Abstract: An edge device can include a processing device, an image sensor, and a memory having instructions that are executable by the processing device for causing the processing device to perform operations. The processing device can receive, from the image sensor, an image of an environment. The processing device can determine a visibility measure corresponding to the environment by determining a dark channel of the image, determining, based on the dark channel of the image, a transmission map of the image, and determining, based on the transmission map, a visual contrast of the image. The processing device can generate information corresponding to the visibility measure.

Abstract: A method of assessing a wound in a subject is provided. The method comprises obtaining one or more optical coherence tomography images of the wound and analysing the one or more optical coherence tomography images using a deep learning model that has been trained to classify pixels in an optical coherence tomography image of a wound between a plurality of classes comprising a plurality of classes associated with different types of wound tissue, thereby obtaining for each image analysed, an indication of the location of tissue likely to belong to each of the different types of wound tissue in the respective image.

Abstract: A mobile roadway sensing unit can include an image sensor that can sense image data, a network sensor that can acquire wireless signal data, and a weather sensor that can sense weather condition data. The mobile roadway sensing unit can include a processor and a memory. The memory can include instructions executable by the processor for causing the processor to acquire the sensor data including at least one of the image data, the wireless signal data, or the weather condition data from the weather sensor, wherein the sensor data represents one or more current conditions proximate the mobile roadway sensing unit. The processor can generate processed data based on the sensor data The processor can generate, based on the processed data, a notification corresponding to the one or more current conditions. The processor can provide the notification to one or more external electronic devices.

Abstract: A computer implemented image processing method is provided for identifying a tissue boundary of a tumor region of a tissue sample, the tissue sample containing non-tumor regions and at least one tumor region. The method includes obtaining an image of a tissue section of the tissue sample, identifying at least one image property of the image, and comparing the image property with classification data. The method further includes, based on the comparison, classifying a region of the image as a tumor region representing a tumor region in the tissue sample or a non-tumor region representing a non-tumor region in the tissue sample. If the region of the image is classified as a tumor region, the method includes identifying a boundary of the region of the image, and using the boundary to identify a tissue boundary of the tumor region of the tissue sample represented by the region of the image.

Abstract: A computer implemented method is provided for determining an amount of tumor cells in a tissue sample. The method includes obtaining first image data describing an image of a tissue sample at a first resolution, obtaining second image data describing the image at a second resolution, wherein the first resolution is lower than the second resolution, selecting a candidate tumor region from the second image data based on texture data determined from the first image data, identifying a tumor structure in the candidate region of the second image data, and determining a number of cells in the tumor structure based on its area and an estimate of tumor cell area to estimate an amount of tumor cells in the tissue sample.

Abstract: A facility comprising methods and systems for discovering travel patterns based at least in part on data collected from a plurality of dynamic and/or mobile sensor devices is disclosed. The mobile sensor devices sense or detect the presence of nearby devices and record information about each detection, such as the location, the date and time, and an identifier of the detected device and provide this information to the facility. Thus, the facility can leverage the communication capabilities of existing mobile devices and networks to provide a broad, wide-ranging, scalable sensor network that detects the presence of individual devices. By recording and analyzing the encounter data received from various mobile sensor devices, the facility can ascertain or estimate the path of devices and their associated users. Given the availability of mobile devices and mobile networks, the disclosed techniques can be deployed without a substantial investment in infrastructure and maintenance.

Abstract: A computer implemented method is provided for determining an amount of tumor cells in a tissue sample. The method includes obtaining first image data describing an image of a tissue sample at a first resolution, obtaining second image data describing the image at a second resolution, wherein the first resolution is lower than the second resolution, selecting a candidate tumor region from the second image data based on texture data determined from the first image data, identifying a tumor structure in the candidate region of the second image data, and determining a number of cells in the tumor structure based on its area and an estimate of tumor cell area to estimate an amount of tumor cells in the tissue sample.

Abstract: A computer implemented image processing method is provided for identifying a tissue boundary of a tumour region of a tissue sample, the tissue sample containing non-tumour regions and at least one tumour region. The method includes obtaining an image of a tissue section of the tissue sample, identifying at least one image property of the image, and comparing the image property with classification data. The method further includes, based on the comparison, classifying a region of the image as a tumour region representing a tumour region in the tissue sample or a non-tumour region representing a non-tumour region in the tissue sample. If the region of the image is classified as a tumour region, the method includes identifying a boundary of the region of the image, and using the boundary to identify a tissue boundary of the tumour region of the tissue sample represented by the region of the image.

Abstract: Systems and methods for detecting and tracking objects, such as motor vehicles, within video data. The systems and method analyze video data, for example, to count objects, determine object speeds, and track the path of objects without relying on the detection and identification of background data within the captured video data. The detection system uses one or more scan lines to generate a spatio-temporal map. A spatio-temporal map is a time progression of a slice of video data representing a history of pixel data corresponding to a scan line. The detection system detects objects in the video data based on intersections of lines within the spatio-temporal map. Once the detection system has detected an object, the detection system may record the detection for counting purposes, display an indication of the object in association with the video data, determine the speed of the object, etc.

Abstract: Systems and methods for detecting and tracking objects, such as motor vehicles, within video data. The systems and method analyze video data, for example, to count objects, determine object speeds, and track the path of objects without relying on the detection and identification of background data within the captured video data. The detection system uses one or more scan lines to generate a spatio-temporal map. A spatio-temporal map is a time progression of a slice of video data representing a history of pixel data corresponding to a scan line. The detection system detects objects in the video data based on intersections of lines within the spatio-temporal map. Once the detection system has detected an object, the detection system may record the detection for counting purposes, display an indication of the object in association with the video data, determine the speed of the object, etc.

Abstract: Traffic induction loop simulator (LOOPSIM) software, and a system and method designed for traffic research and education. The system is able to generate output signals in response to previously recorded real traffic events or simulated traffic. If directly connected to a controller cabinet or a traffic event data collection system, LOOPSIM can be used to simulate output signals from loop detectors and to test algorithms executable in the controller, or to test the performance of a traffic data collection/monitoring system. LOOPSIM can simulate different predefined patterns of traffic distributions and replay loop event data recorded by a detector event data collection (DEDAC) system or an advanced loop event data analyzer (ALEDA) at a pace responsive to controller feedback. Using LOOPSIM, many traffic system tests can be conducted for multi-purpose traffic control applications, and traffic arrival patterns can be generated for researchers and educators to test theoretical models.

Abstract: Traffic induction loop simulator (LOOPSIM) software, and a system and method designed for traffic research and education. The system is able to generate output signals in response to previously recorded real traffic events or simulated traffic. If directly connected to a controller cabinet or a traffic event data collection system, LOOPSIM can be used to simulate output signals from loop detectors and to test algorithms executable in the controller, or to test the performance of a traffic data collection/monitoring system. LOOPSIM can simulate different predefined patterns of traffic distributions and replay loop event data recorded by a detector event data collection (DEDAC) system or an advanced loop event data analyzer (ALEDA) at a pace responsive to controller feedback. Using LOOPSIM, many traffic system tests can be conducted for multi-purpose traffic control applications, and traffic arrival patterns can be generated for researchers and educators to test theoretical models.