Abstract: An encoder includes circuitry and memory. Using the memory, the circuitry: encodes an original image and decodes the original image encoded, to generate a first bitstream and a local decoded image; encodes supplemental information and decodes the encoded supplemental information, to generate a second bitstream and local decoded supplemental information; inputs data based on the local decoded image and the local decoded supplemental information to a post processing network which is a neural network, to cause a reconstructed image to be output from the post processing network, the reconstructed image corresponding to the original image and being to be used to encode a following original image which follows the original image; and concatenates the first bitstream and the second bitstream to generate a concatenated bitstream.

Abstract: In an example, the present invention provides an UWB and FMCW sensor apparatus, with an audio module and inertial motion module. The apparatus has at least three transceiver modules. Each of the transceiver modules has an antenna array to be configured to sense a back scatter of electromagnetic energy from spatial location of a zero degree location in relation to a mid point of the device through a 360 degrees range where each antenna array is configured to sense a 120 degree range. In an example, each of the antenna array has a support member, a plurality of receiving antenna, a receiver integrated circuit coupled to the receiving antenna and configured to receive an incoming rf signal and covert the incoming rf signal into a base band signal, and a plurality of transmitting antenna.

Abstract: In an example, the present invention provides a method for identifying a status of a user and providing feedback to the user based upon the status of the user. In an example, the method includes generating an electromagnetic radio frequency (RF) signal from an RF generating device and emitting the electromagnetic RF signal into a spatial region of a user. In an example, the method includes detecting a back scattered RF signal, using an RF receiving antenna, from the user and converting the back scattered RF signal from an RF analog signal into an RF digital signal.

Abstract: In an example, the present invention provides a method for identifying a status of a user and providing feedback to the user based upon the status of the user. In an example, the method includes generating an electromagnetic radio frequency (RF) signal from an RF generating device and emitting the electromagnetic RF signal into a spatial region of a user. In an example, the method includes detecting a back scattered RF signal, using an RF receiving antenna, from the user and converting the back scattered RF signal from an RF analog signal into an RF digital signal.

Abstract: Inputting an image to a neural network, performing convolution on a current frame included in the image to calculate a current feature map, which is a feature map at a present time, combining a past feature map, which is obtained by performing convolution on a past frame included in the image, and the current feature map, estimating an object candidate area using the combined past feature map and current feature map, estimating positional information and identification information regarding the one or more objects included in the current frame using the combined past feature map and current feature map and the estimated object candidate area, and outputting the positional information and the identification information regarding the one or more objects included in the current frame of the image estimated in the estimating as object detection results are included.

Abstract: In an example, the present invention provides a method for identifying a status of a user and providing feedback to the user based upon the status of the user. In an example, the method includes generating an electromagnetic radio frequency (RF) signal from an RF generating device and emitting the electromagnetic RF signal into a spatial region of a user. In an example, the method includes detecting a back scattered RF signal, using an RF receiving antenna, from the user and converting the back scattered RF signal from an RF analog signal into an RF digital signal.

Abstract: A method for a computing system includes beaming with a radar emitter from a central hub, a plurality of radar signals into an interior room, receiving with a radar receiver within the central hub, a plurality of radar reflections in response to the plurality of radar signals, processing with a processor within the central hub, the plurality of radar reflections to form a plurality of processed radar signals, determining with an AI processor within the central hub, at least one activity from a plurality of activities in response to the plurality of processed radar signals, and determining with a central processing unit within the central hub, a notification action to perform in response to the one or more activities.

Abstract: An image processor includes memory and circuitry. The circuitry performs processing of approximating a decompressed image to an original image by using a neural network model trained to approximate the decompressed image to the original image. The decompressed image is obtained as a result of compression of the original image and decompression of the compressed image. The neural network model includes one or more convolutional blocks, and includes one or more residual blocks. Each of the one or more convolutional blocks is a processing block including a convolutional layer. Each of the one or more residual blocks includes a convolutional group including at least one of the one or more convolutional blocks, inputs data which is input to the residual block to the convolutional group included in the residual block, and adds the data input to the residual block to data to be output from the convolutional group.

Abstract: In an example, the present invention provides an UWB and FMCW sensor apparatus. The apparatus has at least three transceiver modules. Each of the transceiver modules has an antenna array to be configured to sense a back scatter of electromagnetic energy from spatial location of a zero degree location in relation to a mid point of the device through a 360 degrees range where each antenna array is configured to sense a 120 degree range. In an example, each of the antenna array has a support member, a plurality of receiving antenna, a receiver integrated circuit coupled to the receiving antenna and configured to receive an incoming rf signal and covert the incoming rf signal into a base band signal, and a plurality of transmitting antenna. Each antenna array has a transmitter integrated circuit coupled to the transmitting antenna to transmit an outgoing rf signal.

Abstract: In an example, the present invention provides an UWB and FMCW sensor apparatus, with an audio module and inertial motion module. The apparatus has at least three transceiver modules. Each of the transceiver modules has an antenna array to be configured to sense a back scatter of electromagnetic energy from spatial location of a zero degree location in relation to a mid point of the device through a 360 degrees range where each antenna array is configured to sense a 120 degree range. In an example, each of the antenna array has a support member, a plurality of receiving antenna, a receiver integrated circuit coupled to the receiving antenna and configured to receive an incoming rf signal and covert the incoming rf signal into a base band signal, and a plurality of transmitting antenna.

Abstract: In an example, the present invention provides an UWB and FMCW sensor apparatus, with an audio module and inertial motion module. The apparatus has at least three transceiver modules. Each of the transceiver modules has an antenna array to be configured to sense a back scatter of electromagnetic energy from spatial location of a zero degree location in relation to a mid point of the device through a 360 degrees range where each antenna array is configured to sense a 120 degree range. In an example, each of the antenna array has a support member, a plurality of receiving antenna, a receiver integrated circuit coupled to the receiving antenna and configured to receive an incoming rf signal and covert the incoming rf signal into a base band signal, and a plurality of transmitting antenna.

Abstract: Executing a deep neural network by obtaining, during deep neural network inference, a binary intermediate feature map in binary representation by converting a floating-point or fixed-point intermediate feature map into a binary vector using a first transformation module; generating a compressed feature map by compressing the binary intermediate feature map using a nonlinear dimensionality reduction layer; storing the compressed feature map into memory; reconstructing the binary intermediate feature map by decompressing the compressed feature map read from the memory using a reconstruction layer corresponding to the nonlinear dimensionality reduction layer; and converting the reconstructed binary intermediate feature map into a floating-point or fixed-point intermediate feature map using a second transformation module.

Abstract: An information processing method performed using a computer includes: obtaining first sensor data (SD) that is SD of a scene and includes noise; inputting the first SD to a single converter, and obtaining second SD outputted from the single converter as a result of denoising performed on the first SD by the single converter; obtaining third SD that is SD of a scene identical or corresponding to the scene, does not include the noise, and is different from the second SD; obtaining feature information of the second SD and feature information of the third SD, based on the second SD and the third SD, respectively; and training the single converter by machine learning using the second SD and the feature information of the second SD as converted data, and using the third SD and the feature information of the third SD as reference data corresponding to the converted data.

Abstract: The encoder includes processing circuitry, and memory. Using the memory, the processing circuitry: generates a predicted image of an input image that is a current image to be encoded, based on generated data output from a generator network in response to a reference image being input to the generator network, the generator network being a neural network; calculates a prediction error by subtracting the predicted image from the input image; and generates an encoded image by at least transforming the prediction error.

Abstract: A determination method for determining the structure of a convolutional neural network includes acquiring N filters having the weights trained using a training image group as the initial values, where N is a natural number greater than or equal to 1, and increasing the number of the filters from N to M, where M is a natural number greater than or equal to 2 and is greater than N, by adding a filter obtained by performing a transformation used in image processing fields on at least one of the N filters.

Abstract: In an image recognition method executed by a computer of an image recognizer using a convolutional neural network, the convolutional neural network is a first convolutional neural network in which a fully connected layer is changed to a convolutional layer, and the method includes controlling a first convolutional neural network to acquire an input image, controlling the first convolutional neural network to estimate a center area of a recognition target in the acquired input image and to output a value indicating the estimated center area as a location of the recognition target in the input image.

Abstract: An information processing method includes: inputting an input tensor indicating data to a processor having a memory; causing the processor to perform, after elements of the input tensor are subjected to precomputation for conversion into a power-of-two format and are stored in the memory, convolution operation processing with only addition and shift operations by using the precomputed elements of the input tensor stored in the memory and weight tensors that are pre-converted into the power-of-two format in accordance with a predetermined algorithm, that are stored in the memory, and that indicate weights having a possibility of being used for a convolution operation; and outputting, as an output tensor, the elements of the input tensor on which the convolution operation processing is performed.

Abstract: A learning method includes an input process to input, to a neural network, a first image and a second image that constitute a moving image and that are temporally adjacent to each other, where the second image is an image subsequent to the first image with a predetermined time interval therebetween, a learning process to cause the neural network to use the first image and the second image and learn to output a transformation matrix applied to all pixels of the first image and used to convert the first image into the second image, and an output process to output, as a result of estimation of motion between the first image and the second image, a motion amount image generated from the transformation matrix and representing an amount of motion of each of the pixels of the first image that continues until the predetermined time interval elapses.

Abstract: Inputting an image to a neural network, performing convolution on a current frame included in the image to calculate a current feature map, which is a feature map at a present time, combining a past feature map, which is obtained by performing convolution on a past frame included in the image, and the current feature map, estimating an object candidate area using the combined past feature map and current feature map, estimating positional information and identification information regarding the one or more objects included in the current frame using the combined past feature map and current feature map and the estimated object candidate area, and outputting the positional information and the identification information regarding the one or more objects included in the current frame of the image estimated in the estimating as object detection results are included.

Abstract: An aircraft having a fixed wing is operative to perform vertical takeoff and landing while positioned in a nose-down orientation. The aircraft has a fixed wing having a leading edge and a trailing edge; a propulsion system operative to selectively provide forward propulsion and rearward propulsion; and a controller operative to control operation of the propulsion system. The propulsion system provides rearward propulsion during takeoff of the aircraft to move the aircraft in a direction of the trailing edge of the fixed wing, and provides forward propulsion during flight of the aircraft to move the aircraft in a direction of the leading edge of the fixed wing. The aircraft maintains the wing substantially vertical with the trailing edge facing upwards during takeoff, and transitions to having the wing substantially horizontal during flight. A vertical landing procedure is also provided.