Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: A system and method for automatically detecting, classifying, and processing objects captured in an image. The system receives an image from the image source and detects one or more objects in the image. The system then performs a high-level classification of each of the one or more objects in the image and extracts each of the one or more objects from the image. The system then performs a specific classification of each of the one or more objects and determines a price of each of the one or more objects. Finally, the system generates a pricing report comprising a price of each of the one or more objects.

Abstract: Systems and methods for automatic segmentation of lesions from a 3D input medical image are provided. A 3D input medical image depicting one or more lesions is received. The one or more lesions are segmented from one or more 2D slices extracted from the 3D input medical image using a trained 2D segmentation network. 2D features are extracted from results of the segmentation of the one or more lesions from the one or more 2D slices. The one or more lesions are segmented from a 3D patch extracted from the 3D input medical image using a trained 3D segmentation network. 3D features are extracted from results of the segmentation of the one or more lesions from the 3D patch. The extracted 2D features and the extracted 3D features are fused to generate final segmentation results. The final segmentation results are output.

Abstract: A communication system using vector and scalar potential is disclosed. The system uses field-free potentials signaling for many applications where the absence of shielding effects in sea water, plasma or other dense media due to the fact that the absence of (E,B) fields eliminates the possibility of induced charge and current response in the media being transited.

Abstract: An object identification method includes: generating a tracking sample and an adversarial sample; training a teacher model according to the tracking sample; and initializing a student model according to the teacher model. The student model adjusts a plurality of parameters according to the teacher model and the adversarial sample, in response to the vector difference between the output result of the student model and the output result of the teacher model being lower than the learning threshold, the student model is deemed to have completed training, and the student model is extracted as an object identification model.

Abstract: Provided is a device cradle. The device cradle includes a fixer for fixing a device, a body portion including a main body and a penetrating groove which is formed to penetrate the main body from a top surface of the main body and accommodates at least a portion of the fixer, and a first driving portion for rotating the fixer by a specified angle around a horizontal axis with respect to the body portion, wherein the penetrating groove has a spherical shape, and wherein at least part of the fixer may be formed in a spherical shape.

Abstract: The present invention discloses methods and systems for communicating at a cellular router between a first wireless communication module and a first subscriber identity module (SIM). The cellular router receives a first request from a first wireless communication module and encapsulates the first request in a first modified request. The cellular router then sends the first modified request to a first SIM card in a first communication apparatus and waits for a first modified reply. While waiting for the first modified reply the cellular router sends at least one halt message to the first wireless communication module after a first time threshold. After receiving the first modified reply, the cellular router decapsulates the first modified reply to retrieve a first reply and sends the first reply to the first wireless communication module where the first modified reply is a reply to the first modified request.

Abstract: A system includes a sensor capturing sensor data and an imager capturing imager data in combination with a computing device configured to time calibrate the sensor and the imager, detect an occurrence of a first event in the sensor data and extract a first time at which the first event occurred, and identify a first image in the image data having a capture time correlating to the first time at which the first event occurred in combination with retrieving the first image for insertion into a composite image including further image data combined with at least a portion of the first image depicting the first event.

Abstract: For reconstruction in medical imaging, such as reconstruction in MR imaging, the number of iterations in deep learning-based reconstruction may be reduced by including a learnable extrapolation in one or more iterations. Regularization may be provided in fewer than all of the iterations of the reconstruction. The result of either approach alone or both together is better quality reconstruction and/or less computationally expensive reconstruction.

Abstract: A system, method, and apparatus for keying radio transmitters in data transmission. The invention includes an automated Push-To-Talk (PTT) transmitter keying/enable switch to facilitate data mode communication via radio stations. An embodiment of the present invention provides low latency or delay; independent from other firmware/subroutine layers executing properly; independent of computer ‘power management’ arbitrarily and capriciously shutting down vital parts of the communications chain. The PTT transmitter keying switch includes a buffering resistor to smooth a load impedance and a charge pump power demand of a driving audio input signal. A plurality of charge pump capacitors are configured to store a source charge of the driving audio input signal for the voltage multiplier. A collector of a switch transistor is configured for connection with a PTT input of the radio station transmitter. An emitter of the switch transistor coupled to the ground.

Abstract: A radio frequency (RF) aperture includes an interface board. An array of electrically conductive tapered projections have bases disposed on a front side of the interface printed circuit board and extend away from the front side of the interface printed circuit board. RF circuitry is disposed at the back side of the interface board and is electrically connected with the electrically conductive tapered projections.

Abstract: A digital up-converter (DUC) includes conjugate-mixer-combiner. The conjugate-mixer-combiner includes a pre-combiner configured to generate combinations of a first in-phase (I) value to be transmitted at a first frequency of a first frequency band, a first quadrature (Q) value to be transmitted at the first frequency of a first frequency band, a second I value for to be transmitted at a second frequency of a second frequency band, and a second Q value to be transmitted at the second frequency of a second frequency band. The conjugate-mixer-combiner further includes a plurality of multipliers collectively configured to shift the combinations based on an average difference between the first frequency and the second frequency.

Abstract: A radio frequency module includes: a module board; a first electronic component and a second electronic component that are disposed apart from each other on the module board; and a third electronic component that is electrically connected to both the first electronic component and the second electronic component, and is disposed extending across the first electronic component and the second electronic component.

Abstract: An information handling system includes a network interface card and a processor. The network interface card includes first and second virtual network interface cards. The processor communicates with the network interface card, and connects with an infrastructure access point via the first virtual network interface card. The processor forms multiple network connections via the infrastructure access point and the first virtual network interface card. The processor detects a wireless dock is within a predetermined proximity. In response to the wireless dock being within the predetermined proximity, the processor initiates a connection with the wireless dock via the second virtual network interface card. The processor resumes one or more of the network connections through the wireless dock via the second virtual network interface card. In response to the one or more of the network connections being resumed through the wireless dock, the processor drops the connection with the infrastructure access point.

Abstract: In an approach to efficient model adjustment utilizing a dual model system, one or more computer processors create a subset of a dataset utilizing a trained primary model; create a secondary model based on the created subset of the dataset; calculate a confidence of a case utilizing the trained primary model, wherein the confidence is a robustness indicator of a model indicating a capacity of the model to meet or exceed performance when applied to the dataset; responsive to the calculated confidence not exceeding a confidence threshold, generate a score of the case utilizing the created secondary model; responsive to an incorrect classification, update the created subset of the dataset with the case; retrain the secondary model utilizing the updated subset of the dataset.

Abstract: Methods for identifying a disease state in a patient and/or for treating a patient having the identified disease state are disclosed and can be based on characteristics identified through machine learning models such as deep learning convolutional neural networks and that are associated with video recordings, audio recordings, infrared images, photographs, and/or radiologic patient images.

Abstract: A noise reduction structure includes an antenna, a noise source, an electromagnetic conductor, and a grounding member. The antenna has a transmission and reception bandwidth. The noise source radiates an electromagnetic wave. Frequency of the electromagnetic wave falls within the transmission and reception bandwidth. The electromagnetic conductor is closer to the antenna than the noise source. The grounding member is disposed at the noise source in such a manner to face the antenna. The grounding member is electrically isolated from the electromagnetic conductor and forms a good grounding path to the noise source. Furthermore, a transmission dock with the noise reduction structure is provided.

Abstract: An apparatus includes an amplifier circuit and a protection circuit. The amplifier circuit may be configured to generate an output signal by amplifying an input signal received at an input port. The input signal may be a radio-frequency signal. The protection circuit may be configured to (i) generate a detection signal by detecting when a level of the input signal exceeds a corresponding threshold, where the level is a power level, a voltage level or both, (ii) route the input signal away from the input port of the amplifier circuit and disable the amplifier circuit both in response to the detection signal being continuously active at least a first time duration and (iii) route the input signal to the input port of the amplifier circuit and enable the amplifier circuit both in response to the detection signal being continuously inactive at least a second time duration.

Abstract: Implementations are directed to receiving a target object model representative of a target object, receiving a source object model representative of a source object, defining a set of target segments and a set of source segments using a segmentation machine learning (ML) model, for each target segment and source segment pair in a set of target segment and source segment pairs, generating a compatibility score representing a degree of similarity between a target segment and a source segment, the compatibility score calculated based on global feature representations of each of the target segment and the source segment, each global feature representation determined from a ML model, selecting a source segment for style transfer based on compatibility scores, and merging the source segment into the target object model to replace a respective target segment within the target object model and providing a stylized target object model.