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Transfer function design for Fourier volume rendering and implementation using GPU
(2008-01-01)
Cheng, Chang-Chieh
;
Ching, Yu-Tai
;
資訊工程學系
;
Department of Computer Science
Volume rendering is a technique for volume visualization. Given a set of N x N x N volume data, the traditional volume rendering methods generally need O(N-3) rendering time. The FVR (Fourier Volume Rendering), that takes advantage of the Fourier slice theorem, takes O(N-2 log N) rendering time once the Fourier Transform of the volume data is available. Thus the FVR is favor to designing a real-time rendering algorithm with a preprocessing step. But the FVR has a disadvantage that resampling in the frequency domain causes artifacts in the spatial domain. Another problem is that the method for designing a transfer function is not obvious. In this paper, we report that by using the spatial domain zero-padding and tri-linear filtering can reduce the artifacts to an acceptable rendered image quality in spatial domain. To design the transfer function, we present a method that the user can define a transfer function by using a Bezier curve first. Based on the linear combination property of the Fourier transform and Bezier curve equation, the volume rendered result can be obtained by adding the weighted frequency domain signals. That mean, once a transfer function is given, we don't have to recompute the Fourier transform of the volume data after the transfer function applied. This technique makes real-time adjustment of transfer function possible.
Transfer function design for Fourier volume rendering and implementation using GPU - art. no. 691806
(2008)
Cheng, Chang-Chieh
;
Ching, Yu-Tai
;
資訊工程學系
;
Department of Computer Science
Volume rendering is a technique for volume visualization. Given a set of N x N x N volume data, the traditional volume rendering methods generally need O(N-3) rendering time. The FVR (Fourier Volume Rendering), that takes advantage of the Fourier slice theorem, takes O(N-2 log N) rendering time once the Fourier Transform of the volume data is available. Thus the FVR is favor to designing a real-time rendering algorithm with a preprocessing step. But the FVR has a disadvantage that resampling in the frequency domain causes artifacts in the spatial domain. Another problem is that the method for designing a transfer function is not obvious. In this paper, we report that by using the spatial domain zero-padding and tri-linear filtering can reduce the artifacts to an acceptable rendered image quality in spatial domain. To design the transfer function, we present a method that the user can define a transfer function by using a Bezier curve first. Based on the linear combination property of the Fourier transform and Bezier curve equation, the volume rendered result can be obtained by adding the weighted frequency domain signals. That mean, once a transfer function is given, we don't have to recompute the Fourier transform of the volume data after the transfer function applied. This technique makes real-time adjustment of transfer function possible.
Ordered lookup with bypass matching for scalable per-flow classification in layer 4 routers
(2001-04-01)
Lin, YD
;
Wei, HY
;
Wu, KJ
;
資訊工程學系
;
Department of Computer Science
In order to provide different service treatments to individual or aggregated hows, layer 4 routers in Integrated Services networks need to classify packets into different queues. The classification module of layer 4 routers must be fast enough to support gigabit links at a rate of millions of packets per second. In this work, we present a new software method OLBM to lookup multiple fields of a packet, in a dynamically pre-defined order, against the classification database. This algorithm also uses a technique called bypass matching and can classify packets at a rate of well over one million packets per second while scaling to support more than 300k flows. Complexity analysis and experiment measurements are also presented in this study. (C) 2001 Elsevier Science B.V. All rights reserved.
Bayesian classification for data from the same unknown class
(2002-04-01)
Huang, HJ
;
Hsu, CN
;
資訊工程學系
;
Department of Computer Science
In this paper, we address the problem of how to classify a set of query vectors that belong to the same unknown class. Sets of data known to be sampled from the same class are naturally available in many application domains, such as speaker recognition. We refer to these sets as homologous sets. We show how to take advantage of homologous sets in classification to obtain improved accuracy over classifying each query vector individually. Our method, called homologous naive Bayes (HNB), is based on the naive Bayes classifier, a simple algorithm shown to be effective in many application domains. HNB uses a modified classification procedure that classifies multiple instances as a single unit. Compared with a voting method and several other variants of naive Bayes classification, HNB significantly outperforms these methods in a variety of test data sets, even when the number of query vectors in the homologous sets is small. We also report a successful application of HNB to speaker recognition. Experimental results show that HNB can achieve classification accuracy comparable to the Gaussian mixture model (GMM), the most widely used speaker recognition approach, while using less time for both training and classification.
Integrated business agents for processing free text information
(2004-03-01)
Meng, IH
;
Tseng, SM
;
Yang, WP
;
資訊工程學系
;
Department of Computer Science
The Internet functions as an increasingly important channel for retailing commerce and business transactions. Although nearly five billion web sites exist on the World Wide Web, an integrated mediator, called a business agent, is sought to negotiate between suppliers and buyers. Such an intelligent business broker is thus necessary to support efficient and effective sharing of information. This work proposes an entirely new business agent architecture comprised of five elements, namely Business Spy Agent, Supply and Demand Analysis, Supply and Demand Classification, Matching and Negotiation, and Hidden Business Mining. The proposed architecture encompasses four trading processes, including Supply and demand identification, product brokering, merchant brokering and negotiation. The architecture introduces the notion of ontology to increase the precision of classification and more the effectiveness of negotiation. Experiments involving the proposed business agent show a significant successful pairing rate of 82% among suppliers and buyers. Simulation results indicate that the options, including Negotiable, Designated Brand and Split Demand, affect the match ratio differently from various perspectives.
Learning effective classifiers with Z-value measure based on genetic programming
(2004-10-01)
Chien, BC
;
Lin, JY
;
Yang, WP
;
資訊工程學系
;
Department of Computer Science
This paper presents a learning scheme for data classification based on genetic programming. The proposed learning approach consists of an adaptive incremental learning strategy and distance-based fitness functions for generating the discriminant functions using genetic programming. To classify data using the discriminant functions effectively, the mechanism called Z-value measure is developed. Based on the Z-value measure, we give two classification algorithms to resolve ambiguity among the discriminant functions. The experiments show that the proposed approach has less training time than previous GP learning methods. The learned classifiers also have high accuracy of classification in comparison with the previous classifiers. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
A 48.6-to-105.2 mu W Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications
(2014-04-01)
Hsu, Shu-Yu
;
Ho, Yingchieh
;
Chang, Po-Yao
;
Su, Chauchin
;
Lee, Chen-Yi
A machine- learning ( ML) assisted cardiac sensor SoC ( CS- SoC) is designed for mobile healthcare applications. The heterogeneous architecture realizes the cardiac signal acquisition, filtering with versatile feature extractions and classifications, and enables the higher order analysis over traditional DSPs. Besides, the asynchronous architecture with dynamic standby controller further suppresses the system active duty and the leakage power dissipation. The proposed chip is fabricated in a 90- nm standard CMOS technology and operates at 0.5 V- 1.0 V ( 0.7 V- 1.0 V for SRAM and I/ O interface). Examined with healthcare monitoring applications, the CS- SoC dissipates 48.6/ 105.2 mu W for real- time syndrome detections of ECG- based arrhythmia/ VCG- based myocardial infarction with 95.8/ 99% detection accuracy, respectively.
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
(2011-11-10)
Chen, Shih-Wei
;
Lin, Sheng-Huang
;
Liao, Lun-De
;
Lai, Hsin-Yi
;
Pei, Yu-Cheng
Background: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). Method: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. Results and Discussion: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. Conclusion: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.
Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses
(2011-10-01)
Huang, Guan-Hua
;
Wang, Su-Mei
;
Hsu, Chung-Chu
;
統計學研究所
;
Institute of Statistics
Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the conditional independence assumption underlying the latent variable model. The second stage then uses mixture modeling treating the class membership as known. The proposed approach is theoretically justifiable, directly checks the conditional independence assumption, and converges much faster than the full likelihood approach when analyzing high-dimensional data. This paper also develops a new classification rule based on latent variable models. The proposed classification procedure reduces the dimensionality of measured data and explicitly recognizes the heterogeneous nature of the complex disease, which makes it perfect for analyzing high-throughput genomic data. Simulation studies and real data analysis demonstrate the advantages of the proposed method.
A classification tree based on discriminant functions
(2006-05-01)
Chien, BC
;
Lin, JY
;
Yang, WP
;
資訊工程學系
;
Department of Computer Science
The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminant functions which are learned using genetic programming. A discriminant function is a mathematical function for classifying data into a specific class. To learn discriminant functions effectively and efficiently, a distance-based fitness function for genetic programming is designed. After the set of discriminant functions for all classes is generated. a classifier is created as a binary decision tree with the Z-value measure to resolve the problem of ambiguity among discriminant functions. Several popular datasets from the UCI Repository were selected to illustrate the effectiveness of the proposed classifiers by comparing with previous methods. The results show that the proposed classification tree demonstrates high accuracy on the selected datasets.

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