Abstract: New radio download numerology allocation information may be obtained through master information block data, system information block data, radio resource control signals, or signals or a physical downlink numerology indication channel, and used along with a reference signal detected in a search space to obtain resource element positions in an antenna port reference signal in a resource block that belongs to a particular band slice according to a reference signal allocation scheme for a band slice numerology. A physical download control may then be decoded based upon one or more resource elements of the reference signal, allowing the connection of, e.g., an enhanced mobile broadband, massive machine type communication, or ultra-reliable/low-latency application to a communications network thereby. Alternatively, multiple physical downlink control channels may be blindly demodulated at each of a number calculated reference signal locations, and one channel selected based on passing a cyclic redundancy check.

Abstract: A system for 3D medical image segmentation includes a medical imaging device for obtaining a plurality of 2D images forming a volumetric image, processing circuitry, and a display. The processing circuitry is configured with a first stage to divide the volumetric image into 3D image patches, a hierarchical encoder-decoder structure in which resolution of features of the 3D image patches is decreased by a factor of two in each of a plurality of stages of the encoder, an encoder output connected to the decoder via skip connections, and a convolutional block to produce a voxel-wise final segmentation mask. The encoder includes a plurality of efficient paired attention blocks each with a spatial attention branch and a channel attention branch that learn respective spatial and channel attention feature maps. The display displays the final segmentation mask.

Abstract: A computer-implemented method and apparatus for determining morphology of a human breast, the method comprising: obtaining at least one image of a subject; extracting features of at least a portion of the subject's body from the at least one image, wherein the features correspond to a model of standard human anatomy; generating a three-dimensional model of the subject's body based on the extracted features and the model of standard human anatomy; and determining a morphological parameter of the subject's breast from the three-dimensional model of the subject's body. In another aspect, a computer implemented method is presented for adjusting a virtual garment to fit the three-dimensional model and determining a parameter based on the adjusted virtual garment. In a further aspect, there is provided a method for providing a manufactured wearable garment.

Abstract: A method and system for multi-modal prompt learning of vision-language models. Encodings of image-text pairs can be combined with image prompts and text prompts before being input into an image encoder and text encoder of a vision-language model respectively. The image prompt can be generated using the text prompt using a vision-language coupling function to encourage synergy between the two prompts. The combination of encodings and prompts can be fed through the transformer layers of the encoders, and the output of each layer can be combined with a new prompt before entering the next layer, up until a specific depth. The subsequent transformer layers can process the output and generate a final representation for the image and text which can then be used for downstream tasks.

Abstract: An apparatus and method for a machine learning engine for domain generalization which trains a vision transformer neural network using a training dataset including at least two domains for diagnosis of a medical condition. Image patches and class tokens are processed through a sequence of feature extraction transformer blocks to obtain a predicted class token. In parallel, intermediate class tokens are extracted as outputs of each of the feature extraction transformer blocks, where each transformer block is a sub-model. One sub-model is randomly sampled from the sub-models to obtain a sampled intermediate class token. The intermediate class token is used to make a sub-model prediction. The vision transformer neural network is optimized based on a difference between the predicted class token and the sub-model prediction. Inferencing is performed for a target medical image in a target domain that is different from the at least two domains.

Abstract: An object detection system and method in which a machine learning engine is configured with a region-based knowledge distillation stage that generates region embeddings from a training image having bounding boxes. A linear layer learns a region-level vision-language mapping for projecting feature embeddings from the training image to a common feature space shared by text embeddings to obtain the region embeddings. An image-level supervision stage generates pseudo-box labels for a classification training image and region embeddings from the training image having bounding boxes and corresponding class labels and the classification training image having an image-level label as input. Pseudo-box labels are determined on the classification training image as an image-level vision-language mapping. A weight transfer function conditions the image-level vision-language mapping on the learned region-level vision-language mapping.

Abstract: An edge computing system, computer readable storage medium and method for object detection, including processing circuitry. The processing circuitry is configured with a hybrid CNN and vision transformer backbone network in an object detection deep learning network. The backbone network receives an image, and includes a first convolutional encoder to extract local features from feature maps of the image, a second stage having consecutive second convolutional encoders, a positional encoding layer, split depth-wise transpose attention (SDTA) encoders, consecutive convolutional encoders, a third stage and a fourth stage SDTA encoder. Each of the SDTA encoders perform multi-headed self-attention by applying a dot product operation across channel dimensions in order to compute cross-covariance across channels to generate attention feature maps.

Abstract: A system, computer readable medium and method trains a video transformer, using a machine learning engine, for human action recognition in a video. The method includes sampling video clips with varying temporal resolutions in global views and sampling the video clips from different spatiotemporal windows in local views. The machine learning engine is configured to match the global and local views in a framework of student-teacher networks to learn cross-view correspondence between local and global views, and to learn motion correspondence between varying temporal resolutions. The video transformer can output for display video clips in a manner that emphasizes attention to the recognized human action.

Abstract: A system, method, computer readable storage medium for a computer vision system includes at least one video camera, and video processor circuitry. The method includes inputting a stream of video data and generating a sequence of image frames, segmenting and tracking, by the video analysis apparatus, object instances in the stream of video data, including receiving the sequence of image frames, analyzing the sequence of image frames using a video instance segmentation transformer to obtain a video instance mask sequence from the sequence of image frames, the transformer having a backbone network, a transformer encoder-decoder, and an instance matching and segmentation block, The encoder contains a multi-scale spatio-temporal split attention module to capture spatio-temporal feature relationships at multiple scales across multiple frames. The decoder contains a temporal attention block for enhancing a temporal consistency of transformer queries. The method includes displaying the video instance mask sequence.

Abstract: An optical encryption camera includes a sensor array and a filter positioned over the sensor array to receive light prior to the sensor array. The filter includes a multiplexing mask and a scaling mask in sequence. The multiplexing mask and the scaling mask combine to provide an encryption key to encrypt image data prior to capture.

Abstract: A mobile device and mobile application, in which the mobile device includes a camera having an image capture circuit operating in a mode to capture a RAW image burst, and processing circuitry, including a neural network engine, to generate a single enhanced image from the RAW image burst. The neural network engine executing program instructions including an edge boosting feature alignment stage to remove inter-frame spatial and color misalignment from the RAW image burst to obtain aligned burst frames, a pseudo-burst feature fusion stage to create a set of pseudo-burst features that combine complementary information from the aligned burst frames, and an adaptive group upsampling stage to progressively increase spatial resolution while merging the set of pseudo-burst features and output the single enhanced image. The mobile application and mobile device perform super-resolution, low-light image enhancement, and burst denoising using a RAW image burst.

Abstract: A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

Abstract: In one embodiment, system for aggregating, analyzing, and reporting medical information includes a front end module for managing a user interface, a back end module for exchanging patient information with a clinic record system and obtaining one or more medical images therefrom, a machine learning/artificial intelligence (ML/AI) engine for analyzing said one or more medical images and generating analysis results, and a report generator for generating a report that includes the analysis results. The ML/AI engine can include an anatomical plane classifier such as a 20+2 classifier, and the anatomical structure classifier can apply sematic segmentation.

Abstract: New radio download numerology allocation information may be obtained through master information block data, system information block data, radio resource control signals, or signals or a physical downlink numerology indication channel, and used along with a reference signal detected in a search space to obtain resource element positions in an antenna port reference signal in a resource block that belongs to a particular band slice according to a reference signal allocation scheme for a band slice numerology. A physical download control may then be decoded based upon one or more resource elements of the reference signal, allowing the connection of, e.g., an enhanced mobile broadband, massive machine type communication, or ultra-reliable/low-latency application to a communications network thereby. Alternatively, multiple physical downlink control channels may be blindly demodulated at each of a number calculated reference signal locations, and one channel selected based on passing a cyclic redundancy check.

Abstract: A system and computer readable storage medium for automated handwriting generation, including a text input device for inputting a text query having at least one textual word string, an image input device for inputting a handwriting sample with characters in a writing style of a user, and a computer implemented deep learning transformer model including an encoder network and a decoder network in which each are a hybrid of convolution and multi-head self-attention networks. The encoder produces a sequence of style feature embeddings from the input handwriting sample. The decoder takes the sequence of style feature embeddings in order to convert the at least one textual word string into a generated handwritten image having substantially same writing style as the handwriting sample. An output device to output the generated handwriting image.

Abstract: The present application at least describes a frame structure in new radio. The frame structure includes a self-contained transmission time interval. The transmission time interval includes a control information region including plural beams. The interval also includes a downlink transmission channel region including plural beams. The frame structure is configured for downlink control information to be swept through the time interval. The frame structure is also configured for an uplink or downlink grant resource subsequently to be swept through the time interval. The present application is also directed to a method for configuring user equipment.

Abstract: The present invention relates to a flexible thermoelectric device that may be combined and attached to a curved surface and generates an electromotive force based on a temperature difference between one surface and the other surface, and more particularly, to a flexible thermoelectric device having a radiative cooling part which improves power generation performance by increasing cooling efficiency of a cooling side through radiative cooling and minimizes a volume of a heat dissipating part, and a method of manufacturing a radiative cooling part.

Abstract: A vehicle platform with a variety of impact safety features including front and rear impact features as well as side impact features designed to protect the passenger compartment as well as the battery compartment and vehicle chassis components. Some features may include crumple zone components, deflectors and modular energy absorption units.

Abstract: A vehicle platform with a variety of impact safety features including front and rear impact features as well as side impact features designed to protect the passenger compartment as well as the battery compartment and vehicle chassis components. Some features may include crumple zone components, deflectors and modular energy absorption units.

Abstract: It is recognized herein that current LTE reference signals may be inadequate for future cellular (e.g., New Radio) systems. Configurable reference signals are described herein. The configurable reference signals can support mixed numerologies and different reference signal (RS) functions. Further, reference signals can be configured so as to support beam sweeping and beamforming training.