Abstract: A high resolution analog to digital converter (ADC) with improved bandwidth senses an analog signal (e.g., a load current) to generate a digital signal. The ADC operates based on a load voltage produced based on charging of an element (e.g., a capacitor) by a load current and a digital to analog converter (DAC) output current (e.g., from a N-bit DAC). The ADC generates a digital output signal representative of a difference between the load voltage and a reference voltage. This digital output signal is used directly, or after digital signal processing, to operate an N-bit DAC to generate a DAC output current that tracks the load current. In addition, quantization noise is subtracted from the digital output signal thereby extending the operational bandwidth of the ADC. In certain examples, the operational bandwidth of the ADC extends up to 100s of kHz (e.g., 200-300 kHz), or even higher.

Abstract: A delta-sigma modulation circuit has a sampling period and, in operation, generates a delta-sigma modulated signal based on the analog input signal. The delta-sigma modulation circuit includes: a first integrator; an analog-to-digital converter; a feedback-loop coupled between an input of the first integrator and the output interface; a second integrator coupled between the first integrator and the analog-to-digital converter. The delta-sigma modulation circuit has loop-delay compensation circuitry having a plurality of switches. The loop delay compensation circuitry, in operation, controls the plurality of switches based on a time interval of a duration of half the sampling period and generates a loop-delay compensation signal.

Abstract: One embodiment provides a method comprising receiving device setting behavioral data collected from one or more consumer electronic (CE) devices, and generating one or more machine learning (ML) models based on a portion of the device setting behavioral data. In one embodiment, the method comprises predicting, via the one or more ML models, a device setting suitable for a CE device based on a current user context, and providing a recommendation comprising the predicted device setting to the CE device. In another embodiment, the method comprises clustering, via the one or more ML models, at least one user associated with the one or more user-initiated adjustments into at least one user group, and determining one or more user-preferred device settings that the user group prefers most. The one or more user-preferred device settings are provided to a CE device as one or more new device settings available for user selection.

Abstract: A method for training a convolutional neural network comprises sliding a first, a second and a third window in a predefined path simultaneously on each training image of a training data set, a binary image containing a highlighted region of interest of the each training image, and a labeled image containing a plurality of tags representing one or more features of interest of the each training image. A target matrix is obtained for each sample of the labeled image that lies within the highlighted region of interest. The target matrix is a probabilistic distribution of the plurality of tags that is based on a proportion of existence of a feature of interest represented by a tag in the each sample. The CNN is trained to recognize each sample to contain the proportion of the at least one feature of interest based on the target matrix of the each sample.

Abstract: An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.

Abstract: A conversion circuit that performs analog-to-digital conversion is described. During operation, the conversion circuit receives an input signal. Then, the conversion circuit performs analog-to-digital conversion and provides a quantized output corresponding to the input signal based at least in part on a first power-supply voltage and a second power-supply voltage of the conversion circuit. For example, the quantized output may be based at least in part on a comparison of the input signal to the first power-supply voltage and the second power-supply voltage. Moreover, the first power-supply voltage and the second power-supply voltage may specify a full-scale range of the conversion circuit. When the full-scale range exceeds a second full-scale range associated with reference voltages that are other than the first power-supply voltage and the second power-supply voltage, the quantized output may correspond to a larger number of bits than when the full-scale range equals the second full-scale range.

Abstract: Systems, methods, apparatuses, and computer program products for event subscription notification(s) are provided. One method may include providing, from a first network entity, a policy control request trigger about subscriptions for event exposure to a second network entity. The method may also include receiving a notification, from the second network entity, about the policy control request trigger, and installing or updating at least one policy and charging control (PCC) rule based on the information received in the notification from the second network entity.

Abstract: A method includes converting, by n analog to digital converter circuits, n analog signals into n first digital signals having a first data rate frequency; converting, by n digital decimation filtering circuits, the n first digital signals into n second digital signals having a second data rate frequency; and converting, by n digital bandpass filter (BPF) circuits, the n second digital signals into a plurality of outbound digital signals having a third data rate frequency. The coefficients for the taps of a digital BPF circuit is set to produce a bandpass region approximately centered at the oscillation frequency of the analog signal and having a bandwidth tuned for filtering a pure tone component of the analog signal. The first data rate frequency is a first integer multiple of the third data rate frequency. The second data rate frequency is a second integer multiple of the third data rate frequency.

Abstract: A digital-to-time converter (DTC) converts a digital code into a time delay using a capacitor digital-to-analog converter (CDAC) that functions as a charging capacitor. The DTC includes a switched capacitor voltage-to-current converter for the formation of a charging current (or a discharging current) for charging (or for discharging) the charging capacitor responsive to a triggering clock edge that begins the time delay. A comparator compares a voltage on the charging capacitor to a threshold voltage to determine an end of the time delay.

Abstract: An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.

Abstract: A monitoring system for an escalator including: a local gateway device; an analytic engine in communication with the local gateway device through a cloud computing network; a weather data source in communication with the analytic engine through the cloud computing network, the weather data source being configured to obtain weather data at a location of the escalator; a sensing apparatus in wireless communication with the local gateway device through a short-range wireless protocol, the sensing apparatus comprising: an inertial measurement unit sensor configured to detect acceleration data of the escalator, and wherein the CBM health score is adjusted in response to at least the weather data.

Abstract: Embodiments are directed to communicating with and controlling operation of electronically-controlled appliances. In one scenario, a computer system receives a first input from a computing system indicating that an electronically-controlled appliance is permitted to communicate with a cloud computing platform. The computer system generates a notification that is to be sent to a software application, where the software application is configured to control functions of the electronically-controlled appliance. The computer system transmits the generated notification to the software application, where the generated notification indicates that the cloud computing platform is communicably connected to the electronically-controlled appliance.

Abstract: An apparatus and method of intersensor calibration including using a zero airmass response constant proportional to sensor absolute radiometric gain coefficients to monitor sensor radiometric stability. Tracking the ratio of zero airmass response constant values for similar bands between two sensors provides a parameter on a common radiometric scale for evaluating interoperability performance. The method includes imaging a solar signal using a mirror to create an image reference target, detecting the image reference target using a first sensor, generating a zero airmass response constant based on a ground sampling distance of the first sensor and an at-sensor radiance value, computing a radiometric gain coefficient of the first sensor using the zero airmass response constant, and comparing the radiometric gain coefficient of the first sensor to a radiometric gain coefficient of a second sensor to determine a gain ratio between the first sensor and second sensor.

Abstract: Supervised and unsupervised learning schemes may be used to automatically label medical images for use in deep learning applications. Large labeled datasets may be generated from a small initial training set using an iterative snowball sampling scheme. A machine learning powered automatic organ classifier for imaging datasets, such as CT datasets, with a deep convolutional neural network (CNN) followed by an organ dose calculation is also provided. This technique can be used for patient-specific organ dose estimation since the locations and sizes of organs for each patient can be calculated independently.

Abstract: A ramp voltage generator includes: a ramping cell array including a plurality of ramping current cells; a calibration cell array including a plurality of calibration current cells; and a current-voltage converter suitable for converting a current supplied from activated ramping current cells among the ramping current cells and activated calibration current cells among the calibration current cells into a voltage to generate a ramp voltage.

Abstract: A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

Abstract: Techniques for generating multiple-resolutions of time series data are described. An input irregular time series having a plurality of data points is obtained, each data point of the plurality of data points including a timestamp and a feature vector. Based on the input irregular time series, multiple variant time series are generated. A data point in one of the variant time series is based in part on a combination of at least two data points of the input irregular time series. The multiple variant time series can then be used for machine learning tasks such as training a machine learning model or using a machine learning model to infer an output.

Abstract: The positions of multiple user equipments (UEs) are jointly determined by a location server using positioning measurements from a comment set of positioning reference signals (PRS), which may include downlink (DL) PRS, uplink (UL) PRS, sidelink (SL) PRS, or a combination thereof. The common set of PRS may be selected by the location server, e.g., based on a rough estimate of position of the UEs determined by the location server, a recommendation from the UEs, or a position report from the UEs. Once selected by the location server, an indication of the common set of PRS is sent to the UEs. The common set of PRS, alternatively, may be selected by one or more UEs, e.g., by a controlling UE or consensus, and one or more UEs provide an indication of the common set of PRS to the location server.

Abstract: Provided are an analog-to-digital (AD) converter, a sensor system, and a test system capable of reducing the time for test processing. AD converter includes input part, AD conversion part, first output part, and second output part. The analog signal output from sensor is input to input part. AD conversion part digitally converts an analog signal to generate first digital data and second digital data. First output part outputs the first digital data to control circuit. Second output part outputs the second digital data to test controller before first output part outputs the first digital data. In the test mode, test controller determines whether or not sensor system including sensor is in an abnormal state on the basis of the second digital data.

Abstract: An automated system includes transducers, at least one computing device, and at least one automated apparatus. The transducer(s) is/are driven and sensed using drive-sense circuit(s). A drives and senses drive and sense a transducer via a single line, generates a digital signal representative of a sensed analog feature to which the transducer is exposed, and transmits the digital signal to the computing device. The computing device receives digital signals from at least some of drive-sense circuits and process them in accordance with the automation process to produce an automated process command. The automated apparatus executes a portion of an automated process based on the automated process command.