Abstract: Provided is an image compression device including an object extracting unit configured to perform convolution neural network (CNN) training and identify an object from an image received externally, a parameter adjusting unit configured to adjust a quantization parameter of a region in which the identified object is included in the image on the basis of the identified object, and an image compression unit configured to compress the image on the basis of the adjusted quantization parameter.

Abstract: Provided is an image compression device including an object extracting unit configured to perform convolution neural network (CNN) training and identify an object from an image received externally, a parameter adjusting unit configured to adjust a quantization parameter of a region in which the identified object is included in the image on the basis of the identified object, and an image compression unit configured to compress the image on the basis of the adjusted quantization parameter.

Abstract: Provided is an artificial neural network device including pre-synaptic neurons configured to generate a plurality of input spike signals, and a post-synaptic neuron configured to receive the plurality of input spike signals and to generate an output spike signal during a plurality of time periods, wherein the post-synaptic neuron respectively applies different weights in the plurality of time periods according to contiguousness with a reference time period in which input spike signals, which lead generation of the output spike signal from among the plurality of input spike signals, are received.

Abstract: Provided is a convolutional neural network system. The system includes an input buffer configured to store an input feature, a parameter buffer configured to store a learning parameter, a calculation unit configured to perform a convolution layer calculation or a fully connected layer calculation by using the input feature provided from the input buffer and the learning parameter provided from the parameter buffer, and an output buffer configured to store an output feature outputted from the calculation unit and output the stored output feature to the outside. The parameter buffer provides a real learning parameter to the calculation unit at the time of the convolution layer calculation and provides a binary learning parameter to the calculation unit at the time of the fully connected layer calculation.

Abstract: Provided is a convolution neural network system including an image database configured to store first image data, a machine learning device configured to receive the first image data from the image database and generate synapse data of a convolution neural network including a plurality of layers for image identification based on the first image data, a synapse data compressor configured to compress the synapse data based on sparsity of the synapse data, and an image identification device configured to store the compressed synapse data and perform image identification on second image data without decompression of the compressed synapse data.

Abstract: Provided is a convolutional neural network system including a data selector configured to output an input value corresponding to a position of a sparse weight from among input values of input data on a basis of a sparse index indicating the position of a nonzero value in a sparse weight kernel, and a multiply-accumulate (MAC) computator configured to perform a convolution computation on the input value output from the data selector by using the sparse weight kernel.

Abstract: Disclosed herein is a noise cancellation apparatus and method, which select in advance parameters to be used for noise cancellation in a reference voice signal section by generating a reference voice signal in advance before a voice signal is generated, thus improving noise cancellation effects. The noise cancellation apparatus includes a parameter initialization unit for determining an initial value of a parameter to be used for noise cancellation, based on reference signals filtered for respective frequencies, a parameter estimation unit for receiving the initial value of the parameter, and estimating the parameter in response to signals that are input after being filtered for respective frequencies, a gain estimation unit for calculating gains for respective frequencies based on the parameter from the parameter estimation unit, and a gain application unit for cancelling noise by applying the gains to the signals that are input after being filtered for respective frequencies.

Abstract: Disclosed herein are a method and an apparatus of expanding a speech recognition database used for speech recognition. The method of expanding a speech recognition database includes generating a pronunciation text from a corpus; confirming whether or not a non-registered word that is not registered in advance in a pronunciation dictionary among words included in the pronunciation text is present; generating lexical model information on the corresponding non-registered word with reference to a built-up acoustic model in the case in which the non-registered word is present as a confirmation result; and adding the generated lexical model information to a built-up lexical model. According to exemplary embodiments of the present invention, various speeches may be recognized in a stand-along speech recognizer in which an infrastructure is insufficient.

Abstract: An apparatus and method for improving voice recognition are disclosed herein. The apparatus includes a standard voice transmission unit, a Mel-frequency cepstrum coefficient (MFCC) generation unit, and an MFCC compensation unit. The standard voice transmission unit generates a standard voice. The MFCC generation unit generates voice feature data (MFCC) based on the utterance of the standard voice before voice recognition. The MFCC compensation unit stores a gain value generated based on the standard voice, and compensates for the distortion of the voice feature data based on the utterance of a user using the gain value during the voice recognition.

Abstract: A first communication device is provided. The first communication device modulates data to generate a first data symbol. The first communication device generates a first signal by using a first signal waveform allocated among a plurality of mutually orthogonal signal waveforms and the first data symbol. The first communication device outputs the first signal to a serial line connected to a second communication device.

Abstract: Disclosed herein is a noise cancellation apparatus and method, which select in advance parameters to be used for noise cancellation in a reference voice signal section by generating a reference voice signal in advance before a voice signal is generated, thus improving noise cancellation effects. The noise cancellation apparatus includes a parameter initialization unit for determining an initial value of a parameter to be used for noise cancellation, based on reference signals filtered for respective frequencies, a parameter estimation unit for receiving the initial value of the parameter, and estimating the parameter in response to signals that are input after being filtered for respective frequencies, a gain estimation unit for calculating gains for respective frequencies based on the parameter from the parameter estimation unit, and a gain application unit for cancelling noise by applying the gains to the signals that are input after being filtered for respective frequencies.

Abstract: An apparatus and method for improving voice recognition are disclosed herein. The apparatus includes a standard voice transmission unit, a Mel-frequency cepstrum coefficient (MFCC) generation unit, and an MFCC compensation unit. The standard voice transmission unit generates a standard voice. The MFCC generation unit generates voice feature data (MFCC) based on the utterance of the standard voice before voice recognition. The MFCC compensation unit stores a gain value generated based on the standard voice, and compensates for the distortion of the voice feature data based on the utterance of a user using the gain value during the voice recognition.

Abstract: There are provided a distributed video coding apparatus and method capable of controlling an encoding rate, the apparatus including: an intra-frame encoder encoding a key frame and outputting a bit stream of the encoded key frame; an encoder rate control (ERC) module calculating a bit rate according to motion complexity of a present Wyner-Ziv (WZ) frame by using a correlation between the motion complexity and the bit rate; and a turbo encoder encoding the present WZ frame by the bit rate calculated at the ERC module and outputting the encoded WZ bit stream.

Abstract: A multimedia content file management system for and a method of monitoring copyright and watching ratings in real time under the circumstance of the multimedia file sharing service using genetic information are disclosed.

Abstract: An anisotropic diffusion method and apparatus based on the direction of an edge are disclosed. In the anisotropic diffusion apparatus, directional pattern masking is performed to determine the direction of an edge in an image including noise, and values obtained through the directional pattern masking are convoluted to calculate the magnitude of an image. If the calculated magnitude value of the edge is larger than a threshold value, the edge of the image is preserved, while if the calculated magnitude value of the edge is not larger than the threshold value, noise cancellation is strengthened, whereby noise can be effectively canceled (or concealed) while preserving the edge representing the characteristics of the image, and thus, an image of high quality can be obtained.

Abstract: There are provided a distributed video coding apparatus and method capable of controlling an encoding rate, the apparatus including: an intra-frame encoder encoding a key frame and outputting a bit stream of the encoded key frame; an encoder rate control (ERC) module calculating a bit rate according to motion complexity of a present Wyner-Ziv (WZ) frame by using a correlation between the motion complexity and the bit rate; and a turbo encoder encoding the present WZ frame by the bit rate calculated at the ERC module and outputting the encoded WZ bit stream.