This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) signals. expectation-maximization (EM) algorithm was used for train...
详细信息
ISBN:
(纸本)0780387406
This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) signals. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.
Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide diagnosing of breast cancer. expectation-maximization (EM) algo...
详细信息
Multichannel audio is all emerging technology with continuously increasing applications. Audio reproduction through multiple channels has the advantage of recreating file acoustic Scene with unprecedented fidelity and...
详细信息
ISBN:
(纸本)0780391543
Multichannel audio is all emerging technology with continuously increasing applications. Audio reproduction through multiple channels has the advantage of recreating file acoustic Scene with unprecedented fidelity and of immersing the listener in an acoustic environment that is virtually indistinguishable from reality. However. one of the greatest challenges of this scheme is its high transmission requirements especially since accurate rendering through as many possible channels is the main purpose. This paper follows previous techniques Oil spectral conversion and it recently introduced concept called audio resynthesis. In audio resynthesis. a reference channel is transmitted and then used to recreate the remaining channels at the receiver. An alternative approach to audio resynthesis is presented based oil the Generalized Gaussian Mixture model. This model incorporates most of the standard Mixtures (Laplace. Gaussian etc) but this flexibility comes with high structural complexity due to file increased number of model parameters. A scheme is presented here that bypasses this issue and avoids the use of the expectation-maximization (EM) algorithm. A smoothing technique is also introduced which optimizes the performance during the spectral conversion stage and significantly improves file resynthesis results.
Conventional process monitoring based on principal component analysis (PCA) has been applied to many industrial chemical processes. However, such PCA-based approaches assume that the process is operating in a steady s...
详细信息
Conventional process monitoring based on principal component analysis (PCA) has been applied to many industrial chemical processes. However, such PCA-based approaches assume that the process is operating in a steady state and consequently that the process data are normally distributed and contain no time correlations. These assumptions significantly limit the applicability of PCA-based approaches to the monitoring of real processes. In this paper, we propose a more exact and realistic process monitoring method that does not require that the process data be normally distributed. Specifically, the concept of conventional PCA is expanded such that a Gaussian mixture model (GMM) is used to approximate the data pattern in the model subspace obtained by PCA. The use of a mixture of local Gaussian models means that the proposed approach can be applied to arbitrary datasets, not just those showing a normal distribution. To use the GMM for monitoring, the overall T-2 and Q statistics were used as the monitoring guidelines for fault detection. The proposed approach significantly relaxes the restrictions inherent in conventional PCA-based approaches in regard to the raw data pattern, and can be expanded to dynamic process monitoring without developing a complicated dynamic model. In addition, a GMM via discriminant analysis is proposed to isolate faults. The proposed monitoring method was successfully applied to three case studies: (1) simple two-dimensional toy problems, (2) a simulated 4 x 4 dynamic process, and (3) a simulated non-isothermal continuous stirred tank reactor (CSTR) process. These application studies demonstrated that, in comparison to conventional PCA-based monitoring, the proposed fault detection and isolation (FDI) scheme is more accurate and efficient. (C) 2003 Elsevier Ltd. All rights reserved.
One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments ...
详细信息
One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcala, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human - robot interfaces ( basically speech) and user - robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.
The simplified joint channel estimation and symbol detection based on the EM (expectation-maximization) algorithm for space-time block code (STBC) are proposed. By assuming channel to be invariant within only one STBC...
详细信息
The simplified joint channel estimation and symbol detection based on the EM (expectation-maximization) algorithm for space-time block code (STBC) are proposed. By assuming channel to be invariant within only one STBC word and utilizing the orthogonal structure of STBC, the computational complexity and cost of this algorithm are both very low, so it is very suitable to implementation in real systems.
A new joint channel estimation with ST-ring TCM codes suitable for QAM modulation is proposed. The signal-to-point mapping is carefully studied and methods for further improving performance of QAM ST codes are investi...
详细信息
A new joint channel estimation with ST-ring TCM codes suitable for QAM modulation is proposed. The signal-to-point mapping is carefully studied and methods for further improving performance of QAM ST codes are investigated. The criteria for the design of ST codes are developed to include other factors and results of an a priori computer search to find good QAM ST-ring TCM codes are provided.
This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble o...
详细信息
This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framework of the compound classification (CC) decision rule. To develop as uncorrelated as possible classification procedures, the estimates of statistical parameters of classifiers are carried out according to different approaches (i.e., multilayer perceptron neural networks, radial basis functions neural networks, and k-nearest neighbour technique). The outputs provided by different classifiers are combined according to three standard stratcaies extended to the compound classification case (i.e., Majority voting, Bayesian average, and Bayesian,weighted average). Experiments, carried out on a multitemporal. remote-sensing data set, confirm the effectiveness of the proposed system. (C) 2004 Elsevier B.V. All rights reserved.
In this paper we present a new framework for analyzing and segmenting point-sampled 3D objects. Our method first computes for each surface point the surface curvature distribution by applying the Principal Component A...
详细信息
ISBN:
(纸本)0769521401
In this paper we present a new framework for analyzing and segmenting point-sampled 3D objects. Our method first computes for each surface point the surface curvature distribution by applying the Principal Component Analysis on local neighborhoods with different sizes. Then we model in the four dimensional space the joint distribution of surface curvature and position features as a mixture of Gaussians using the expectationmaximizationalgorithm. Central to our method is the extension of the scale-space theory from the 2D domain into the three-dimensional space to allow feature analysis and classification at different scales. Our algorithm operates directly on points requiring no vertex connectivity information. We demonstrate and discuss the performance of our framework on a collection of point sampled 3D objects.
This paper presents it Turbo Multiuser Detector for Turbo-Coded DS-CDMA systems, based on the utilization of a PIC and a bank of turbo decoders, in which the PIC performs interference cancellation after each constitue...
详细信息
ISBN:
(纸本)0780383443
This paper presents it Turbo Multiuser Detector for Turbo-Coded DS-CDMA systems, based on the utilization of a PIC and a bank of turbo decoders, in which the PIC performs interference cancellation after each constituent decoder of the turbo decoding scheme. Moreover, the soft output of turbo decoders are used iteratively to improve the updating step of the channel parameter estimation which is formally equivalent to one step of the expectation-maximization algorithm. By means of computer simulations, we will show that the proposed receiver achieves performance comparable with systems which suppose perfect channel parameters knowledge for medium to high system loads. in AWGN channel. The proposed receiver is also tested in a satellite channel.
暂无评论