A method of monitoring a conveyor belt state, the method including the steps of: (a) monitoring the acoustic emissions from a conveyor belt using an optical waveguide interrogator, the optical waveguide interrogator outputting a series of data bins representing the temporal evolution of the acoustic emissions received along the optical waveguide; and (b) utilizing the temporal evolution of the acoustic emissions of adjacent data bins to detect anomalies in the conveyor belt state.

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure includes: receiving a request to determine a blood pressure of a first subject from a terminal, obtaining data relating to the first subject, the data relating to the first subject including data relating to heart activity of the first subject and personal information relating to the first subject, extracting target features relating to the first subject from the data relating to the first subject, determining a preliminary blood pressure of the first subject using a prediction model based on the target features relating to the first subject, determining a predicted blood pressure of the first subject using an optimization model based on the preliminary blood pressure and sending the predicted blood pressure of the first subject to the terminal in response to the request.

A method implemented on a computing device having at least one processor, storage, and a communication platform connected to a network for determining blood pressure includes: receiving a request to determine a blood pressure of a first subject from a terminal, obtaining data relating to the first subject, the data relating to the first subject including data relating to heart activity of the first subject and personal information relating to the first subject, extracting target features relating to the first subject from the data relating to the first subject, determining a preliminary blood pressure of the first subject using a prediction model based on the target features relating to the first subject, determining a predicted blood pressure of the first subject using an optimization model based on the preliminary blood pressure and sending the predicted blood pressure of the first subject to the terminal in response to the request.

Two-dimensional face presentation attacks are one of most notorious and pervasive face spoofing types, causing security issues to facial authentication systems. To tackle these issues, a cost-effective face anti-spoofing (FAS) system based on acoustic modality, named as Echo-FAS, is devised, which employs a crafted acoustic signal to probe the presented face. First, a large-scale, high-diversity, acoustic-based FAS database, named as Echo-Spoof, is built. Based upon Echo-Spoof, we design a two-branch framework combining global and local frequency features of the presented face to distinguish live vs. spoofing faces. Echo-FAS has the following merits: (1) it only needs one speaker and one microphone; (2) it can capture three-dimensional geometrical information of the presented face and achieve a remarkable FAS performance; and (3) it can be handily allied with RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust.

Two-dimensional face presentation attacks are one of most notorious and pervasive face spoofing types, causing security issues to facial authentication systems. To tackle these issues, a cost-effective face anti-spoofing (FAS) system based on acoustic modality, named as Echo-FAS, is devised, which employs a crafted acoustic signal to probe the presented face. First, a large-scale, high-diversity, acoustic-based FAS database, named as Echo-Spoof, is built. Based upon Echo-Spoof, we design a two-branch framework combining global and local frequency features of the presented face to distinguish live vs. spoofing faces. Echo-FAS has the following merits: (1) it only needs one speaker and one microphone; (2) it can capture three-dimensional geometrical information of the presented face and achieve a remarkable FAS performance; and (3) it can be handily allied with RGB-based FAS models to mitigate the overfitting problem in the RGB modality and make the FAS model more accurate and robust.

Provided is a fault signal locating and identifying method of industrial equipment based on a microphone array. The method includes the steps of: acquiring sound signals and dividing the acquired signals into a training set, a verifying set and a test set; performing feature extraction on the sound signals in the training set, and extracting a phase spectrogram and an amplitude spectrogram of a spectrogram; sending an output of a feature extraction module, as an input, to a CNN, and in each layer of the CNN, learning a translation invariance in the spectrogram by using a 2D CNN; in between the layers of the CNN, normalizing the output by using a batch normalization, and reducing a dimension by using a maximum pooling layer along a frequency axis; sending an output from the layers of the CNN to layers of RNN; using a linear activation function; and inputting an output of a full connection layer to two parallel full connection layer branches for fault identification and fault location, respectively.

Images of a hand are obtained by a camera. A pose of the hand relative to the camera may vary due to rotation, translation, articulation of joints in the hand, and so forth. Avatars comprising texture maps from images of actual hands and three-dimensional models that describe the shape of those hands are manipulated into different poses and articulations to produce synthetic images. Given that the mapping of points on an avatar to the synthetic image is known, highly accurate annotation data is produced that relates particular points on the avatar to the synthetic image. An artificial neural network (ANN) is trained using the synthetic images and corresponding annotation data. The trained ANN processes a first image of a hand to produce a second image of the hand that appears to be in a standardized or canonical pose. The second image may then be processed to identify the user.

Images of a hand are obtained by a camera. A pose of the hand relative to the camera may vary due to rotation, translation, articulation of joints in the hand, and so forth. Avatars comprising texture maps from images of actual hands and three-dimensional models that describe the shape of those hands are manipulated into different poses and articulations to produce synthetic images. Given that the mapping of points on an avatar to the synthetic image is known, highly accurate annotation data is produced that relates particular points on the avatar to the synthetic image. An artificial neural network (ANN) is trained using the synthetic images and corresponding annotation data. The trained ANN processes a first image of a hand to produce a second image of the hand that appears to be in a standardized or canonical pose. The second image may then be processed to identify the user.

A computer-implemented method for detecting anomaly of an operation of a machine based on a signal indicative of the operation of the machine performing a task, comprises collecting hyperbolic embeddings of the signal indicative of the operation of the machine. The hyperbolic embeddings lie in a hyperbolic space. The method further comprises performing the detection of the anomaly of the operation of the machine based on the hyperbolic embeddings to determine an anomaly score and rendering the anomaly score. The machine operation is controlled based on the rendered anomaly score.

A computing platform may be configured to: (i) based on a set of data points defining a feature space, construct a graph structure associated with the set of data points; (ii) generate a multi-scale representation that represents the feature space, wherein each feature of a plurality of features from the feature space is represented in a respective plurality of scales with respect to the graph structure; (iii) regularize the multi-scale representation; (iv) based on the regularized multi-scale representation, identify a plurality of clusters associated with the set of data points; and (v) transmit, to a client station, data regarding the plurality of clusters and thereby cause an indication of the plurality of clusters to be presented at a user interface of the client station.