Abstract: A device for monitoring synchronous signals of a motor includes a detection module and a processor. The detection module includes a rotational speed detector and an electric detector. The rotational speed detector is adapted to detect a rotational speed of a motor to generate a rotational speed signal. The electric detector is adapted to detect a current value and a voltage value between the motor and a power supply to generate a current signal and a voltage signal, respectively. The processor is electrically connected to the detection module to receive the rotational speed signal, the current signal, and the voltage signal. The processor generates an analysis result according to instant values of the rotational speed signal, the current signal, and the voltage signal at a same time point. The detection accuracy of the operating status of the motor is, thus, increased.

Abstract: An electronic apparatus for establishing prediction model based on electroencephalogram (EEG). The electronic apparatus is configured for: acquiring an EEG signal segment related to an epilepsy patient; dividing each EEG signal into EEG components according to a predetermined window size; retrieving datasets corresponding to EEG features from the EEG components of each EEG signal segment; acquiring statistical feature values of each dataset of each EEG signal segment; determining a gain ratio of each of the statistical feature values of each EEG signal segment based on the statistical feature values corresponding to each of the EEG features; selecting specific statistical feature values from the statistical feature values according to the gain ratio of each of the statistical feature values of each EEG signal segment; establishing a prediction model based on the specific statistical feature values of the epilepsy patient.

Abstract: An evaluation device is adapted for providing a transceiver system with performance information thereof. The transceiver system models a channel between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a signal-to-noise ratio (SNR) computing module, a capacity computing module, and an output module. The SNR computing module is operable to set an average SNR for the channel between the transmitter and the receiver, and to compute an expected value and variance of an effective SNR of the transceiver system according to the fading parameter and the average SNR. The capacity computing module is operable to compute an outage capacity of the transceiver system based upon the expected value and the variance of the effective SNR and a transmission outage parameter. The output module is operable to provide the transceiver system with the average SNR and the outage capacity.

Abstract: A method for extracting feature vectors from a palm image includes the steps of: determining a palm contour in the palm image, and labeling pixels on the palm contour as contour pixels in order along the palm contour; determining a rotation angle of the palm contour relative to a coordinate system; obtaining corrected contour pixels to offset the rotation angle; determining a plurality of feature points from the corrected contour pixels; obtaining a plurality of sub-images from the palm image with reference to the feature points, one of the sub-images corresponding to a palm center and another one of the sub-images corresponding to a corresponding palm finger; and determining the feature vectors with reference to the sub-images, each of the feature vectors corresponding to a corresponding one of the sub-images.

Abstract: An evaluation device is configured to provide a transceiver system with performance information thereof. The transceiver system models a channel between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a setting module operable to set an average signal-to-noise rate (SNR) for the channel between the transmitter and the receiver, a computing module operable to estimate a symbol error rate related to a signal received by the receiver from the transmitter based upon the fading parameter and the average SNR, and an output module operable to provide the average SNR and the symbol error rate as the performance information of the transceiver system.

Abstract: A modeling method of neuro-fuzzy system including a rule-defining process and a network-building process is disclosed. The rule-defining process divides a plurality of training data into a plurality of groups to accordingly define a plurality of fuzzy rules, and the network-building process constructs a fuzzy neural network based on the fuzzy rules obtained by the rule-defining process. The provided modeling method of neuro-fuzzy system is capable of building a neuro-fuzzy system extremely similar to an original function that generates training data of the neuro-fuzzy system.

Abstract: An analysis method for turbine-generator torsional vibrations affected by power transmission system, which is processed by a computer system with a simulation software, is proposed. This analysis method comprises: building structures of a first system model and a second system model to respectively simulate a first system and a second system; building detailed models of the first and second system model; and analyzing the detailed models of the first and second system model in frequency- and time-domain.

Abstract: A system for computing machine parameters of an induction machine includes a relation providing module and a circuit parameter computing module. The relation providing module is operable to provide a relationship between slip rates and primary side resistances of the induction machine and a relationship between the slip rates and primary side reactances of the induction machine. The circuit parameter computing module is operable to perform a parameter computing process according to the relationships obtained from the relation providing module. The parameter computing process includes the steps of: a) generating a set of initial values of equivalent circuit parameters of the induction machine; b) updating the initial values; c) calculating a cumulative resistance error and a cumulative reactance error; and d) repeating steps b) and c) until the cumulative resistance error and the cumulative reactance error are smaller than first and second error thresholds, respectively.

Abstract: An electroencephalogram signal processing method includes a recording step, a retrieving step, a removing step and a synthesizing step. The recording step retrieves EEG signal components of a testee via a plurality of electrodes, wherein the EEG signal components serve as an input signal. The retrieving step filters the input signal to obtain a predetermined frequency band signal, and subtracts the predetermined frequency band signal from the input signal to obtain a difference signal. The removing step performs an independent component analysis operation between the difference signal and a separating matrix to obtain an analysis signal, generates a separating pseudo inverse and an independent analysis signal, and performs a matrix operation between the separating pseudo inverse and the independent analysis signal to obtain a corrected signal. The synthesizing step adds the corrected signal and the predetermined frequency band signal together to obtain an output signal.

Abstract: An evaluation device provides a transceiver system with performance information. The transceiver system models channels between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a setting module, a computing module and an output module. The computing module is operable, based upon the fading parameter, an average SNR of the channels, a number of transmit antennas and a number of receive antennas, to estimate an average output SNR, a bit error rate and an outage probability related to signals received by the receiver. The output module is operable to provide the transceiver system with the average SNR and the estimated information as the performance information.

Abstract: An OFDM beamformer of a signal receiving system receives multiple frequency-domain sample signal vectors each transformed from arrival signals received by a corresponding antenna element of an antenna array, and includes: multiple signal splitters each splitting a corresponding frequency-domain sample signal vector into a data signal subvector and a pilot signal subvector based on predetermined position information; a weight generator generating an estimated auto-correlation matrix and an estimated cross-correlation vector based on the data signal subvectors, the pilot signal subvectors, and a reference pilot signal vector associated with a desired user, and generating an optimal weight vector based on the estimated auto-correlation matrix and the estimated cross-correlation vector; and a combining unit combining, based on the optimal weight vector, serial data signals converted from each data signal vector by a corresponding parallel-to-serial converter to generate an estimated data symbol output correspondi

Abstract: A signal analyzer includes a sampling unit, a filter unit coupled to the sampling unit, a computing unit coupled to the filter unit, and an output unit coupled to the computing unit. The sampling unit is operable to sample a time domain signal from a target system according to a predetermined sampling frequency to obtain a sampling signal. The filter unit is configured to perform filter processing upon the sampling signal so as to filter out harmonic frequency components from the sampling signal, thereby obtaining a fundamental frequency signal having a plurality of sample points. The computing unit is operable to compute a signal parameter set for each of temporally adjacent pairs of the sample points of the fundamental frequency signal. The output unit is configured to output information about dynamic behavior of the target system based upon the signal parameter sets computed by the computing unit.

Abstract: A modeling method of neuro-fuzzy system including a rule-defining process and a network-building process is disclosed. The rule-defining process divides a plurality of training data into a plurality of groups to accordingly define a plurality of fuzzy rules, and the network-building process constructs a fuzzy neural network based on the fuzzy rules obtained by the rule-defining process. The provided modeling method of neuro-fuzzy system is capable of building a neuro-fuzzy system extremely similar to an original function that generates training data of the neuro-fuzzy system.

Abstract: An evaluation device is configured to provide a transceiver system with performance information thereof. The transceiver system models a channel between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a setting module operable to set an average signal-to-noise rate (SNR) for the channel between the transmitter and the receiver, a computing module operable to estimate a symbol error rate related to a signal received by the receiver from the transmitter based upon the fading parameter and the average SNR, and an output module operable to provide the average SNR and the symbol error rate as the performance information of the transceiver system.

Abstract: An evaluation device is adapted for providing a transceiver system with performance information thereof. The transceiver system models a channel between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a threshold value computing module, a signal-to-noise ratio (SNR) setting module, a probability computing module, and an output module. The threshold value computing module is operable to compute a threshold value based upon a given capacity. The SNR setting module is operable to set an average SNR for the channel between the transmitter and the receiver of the transceiver system. The probability computing module is operable, based upon the fading parameter, the average SNR and the threshold value, to compute an outage probability of the transceiver system corresponding to the given capacity. The output module is operable to provide the transceiver system with the average SNR and the outage probability.

Abstract: A method for determining an optimum sampling frequency to be performed by a power analyzer includes the following computer-implemented steps: sampling a time domain signal to obtain a sampling signal according to a predetermined sampling frequency; obtaining two reference sampling signals using higher and lower sampling frequencies compared to the predetermined sampling frequency; transforming the sampling signal and the reference sampling signals to frequency domain signals; computing a sum-of-amplitudes for each of the three frequency domain signals; estimating a minimum sum-of-amplitudes value and a corresponding re-sampling frequency; obtaining a new reference sampling signal using the re-sampling frequency; transforming the new reference sampling signal to a frequency domain signal, and computing a sum-of-amplitudes therefor; and re-estimating the minimum sum-of-amplitudes value and the corresponding re-sampling frequency.

Abstract: An evaluation device provides a transceiver system with performance information. The transceiver system models channels between a transmitter and a receiver thereof using Nakagami distribution with a fading parameter. The evaluation device includes a setting module, a computing module and an output module. The computing module is operable, based upon the fading parameter, an average SNR of the channels, a number of transmit antennas and a number of receive antennas, to estimate an average output SNR, a bit error rate and an outage probability related to signals received by the receiver. The output module is operable to provide the transceiver system with the average SNR and the estimated information as the performance information.

Abstract: A method for extracting feature vectors from a palm image includes the steps of: determining a palm contour in the palm image, and labeling pixels on the palm contour as contour pixels in order along the palm contour; determining a rotation angle of the palm contour relative to a coordinate system; obtaining corrected contour pixels to offset the rotation angle; determining a plurality of feature points from the corrected contour pixels; obtaining a plurality of sub-images from the palm image with reference to the feature points, one of the sub-images corresponding to a palm center and another one of the sub-images corresponding to a corresponding palm finger; and determining the feature vectors with reference to the sub-images, each of the feature vectors corresponding to a corresponding one of the sub-images.

Abstract: An OFDM beamformer of a signal receiving system receives multiple frequency-domain sample signal vectors each transformed from arrival signals received by a corresponding antenna element of an antenna array, and includes: multiple signal splitters each splitting a corresponding frequency-domain sample signal vector into a data signal subvector and a pilot signal subvector based on predetermined position information; a weight generator generating an estimated auto-correlation matrix and an estimated cross-correlation vector based on the data signal subvectors, the pilot signal subvectors, and a reference pilot signal vector associated with a desired user, and generating an optimal weight vector based on the estimated auto-correlation matrix and the estimated cross-correlation vector; and a combining unit combining, based on the optimal weight vector, serial data signals converted from each data signal vector by a corresponding parallel-to-serial converter to generate an estimated data symbol output correspondi

Abstract: A method for establishing a simulating signal suitable for estimating a complex exponential signal includes the following computer-implemented steps: sampling a time domain signal of a physical system to obtain a sampling signal; transforming the sampling signal to a frequency domain signal using Fast Fourier Transform; determining parameters of the frequency domain signal; establishing a simulating signal; establishing a target function which is a deviation of the simulating signal from the sampling signal; obtaining correcting factors; iterating the target function using a gradient method and the correcting factors to obtain three sets of iterated signal parameters; obtaining corrected parameters using quadratic interpolation; and using the corrected parameters to correct the simulating signal, and establishing an updated target function. The simulating signal can be used to estimate dynamic behavior of the physical system if the updated target function converges to a tolerable range.