Abstract: A system that can determine states of human activity and transitions between those states using wireless signal data. A machine learning model such as a Hidden Markov Model (HMM) may be trained to determine transitions between states of human activity (e.g., static, slow movement, fast movement) using information from wireless signal data, such as channel state information gathered from Wi-Fi signal beacons. Depending on the state of the human activity the system may then cause certain commands to be executed corresponding to the human activity such as turning on a certain configuration of lights, playing certain music, or the like.

Abstract: Apparatuses, methods, and systems for touch pad sensors that are further operative as antennas, are disclosed. One apparatus includes a plurality of touch sensors, a controller, and a radio. The controller is operative to receive sensed signals of the plurality of touch sensors, and provide controls of the apparatus in response to the receive sensed signals of the plurality of touch sensors. The radio is operative to wirelessly communicate with an external wireless device. One or more conductive lines electrically connect the radio with one or more of the plurality of touch sensors, wherein the plurality of touch sensors operate as one or more antennas for facilitating the wireless communication.

Abstract: A method, apparatus, device, and storage medium for voice interaction. A specific embodiment of the method includes: extracting an acoustic feature from received voice data, the acoustic feature indicating a short-term amplitude spectrum characteristic of the voice data; applying the acoustic feature to a type recognition model to determine an intention type of the voice data, the intention type being one of an interaction intention type and a non-interaction intention type, and the type recognition model being constructed based on the acoustic feature of training voice data; and performing an interaction operation indicated by the voice data, based on determining that the intention type is the interaction intention type.

Abstract: Microfluidic devices, systems, and methods.

Abstract: Disclosed are systems, devices and methods for imaging and image-based sorting of particles in a flow system, including cells in a flow cytometer. In some aspects, a system includes a particle flow device to flow particles through a channel, an imaging system to obtain image data of a particle during flow through the channel, a processing unit to determine a property associated with the particle and to produce a control command for sorting the particle based on sorting criteria associated with particle properties, and an actuator to direct the particle into one of a plurality of output paths of the particle flow device in real-time.

Abstract: Apparatuses, methods, and systems for touch pad sensors that are further operative as antennas, are disclosed. One apparatus includes a plurality of touch sensors, a controller, and a radio. The controller is operative to receive sensed signals of the plurality of touch sensors, and provide controls of the apparatus in response to the receive sensed signals of the plurality of touch sensors. The radio is operative to wirelessly communicate with an external wireless device. One or more conductive lines electrically connect the radio with one or more of the plurality of touch sensors, wherein the plurality of touch sensors operate as one or more antennas for facilitating the wireless communication.

Abstract: A method and an apparatus for recognizing a voice are provided. The method may include: inputting a target voice into a pre-trained voice recognition model to obtain an initial text output by at least one recognition network in the voice recognition model, the recognition network including a plurality of preset types of processing layers, and at least one type of processing layer of the recognition network being obtained by training based on a voice sample in a preset direction interval; and determining a voice recognition result of the target voice, based on the initial text.

Abstract: The present disclosure provides a method and apparatus for detecting a voice, relates to the fields of voice processing and deep learning technology. The method may include: acquiring a target voice; and inputting the target voice into a pre-trained deep neural network to obtain whether the target voice has a sub-voice in each of a plurality of preset direction intervals, the deep neural network being used to predict whether the voice has a sub-voice in each of the plurality of direction intervals.

Abstract: The disclosure provides a speech recognition method, a device and a computer-readable storage medium. The method includes obtaining a first voice signal collected from a first microphone in a microphone array and a second voice signal collected from a second microphone in the microphone array, the microphone array including at least two microphones, such as two, three or six microphones. The method further includes extracting enhanced features associated with the first voice signal and the second voice signal through a neural network, and obtaining a speech recognition result based on the enhanced features extracted.

Abstract: A method, apparatus, device, and storage medium for voice interaction. A specific embodiment of the method includes: extracting an acoustic feature from received voice data, the acoustic feature indicating a short-term amplitude spectrum characteristic of the voice data; applying the acoustic feature to a type recognition model to determine an intention type of the voice data, the intention type being one of an interaction intention type and a non-interaction intention type, and the type recognition model being constructed based on the acoustic feature of training voice data; and performing an interaction operation indicated by the voice data, based on determining that the intention type is the interaction intention type.

Abstract: Disclosed are systems, devices and methods for imaging and image-based sorting of particles in a flow system, including cells in a flow cytometer. In some aspects, a system includes a particle flow device to flow particles through a channel, an imaging system to obtain image data of a particle during flow through the channel, a processing unit to determine a property associated with the particle and to produce a control command for sorting the particle based on sorting criteria associated with particle properties, and an actuator to direct the particle into one of a plurality of output paths of the particle flow device in real-time.

Abstract: Apparatuses, methods, and systems for touch pad sensors that are further operative as antennas, are disclosed. One apparatus includes a plurality of touch sensors, a controller, and a radio. The controller is operative to receive sensed signals of the plurality of touch sensors, and provide controls of the apparatus in response to the receive sensed signals of the plurality of touch sensors. The radio is operative to wirelessly communicate with an external wireless device. One or more conductive lines electrically connect the radio with one or more of the plurality of touch sensors, wherein the plurality of touch sensors operate as one or more antennas for facilitating the wireless communication.

Abstract: Apparatuses, methods, and systems for a hinge feed antenna of eyewear, are disclosed. One apparatus includes a front frame, a temple arm, and a hinge rotatably connecting the front frame and the temple arm. For an embodiment, the temple arm includes a circuit assembly including at least a radio that supports wireless communication. For an embodiment, the front frame includes one or more antennas traces, wherein the hinge conductively connects the circuit assembly to the one or more antenna traces, and wherein the hinge in combination with the one or more antenna traces form one or more antennas for supporting the wireless communication.

Abstract: The present disclosure discloses an interactive voice-control method and apparatus, a device and a medium. The method includes: obtaining a sound signal at a voice interaction device and recognized information that is recognized from the sound signal; determining an interaction confidence of the sound signal based at least on at least one of an acoustic feature representation of the sound signal and a semantic feature representation associated with the recognized information; determining a matching status between the recognized information and the sound signal; and providing the interaction confidence and the matching status for controlling a response of the voice interaction device to the sound signal.

Abstract: Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. A novel joint deep learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neutral network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representation. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration.

Abstract: An object-based convolutional neural network (OCNN) method and system for urban land use classification from VFSR imagery are described. In the OCNN, segmented objects consisting of linearly shaped objects (LS-objects) and other general objects (G-objects), are utilized as functional units. The G-objects are precisely identified and labelled through a single large input window (128×128) CNN with a deep (eight-layer) network to perform a contextual object-based classification. Whereas the LS-objects are each distinguished accurately using a range of small input window (48×48) CNNs with less deep (six-layer) networks along the objects' lengths through majority voting. The locations of the input image patches for both CNN networks are determined by considering both object geometry and its spatial anisotropy, such as to accurately classify the objects into urban land use classes.

Abstract: The present invention relates to methods for a blockchain search system, and particularly, the present invention relates to methods for assuring the data integrity on blockchain hybrid storage. Furthermore, the present invention provides a cost-efficient update scheme to maintain the on-chain index and authenticated data structure and is efficient and scalable under various operational parameters.

Abstract: An object-based convolutional neural network (OCNN) method and system for urban land use classification from VFSR imagery are described. In the OCNN, segmented objects consisting of linearly shaped objects (LS-objects) and other general objects (G-objects), are utilized as functional units. The G-objects are precisely identified and labelled through a single large input window (128×128) CNN with a deep (eight-layer) network to perform a contextual object-based classification. Whereas the LS-objects are each distinguished accurately using a range of small input window (48×48) CNNs with less deep (six-layer) networks along the objects' lengths through majority voting. The locations of the input image patches for both CNN networks are determined by considering both object geometry and its spatial anisotropy, such as to accurately classify the objects into urban land use classes.

Abstract: Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. A novel joint deep learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neutral network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representation. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration.

Abstract: Disclosed are systems, devices and methods for imaging and image-based sorting of particles in a flow system, including cells in a flow cytometer. In some aspects, a system includes a particle flow device to flow particles through a channel, an imaging system to obtain image data of a particle during flow through the channel, a processing unit to determine a property associated with the particle and to produce a control command for sorting the particle based on sorting criteria associated with particle properties, and an actuator to direct the particle into one of a plurality of output paths of the particle flow device in real-time.