial rocking motion, all constructs were loaded to failure point (12 mm axial displacement) or up to 20,000 cycles of 1.45 kN peak magnitude were achieved, whichever occurred first. The migration kinematics was continuously monitored and recorded. The final tip-to-apex distance, rotational angle and varus deformation were also recorded. The results showed that the ODRC DHS sustained significantly more loading cycles and exhibited less axial migration in comparison to the conventional DHS. The ODRC DHS showed a significantly smaller bending strain and larger torsional strain compared to the conventional DHS. The changes in tip-to-apex distance (TAD), post-study varus angle, post-study rotational angle of the ODRC DHS were all significantly less than that of the conventional DHS (p < 0.05). We concluded that the ODRC DHS augmented with two horizontal wings would increase the bone-implant interface contact surface, dissipate the load to the screw itself, which improves the migration resistance and increases the anti-rotational implant effect. In conclusion, the proposed ODRC DHS demonstrated significantly better migration resistance and anti-rotational effect in comparison to the conventional DHS construct. (C) 2017 IPEM. Published by Elsevier Ltd. All rights reserved.