Abstract: An emotion recognition method includes the following steps: acquiring video data of a human-object interaction behavior process; performing data labeling on the positions of a person and an object and the interaction behaviors and emotions expressed by the person; constructing a feature extraction model based on deep learning, extracting features of interaction between the person and the object in a time-space dimension, and detecting the position and category of the human-object interaction behavior; mapping the detected interaction behavior category into a vector form through a word vector model; and finally, constructing a fusion model based on deep learning, fusing the interaction behavior vector and the time-space interaction behavior features, and identifying the emotion expressed by the interaction person.

Abstract: A recessed downlight with a modular design is specifically provided, which relates to the technical field of downlights. A clamping ring, two positioning latches, and a light reflection cup are together put into an inner wall of a light cylinder. When the positioning latches are in contact with the inner wall of the light cylinder, the two positioning latches are pressed to deform. After the positioning latches are aligned with positioning slots, the positioning latches restore original shapes and are clamped into positioning slots to complete mounting of the clamping ring. During mounting of a lampshade, two positioning clamping blocks are aligned with two gaps, the lampshade is moved upwards to cause tops of the positioning clamping blocks to penetrate through the two gaps.

Abstract: The present disclosure discloses a place recognition method based on knowledge graph inference; and provides, on the basis of giving a knowledge graph construction method in the place field, a recognition method of general places that is based on knowledge graph inference and can integrate various heterogeneous environmental information, including the following steps: (1) extracting main clues such as the main items that make up the place, the produced events, and the spatial structure from various heterogeneous information, and describing these clues in natural language text; (2) screening the foregoing descriptions by using natural language processing methods, to form place description entities; (3) constructing a knowledge graph in the place field according to the occurrence frequencies of the description entities in an actual environment; and (4) implementing inference and classification based on the knowledge graph by using a Deep Neural Network (DNN), to give a final recognition result.

Abstract: An emotion recognition method includes the following steps: acquiring video data of a human-object interaction behavior process; performing data labeling on the positions of a person and an object and the interaction behaviors and emotions expressed by the person; constructing a feature extraction model based on deep learning, extracting features of interaction between the person and the object in a time-space dimension, and detecting the position and category of the human-object interaction behavior; mapping the detected interaction behavior category into a vector form through a word vector model; and finally, constructing a fusion model based on deep learning, fusing the interaction behavior vector and the time-space interaction behavior features, and identifying the emotion expressed by the interaction person.

Abstract: The present disclosure discloses a place recognition method based on knowledge graph inference; and provides, on the basis of giving a knowledge graph construction method in the place field, a recognition method of general places that is based on knowledge graph inference and can integrate various heterogeneous environmental information, including the following steps: (1) extracting main clues such as the main items that make up the place, the produced events, and the spatial structure from various heterogeneous information, and describing these clues in natural language text; (2) screening the foregoing descriptions by using natural language processing methods, to form place description entities; (3) constructing a knowledge graph in the place field according to the occurrence frequencies of the description entities in an actual environment; and (4) implementing inference and classification based on the knowledge graph by using a Deep Neural Network (DNN), to give a final recognition result.

Abstract: Various embodiments are described herein for a system and method for adaptively detecting a desired signal S(n) in a noisy environment. A main signal is obtained that the desired signal and at least one of noise and interference from a collective noise source, an auxiliary signal is obtained comprising a version of the noise and/or a version of the interference from the collective noise source; and at a processing unit at least one signal feature is defined, noise and interference components corresponding to the noise and/or interference and the version of the noise and/or version of the interference are defined, and strengths thereof are estimated. The desired signal is estimated using the at least one signal feature and corresponding strengths, and the noise and/or interference components are estimated using the components and corresponding strengths.

Abstract: Handheld medical diagnostic instrument that provides high time-resolution pulse waveforms associated with multiple parameters including blood pressure measurements, blood oxygen saturation levels, electrocardiograph (ECG) measurements, and temperature measurements. The device stores and analyzes the pulse waveforms simultaneously obtained from all tests, and thereby allows an unusually detailed view into the functioning of the user's cardiovascular heart-lung system. The device is designed for use by unskilled or semi-skilled users, thus enabling sophisticated cardiovascular measurements to be obtained in a home environment. Data from the device can be analyzed onboard, with local computerized devices, and with remote server based systems. The remote server may be configured to analyze this data according to various algorithms chosen by the physician to be most appropriate to that patient's particular medical condition (e.g. COPD patient algorithms).

Abstract: Various embodiments are described herein for a system and method for adaptively detecting a desired signal S(n) in a noisy environment. A main signal is obtained that the desired signal and at least one of noise and interference from a collective noise source, an auxiliary signal is obtained comprising a version of the noise and/or a version of the interference from the collective N noise source; and at a processing unit at least one signal feature is defined, noise and interference components corresponding to the noise and/or interference and the version of the noise and/or version of the interference are defined, and strengths thereof are estimated. The desired signal is estimated using the at least one signal feature and corresponding strengths, and the noise and/or interference components are estimated using the components and corresponding strengths.

Abstract: Handheld medical diagnostic instrument that provides high time-resolution pulse waveforms associated with multiple parameters including blood pressure measurements, blood oxygen saturation levels, electrocardiograph (ECG) measurements, and temperature measurements. The device stores and analyzes the pulse waveforms simultaneously obtained from all tests, and thereby allows an unusually detailed view into the functioning of the user's cardiovascular heart-lung system. The device is designed for use by unskilled or semi-skilled users, thus enabling sophisticated cardiovascular measurements to be obtained in a home environment. Data from the device can be analyzed onboard, with local computerized devices, and with remote server based systems. The remote server may be configured to analyze this data according to various algorithms chosen by the physician to be most appropriate to that patient's particular medical condition (e.g. COPD patient algorithms).

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.

Abstract: A system and method for use in a real time system and for processing a signal with a low signal-to-noise ratio (SNR). The system comprises a model for modeling an expected signal and a filter that uses the model for filtering the signal. The filter is used for generating a prediction of the signal and an error variance matrix. The system further comprises an adaptive element for modifying the error variance matrix such that the bandwidth of the filter is widened, wherein the filter behaves like an adaptive filter.