A hydrant apparatus may be employed to monitor a water distribution system, and may include a sensor, a processor, and a local clock source. The apparatus may wake from a low power mode to a sensing mode, receive the sensor data, associate the sensor data with a first local clock time, and return the apparatus to the low power mode from the sensing mode. The apparatus may subsequently wake to an operational mode, determine a second local clock time subsequent to the first local clock time, associate an external clock time with the second local clock time, determine an offset for the received sensor data based on the first local clock time and the association between the second local clock time and the external clock time, and transmit the sensor data and the offset to an external monitoring system.

Systems and methods for identifying and/or measuring displacement of at least one sensor in system comprising at least two sensors, each sensor comprising a signal generator wherein the signal produced by the generator is used as a transmitted signal and as a local oscillator for down converting signals received from other sensors to produce an IF (intermediate frequency) signal; a data acquisition subsystem configured to generate data samples comprising phase information of the plurality of IF signals and record said data samples; at least one processor, said at least one processor is configured to: receive the recorded data samples from each sensor of said at least two sensors; jointly process the recorded data samples from each sensor of said at least two sensors to extract a phase value which depends on the distance between the at least two sensors; measure over time said phase value to yield a phase change value; identify displacement of at least one sensor of the at least two sensors based on the extracted phase change value.

A data processing device for processing telemetry data obtains sampled data based on output data from a plurality of sensors associated with a vehicle. The data processing device generates first and second sets of sampled values using the sampled data. The first set of sampled values are associated with a first sampling time and the second set of sampled values are associated with a second, subsequent sampling time. The data processing device derives a set of data elements, a data element being indicative of a measure of a change between a sampled value in the first set and a corresponding sampled value in the second set, a position of a given data element in the set of data elements having been determined based on at least one mapping rule. The data processing device encodes the set of data elements and outputs data comprising at least the encoded set of data elements for transmission to a remote data processing unit.

A method includes obtaining foot force data that is created by sampling, in accordance with a sampling signal, data from pressure sensing elements of a shoe sensor system. The method further includes obtaining three-dimensional foot data that is created by sampling, in accordance with the sampling signal, data from an accelerometer of the shoe sensor system. The method further includes correlating the foot force data and the three-dimensional foot data in accordance with the sampling signal to produce correlated foot data. The method further includes processing the correlated foot data to determine one or more of: distance traveled during a time period, stride length data, time duration, fatigue indication, injury prevention indicators, elevation tracking for the time period, running optimization, and rotational sport optimization.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

The invention relates to a communication device (8) for wirelessly communicating with a sensor (4). The communication device (8) comprises a receiver for receiving signals, an amplifier for amplifying the received signals, a transmitter for transmitting signals, and a controller. The controller is operable in a first mode in which signal strength values are determined over time, which are indicative of a strength of an amplified received signal, and in a second mode in which signal transmission and reception are controlled based on the determined signal strength values. The determined signal strength values can be indicative of, for instance, a saturation of the amplifier caused by a transmission operation of another, neighboring communication device such that, by considering the determined signal strength values, the transmission carried out by the communication device (8) can be synchronized with the corresponding operation of the neighboring communication device. This can lead to reduced disturbances.

A device and computer-executable method is provided for adaptively determining a sampling scheme to be applied at a first sensor from among a plurality of sensors for sampling sensor data values corresponding to a signal. A sparsifying transform for a subsequent sampling time window of the first sensor is predicted, wherein the sparsifying transform is determined based on a predictive model of the sparsity of the signal. Moreover, a subsampling parameter for the subsequent sampling time window is determined. The subsampling parameter corresponds to a number of sensor data values to be acquired within the sampling time window. This subsampling parameter is determined based on the predicted sparsifying transform. Further determined is a compressive sampling scheme for the subsequent sampling time window of the first sensor. The compressive sampling scheme is determined based on the predicted sparsifying transform.

Systems and methods for taking sensor measurements is provided. The system includes a sensor configured to detect sensor inputs from a signal source and generate a sensor output signal. The system further includes a synchronization adaptor configured to receive the sensor output signal, to transmit a timing synchronization signal on a communication channel, and to transmit the sensor output signal on the communication channel.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit configured to synchronize, when a malfunctioning sensor is detected from among the plurality of sensors, pieces of detection data output from remaining sensors other than the detected sensor to substantially simultaneously output the synchronized data as sensing data.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, an interface unit configured to receive pieces of detection data outputted from the plurality of sensors and convert the received detection data into a predetermined data format, and a signal processing unit configured to simultaneously output pieces of converted detection data from the interface unit on the basis of the sensing period of one among the plurality of sensors.