Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. A disambiguated depth of a surface may be determined from the first image data and the second image data. If the disambiguated depth is greater than a nominal maximum depth of the sensor in the first configuration, an intensity of the surface may be determined from the first image data. If the intensity meets or exceeds a threshold intensity, the surface may be determined to be beyond the nominal maximum depth. If the intensity is less than the threshold intensity, an actual depth of the surface may be determined form the second image data as a distance less than the nominal maximum depth.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over-exposure or saturation. In some examples, multiple exposures captured at different exposure times can be used to determine an overall saturation value or metric representative of the sensor data. The saturation value may be used to control parameters of the sensor. For instance, the saturation value may be used to determine power control parameters for the sensor, e.g., to reduce over- and/or under-exposure.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include saturated pixels, e.g., due to over-exposure, sensing highly-reflective objects, and/or excessive ambient light. In some examples, parameters associated with power of a time-of-flight sensor can be altered based on characteristics of the saturated pixels, as well as information about non-saturated pixels neighboring the saturated pixels. For example, the neighboring pixels may provide information about whether saturation is due to ambient light, e.g., sunlight, or due to emitted light from the sensor.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return pixels subject to over-exposure or saturation, which may be from stray light. In some examples, multiple exposures captured at different exposure times can be used to determine a saturation value for sensor data. The saturation value may be used to determine a threshold intensity against which intensity values of a primary exposure are compared. A filtered data set can be obtained based on the comparison.

Abstract: Techniques for facilitating a robust clock synchronization across a computer network that presumes network jitter exists are discussed herein. A first device and a second device transceive a plurality of sets of time-synchronization messages to synchronize a synchronization clock of the second device to a first clock of the first device. The second device calculates a smoothing of time delay data of a plurality of sets. The time delay data is associated with a transmission duration of time-synchronization messages of the sets of the plurality. The second device sets a synchronization clock based on a time at the first device and the smoothed time delay data.

Abstract: Techniques for facilitating a robust clock synchronization across a computer network that presumes network jitter exists are discussed herein. A first device and a second device transceive a plurality of sets of time-synchronization messages to synchronize a synchronization clock of the second device to a first clock of the first device. The second device calculates a smoothing of time delay data of a plurality of sets. The time delay data is associated with a transmission duration of time-synchronization messages of the sets of the plurality. The second device sets a synchronization clock based on a time at the first device and the smoothed time delay data.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. A disambiguated depth of a surface may be determined from the first image data and the second image data. If the disambiguated depth is greater than a nominal maximum depth of the sensor in the first configuration, an intensity of the surface may be determined from the first image data. If the intensity meets or exceeds a threshold intensity, the surface may be determined to be beyond the nominal maximum depth. If the intensity is less than the threshold intensity, an actual depth of the surface may be determined form the second image data as a distance less than the nominal maximum depth.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. An estimated depth of an object may be determined from the first image data, and an actual depth of the object may be determined from the second image data, based on the estimated depth. In examples, the first and second configurations have different modulation frequencies such that a nominal maximum depth in the first configuration is greater than the nominal maximum depth in the second configuration.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can include returns associated with highly reflective objects that cause glare. In some examples, a depth of a sensed surface is determined from the sensor data and additional pixels at the same depth are identified. The subset of pixels at the depth are filtered by comparing a measured intensity value to a threshold intensity value for the depth. Other threshold intensity values can be applied to subsets of pixels at different depths.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can be impacted by glare. In some examples, corrected data is generated by quantifying glare. A glare region including pixels that are not associated with an object in a range of the time-of-flight sensor may provide glare intensity and glare depth values used to quantify the glare. The glare intensity and glare depth may be used to correct measured data.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return pixels subject to over-exposure or saturation, which may be from stray light. In some examples, multiple exposures captured at different exposure times can be used to determine a saturation value for sensor data. The saturation value may be used to determine a threshold intensity against which intensity values of a primary exposure are compared. A filtered data set can be obtained based on the comparison.

Abstract: Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. Sensor data generated by the time-of-flight sensor can return unreliable pixels, e.g., in the case of over-exposure or saturation. In some examples, multiple exposures captured at different exposure times can be used to determine an overall saturation value or metric representative of the sensor data. The saturation value may be used to control parameters of the sensor. For instance, the saturation value may be used to determine power control parameters for the sensor, e.g., to reduce over- and/or under-exposure.

Abstract: Apparatus and method for determining characteristics of a bit stream of binary pulses. The apparatus has control apparatus for defining a window comparator and logic apparatus for accumulating and mapping numbers derived from a count of the number of times the bit stream pulses fall at points inside the window comparator and drawing an eye diagram defining characteristics of the bit stream.

Abstract: An apparatus and method for measuring errors and event occurrences in a multi-valued data stream by using a dual decision bit error rate tester is disclosed. The Bit error rate tester (BERT) includes a plurality of decision circuits operative to provide a respective bit decision output signal in response to an input signal. The bit decision output signal magnitude information of a signal under test as measured over a sample window period. A comparator circuit is coupled to each of the plurality of decision circuits, and is operative to provide an event occurrence signal in response to the bit decision output signals from each of the plurality of decision circuits. The BERT provides the ability to supply additional information and feedback about the behavior and performance of the targeted device or subsystem being tested and to perform error measurements in non-constrained data (i.e. live data).

Abstract: A frequency response measurement circuit includes a generation circuit operative to provide an input signal having a voltage and programmable frequency characteristics in response to a frequency control signal. A decision circuit is coupled to the generation circuit and is operative to sample the input signal at predetermined intervals in response to a sampling clock signal and determine the amplitude characteristics of the input signal relative to a variable threshold signal. A control circuit is coupled to the decision circuit, and is operative to determine the frequency response characteristics of the input signal at varying frequencies and threshold voltages in response to the frequency control signal.

Abstract: A signal analysis circuit includes a sampling circuit operative to sample the characteristics of an input signal at various points within a bit window in response to a sample clock signal. A sampling control circuit is coupled to the sampling circuit and is operative to provide the sample clock signal in response to a sample control signal. The sample clock signal provides a variable time function such that the input signal characteristics may be sampled at several times during the input signal or bit window period. A control circuit is coupled to the sampling circuit and the sampling control circuit, and is operative to provide the sample control signal in response to the number of times the input signal is within a signal characteristic of interest. In an exemplary embodiment, the characteristic of interest is a reference pattern that may be synchronized with the input data signal.

Abstract: A method and apparatus to draw eye diagrams of multi-valued signals that remove non-data dependent effects is disclosed. An exemplary method includes collecting event counts at variable bit offsets, desired time offsets within one or more bit periods and desired voltage offsets within a voltage region of interest; removing non-data dependent effects from the collected event counts; generating a composite diagram of the desired time offsets within the one or more bit periods of interest and desired voltage offsets within the voltage region of interest; and displaying the composite diagram.

Abstract: A measurement device includes a measurement circuit that generates a parametric measurement data signal including parametric characteristics of an input signal. In an exemplary embodiment, the parametric characteristics are measured at predetermined increments of time. A population limit analyzer is coupled to the measurement circuit and generates limit data in response to the parametric measurement data signal. A measurement limit checker is coupled to the population limit analyzer and generates a signal indicating that the characteristics of the parametric measurement data signal is within acceptable limits. With this information, the user is able to quickly grade a selected device under test (DUT). A device performance measurement method includes receiving an input signal. Next, statistical characteristics are determined from the parametric measurements of the input signal. Performance limits are extrapolated from the statistical characteristics of the parametric measurements.