Embodiments of the present disclosure provides a device control method, an apparatus, an electronic device, and a computer readable storage medium, the method including: acquiring a device control instruction input by a user, acquiring at least one of user information, environment information, and device information, and controlling at least one target device to perform a corresponding operation based on the acquired information and the device control instruction. The embodiments of the present disclosure implement the safe and convenient control of a smart device to perform corresponding operations.

Methods, systems, and apparatus for operations for performing a biometric recognition attack test on a biometric recognition device. An example method includes obtaining a biometric feature object for performing the biometric recognition attack on the biometric recognition device; Perform the biometric recognition attack test on the biometric recognition device, comprising: controlling a mechanical arm to place the biometric feature object in a recognition area of the biometric recognition device; and controlling the mechanical arm to press the biometric feature object to the biometric recognition device to trigger the biometric feature object to input the biometric features in the feature attachment part into the biometric recognition device through the conductive part; obtaining an attack test result corresponding to the biometric feature object; and determining a test result of the biometric recognition attack test performed on the biometric recognition device.

Provided is an analysis apparatus including an acquisition unit that acquires vibration data showing a vibration generated in a first object by having a second object come into contact with a first position on the first object, a first analysis processing unit that specifies the first position by comparing a vibration characteristic shown by the vibration data, and a vibration characteristic defined for each position where the second object may come into contact with the first object, and a second analysis processing unit that estimates a velocity after the contact of the second object based on a velocity of the first object and the first position.

A system and method for a laryngoscopic device which incorporates the electronic and imaging elements within the device with advanced functional iterations that incorporate mathematical pattern recognition/predictive modeling as well as parallel computing and supercomputing for data analysis as well as and integrating it with the patient's record and compiling images from multiple scopes in a cloud-based system for an AI platform to use in diagnosis and disease monitoring.

Embodiments of the present invention relate to a method, system and computer program product for histogram generation. In an embodiment, a first set of bins are acquired for a histogram based on the plurality of data points. In response to receiving a data point, a bin closest to the data point is determined from the first set of bins. In response to a distance between the data point and the bin not exceeding a threshold, the data point is merged into a target bin of the first set of bins, where the width of the target bin after merging the data point is closest to an average width of the first set of bins before the merging. In other embodiments, a system and a computer program product are disclosed.

Systems and methods are provided for receiving a two-dimensional (2D) image comprising a 2D object; identifying a contour of the 2D object; generating a three-dimensional (3D) mesh based on the contour of the 2D object; and applying a texture of the 2D object to the 3D mesh to output a 3D object representing the 2D object.

Large amounts of human body movement data may be collected, possibly via streaming data, from one or more sensors worn by a user. The data may be analyzed along with other classification data to generate feedback for the user or for other interested people (e.g., a trainer, a coach, a team member, health professional, etc.). The analysis may utilize one or more machine learning (ML) algorithms that use training data to create one or more ML models. When a user is evaluated after receiving feedback, accuracy of the feedback may be evaluated and fed back to the ML model to continue training the ML model(s).

Systems and methods for data collection in an industrial environment can include a data collector to route analog signals from a plurality of analog sensor inputs to a plurality of output channels of in accordance with a first routing scheme and a controller configured to adjust the routing scheme to a second routing scheme. The first routing scheme may include providing at least two of the plurality of analog sensor inputs at one of the plurality of output channels and the second routing scheme may include providing at least one of the at least two of the plurality of analog sensor inputs to a different one of the plurality of output channels.

Genetic-variant data is obtained that corresponds to one or more variants associated with a client. Each of the one or more variants corresponds to an instance of one or more bases positioned at one or more first positions in a first genetic sequence differ from corresponding one or more bases positioned in a reference genetic sequence. The first genetic sequence is a genetic sequence of the client. Sensor data is obtained that provides an indication of one or more characteristics of a current or past environment of the client. The genetic-variant data and the sensor data is processed to generate a disease-risk metric corresponding to a predicted risk of the client developing a particular disease. A communication is generated that is indicative of the disease-risk metric. The communication is transmitted to a remote device.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals. One of the methods includes: determining an RF signal configured to be transmitted through an RF band of a communication medium; determining first classification information that is associated with the RF signal, and that includes a representation of a characteristic of the RF signal or a characteristic of an environment in which the RF signal is communicated; using at least one machine-learning network to process the RF signal and generate second classification information as a prediction of the first classification information; calculating a measure of distance between (i) the second classification information that was generated by the at least one machine-learning network, and (ii) the first classification information associated with the RF signal; and updating the at least one machine-learning network based on the measure of distance.