Abstract: An image-based livestock inventory management device includes a data transmission and reception module, a memory storing an image-based livestock inventory management program, and a processor configured to execute the image-based livestock inventory management program stored in the memory, wherein the image-based livestock inventory management program detects each livestock individual by inputting, to a livestock individual detection model, an image collected by at least one camera that captures a doorway of each livestock room which is set as one region of interest (ROI), tracks a movement state of each livestock individual by accumulating and storing center coordinates of detected each livestock individual, generates entry and exit state information of each livestock room by counting an entry action and an exit action of each livestock individual at the doorway of each livestock room for the ROI, and provides inventory information.

Abstract: An animal object identification apparatus includes a memory storing a program for identifying an animal object based on the image, and a processor configured to identify the animal object based on the image by executing the program, wherein the processor detects specific body images of animal objects imaged in an input image as object regions to set the specific body images as input data, extracts flattened features for the input data by inputting the input data to a preset flattened-feature calculation model, generates clustering data by performing clustering for the flattened features and assigning identifiers for classifying the animal objects to at least one cluster, and assigns the identifiers to animal objects included in the object regions by comparing the flattened features calculated during extracting the flattened features with the clustering data.

Abstract: An apparatus for detecting mounting behavior of an animal object includes: a memory that stores a program; and a processor that executes the program. The program extracts animal detection information about an animal object detected from the image by inputting the received image into an animal detection model. Also, the program extracts bounding boxes of which a distance between coordinates of central points is smaller than a first set value, bounding boxes of which a difference in rotational angle is smaller than a second set value, and bounding boxes of which a difference between a vector connecting the central points of the extracted bounding boxes and an orientation of each bounding box is smaller than a third set value. If activity information of the animal object is extracted based on an MHI of the image, it is determined that mounting behavior occurs.

Abstract: An image-based animal object condition identification apparatus includes: a communication module that receives an image of an object; a memory that stores therein a program configured to extract animal condition information from the received image; and a processor that executes the program. The program extracts continuous animal detection information of each object by inputting the received image into an animal detection model that is trained based on learning data composed of animal images and determines predetermined animal condition information for each class of each animal object by inputting the continuous animal detection information of each object into an animal condition identification model.

Abstract: An image-based animal object detection apparatus includes: a communication module; a memory that stores therein a program configured to extract animal detection information; and a processor that executes the program. Herein, the program configured to extract animal detection information extracts the animal detection information by inputting received image into an animal detection model that is trained based on learning data composed of animal images. The animal detection information includes, as information about a bounding box created to be suitable for an animal object detected from the image, coordinates of a central point, a width and a length of the bounding box and a rotational angle of the bounding box, and includes, a position information indicating keypoints of the animal object.

Abstract: A face recognition body temperature measuring apparatus includes a communication module receiving a video image and a thermal image obtained by simultaneously imaging at least one target object from a camera module, a memory storing a body temperature measurement program for measuring a body temperature by using the video image and the thermal image, and a processor executing the program.

Abstract: A low frequency ultrasonic communication system includes: a low frequency ultrasonic transmitting apparatus configured to transmit data using a low frequency ultrasonic wave; and a low frequency ultrasonic receiving apparatus configured to recover the data by processing a low frequency ultrasonic reception signal transmitted from the low frequency ultrasonic transmitting apparatus. In communication between the transmitting apparatus and the receiving apparatus, modulation is performed using a DPSK scheme, and FEC coding is used to perform channel coding before the modulation.

Abstract: A method of detecting a particular abnormal sound in an environment with background noise is provided. The method includes acquiring a sound from a microphone, separating abnormal sounds from the input sound based on non-negative matrix factorization (NMF), extracting Mel-frequency cepstral coefficient (MFCC) parameters according to the separated abnormal sounds, calculating hidden Markov model (HMM) likelihoods according to the separated abnormal sounds, and comparing the likelihoods of the separated abnormal sounds with a reference value to determine whether or not an abnormal sound has occurred. According to the method, based on NMF, a sound to be detected is compared with ambient noise in a one-to-one basis and classified so that the sound may be stably detected even in an actual environment with multiple noises.

Abstract: A low frequency ultrasonic communication system includes: a low frequency ultrasonic transmitting apparatus configured to transmit data using a low frequency ultrasonic wave; and a low frequency ultrasonic receiving apparatus configured to recover the data by processing a low frequency ultrasonic reception signal transmitted from the low frequency ultrasonic transmitting apparatus. In communication between the transmitting apparatus and the receiving apparatus, modulation is performed using a DPSK scheme, and FEC coding is used to perform channel coding before the modulation.

Abstract: A method of detecting a particular abnormal sound in an environment with background noise is provided. The method includes acquiring a sound from a microphone, separating abnormal sounds from the input sound based on non-negative matrix factorization (NMF), extracting Mel-frequency cepstral coefficient (MFCC) parameters according to the separated abnormal sounds, calculating hidden Markov model (HMM) likelihoods according to the separated abnormal sounds, and comparing the likelihoods of the separated abnormal sounds with a reference value to determine whether or not an abnormal sound has occurred. According to the method, based on NMF, a sound to be detected is compared with ambient noise in a one-to-one basis and classified so that the sound may be stably detected even in an actual environment with multiple noises.

Abstract: A speech analysis apparatus is provided. An F0 extraction part extracts a pitch value from speech information. A spectrum extraction part extracts spectrum information from the speech information. An MVF extraction part extract a maximum voiced frequency and allows boundary information for respectively filtering a harmonic component and a non-harmonic component to be obtained. According to the speech analysis apparatus, speech synthesis apparatus, and speech analysis synthesis system of the present invention, speech that is closer to the original voice and is more natural may be synthesized. Also, speech may be represented with less data capacity.

Abstract: A method and apparatus for audio signal encoding for noise reduction are provided. The method includes: receiving an audio signal and performing modified discrete cosine transformation (MDCT) on the audio signal to convert the audio signal into a long block or a short block; reducing noise included in the audio signal in accordance with the long block or the short block; and performing advanced audio coding (AAC) on the long block or the short block in which noise is reduced.

Abstract: A speech analysis apparatus is provided. An F0 extraction part extracts a pitch value from speech information. A spectrum extraction part extracts spectrum information from the speech information. An MVF extraction part extract a maximum voiced frequency and allows boundary information for respectively filtering a harmonic component and a non-harmonic component to be obtained. According to the speech analysis apparatus, speech synthesis apparatus, and speech analysis synthesis system of the present invention, speech that is closer to the original voice and is more natural may be synthesized. Also, speech may be represented with less data capacity.

Abstract: A method and apparatus for audio signal encoding for noise reduction are provided. The method includes: receiving an audio signal and performing modified discrete cosine transformation (MDCT) on the audio signal to convert the audio signal into a long block or a short block; reducing noise included in the audio signal in accordance with the long block or the short block; and performing advanced audio coding (AAC) on the long block or the short block in which noise is reduced.