Abstract: An automated driving system (ADS) of an autonomous vehicle includes a communication module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message including source vehicle data and receive a fusion data message including fusion data from a mobile edge computing (MEC) system including a roadside unit (RSU). The source vehicle data includes a source vehicle location. The fusion data is based on RSU sensed data and on vehicle sensed data received at the RSU from at least one vehicle. The misbehavior detection module is configured to determine whether a source vehicle is disposed at the source vehicle location based on the fusion data. The processor is configured to manage performance of the autonomous vehicle in accordance with the source vehicle data based at least in part on the determination. Other embodiments are described and claimed.

Abstract: A guided pairing method includes generating a singles list by detecting a plurality of singles at a plurality of detector elements in a detector array, the plurality of singles falling within a plurality of detection windows; for each detection window of the plurality of detection windows in the singles list having exactly two singles of the plurality of singles, determining the line of responses (LORs) for each of the two singles of the plurality of singles; for each detection window of the plurality of detection windows in the singles list having more than two singles of the plurality of singles, determining all coincidences possible based on the more than two singles; generating a weight for said each coincidence of the coincidences based on the determined LORs for said each of the two singles of the plurality of singles; and pairing the more than two singles based on the generated weight for said each coincidence of the coincidences.

Abstract: A PET scanner includes gamma-ray detector rings that form a bore through which an imaging subject is translated, a length of the bore defining an axial length of the PET scanner, the gamma-ray detector rings being movable along the axial length, the gamma-ray detector rings including gamma-ray detector modules therein, and processing circuitry configured to receive PET data associated with a plurality of transaxial slices of the imaging subject, the PET data including a first set of spatial information and timing information corresponding to a first data acquisition period for the gamma-ray detector modules in a first axial position and a second set of spatial information and timing information corresponding to a second data acquisition period for the gamma-ray detector modules in a second axial position, and reconstruct a PET image based on the first set of spatial and timing information and the second set of spatial and timing information.

Abstract: A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A method is provided for determining a scatter fraction for a radiation diagnosis apparatus. The method includes acquiring an energy spectrum from list mode data obtained from a scan performed using the radiation diagnosis apparatus; determining, from the acquired list mode data, a first number of events occurring in a first energy window spanning a first energy range; determining, from the acquired list mode data, a second number of events occurring in a second window, the second energy window spanning a second energy range different from the first energy range; calculating a singles scatter fraction based on the determined first number of events and the determined second number of events; and reconstructing an image based on the acquired list mode data and the calculated singles scatter fraction.

Abstract: A dynamic frame reconstruction apparatus and method for medical image processing is disclosed which reduces the computationally expensive reconstruction of images but which retains the accuracy of the image reconstruction. A convolutional neural network is used to cluster the dynamic data into groups of frames, each group sharing similar radiotracer distribution. In one embodiment, groups of frames that have similar reconstruction parameters are determined, and scatter and random estimations are computed once and shared among each of the frames in the same frame group.

Abstract: A method of detecting a malicious wireless vehicle-to-everything (V2X) communication includes retrieving perception data from a perception system on an ego vehicle, determining information about nearby objects from the perception data, receiving a first Basic Safety Message (BSM) from a first V2X source and determining if a vehicle location indicated in the first BSM corresponds to a location visible to the ego vehicle. In the event that the vehicle location indicated in the first BSM does not correspond to a location visible to the ego vehicle, the method further includes receiving a second V2X communication from a second V2X source and determining if the second V2X communication indicates a vehicle at the vehicle location indicated in the first BSM. In the event that the second V2X communication does not indicate a vehicle at the vehicle location indicated in the first BSM, the first BSM data is flagged as malicious.

Abstract: A method is provided for count loss correction based on detector dead time in a radiation diagnosis apparatus. The method includes acquiring scan data from a scan of a patient performed using the radiation diagnosis apparatus; determining, from the acquired scan data, a first count rate occurring in a first energy window spanning a first energy range; determining, from the acquired scan data, a second count rate occurring in a second window spanning a second energy range, the second energy range being different from the first energy range; calculating a particular count loss correction factor based on the determined first count rate and the determined second count rate; and reconstructing an image based on the acquired scan data and the calculated particular count loss correction factor.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: determine, via a trained neural network model, a route for a vehicle to traverse based on vehicle sensor data, and update the trained neural network model based on data received from at least one of an edge computing device or an infrastructure (V2I) device.

Abstract: A method of normalizing detector elements in an imaging system is described herein. The method includes a line source that is easier to handle for a user, and decouples the normalization of the detector elements into a transaxial domain and an axial domain in order to isolate errors due to positioning of the line source. Additional simulations are performed to augment the real scanner normalization. A simulation of a simulated line source closely matching the real line source can be performed to isolate errors due to physical properties of the crystals and position of the crystals in the system, wherein the simulated detector crystals are otherwise modeled uniformly. A simulation of a simulated cylinder source can be performed to determine errors due to other effects stemming from gaps between the detector crystals.

Abstract: A method, apparatus, and computer instructions stored on a computer-readable medium perform latent image feature extraction by performing the functions of receiving image data acquired during an imaging of a patient, wherein the image data includes motion by the patient during the imaging; segmenting the image data to include M image data segments corresponding to at least N motion phases having shorter durations than a duration of the motion by the patient during the imaging, wherein M is a positive integer greater than or equal to a positive integer N; producing, from the M image data segments, at least N latent feature vectors corresponding to the motion by the patient during the imaging; and performing a gated reconstruction of the N motion phases by reconstructing the image data based on the at least N latent feature vectors.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: execute an autonomous vehicle algorithm simulating vehicle operations determine whether a notification that provides information on how a driver intervention for actuation by system feature functions has been at least one of previously presently or confirmed and cause the notification to be generated. The notification includes information for responding to a finite number of driving options determined by the driver intervention for actuation by system.

Abstract: A method of generating an image, including: receiving, via a monitoring device, time-dependent data corresponding to a patient parameter; obtaining emission data representing radiation detected during a medical imaging scan; identifying time frames during the medical imaging scan to exclude from the obtained emission data, the identified time frames corresponding to a stressed emotional state for the patient based on the patient parameter; modifying the obtained emission data to exclude the emission data corresponding to the time frames corresponding to the stressed emotional state; and generating an emotional-state-corrected image based on the modified emission data excluding the emission data corresponding to the time frames corresponding to the stressed emotional state.

Abstract: At least two vehicle behavior reports are received at a reputation score management system. Each of the vehicle behavior reports is received from a corresponding traffic autonomous vehicle and includes a unique vehicle identifier and a classification result associated with a source autonomous vehicle. A reputation score is generated for association with the unique vehicle identifier based at least in part on the classification results received in the at least two vehicle behavior reports. A request for the reputation score associated with the unique vehicle identifier is received from an ego autonomous vehicle. The requested reputation score is transmitted to the ego autonomous vehicle to enable the ego autonomous vehicle to determine whether a V2V message including the unique vehicle identifier associated with the requested reputation score is one of an honest V2V message and a malicious V2V message based in part on the reputation score.

Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.

Abstract: An automated driving system (ADS) of an autonomous vehicle includes a communication module, a perception module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message comprising message-based vehicle data. The perception module configured to receive sensor data from at least one vehicle sensing device. The misbehavior detection module is configured to determine whether the V2V message is one of a legitimate message and a malicious message based at least in part on a comparison of the message-based vehicle data with sensor-based vehicle data generated based on the sensor data. The processor is configured to manage performance of the autonomous vehicle in accordance with the message-based vehicle data based on the determination. Other embodiments are described and claimed.

Abstract: A vehicle collaboration system of a first autonomous vehicle includes a transceiver and a collaboration controller. The transceiver is configured to broadcast a first collaboration message including first vehicle data associated with the first autonomous vehicle in connection with a collaboration application to a collaboration domain group via a hybrid distributed-connected network (HYDIN). The collaboration domain group includes at least the first autonomous vehicle and a second autonomous vehicle. The transceiver is configured to receive a second collaboration message including second vehicle data associated with the second autonomous vehicle in connection with the collaboration application via the HYDIN. The second collaboration message is broadcast from the second autonomous vehicle to the collaboration domain group via the HYDIN.

Abstract: Upon receiving list-mode data by detecting radiation emitted from a radiation source positioned with a field of view of a medical imaging scanner, each photon included in the list-mode data can be classified according to one or more interaction properties, such as energy or number of crystals interacted with. Grouped pairs of photons can be generated based on the classifying, and a corresponding interaction-property-specific correction kernel (e.g., a corresponding interaction-property-specific point spread function correction kernel) can be selected for each group. Corresponding interaction-property-specific correction kernels can then be utilized to generate higher quality images.

Abstract: An automated driving system (ADS) of an autonomous vehicle includes a communication module, a misbehavior detection module, and a processor. The communication module is configured to receive a vehicle-to-vehicle (V2V) message including source vehicle data and receive a fusion data message including fusion data from a mobile edge computing (MEC) system including a roadside unit (RSU). The source vehicle data includes a source vehicle location. The fusion data is based on RSU sensed data and on vehicle sensed data received at the RSU from at least one vehicle. The misbehavior detection module is configured to determine whether a source vehicle is disposed at the source vehicle location based on the fusion data. The processor is configured to manage performance of the autonomous vehicle in accordance with the source vehicle data based at least in part on the determination. Other embodiments are described and claimed.

Abstract: A method, apparatus, and non-transitory computer-readable storage medium for image denoising whereby a deep image prior (DIP) neural network is trained to produce a denoised image by inputting the second medical image to the DIP neural network and combining a converging noise and an output of the DIP network during the training such that the converging noise combined with the output of the DIP network approximates the first medical image at the end of the training, wherein the output of the DIP network represents the denoised image.