Abstract: An improved connection between supported and supporting structural members, particularly for use in structures where the supported member needs to move with respect to the supporting member. The connector includes a mounting clip and one or more sliders receiving in one or more openings in the mounting clip.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method of classifying signals using non-linear sparse representations includes learning a plurality of non-linear dictionaries based on a plurality of training signals, each respective nonlinear dictionary corresponding to one of a plurality of class labels. A non-linear sparse coding process is performed on a test signal for each of the plurality of non-linear dictionaries, thereby associating each of the plurality of non-linear dictionaries with a distinct sparse coding of the test signal. For each respective non-linear dictionary included in the plurality of non-linear dictionaries, a reconstruction error is measured using the test signal and the distinct sparse coding corresponding to the respective non-linear dictionary. A particular nonlinear dictionary corresponding to a smallest value for the reconstruction error among the plurality of non-linear dictionaries is identified and a class label corresponding to the particular non-linear dictionary is assigned to the test signal.

Abstract: A connection is provided between first and second building structural members, wherein a connector is made up of a stiffener and a connection element, and the stiffener nests within the connection element, and both the stiffener and the connection element are attached to the second building structural member with the same fastener or fasteners, and similarly, both the stiffener and the connection element are attached to the first building structural member with the same fastener or fasteners.

Abstract: A method and system for anatomical landmark detection in medical images using deep neural networks is disclosed. For each of a plurality of image patches centered at a respective one of a plurality of voxels in the medical image, a subset of voxels within the image patch is input to a trained deep neural network based on a predetermined sampling pattern. A location of a target landmark in the medical image is detected using the trained deep neural network based on the subset of voxels input to the trained deep neural network from each of the plurality of image patches.

Abstract: A tissue retractor device is provided. The tissue retractor device includes a handle attached to an inflatable rake-shaped tissue retractor head.

Abstract: A method and apparatus for cross-domain medical image synthesis is disclosed. A source domain medical image is received. A synthesized target domain medical image is generated using a trained contextual deep network (CtDN) to predict intensities of voxels of the target domain medical image based on intensities and contextual information of voxels in the source domain medical image. The contextual deep network is a multi-layer network in which hidden nodes of at least one layer of the contextual deep network are modeled as products of intensity responses and contextual response.

Abstract: Computer-implemented methods and systems are disclosed for optimizing the performance of wireless networks by automatically capturing wireless traffic and other radio frequency (RF) signal data in the network and analyzing the data to identify network anomalies and to determine one or more solutions, without human intervention.

Abstract: Computer-implemented methods and systems are disclosed for optimizing the performance of wireless networks by automatically capturing wireless traffic and other radio frequency (RF) signal data in the network and analyzing the data to identify network anomalies and to determine one or more solutions, without human intervention.

Abstract: A small form factor wiring connector provides a two wire to two point connector with separate-hole insertion for two wires, by having two holes on the first casing side of the connector and a terminal connection on the opposite second side of the connector. The design works well for a pluggable or fixed-mounted connector. The separate wire insertion holes and the internal screw-driven clamping means with U-shaped wire clamp and U-shaped fixed terminal surfaces provides positive connections which will not easily loosen.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: Object detection uses a deep or multiple layer network to learn features for detecting the object in the image. Multiple features from different layers are aggregated to train a classifier for the object. In addition or as an alternative to feature aggregation from different layers, an initial layer may have separate learnt nodes for different regions of the image to reduce the number of free parameters. The object detection is learned or a learned object detector is applied.

Abstract: A protective case can house a portable electronic device. The portable electronic device can have a switch (e.g., for switching an operational mode of the portable electronic device). The protective case can have a switch cover that engages the switch of the portable electronic device when the portable electronic device is in the protective case. The switch cover can be moved between positions corresponding to positions of the switch of the portable electronic device. As the switch cover is moved, the switch cover correspondingly moves the switch.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective sparse deep neural network is trained for each of the marginal search spaces, resulting in a series of trained sparse deep neural networks. Each of the trained sparse deep neural networks is trained by injecting sparsity into a deep neural network by removing filter weights of the deep neural network.

Abstract: An improved connection between supported and supporting structural members, particularly for use in structures where the supported member needs to move with respect to the supporting member. The connector includes a mounting clip and one or more sliders receiving in one or more openings in the mounting clip.

Abstract: An improved connection between supported and supporting structural members, particularly for use in structures where the supported member needs to move with respect to the supporting member. The connector has an anchoring leg that allows for the back of the anchoring leg on either side of the sliding leg.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.