A method for analysis of algae, comprising: receiving a microscopic image of algae by a cloud server (2501), the microscopic image including a scaling pattern for determining a magnification; determining the magnification by the cloud server based on the scaling pattern (2502); and analyzing the microscopic image by the cloud server based on the magnification to obtain an analysis result (2503).

A graphics processor including geometry and fragment processing logic, and a memory manager arranged to allocate and deallocate memory for use to hold tile data. The memory manager tracks which memory regions are allocated to hold tile data of which subdivisions (e.g. macrotiles) of the render area. Once the fragment processing logic has finished processing the tile data of a subdivision, it sends an identifier of that subdivision to the memory manager for deallocation. The processor further comprises a blocking circuit enabling the fragment processing logic to start processing tile data of a second task while the memory manager is still deallocating some of the memory regions allocated to the subdivisions of a first task; by preventing identifiers of subdivisions of the second task being passed to the memory manager until it has completed deallocating the memory regions allocated to the first task.

Dynamic routing of texture-load in graphics processing is described. An example of a processor includes one or more processing resources, the one or more processing resources to load a message including a texture load; a texture sampler and a data port; and a message router to route the texture load to a destination, wherein the destination may be either the texture sampler or the data port; wherein the message router includes arbitration circuitry to select the destination for the texture load, the arbitration circuitry to base selection of the destination at least in part on support by the data port for a format of a memory surface for the texture load; and a utilization metric for the data port representing availability of the data port.

Techniques that facilitate matrix factorization associated with graphics processing units are provided. In one example, a system includes a first graphics processing unit, a second graphics processing unit and a central processing unit. The first graphics processing unit processes a first data block of a data matrix associated with a matrix factorization system to generate first information for the matrix factorization system. The second graphics processing unit processes a first portion of a second data block of the data matrix separate from a second portion of the second data block to generate second information for the matrix factorization system. The central processing unit processes a machine learning model for the matrix factorization system based on at least the first information provided by the first graphics processing unit and the second information provided by the second graphics processing unit.

Provided are a method and system for detecting a UAV using a plurality of image sensors. A method of detecting a UAV includes detecting, by each of a plurality of detection image sensors, a UAV in a UAV detection area, transmitting, when the UAV is detected, position information of the detection image sensor detecting the UAV and distance information of the UAV to a classification image sensor, acquiring, by the classification image sensor, a magnified image of the UAV by setting a parameter of a camera of the classification image sensor according to the position information and the distance information, and classifying a type of the UAV by analyzing the magnified image.

A system for computer-aided triage can include a router, a remote computing system, and a client application. A method for computer-aided triage can include determining a parameter associated with a data packet, determining a treatment option based on the parameter, and transmitting information to a device associated with a second point of care.

Disclosed is an electronic apparatus comprising, a memory configured to store instructions; and at least one processor connected to the memory, and configured to detect at least one object of a first-class object or a second-class object included in a target image by the electronic apparatus using an artificial intelligent algorithm to apply the target image to a learned neural network model, and identify and apply an image-quality processing method to be individually applied to at least one detected object, the neural network model is set to detect an object included in an image, as trained based on learning data such as an image, a class to which the image belongs, information about the first-class object included in the image, and information about the second-class object included in the image.

The disclosure discloses a reconfigurable hardware acceleration method and system for Gaussian pyramid construction and belongs to the field of hardware accelerator design. The system provided by the disclosure includes a static random access memory (SRAM) bank, a first in first out (FIFO) group, a switch network, a shift register array, an adder tree module, a demultiplexer, a reconfigurable PE array, and a Gaussian difference module. In the disclosure, according to the requirements of different scenarios and different tasks for the system, reconfigurable PE array resources can be configured to realize convolution calculations of different scales. The disclosure includes methods of fast and slow dual clock domain design, dynamic edge padding design, and input image partial sum reusing design.

Enhancing edges of objects in a thermal image comprises receiving a thermal image and generating a gradient magnitude image comprising a plurality of pixels having associated gradient magnitude values. The gradient magnitude image is partitioned into subregions and gradient magnitude statistics are calculated for each. Mapping parameters are calculated for each of the subregions that equalize and smooth a dynamic range of the corresponding gradient magnitude statistics across the subregions. The mapping parameters calculated for each of the subregions are applied to pixels in the subregions to generate enhanced gradient magnitude values having equalized luminosity and contrast, and a wireframe image is formed therefrom having enhanced edges of objects. The wireframe image is displayed on a display device, wherein the wireframe image appears as a decluttered line drawing where the enhanced edges have increased luminosity and contrast compared to the thermal image to reduce the cognitive load of the user.

Methods, apparatus, systems, and articles of manufacture to optimize pipeline execution are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to determine a value associated with a first location of a first pixel of a first image and a second location of a second pixel of a second image by calculating a matching cost between the first location and the second location, generate a disparity map including the value, and determine a minimum value based on the disparity map corresponding to a difference in horizontal coordinates between the first location and the second location.