Abstract: Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Abstract: A process of applying an adhesive to a substrate is provided that includes the mixing together of the components of a two-part formulation of a Part A and a Part B. The Part A includes a reactive methacrylate phosphate monomer or oligomer, a methacrylate monomer, a high flash point (meth)acrylate monomer, a free-radical polymerization inhibitor, an adhesion promoter, an impact modifier, a toughening agent, and a free-radical polymerization initiator. The Part B includes an impact modifier, a toughening agent, and a polymerization accelerator. The A and B Parts are combined together to form an adhesive mixture. The adhesive mixture is applied to the substrate. A formulation for the Part A and B is also provided. A bonded structure is provided in which the adhesive so created is intermediate between a first substrate and a second substrate.

Abstract: The disclosure is generally directed to systems and methods for inference quality determination of a deep neural network (DNN) without requiring ground truth information for use in driver-assisted vehicles, including receiving an image frame from a source; applying a normal inference DNN model to the image frame to produce a first inference with a first bounding box using a normal inference DNN model; applying a deep inference DNN model to a plurality of filtered versions of the image frame to produce a plurality of deep inferences with a plurality of bounding boxes; comparing the plurality of bounding boxes to identify a cluster condition of the plurality of bounding boxes; and determining an inference quality of the image frame of the normal inference DNN model as a function of the cluster condition.

Abstract: Systems and methods for transforming autonomous aerial vehicle sensor data between platforms are disclosed herein. An example method can include receiving, by an UAV, vehicle radar data and radar calibration data from a vehicle, as well as location information for a location of the vehicle, determining a simulated UAV at the location, establishing an orientation for a simulated radar on a bottom of the simulated UAV, determining a height for the simulated UAV to match a field of view of the simulated radar, performing a geometrical transformation to convert the vehicle radar data set into a UAV perspective, converting the vehicle radar data into a vehicle coordinate frame using the radar calibration using vehicle global positioning system (GPS) coordinates, converting the vehicle coordinate frame from a global frame into a UAV coordinate frame using UAV GPS coordinates, and converting the UAV coordinate frame into simulated radar sensor frame.

Abstract: The disclosure is generally directed to systems and methods for inference quality determination of a deep neural network (DNN) without requiring ground truth information for use in driver-assisted vehicles, including receiving an image frame from a source; applying a normal inference DNN model to the image frame to produce a first inference with a first bounding box using a normal inference DNN model; applying a deep inference DNN model to a plurality of filtered versions of the image frame to produce a plurality of deep inferences with a plurality of bounding boxes; comparing the plurality of bounding boxes to identify a cluster condition of the plurality of bounding boxes; and determining an inference quality of the image frame of the normal inference DNN model as a function of the cluster condition.

Abstract: Devices and methods are provided for measuring, on a digital microfluidic platform, electrical signals associated with the impedance of adherent cells. In one embodiment, a sub-droplet of cell culture media containing adherent cells is passively dispensed at a pre-selected electrode location where a local hydrophilic surface region is provided, and adherent cells are attached to the local hydrophilic surface region. The cell culture media sub-droplet is replaced with a sub-droplet of a low-conductivity medium in a passive dispensing step, retaining the attached adherent cells. An AC voltage with a suitable frequency is applied between electrodes of the device and a signal associated with the impedance of the adherent cells is obtained. One of the electrodes to which the AC voltage is applied may be a dedicated sensing electrode. The local thickness of a dielectric layer coating the pre-selected electrode may be reduced to increase the detection sensitivity of the device.

Abstract: Nucleotide and amino acid sequences of cartilage-derived morphogenetic proteins (CDMP-1 and CDMP-2) from human and bovine cartilage extracts. These proteins exhibit chondrogenic activity and can be used to repair cartilage defects in a mammal.

Abstract: The present invention is a purified cartilage extract that stimulates local cartilage formation when combined with a matrix and implanted into a mammal. This extract can conveniently be produced by a method which includes the steps of: obtaining cartilage tissue; homogenizing the cartilage tissue in the presence of chaotropic agents under conditions that permit separation of proteins from proteoglycans; separating the proteins from the proteoglycans and then obtaining the proteins. The step for separating the proteins from the proteoglycans can be carried out using a sepharose column. The extract can also be obtained by additionally including the steps of separating the proteins on a molecular sieve and then collecting the proteins having molecular weights in the 30 kDa to 60 kDa size range. Articular cartilage or epiphyseal cartilage can be used in the preparation of this purified extract.

Abstract: the present invention is a purified cartilage extract that stimulates local cartilage formation when combined with a matrix and implanted into a mammal. This extract can conveniently be produced by a method which includes the steps of: obtaining cartilage tissue; homogenizing the cartilage tissue in the presence of chaotropic agents under conditions that permit separation of proteins from proteoglycans; separating the proteins from the proteoglycans and then obtaining the proteins. The step for separating the proteins from the proteoglycans can be carried out using a sepharose column. The extract can also be obtained by additionally including the steps of separating the proteins on a molecular sieve and then collecting the proteins having molecular weights in the 30 kDa to 60 kDa size range. Articular cartilage or epiphyseal cartilage can be used in the preparation of this purified extract.

Abstract: Nucleotide and amino acid sequences of cartilage-derived morphogenetic proteins (CDMP-1 and CDMP-2) from human and bovine cartilage extracts. These proteins exhibit chondrogenic activity and can be used to repair cartilage defects in a mammal.

Abstract: Nucleotide and amino acid sequences of cartilage-derived morphogenetic proteins (CDMP-1 and CDMP-2) from human and bovine cartilage extracts. These proteins exhibit chondrogenic activity and can be used to repair cartilage defects in a mammal.

Abstract: Nucleotide and amino acid sequences of cartilage-derived morphogenetic proteins (CDMP-1 and CDMP-2) from human and bovine cartilage extracts. These proteins exhibit chondrogenic activity and can be used to repair cartilage defects in a mammal.