Abstract: A device includes a vertical transistor comprising a first gate in a first trench, wherein the first gate comprises a dielectric layer and a gate region over the dielectric layer, and a second gate in a second trench, a high voltage lateral transistor immediately adjacent to the vertical transistor and a low voltage lateral transistor, wherein the high voltage lateral transistor is between the vertical transistor and the low voltage lateral transistor.

Abstract: A home computing system (cloud) integrates a protocols gateway, WiFi router, cloud server, and mass storage device to support one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment such as a residential home. The home computing cloud often reduces the amount of data traffic sent to a public computing cloud by locally processing collected device data rather than by sending the device data to the public computer cloud for processing. The home computing cloud may download an appropriate data analytic model from the public computing cloud, locally train (for example, reinforcement learning) the model, and locally execute the trained model to obtain prediction information from collected IoT device data. The home computing cloud also allows direct access of the connected IoT devices by user applications via the internet, through a protocols gateway and an IoT message translator.

Abstract: An aspect of the embodiments provides a compensation for the temperature rise effect on the printed circuit board (PCB) of an electronic thermostat to obtain better precision and performance. Once the measurements from the temperature sensors have stabilized, the compensated ambient temperature may be used by an associated system (for example, a Heating, Ventilation, and Air Conditioning (HVAC) system).

Abstract: A method 300 is disclosed for generating and training a model to detect interference conditions at a cell in a wireless cellular network and to classify the impact of detected interference conditions on performance of the wireless cellular network in the cell. The method comprises, for each of a plurality of cells in the wireless cellular network (360), obtaining data representing received signal power at a base station serving the cell over a period of time (310) and obtaining data representing a plurality of performance metrics for the cell over the time period (330). The method further comprises obtaining classifications of the received signal power data into one of a plurality of cell interference conditions (320) and the performance metric data into one of a plurality of cell impact classes (340).

Abstract: Systems and methods related to centrifuge energy harvesting chambers (CEHCs) for gas production simulation are provided. Certain CEHCs may include a high-pressure chamber, high-pressure syringe pumps, cooling systems, an actuator and surcharge, backpressure control inside the wellbore, a heating element on the wellbore, water gas separation systems, and flow measurement systems. Certain CEHCs may also provide software operably connected to sensors and instrumentation, comprising a module to continuously, in real-time, periodically, or asynchronously, measure and monitor simulation variables.

Abstract: A method and a system for dialogue enhancement of an audio signal, comprising receiving (step S1) the audio signal and a text content associated with dialogue occurring in the audio signal, generating (step S2) parameterized synthesized speech from the text content, and applying (step S3) dialogue enhancement to the audio signal based on the parameterized synthesized speech. With the invention text captions, subtitles, or other forms of text content included in an audio stream, can be used to significantly improve dialogue enhancement on the playback side.

Abstract: Various embodiments of a vision-based privacy-preserving embedded fall-detection system are disclosed. This embedded fall-detection system can include one or more cameras for capturing video images of one or more persons. Moreover, this embedded fall-detection system can include various fall-detection modules for processing the captured video images including: a pose-estimation module, an action-recognition module, and a fall-detection module, all of which can perform the intended fall-detection functionalities within the embedded system environment in real-time in order to detect falls of the one or more persons. When a fall is detected, instead of sending the original captured images, the embedded fall-detection system can transmit sanitized video images to the server, wherein each detected person is represented by a skeleton figure in place of the actual person images, thereby preserving the privacy of the detected person.

Abstract: Electronic thermostats are expanding to a higher integration of services that includes, for example, the integration of a home Internet-of-Things (IoT) gateway, local rule control, local machine learning capability, and higher resolution thin-film transistor (TFT) display. However, the required incorporation of higher processing power control units and/or large display typically results in a higher heat dissipation of electronic components, thus increasing the temperature around the components of an electronic thermostat. The system heat dissipation consequently degrades the accuracy of the on-board temperature sensors. An aspect of the embodiments provides a compensation for the temperature rise effect on the printed circuit board (PCB) of the electronic thermostat to obtain better precision and performance. Once the measurements from the temperature sensors have stabilized, the compensated ambient temperature may be used by an associated system (for example, a HVAC system).

Abstract: A home computing system (cloud) integrates a protocols gateway, WiFi router, cloud server, and mass storage device to support one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment such as a residential home. The home computing cloud often reduces the amount of data traffic sent to a public computing cloud by locally processing collected device data rather than by sending the device data to the public computer cloud for processing. The home computing cloud may download an appropriate data analytic model from the public computing cloud, locally train (for example, reinforcement learning) the model, and locally execute the trained model to obtain prediction information from collected IoT device data. The home computing cloud also allows direct access of the connected IoT devices by user applications via the internet, through a protocols gateway and an IoT message translator.

Abstract: The present disclosure relates to salvianolic acid-gelatin conjugate load retentive hydrogel nanoparticles useful for oral delivery of salvianolic acid, pharmaceutical compositions comprising the same, and methods of use and preperation thereof.

Abstract: In some embodiments, a method for performing enhancement on an audio signal to generate an enhanced audio signal in response to feedback indicative of amount of compression applied to at least one frequency band of the enhanced audio signal. In typical embodiments, the enhancement is or includes bass enhancement. Examples of other types of enhancement performed in other embodiments include dialog enhancement, upmixing, frequency shifting, harmonic injection or transposition, subharmonic injection, virtualization, and equalization Other aspects are systems (e.g., programmed processors) and devices (e.g., devices having physically-limited bass reproduction capabilities, such as, for example, a notebook, tablet, mobile phone, or other device with small speakers) configured to perform any embodiment of the method.

Abstract: A hardware accelerator for accelerating the zk-SNARK protocol by reducing the computation time of the cryptographic verification is disclosed. The accelerator includes a zk-SNARK engine having one or more processing units running in parallel. The processing unit can include one or more multiply-accumulate operation (MAC) units, one or more fast Fourier transform (FFT) units; and one or more elliptic curve processor (ECP) units. The one or more ECP units are configured to reduce a bit-length of a scalar cl, in an ECP algorithm used for generating a proof, thereby the cryptographic verification requires less computation power.

Abstract: An apparatus supports smart assistant services with a plurality of smart service providers. The apparatus includes an audio device that receives a speech signal having a user utterance, captures the user utterance when the user utterance includes a user wake word, and sends the captured utterance to a backend computing device. The backend computing device replaces the user wake word with specific wake words associated with different smart service providers. The processed utterances are then sent to selected smart service providers. The backend computing device subsequently constructs feedback to the user utterance based on voice responses from the different smart service providers. The backend computing device then passes a digital representation of the feedback to the audio device, and the audio device converts the digital representation to an audio reply to the user utterance.

Abstract: A method includes forming a first semiconductor layer over a substrate, forming a second semiconductor layer over the first semiconductor layer, forming a first trench and a second trench through in the first semiconductor layer and the second semiconductor layer, wherein a width of the second trench is different from a width of the first trench, forming a dielectric region in the first trench and forming a first gate region in the first trench and over the dielectric region, and a second gate region in the second trench.

Abstract: An apparatus supports smart assistant services with a plurality of smart service providers. The apparatus includes an audio device that receives a speech signal having a user utterance, captures the user utterance when the user utterance includes a user wake word, and sends the captured utterance to a backend computing device. The backend computing device replaces the user wake word with specific wake words associated with different smart service providers. The processed utterances are then sent to selected smart service providers. The backend computing device subsequently constructs feedback to the user utterance based on voice responses from the different smart service providers. The backend computing device then passes a digital representation of the feedback to the audio device, and the audio device converts the digital representation to an audio reply to the user utterance.

Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: Building blocks for a smart device such as a thermostat include a user interface (UI) unit and a terminal (TML) unit. A UI unit may support one or more input data from a user and/or sensors and/or one or more control terminals. The UI unit may process each input datum or a combination of the input data, generate a control signal to one or more control terminals based on the processing, and send the control signal to one or more control terminals over a communication channel. A terminal unit, which may consist of one or more control terminals, transforms the received control signal into one or more controls to one or more associated environmental generators. One or more UI units may control one or more controlled apparatuses in conjunction with a mobile app to allow a unified user experience.

Abstract: Embodiments described herein provide various examples of a face-image training data preparation system for performing large-scale face-image training data acquisition, preprocessing, cleaning, balancing, and post-processing. The disclosed training data preparation system can collect a very large set of loosely-labeled images of different people from the public domain, and then generate a raw training dataset including a set of incorrectly-labeled face images. The disclosed training data preparation system can then perform cleaning and balancing operations on the raw training dataset to generate a high-quality face-image training dataset free of the incorrectly-labeled face images. The processed high-quality face-image training dataset can be subsequently used to train deep-neural-network-based face recognition systems to achieve high performance in various face recognition applications.

Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: A device includes a vertical transistor comprising a first gate in a first trench, wherein the first gate comprises a dielectric layer and a gate region over the dielectric layer, and a second gate in a second trench, a high voltage lateral transistor immediately adjacent to the vertical transistor and a low voltage lateral transistor, wherein the high voltage lateral transistor is between the vertical transistor and the low voltage lateral transistor.