Abstract: The embodiments of the present disclosure disclose data simulation method and device for event camera. A specific embodiment of the method includes: decoding the video to be processed to obtain a video frame sequence; inputting a target video frame to a fully convolutional network UNet to obtain event camera contrast threshold distribution information; sampling each pixel in the target video frame to obtain an event camera contrast threshold set; performing processing on the event camera contrast threshold set and the video frame sequence, to obtain the simulated event camera data; performing generative adversarial learning on the simulated event camera data and event camera shooting data, to obtain updated event camera contrast threshold distribution information; generating simulated event camera data. The present disclosure is a computer vision system that can be widely applied to such fields as national defense and military, film and television production, public security, and etc.

Abstract: A computer-implemented method according to one embodiment includes generating a first matrix based on words extracted from documents, and generating a second matrix based on deduplication chunks. The deduplication chunks include words of the documents. Word clustering is performed based on an analysis performed on the second matrix. Each cluster of the words represents a feature of at least one of the documents. The method further includes generating a third matrix based on the first matrix and the clusters, and performing text mining using the third matrix. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating transportation matches for transportation requests utilizing one or more efficiency metrics based on network coverage in a region. For example, the systems utilize regional and sub-regional network coverage features to generate a predicted network coverage improvement metric resulting from not assigning a particular provider device to a transportation request according to the regional network coverage features. Additionally, the systems utilize regional network coverage features to determine possible request states for a transportation request. The systems then utilize a Markov decision model policy based on the request states to generate an unmatched requester device efficiency metric associated with leaving the request unassigned for a time period.

Abstract: This disclosure describes a request distribution system that can intelligently distribute transportation requests to provider devices to improve transportation system network coverage and efficiency. For example, the disclosed systems can identify one or more provider devices (e.g., limited-eligibility provider devices or full-eligibility provider devices) as potential recipients for a transportation request. In some embodiments, the disclosed systems can further determine an expected change in network coverage time associated with providing the transportation request to one provider device versus another. The disclosed systems can provide the transportation request to a provider device according to the expected change in network coverage time and/or other transportation request metrics.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that improve efficiency and flexibility of implementing computer devices by providing efficient user interfaces to provider devices that include optimal digital routes selected based on dynamic route efficiency metrics. In particular, the disclosed systems can identify and surface an optimal alternative route from a pickup location to a drop-off location associated with a transportation match based on a variety of dynamic non-temporal factors. The disclosed systems can utilize a variety of computer implemented models to determine non-temporal factors, such as distance efficiency metrics, route-segment access efficiency metrics, congestion efficiency metrics, risk efficiency metrics, and match-based efficiency metrics. The disclosed systems can then combine these non-temporal factors utilizing a common efficiency framework to determine and surface an optimal alternative route.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for identifying provider devices and corresponding vehicles as candidates to transport time-sensitive (or otherwise prioritized) requestors and providing prioritized transportation options as fast passes for display on such requestors' devices. To provide such a prioritized transportation option, the disclosed systems can identify provider devices either matched or unmatched with requestors as candidates for prioritized transport based on estimated times of arrivals (ETAs) of vehicles for the candidate provider devices at the requestor's pickup location. Based on the ETAs at the pickup location, the disclosed systems can select a closest provider device from among the candidate provider devices to transport a prioritized requestor. After matching the prioritized requestor to the closest provider device, the disclosed systems can further search for providers with sooner ETAs.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for detecting a potential (or active) shared transportation request from a new requestor's device and an ongoing (or otherwise active) transportation for an existing requestor by a vehicle and then extemporaneously generating an ephemeral-transportation option for display on the new requestor's device to share the ongoing transportation by the vehicle. For example, the disclosed systems can detect a pickup location and a drop-off location associated with a new requestor and one or more active transportations that correlate with the detected locations. Based on the detected pickup and drop-off locations, the disclosed systems match the new requestor with the active transportation by the vehicle for the existing requestor.

Abstract: This disclosure describes an intersection-location system that can identify a street intersection indicated by a transportation request and select an event location at the street intersection based on intersection-location attributes. For example, the disclosed systems can receive a transportation request identifying a requested location at a street intersection and compare various candidate designated locations at the intersection as potential pickup, drop-off, or waypoint locations. The disclosed systems can compare intersection-location attributes for the different candidate designated locations by comparing predicted arrival times or predicted routes of different transportation providers at (or from) the candidate designated locations. The disclosed systems can further select a designated pickup, drop-off, or waypoint location from among the candidate designated locations for a transportation route based on the transportation request.

Abstract: The disclosed computer-implemented method may include (i) receiving, by a dynamic transportation matching system, a request for transportation between initial waypoints, (ii) calculating, by the dynamic transportation matching system, a value metric for an initial driving route between the initial waypoints (iii) calculating a value metric for a walk-enabled driving route that comprises at least one alternate waypoint that is within a predetermined walking range of one of the initial waypoints, and (iv) improving, by the dynamic transportation matching system, a value of fulfilling the request for transportation by determining that a difference between the value metric of the walk-enabled driving route and the value metric of the initial driving route satisfies a walking-value threshold and selecting, based on the determination that the difference satisfies the walking-value threshold, the walk-enabled driving route for fulfilling the request for transportation.

Abstract: The disclosed computer-implemented method may include (i) receiving, from a transportation requestor device, a request for transportation that comprises a transportation stop at a location next to a street, (ii) matching, by a dynamic transportation matching system, the transportation requestor device with a transportation provider, and (iii) improving, by the dynamic transportation matching system, a value of fulfilling the request by (a) determining that a near side of the street is closer to the location of the transportation stop than a far side of the street, (b) mapping, based on the determination that the near side of the street is closer to the location of the transportation stop, a driving route that traverses the street, and (c) directing the transportation provider to fulfill the request for transportation by traversing the driving route. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: An ultrasound probe calibration system and method. The system comprises an ultrasound probe calibration phantom (100), and the ultrasound probe calibration phantom (100) is provided with a sunken recess (110) at a middle position of an upper surface thereof and with several conical holes on a side surface thereof. The sunken recess (110) is fixedly connected with a two dimensional ultrasound probe (220) therein. The conical hole is inserted with an NDI insertion stylus (230), and a tip of the NDI insertion stylus (230) can be acquired in ultrasound imaging. An ultrasound probe calibration system employing the above structure enables a midplane of an ultrasound plane to pass a midplane of an ultrasound probe calibration phantom (100) along a middle gap, such that a tip of an NDI insertion stylus (230) is used for the midplane of the ultrasound plane, thereby addressing the problem of misalignment of a point-type phantom or a two-dimensional plane-type phantom to the ultrasound plane.

Abstract: An ultrasound probe calibration system and method. The system comprises an ultrasound probe calibration phantom (100), and the ultrasound probe calibration phantom (100) is provided with a sunken recess (110) at a middle position of an upper surface thereof and with several conical holes on a side surface thereof. The sunken recess (110) is fixedly connected with a two dimensional ultrasound probe (220) therein. The conical hole is inserted with an NDI insertion stylus (230), and a tip of the NDI insertion stylus (230) can be acquired in ultrasound imaging. An ultrasound probe calibration system employing the above structure enables a midplane of an ultrasound plane to pass a midplane of an ultrasound probe calibration phantom (100) along a middle gap, such that a tip of an NDI insertion stylus (230) is used for the midplane of the ultrasound plane, thereby addressing the problem of misalignment of a point-type phantom or a two-dimensional plane-type phantom to the ultrasound plane.

Abstract: The examples of the present invention disclose a method for dynamically linking a program on an embedded platform and an embedded platform. The embedded platform includes a middle layer which comprises at least one class. An application program to be linked includes at least one class. The application program is compiled into PICs, wherein storage positions of different code sections in the PICs which are obtained by compiling different functions of the application program do not overlap with each other; all the PICs are copied into a memory of the embedded operation system; a class pointer is created, and the class pointer is transferred between the class of the middle layer and the class of the application program to dynamically link the application program.

Abstract: Automated image quality assessment algorithms, which perform the functions of locating a region of interest, maximizing the image contrast, and ensuring the region of interest is properly centered in the image. Wherein the region of interest is located by spectral matching filter using a target spectrum obtained from samples of the image itself.

Abstract: Automated image quality assessment methods, which include locating a region of interest, region assessment, contrast assessment, blur assessment, and contaminant detection, on video data and high-resolution imagery. Where the blur assessment is performed without a reference image by dividing the region into non-overlapping block, measuring the wavenumber frequency of the blocks and calculating the ratio of the low frequency to high frequency areas.

Abstract: The examples of the present invention disclose a method for dynamically linking a program on an embedded platform and an embedded platform. The embedded platform includes a middle layer which comprises at least one class. An application program to be linked includes at least one class. The application program is compiled into PICs, wherein storage positions of different code sections in the PICs which are obtained by compiling different functions of the application program do not overlap with each other; all the PICs are copied into a memory of the embedded operation system; a class pointer is created, and the class pointer is transferred between the class of the middle layer and the class of the application program to dynamically link the application program.

Abstract: Automated image quality assessment methods, which include locating a region of interest, region assessment, contrast assessment, blur assessment, and contaminant detection, on video data and high-resolution imagery. Where the blur assessment is performed without a reference image by dividing the region into non-overlapping block, measuring the wavenumber frequency of the blocks and calculating the ratio of the low frequency to high frequency areas.

Abstract: Automated image quality assessment algorithms, which perform the functions of locating a region of interest, maximizing the image contrast, and ensuring the region of interest is properly centered in the image. Wherein the region of interest is located by spectral matching filter using a target spectrum obtained from samples of the image itself.

Abstract: A process for providing computer aided diagnosis from video data of an organ during an examination with an endoscope, comprising analyzing and enhancing image frames from the video and detecting and diagnosing any lesions in the image frames in real time during the examination. Optionally, the image data can be used to create a 3 dimensional reconstruction of the organ.