Abstract: A method and wireless device with a counterpart presence detector is provided. The detector includes only memory and logic circuits to detect a change in a ringing artifact from the presence of a RF device. The memory stores template signals and threshold values. The template signals may be generated using a metal object positioned various distances from the RF device. The ringing artifact, also called an RF-off signal, is sampled. A difference between subsequent ringing artifact samples is taken to produce a ringing artifact difference. Also, a difference between subsequent samples of a template signal is taken to produce a template difference. A Euclidean distance between the ringing artifact difference and the template difference is determined. The distance is compared to a threshold value. A presence of another RF device within the RF field of the RF device is detected if the distance compares favorably to the threshold value.

Abstract: Systems and methods implement of high-speed delay scanning for spectroscopic SRS imaging characterized by scanning a first pulsed beam across a stepwise reflective surface (such as a stepwise mirror or a reflective blazed grating) in a Littrow configuration to generate near continuous temporal delays relative to a second pulsed beam. Systems and methods also implement deep learning techniques for image restoration of spectroscopic SRS images using a trained encoder-decoder convolution neural network (CNN) which in some embodiments may be designed as a spatial-spectral residual net (SS-ResNet) characterized by two parallel filters including a first convolution filter on the spatial domain and a second convolution filter on the spectral domain.

Abstract: A device may include an image sensor, configured to generate image sensor data representing a vicinity of the device; an image processor, configured to generate a first code from the image sensor data using a code generation protocol; an input validity checker, configured to compare the first code to a second code; and if a similarity of the first code and the second code is within a predetermined range, operate according to a first operational mode; and if the similarity of the first code and the second code is not within the predetermined range, operate according to a second operational mode.

Abstract: Systems and methods are disclosed for processing digital images to identify diagnostic tests, the method comprising receiving one or more digital images associated with a pathology specimen, determining a plurality of diagnostic tests, applying a machine learning system to the one or more digital images to identify any prerequisite conditions for each of the plurality of diagnostic tests to be applicable, the machine learning system having been trained by processing a plurality of training images, identifying, using the machine learning system, applicable diagnostic tests of the plurality of diagnostic tests based on the one or more digital images and the prerequisite conditions, and outputting the applicable diagnostic tests to a digital storage device and/or display.

Abstract: Improvement in heat dissipation capability is intended. A radio-frequency module includes a mounting substrate, a first transmission filter, a second transmission filter, a resin layer, and a shield layer. The second transmission filter is higher in power class than the first transmission filter. The resin layer covers at least part of an outer peripheral surface of the first transmission filter and covers at least part of an outer peripheral surface of the second transmission filter. The shield layer overlaps at least part of the second transmission filter in plan view in a thickness direction of the mounting substrate. At least part of a major surface of the second transmission filter on an opposite side to the mounting substrate side is in contact with the shield layer.

Abstract: Systems and methods are provided for estimating the 3D joint location of skeleton joints from an image segment of an object and a 2D joint heatmaps comprising 2D locations of skeleton joints on the image segment. This includes applying the image segment and 2D joint heatmaps to a convolutional neural network containing at least one 3D convolutional layer block, wherein the 2D resolution is reduced at each 3D convolutional layer and the depth resolution is expanded to produce an estimated depth for each joint. Combining the 2D location of each kind of joint with the estimated depth of the kind of joint generates an estimated 3D joint position of the skeleton joint.

Abstract: Systems and methods for detecting bad installations of telematics devices are provided. The method involves operating at least one processor to: receive image data associated with an installation of a telematics device in a vehicle; extract a portion of the image data containing the telematics device using a first machine learning model trained to detect the telematics device in the image data; determine whether the telematics device was correctly installed using a second machine learning model on the extracted portion of the image data, the second machine learning model trained to classify the telematics device in the extracted portion of the image data based on a position and orientation of at least one fastener attached to the telematics device; and automatically execute at least one action in response to and based on the determination of whether the telematics device was correctly installed.

Abstract: A learning device includes: a data acquisition unit that acquires learning target data that is full-size data of a learning target; a data generation unit that divides the learning target data to generate multiple pieces of first divided data that is divided data of the learning target data, and adds, to each piece of the first divided data, first identification information for identifying a region of the first divided data in the learning target data; and a model generation unit that generates a learned model for determining an anomaly in the first divided data using first correspondence information that is a set of the first divided data and the first identification information corresponding to the first divided data.

Abstract: It is desirable to provide a technology making it possible to enhance noticeability of a virtual object. Provided is an information processing apparatus that includes a display controlling section that causes a display device to start displaying a first virtual object while a user is moving in a real space, performs distance keeping control to substantially keep a distance between the user and the first virtual object, ends the distance keeping control in a case where a predetermined condition is satisfied, and changes the distance between the user and the first virtual object.

Abstract: A computer implemented method is disclosed for identifying a pose of a probe by registering an ultrasound image with volumetric scan data. The volumetric scan data is processed (31) to determine simulated ultrasound images corresponding with a plurality of different poses of the probe. A feature vector is extracted (32) from each of the simulated ultrasound images and from the ultrasound image. The feature vector from the ultrasound image is compared (33) with each feature vector of the simulated ultrasound images to determine a distance or similarity for each simulated ultrasound image. At least one candidate image is selected (34), the at least one candidate image comprising a subset of the simulated ultrasound images that best matches the ultrasound image, based on the distance or similarity. The pose of the probe is identified (35) from the at least one candidate image.

Abstract: Described here are systems and method for predicting clinically relevant brain features using geometric deep learning techniques, such as may be implemented with graph convolutional neural networks or autoencoder networks that are applied to graph representations of brain surface morphology derived from medical images. As an example, graph convolutional neural networks can be applied to brain surface morphology data derived from magnetic resonance images (e.g., T1-weighted) using surface extraction techniques in order to predict brain feature data.

Abstract: Example systems and methods of selection of video frames using a machine learning (ML) predictor program are disclosed. The ML predictor program may generate predicted cropping boundaries for any given input image. Training raw images associated with respective sets of training master images indicative of cropping characteristics for the training raw image may be input to the ML predictor, and the ML predictor program trained to predict cropping boundaries for raw image based on expected cropping boundaries associated training master images. At runtime, the trained ML predictor program may be applied to a sequence of video image frames to determine for each respective video image frame a respective score corresponding to a highest statistical confidence associated with one or more subsets of cropping boundaries predicted for the respective video image frame. Information indicative of the respective video image frame having the highest score may be stored or recorded.

Abstract: Disclosed is a system and method for presenting a graphical representation of an oral situation of a patient over time. In particular the system and method relates to a method of presenting a plurality of scans taken over time in manner where the focus on the changes in the oral situation of the patient is maintained.

Abstract: A method for detecting a surface defect based on a perceptual autoencoder is provided, which belongs to the field of integrated circuit defect detection. By using perceptual loss instead of pixel-by-pixel loss, the problem of blurred single-pixel-loss output image with serious distortion at the edges is solved. This manner is highly migratory, and it may migrate well to the new target to be detected. On the other hand, by using the feature pyramid instead of the sliding window manner, the feature pyramid does not require a plurality of redundant computation by sliding on the original image, which effectively reduces the memory and computation cost. A system for detecting a surface defect based on a perceptual autoencoder is also provided, which includes specific modules for implementing the above method. In addition, a computer device and a storage medium of the above method are provided, for accomplishing operations of the above method.

Abstract: Provided are a scrap determination system and method that can improve scrap determination techniques. A scrap determination system (1) comprises: a first scrap determination model (221) generated using teaching data including first learning images, and determines, based on a camera image, grade of scrap in the image and a ratio of the grade; a second scrap determination model (222) generated using teaching data including second learning images, and determines, based on the image, grade of scrap in the image and a ratio of the grade; a selection model (223) configured to determine which of the first scrap determination model (221) and the second scrap determination model (222) is to be used, based on the image; and an output section (24) configured to output information of each grade of scrap and a ratio of the grade determined based on the image using the model selected by the selection model (223).

Abstract: Systems and techniques are provided for deep learning model optimizations. An example process can include generating, based on processing data using a neural network model, a plurality of prediction outputs associated with a plurality of output channels corresponding to a multi-channel prediction target; determining that a prediction output from the plurality of prediction outputs has a value that is outside of a quantization range and one or more remaining prediction outputs from the plurality of prediction outputs have a respective value that is within the quantization range; clamping the prediction output based on the quantization range; and generating a single channel output based on the clamped prediction output and the one or more remaining prediction outputs.

Abstract: Training of a machine learning model is included. An electronic device includes a memory storing a machine learning model including a discriminator model, and a processor configured to extract, from a training image, a first image patch and a second image patch at least partially overlapping the first image patch, extract, from the first image patch, a first feature map based on a layer of the discriminator model, extract, from the second image patch, a second feature map based on the layer, extract a first partial feature map from a projected map that is projected based on the first feature map, and train the discriminator model based on a first objective function value generated based on a second partial feature map and the first partial feature map, wherein the second partial feature map corresponds to a part of the extracted second feature map.

Abstract: Image processing technology embeds signal (e.g., digital watermarks) within imagery during a raster image process(or). One claim recites: an image processing method of embedding a signal within imagery using a raster image processing (RIP), comprising: obtaining a plurality of elements representing a signal; and modulating a plurality of print structures within the RIP according to the plurality of elements, in which said modulating varies density, and direction or angle, of the plurality of print structures, and in which said modulating introduces the signal within the imagery. Of course, other claims, combinations and technology are described too.

Abstract: A process for right-sizing a software stack includes: identifying a first wireless communications node (1WCN) for a wireless communications system (WCS) that includes a second wireless communications node (2WCN), obtaining a first adaptive software stack (1ASS) which configures the Device to communicate data to a Destination using the 1WCN, and determining if the 1ASS optimizes the Device for the communicating of the data with the Destination via the 1WCN. If not optimized, a second adaptive software stack (2ASS) may be obtained and a determination made as to whether the 2ASS second optimizes the Device. At least one of the 1ASS and the 2ASS may be implemented and the Device is coupled with the 1WCN by a communications links. A first application may be selected and executed. During software execution application data may be communicated with the Destination using the 1WCN and the as implemented 1ASS or 2ASS.

Abstract: An image processing device includes an input/output interface, and at least one processor. The at least one processor executes: detecting feature points in a facial image of an object included in an image taken from the input/output interface; acquiring first distance information between the feature points before a beauty treatment is performed; acquiring second distance information between the feature points at a second timing after the beauty treatment is performed; acquiring a difference value between the first distance information and the second distance information; and determining whether the beauty treatment is correctly given or has been given based on a different value between the acquired distance information.