Abstract: A fluid phase change thermal management cooling method for removing heat from a source of heat includes the steps of: filling a cooling chamber with volume V1 of a fluid phase change thermal management cooling apparatus with a fluid in its liquid phase; increasing the volume of the cooling chamber to volume V2 to vapourise a portion of the fluid from its liquid phase to its vapour phase such that there is substantially only the fluid in its liquid phase and fluid in its vapour phase within the volume V2; allowing a dwell time that provides for at least some of the fluid in its liquid phase that has contact with a heated surface of the cooling chamber to be vaporised; and repeating the steps where timing of the steps and dwell time between steps is selected to control heat build-up within selected limits.

Abstract: This disclosure relates to a system that generates data describing physical surroundings of vehicles during operation. Individual vehicles carry sensors configured to generate output signals conveying information related to one or both of the physical surroundings of the vehicles and/or the operation of the vehicles. Based on the generated output signals, the physical surroundings in which the first vehicle is operating are derived, such as, for example, the speeds of different nearby vehicles, and their distances to each other.

Abstract: A cash trapping monitor apparatus is provided. The apparatus includes a monitor printed circuit board (PCB) interposed between a shutter assembly PCB and a dispenser/recycler PCB. The monitor PCB is connected/interfaced to a time-of-flight (TOF) sensor and an ultrasonic sensor both of which are situated behind a shutter blade in a media chute of a media handling device. Blade states reported by the shutter are changed by the monitor PCB before reporting to the dispenser/recycler PCB when distance measurements provided by the sensors indicate a cash trapping device is present in the chute, indicate that the shutter blade is missing, and/or indicate an original shutter blade was replaced with a different shutter blade.

Abstract: An edge device receives sensor data from a sensor affixed to a moveable gate. The edge device determines the positional state of the moveable gate based on the sensor data by inputting the received data into a machine learning model or by comparing the sensor data to values associated with a positional state through a calibration process. The edge device stores a log that associates the positional state and sensor data. The edge device determines the health state of the moveable gate using a machine learning model that is trained to predict, based on input of a new log, the health state of the gate. Responsive to determining that the health state of the gate is unhealthy, the edge device triggers a remedial action.

Abstract: A method of forming a nanopatterned substrate includes imprinting a deposited photoresist on a substrate with a stamp to form a nanopattern including nanofeatures on the substrate, the nanofeatures including a gap therebetween. The method includes performing glancing angle deposition of a metal on the nanopattern to deposit the metal on the nanofeatures. The method includes directionally etching the nanopattern including the metal in a direction normal to a surface of the nanopattern to remove the photoresist in the gap between the nanofeatures and to expose the substrate in the gap between the nanofeatures. The method includes depositing a deposition material on the directionally etched nanopattern such that the deposition material is deposited on the exposed substrate in the gap between the nanofeatures and on the metal that is on the nanofeatures.

Abstract: This disclosure relates to a system that generates data describing physical surroundings of vehicles during operation. Individual vehicles carry sensors configured to generate output signals conveying information related to one or both of the physical surroundings of the vehicles and/or the operation of the vehicles. Based on the generated output signals, the physical surroundings in which the first vehicle is operating are derived, such as, for example, the speeds of different nearby vehicles, and their distances to each other.

Abstract: A device detects, using input from one or more sensors installed at a parking facility of a plurality of parking facilities, an infraction caused by a vehicle. Responsive to detecting the infraction, the device generates a vehicle fingerprint by inputting a depiction of the vehicle into a supervised machine learning model, the depiction derived from one or more images of the vehicle captured at the parking facility, and receiving a feature vector of the vehicle as output from the supervised machine learning model, the feature vector comprising a plurality of embeddings each describing a dimension of the vehicle. The device monitors for entry of the vehicle at each of the plurality of parking facilities using the vehicle fingerprint, and, responsive to detecting entry of the vehicle at a given one of the plurality of parking facilities, triggers a remediation action.

Abstract: An edge device generates an exit event for a vehicle exiting a parking facility. The edge device determines whether the exit event matches with an entry event. Responsive to determining that the exit event does not match to an entry event, the edge device inputs images of the vehicle into a supervised machine learning model and receives, as output from the model, an exit feature vector. The edge device retrieves entry feature vectors corresponding to hanging entry events. A hanging entry event is an entry event for a vehicle with an unknown vehicle identifier. Edge device inputs the exit feature vector and the entry feature vectors into an unsupervised machine learning model and receives, as output from the model, matching scores for each entry feature vector. Edge device matches the exit event to one of the hanging entry events based on the matching scores.

Abstract: An edge device receives sensor data from a sensor affixed to a moveable gate. The edge device determines the positional state of the moveable gate based on the sensor data by inputting the received data into a machine learning model or by comparing the sensor data to values associated with a positional state through a calibration process. The edge device stores a log that associates the positional state and sensor data. The edge device determines the health state of the moveable gate using a machine learning model that is trained to predict, based on input of a new log, the health state of the gate. Responsive to determining that the health state of the gate is unhealthy, the edge device triggers a remedial action.

Abstract: An edge device receives sensor data from a sensor affixed to a moveable gate. The edge device determines the positional state of the moveable gate based on the sensor data by inputting the received data into a machine learning model or by comparing the sensor data to values associated with a positional state through a calibration process. The edge device stores a log that associates the positional state and sensor data. The edge device determines the health state of the moveable gate using a machine learning model that is trained to predict, based on input of a new log, the health state of the gate. Responsive to determining that the health state of the gate is unhealthy, the edge device triggers a remedial action.

Abstract: This disclosure relates to a system that determines driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine. Individual vehicle event scenarios correspond to vehicle events. Individual simulation scenarios correspond to individual vehicle event scenarios. A vehicle operator, e.g., an autonomous driving algorithm, operates the simulated vehicle in the simulation engine for a set of simulation scenarios. One or more metrics quantify the performance of the vehicle operator based on simulated results.

Abstract: This disclosure relates to a system that determines driving performance by a vehicle operator for simulated driving of a simulated vehicle in a simulation engine. Individual vehicle event scenarios correspond to vehicle events. Individual simulation scenarios correspond to individual vehicle event scenarios. A vehicle operator, e.g., an autonomous driving algorithm, operates the simulated vehicle in the simulation engine for a set of simulation scenarios. One or more metrics quantify the performance of the vehicle operator based on simulated results.

Abstract: The invention provides a microfluidic system for monitoring the condition of lubricating oil. The microfluidic system comprises a microfluidic device with at least one microchannel (40) configured to allow a sample of lubricating oil (52a) to pass therethrough as a laminar flow. The device has at least one separating device (41) configured to selectively separate at least one component (54b) from the lubricating oil (52a) in the fluid flow. The microfluidic system also comprises a detector device (20, 22) configured to detect the presence and/or measure at least one property of the at least one component passing through the microfluidic device after separation in the microchannel (40).

Abstract: A device captures a series of images over time in association with a gate, each image having a timestamp. The device determines, for a vehicle approaching the entry side, from a subset of images of the series of images featuring the vehicle, a first data set comprising a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model. The device stores the data set in association with one or more timestamps with the subset of images, determines a second data set for a second vehicle approaching the exit side, and responsive to determining that the first data set and the second data set match, instructs the gate to move.

Abstract: A device captures a series of images over time in association with a gate, each image having a timestamp. The device determines, for a vehicle approaching the entry side, from a subset of images of the series of images featuring the vehicle, a first data set comprising a plurality of parameters that describe attributes of the vehicle by inputting the subset of images into a first machine learning model and a vehicle identifier of the vehicle by inputting images of the subset featuring a depiction of a license plate of the vehicle into a second machine learning model. The device stores the data set in association with one or more timestamps with the subset of images, determines a second data set for a second vehicle approaching the exit side, and responsive to determining that the first data set and the second data set match, instructs the gate to move.

Abstract: Systems and methods for analyzing physical characteristics of a battery include arrangements of two or more transducers coupled to the battery. A control module controls one or more of the two or more transducers to transmit acoustic signals through at least a portion of the battery, and one or more of the two or more transducers to receive response acoustic signals. Distribution of physical properties of the battery is determined based at least on the transmitted acoustic signals and the response acoustic signals.

Abstract: This disclosure relates to a system that generates data describing physical surroundings of vehicles during operation. Individual vehicles carry sensors configured to generate output signals conveying information related to one or both of the physical surroundings of the vehicles and/or the operation of the vehicles. Based on the generated output signals, the physical surroundings in which the first vehicle is operating are derived, such as, for example, the speeds of different nearby vehicles, and their distances to each other.

Abstract: This disclosure relates to a system configured to generate and provide timely vehicle event information for a fleet of vehicles including at least a first vehicle. Individual vehicles detect vehicle events and transmit related information to a remote computing server. The remote computing server determines whether the detected vehicles events are relevant to add to a set of vehicle events scenarios. For example, if a particular vehicle event is duplicative of a previous vehicle event, if may not need to be added. The newest vehicles events may be reported at certain intervals, in particular if they are indicative of a trend.

Abstract: An encapsulated gear train gear train for a machine comprising a support frame, which is non-encasing, an input shaft rotatably mounted in the support frame, an output shaft rotatably mounted in the support frame, gears configured to operatively connect the input shaft with the output shaft, and an encasement configured to encapsulate the input shaft, the output shaft, the gears, and at least a portion of the support frame, wherein the support frame is configured to be mounted on a structure of the machine such that the support frame does not apply any loads onto the encasement.

Abstract: This disclosure relates to a system that generates data describing physical surroundings of vehicles during operation. Individual vehicles carry sensors configured to generate output signals conveying information related to one or both of the physical surroundings of the vehicles and/or the operation of the vehicles. Based on the generated output signals, the physical surroundings in which the first vehicle is operating are derived, such as, for example, the speeds of different nearby vehicles, and their distances to each other.