Power consumption of a circuit which makes a determination is reduced. The accuracy of a system which makes a determination is improved. The safety of a target object which is monitored by a sensor element is increased. A system which easily monitors a target object is provided. A semiconductor device includes a detection circuit having a function of analyzing first data and making a first determination of selecting a first value or a second value, a first determination circuit and a second determination circuit having a function of performing feature extraction of an image, a power supply circuit, and a power management unit. The power management unit has a function of allowing a voltage to be supplied from the power supply circuit to the first determination circuit in the case where the first value is selected by the first determination. The first determination circuit has a function of analyzing the first data and making a second determination. The second determination circuit has a function of analyzing the first data and making a third determination in the case where an occurrence of an event is detected in the second determination.

Provided are an edge computing-based control method and apparatus, an edge device and a storage medium. The method includes that: an analysis processing tool for implementing image analysis processing in a cloud server is acquired; in a case where the cloud server is in a fault state, image analysis processing is performed on a to-be-processed image with the analysis processing tool to obtain an analysis processing result; and the analysis processing result is synchronized to the cloud server.

A console includes a CPU and a GPU. Of the CPU and GPU, the CPU acquires an image to be processed, which is an object to be subjected to a support process that is a diagnosis support process or an imaging support process, and distributes a process to any one of the CPU, the GPU, or another GPU to execute the support process according to the content of the support process executed for the image to be processed.

Fast and scalable architectures and methods adaptable to available resources, that (1) compute 2-D convolutions using 1-D convolutions, (2) provide fast transposition and accumulation of results for computing fast cross-correlations or 2-D convolutions, and (3) provide parallel computations using pipelined 1-D convolvers. Additionally, fast and scalable architectures and methods that compute 2-D linear convolutions using Discrete Periodic Radon Transforms (DPRTs) including the use of scalable DPRT, Fast DPRT, and fast 1-D convolutions.

The present disclosure provides an object detection method and an object detection device. The object detection device includes: a heterogeneous processor and a memory, the heterogeneous processor including: a processing unit and a programmable logic unit, wherein the programmable logic unit is configured to receive a to-be-detected image, perform feature extraction on the to-be-detected image, and write an extracted feature into the memory; the processing unit is configured to read the feature from the memory, perform target object detection according to the feature, and output a detection result to the programmable logic unit; and the programmable logic unit is further configured to receive the detection result, generate prompt information according to the detection result, and output the prompt information.

A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.

Disclosed herein is a system for facilitating managing incidents occurring in areas monitored by low data-rate monitoring devices using the low data-rate monitoring devices, in accordance with some embodiments. Accordingly, the system comprises a processor, a device server, and a data visualization device. Further, a camera of a low data-rate monitoring device is configured for capturing video of an area. Further, the processor comprises a machine learning (ML) hardware accelerator configured for performing machine learning processing of the video. Further, the processor is configured for generating processed data. Further, the device server is configured for receiving the processed data based on the transmitting of the processed data from a low data-rate transceiver of the low data-rate monitoring device and transmitting a notification to a device. Further, the data visualization device is configured for visualizing the processed data, identifying an incident in the area, and generating the notification for the incident.

A method for navigational assistance for the visually impaired can include determining that an image captured by an imaging device contains an unknown object and determining that the unknown object is not resolvable within a threshold period of time. The method can further include performing an operation to reallocate computing resources between memory devices couplable to the imagining device in response to determining that the unknown object is not resolvable within the threshold period of time. Data corresponding to the unknown object can be written to the reallocated computing resources and an operation involving the data corresponding to the unknown object can be performed to resolve the unknown object using the reallocated computing resources.

The present disclosure generally relates to a method for authenticating a user using an electronic device, where the electronic device comprises a fingerprint sensor as well as a first and a second control unit. Preferably, the first control unit comprises a secure element and/or a secure block adapted to provide a secure processing environment. The present disclosure also relates to a corresponding electronic device and to a computer program product.

A method includes receiving image data representing an environment of a robotic device from a camera on the robotic device. The method further includes applying a trained dense network to the image data to generate a set of feature values, where the trained dense network has been trained to accomplish a first robot vision task. The method additionally includes applying a trained task-specific head to the set of feature values to generate a task-specific output to accomplish a second robot vision task, where the trained task-specific head has been trained to accomplish the second robot vision task based on previously generated feature values from the trained dense network, where the second robot vision task is different from the first robot vision task. The method also includes controlling the robotic device to operate in the environment based on the task-specific output generated to accomplish the second robot vision task.