Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communicatio...
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We present a tensor product formulation for Hilbert space-filling curves. Both recursive and iterative formulas are expressed. We view a Hilbert space-filling curve as a permutation which maps two-dimensional 2 n time...
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We present a tensor product formulation for Hilbert space-filling curves. Both recursive and iterative formulas are expressed. We view a Hilbert space-filling curve as a permutation which maps two-dimensional 2 n times2 n data elements stored in the row major or column major order to the order of traversing a Hilbert space-filling curve. The tensor product formula of Hilbert space-filling curves uses several permutation operations: stride permutation, radix-2 gray permutation, transposition, and antidiagonal transposition. The iterative tensor product formula can be manipulated to obtain the inverse Hilbert permutation. Also, the formulas are directly translated into computer programs which can be used in various applications including R-tree indexing, image processing, and process allocation, etc
In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were in...
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In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery. And learning methods were investigated. Also, correlation between C3 and C4 signals were investigated, and thereby which AR (combine AR or multivariable AR) model must be used in each EEC recording method.
Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communicatio...
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Electroencephalograph (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by e.g., handicap users with amyotrophic lateral sclerosis (ALS). In this study, statistical pattern recognition method based on AR model was introduced to discriminate the EEG signals recorded during right and left motor imagery. And learning methods (processing period for parameter estimation, AR order, etc.) were investigated. Finally, the effectiveness of our method was confirmed through the experimental studies.
Annotating maps, graphs, and diagrams with pieces of text is an important step in information visualization that is usually referred to as label placement. We define nine label-placement models for labeling points wit...
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Much work is being conducted in the area of business process modeling using workflow technology. HiWorD is a hierarchical workflow modeling prototype with simulation capability. It models business processes using Petr...
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Much work is being conducted in the area of business process modeling using workflow technology. HiWorD is a hierarchical workflow modeling prototype with simulation capability. It models business processes using Petri nets in a hierarchical manner and implements recovery transitions as a technique to recover from exceptions. The workflow hierarchy is created by refining places and transitions using predefined patterns. By using these patterns, it is proven that the resulting workflow will be sound.
A wearable intelligent platform for the monitoring of the health condition and rehabilitation of athletes is presented. The system provides doctors and trainers with real-time monitoring, alerting and medical decision...
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A wearable intelligent platform for the monitoring of the health condition and rehabilitation of athletes is presented. The system provides doctors and trainers with real-time monitoring, alerting and medical decision support. It aims to help them optimize treatment and training procedures during rehabilitation, to prevent injury relapses and to ensure a prompt return to peak athletic condition. The system's design requirements are laid out along with its functional description. It is composed of three main subsystems: the athlete subsystem, the rehabilitation station and the portal. The athlete subsystem includes all the monitoring sensors worn by the athlete during training, along with a signal collector and a processing-transceiver unit for preliminary evaluation and for exchanging information with the rehabilitation station. The rehabilitation station is the main control, access and communication point of the system carrying most of the processing. The measurements derived from the received signals are fed into an intelligent module, which provides with the monitoring, alerting and decision support mechanisms. A virtual reality interface is used by the athlete to communicate with the system. The portal is a long-term data storage facility collecting selected data for research purposes.
Given a query DNA sequence, our goal is to find in the DNA sequence database all the sequence segments that are similar' to the query. In this paper we present a stringto-signal transform technique that can transf...
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