Although dynamic non-dominated sorting algorithm II (DNSGA-II) is a well-known dynamic evolutionary multi-objective optimization algorithm, it lacks memory ability. For this reason, we propose a memory enhanced DNSGA-...
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In this study we show how healthy subjects are able to use a non-invasive Motor Imagery (MI)-based braincomputer Interface (BCI) to achieve linear control of an upper-limb neuromuscular electrical stimulation (NMES) ...
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In this study we show how healthy subjects are able to use a non-invasive Motor Imagery (MI)-based braincomputer Interface (BCI) to achieve linear control of an upper-limb neuromuscular electrical stimulation (NMES) controlled neuroprosthesis in a simple binary target selection task. Linear BCI control can be achieved if two motor imagery classes can be discriminated with a reliability over 80% in single trial. The results presented in this work show that there was no significant loss of performance using the neuroproshesis in comparison to MI where no stimulation was present. However, it is remarkable how different the experience of the users was in the same experiment. The stimulation either provoked a positive reinforcement feedback, or prevented the user from concentrating in the task.
To understand the function of networks we have to identify the structure of their interactions, but also interaction timing, as compromised timing of interactions may disrupt network function. We demonstrate how both ...
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ISBN:
(纸本)9781457717871
To understand the function of networks we have to identify the structure of their interactions, but also interaction timing, as compromised timing of interactions may disrupt network function. We demonstrate how both questions can be addressed using a modified estimator of transfer entropy. Transfer entropy is an implementation of Wiener’s principle of observational causality based on information theory, and detects arbitrary linear and non-linear interactions. Using a modified estimator that uses delayed states of the driving system and independently optimized delayed states of the receiving system, we show that transfer entropy values peak if the delay of the state of the driving system equals the true interaction delay. In addition, we show how reconstructed delays from a bivariate transfer entropy analysis of a network can be used to label spurious interactions arising from cascade effects and apply this approach to local field potential (LFP) and magnetoencephalography (MEG) data.
A new learning algorithm for categorical data, named CRN (Classification by Rule-based Neighbors) is proposed in this paper. CRN is a nonmetric and parameter-free classifier, and can be regarded as a hybrid of rule in...
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Currently, relatively popular and representative group key agreement management schemes are Group Diffie-Hellman (GDH), Centralized Key Distribution (CKD), Tree Group Diffie-Hellman (TGDH), Steer(STR) and Burmester-De...
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In this paper, we propose a novel method of frequency bands selection based on the analysis of a channel-frequency map, which we call 'channel-frequency map'. The spatial filtering, feature extraction, and cla...
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This paper addresses the denoising problem associated with diffusion MR imaging. Building on previous approaches to this problem, this paper presents a new method for joint denoising of a sequence of diffusion-weighte...
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ISBN:
(纸本)9781457718571
This paper addresses the denoising problem associated with diffusion MR imaging. Building on previous approaches to this problem, this paper presents a new method for joint denoising of a sequence of diffusion-weighted (DW) magnitude images. The proposed method uses a maximum a posteriori (MAP) estimation formulation to incorporate a Rician likelihood (for modeling the noisy magnitude data), a low rank model (for the DW image sequences) and a spatial prior (for imposing joint edge constraints). An efficient algorithm to solve the associated optimization problem is also described. The proposed method has been evaluated using both simulated and experimental diffusion tensor imaging (DTI) data, which yields very encouraging results both qualitatively and quantitatively.
In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and...
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ISBN:
(纸本)9783037851579
In order to overcome these deficiencies that computation of recognition algorithm based on template matching is very high and the recognition rate of recognition algorithms based on skin-color segmentation is low, and is vulnerable to the impact of background which is similar with skin-color, face recognition algrithom based on skin color segmentation and template matching is presented in this paper. According to the clustering properties that the skin-color of human faces have emerged in the YCb Cr color space, the regions closing to facial skin color are separated from the image by using Gaussian mixture model in order to achieve the purpose of rapidly detecting the external face of human face. Adaptive template matching is used to overcome the affect of the backgrounds which are similar with skin color on face recognition. Computation in the matching process is reduced by using the second matching algorithm. Extraction of face images by using singular value features is used to identify faces and to reduce the dimensions of the eigenvalue matrix in the course of facial feature extraction. Experimental results show that proposed method can rapidly recongnise human faces, and improve the accuracy of face recognition.
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