Abstract: Provided is a source localization in an apparatus for performing a source localization, a target sound source enhancement or speech recognition. The source localization method using input signals input from a plurality of microphones, comprising steps of: (a) obtaining a log likelihood function or an auxiliary function under the assumption that a target source signal mixed with noises satisfies a CGMM model; (b) obtaining an equation for estimating parameter values of the log likelihood function or the auxiliary function so that a value of the log likelihood function or the auxiliary function is maximized recursively in each time frame; (c) estimating a covariance matrix recursively in each time frame; and (d) estimating a steering vector recursively by using the estimated covariance matrix, wherein the steering vector of the target sound source is estimated from the input signals.

Abstract: An oral solid formulation includes irinotecan or a pharmaceutically acceptable salt thereof as an active ingredient, and an acidifying agent. A method of preparing an oral solid formulation includes forming granules containing irinotecan or a pharmaceutically acceptable salt thereof, a diluent, and a binder, mixing the granules with a disintegrant and a lubricant to obtain a mixture, and includes adding an acidifying agent in step of forming granules and/or mixing the granules.

Abstract: An oral solid formulation includes irinotecan or a pharmaceutically acceptable salt thereof as an active ingredient, and an acidifying agent.

Abstract: A method of noise cancellation comprises providing a first estimated output signal based on an input signal and a first prior far-end channel filter and a second prior far-end channel filter corresponding to a prior frame, updating a first current far-end channel filter corresponding to a current frame according to the first estimated output signal and the first prior far-end channel filter, providing a second estimated output signal based on the input signal, the first current far-end channel filter, and the second prior far-end channel filter, updating a second current far-end channel filter corresponding to the current frame according to the second estimated output signal and the second prior far-end channel filter, and providing a resultant signal based on the input signal, the first current far-end channel filter, and the second current far-end channel filter.

Abstract: A target speech signal extraction method for robust speech recognition includes: initializing a steering vector for a target speech source and an adaptive vector, setting a real output channel of the target speech source as an output by the adaptive vector, initializing adaptive vectors for a noise and setting a dummy channel as an output by the adaptive vectors for the noise; setting a cost function for minimizing dependency between a real output for the target speech source and a dummy output for the noise; setting an auxiliary function to the cost function, and updating the adaptive vector for the target speech source and the adaptive vectors for the noise by using the auxiliary function and the steering vector; estimating the target speech signal by using the adaptive vector thereby extracting the target speech signal from the input signals; and updating the steering vector for the target speech source.

Abstract: A motor drive system with a correction function of a temperature deviation of an IGBT module is provided. The system may include: a gate board configured to detect a plurality of sensing temperature information by sensing the IGBT module, acquire a difference value between maximum temperature information among the plurality of sensing temperature information and at least one sensing temperature information, and correct and output the at least one sensing temperature information by using the acquired difference value.

Abstract: A multi-master system includes a first master, a second master, and an integrated control circuit controlled by each of the first and second masters. The integrated control circuit includes a first dedicated block configured to provide a first function to the first master, a second dedicated block configured to provide a second function to the second master, and a global using block configured to provide a common function to each of the first and second masters.

Abstract: Provided is a target speech signal extraction method for robust speech recognition including: receiving information on a direction of arrival of the target speech source with respect to the microphones; generating a nullformer by using the information on the direction of arrival of the target speech source to remove the target speech signal from the input signals and to estimate noise; setting a real output of the target speech source using an adaptive vector as a first channel and setting a dummy output by the nullformer as a remaining channel; setting a cost function for minimizing dependency between the real output of the target speech source and the dummy output using the nullformer by performing independent component analysis (ICA) or independent vector analysis (IVA); setting an auxiliary function to the cost function; and estimating the target speech signal by using the cost function and the auxiliary function.

Abstract: Provided are a prediction value correction method and apparatus. The prediction value correction method includes steps of: (a) determining a prediction condition to be predicted; (b) receiving past prediction values and past measurement values according to the determined prediction condition; (c) filtering the past prediction values and the past measurement values by using an H-infinity filter to obtain an output value for a final time point; (d) estimating a future bias for a date and time point to be predicted by using the output value of the H-infinity filter; and (e) correcting a future prediction value for the date and time point to be predicted by using the estimated future bias to obtain a corrected future prediction value for the date and time point to be predicted.

Abstract: Provided is a method for beamforming by using maximum likelihood estimation in a speech recognition apparatus, including: (a) receiving an input signal (Xn,k) at a time frame n and a frequency k where noise is mixed: (b) determining a probability density function for a target signal (Yn,k) obtained by removing the noise from the input signal satisfies a complex generalized Guassian distribution or a complex gamma distribution where an average value is zero in a time-frequency domain; (c) estimating a variance (?n,k) of the target signal so as to maximize log likelihood for the probability density function; (d) estimating a filter (wk) maximizing a cost function so as to maximize the log likelihood for the probability density function; and (e) repeatedly performing the estimation of the steps (c) and (d) until the filter (wk) coverages, and finally acquiring a final filter (wk).

Abstract: According to an embodiment, a noise cancellation method comprises updating a first far-end channel filter according to a first variance yielded based on an input signal and a first far-end signal included in the input signal, providing a first output signal obtained by removing a first far-end noise signal from the input signal through the first far-end channel filter, updating a second far-end channel filter according to a second variance yielded based on the first output signal and a second far-end signal included in the input signal, and providing a second output signal obtained by removing a second far-end noise signal from the first output signal through the second far-end channel filter.

Abstract: A method of noise cancellation comprises providing a first estimated output signal based on an input signal and a first prior far-end channel filter and a second prior far-end channel filter corresponding to a prior frame, updating a first current far-end channel filter corresponding to a current frame according to the first estimated output signal and the first prior far-end channel filter, providing a second estimated output signal based on the input signal, the first current far-end channel filter, and the second prior far-end channel filter, updating a second current far-end channel filter corresponding to the current frame according to the second estimated output signal and the second prior far-end channel filter, and providing a resultant signal based on the input signal, the first current far-end channel filter, and the second current far-end channel filter.

Abstract: Provided is a target speech signal extraction method for robust speech recognition including: receiving information on a direction of arrival of the target speech source with respect to the microphones; generating a nullformer by using the information on the direction of arrival of the target speech source to remove the target speech signal from the input signals and to estimate noise; setting a real output of the target speech source using an adaptive vector as a first channel and setting a dummy output by the nullformer as a remaining channel; setting a cost function for minimizing dependency between the real output of the target speech source and the dummy output using the nullformer by performing independent component analysis (ICA) or independent vector analysis (IVA); setting an auxiliary function to the cost function; and estimating the target speech signal by using the cost function and the auxiliary function.

Abstract: Provided is an operating method of a monitoring device configured to monitor a microcontroller unit including a plurality of cores. The operating method includes: transmitting a question message to a first core among the plurality of cores; receiving an answer message from a second core among the plurality of cores; determining whether an operation of the microcontroller unit is abnormal, by using the answer message; receiving answer messages from cores, except for the second core, among the plurality of cores when the operation of the microcontroller unit is abnormal; and detecting an error of each of the plurality of cores by using the received answer messages.

Abstract: A real time clock device for a vehicle may include: a register set provided with a first register, in which second time information is stored, and configured to store Real Time Clock (RTC) data including the time or date; and a data compensation circuit to block an input of a 1 second (1 s) tick to the first register and compensate for a delay time according to the block of the input of the 1 s tick to the first register by an RTC lock signal during a process of transmitting the RTC data to an external device or receiving setting data for the RTC data from the external device.

Abstract: Provided is a target speech signal extraction method for robust speech recognition including: (a) receiving information on a direction of arrival of the target speech source with respect to the microphones; (b) generating a nullformer by using the information on the direction of arrival of the target speech source to remove the target speech signal from the input signals and to estimate noise; (c) setting a real output of the target speech source using an adaptive vector w(k) as a first channel and setting a dummy output by the nullformer as a remaining channel; (d) setting a cost function for minimizing dependency between the real output of the target speech source and the dummy output using the nullformer by performing independent component analysis (ICA); and (e) estimating the target speech signal by using the cost function, thereby extracting the target speech signal from the input signals.

Abstract: The present disclosure relates to a method for generating a monochrome permutation structured-light pattern and a structured-light system using the method, and the method for generating a monochrome structured-light pattern that is represented with two or more luminous intensities of a single color includes selecting two different luminous intensities of a single color, generating a stripe for each selected luminous intensity, apart from the two luminous intensities selected previously, generating at least one combination stripe by combining regions of two or more different luminous intensities that are identical to or different from the previously selected two luminous intensities, generating permutations using the generated stripes as elements, setting each permutation as a subpattern, and introducing permutation overlapping into the set subpatterns to generate a monochrome structured-light pattern in which each subpattern is connected in sequential overlapping manner.

Abstract: Provided are an object tracking method and an object tracking system. The object tracking method uses a particle filter configured by combining a Minimax estimation technique with a Monte Carlo sampling method. In the object tracking method, for each of the particles, a feature having a maximum cost among measurement values of at least two or more features is selected, a weight for the particle is calculated by using the measurement value of the selected feature, and a position of the target, object in the frame is estimated by using the weight of each of the candidate particles.

Abstract: Provided is a sound source localization method including steps of: (a) receiving a mixed signal of a target sound source signal and noise and echo signals through multiple microphones including at least two microphones; (b) generating a binarized mask based on a diffuseness by using a coherence-to-diffuseness ratio CDR, which is information on the target sound source and the noise source, by using the input signal; (c) pre-processing an input signal to multiple microphones by using the generated binarized mask; and (d) performing a predetermined algorithm such as the GCC-PHAT or the SRP-PHAT on the pre-processed input signal to estimate a direction of the target sound source.

Abstract: A method of noise cancellation comprises providing a first estimated output signal based on an input signal and a first prior far-end channel filter and a second prior far-end channel filter corresponding to a prior frame, updating a first current far-end channel filter corresponding to a current frame according to the first estimated output signal and the first prior far-end channel filter, providing a second estimated output signal based on the input signal, the first current far-end channel filter, and the second prior far-end channel filter, updating a second current far-end channel filter corresponding to the current frame according to the second estimated output signal and the second prior far-end channel filter, and providing a resultant signal based on the input signal, the first current far-end channel filter, and the second current far-end channel filter.