A method of determining residual intensity noise (RIN) of a sensor may comprise determining a first amplitude of a first harmonic of the sensor while a signal propagating through the sensor is modulated at a modulating frequency corresponding to twice an eigenfrequency of the sensor. The method may further comprise determining a second amplitude of a second harmonic of the sensor while the signal propagating through the sensor is modulated the modulating frequency, and determining the RIN of the sensor as a ratio of the first amplitude and the second amplitude.

A digital measuring device implemented on a photonic integrated circuit, the digital measuring device including a laser source configured to provide light, a first ring resonator configured to produce a first frequency comb of light from the laser source, wherein at least a portion of the first frequency comb of light is directed at a moving object, a local oscillator configured to provide a reference beam, at least one waveguide structure configured to combine the reference beam with light reflected from the moving object to produce a measurement beam, a first multiplexer configured to split the measurement beam into a plurality of channels spaced in frequency, and a plurality of detectors configured to detect an intensity value of each channel of the plurality of channels to measure a distance between the digital measuring device and the moving object.

Improved optical coherence tomography systems and methods to measure retinal data are presented. The systems may be compact, provide in-home monitoring, and have automation to allow the patient to measure himself or herself.

An analysis apparatus includes an acquisition part that acquires a plurality of interference images of the object to be measured from the interference measurement apparatus, a calculation part that calculates a sine wave component and a cosine wave component of an interference signal for each pixel in the plurality of interference images, respectively, an error detection part that detects an error between a first Lissajous figure constructed on the basis of the sine wave component and the cosine wave component for each pixel and an ideal second Lissajous figure, a correction part that corrects the sine wave component and the cosine wave component for each pixel on the basis of the error, and a geometry calculation part that calculates surface geometry of the object to be measured on the basis of the corrected sine wave component and cosine wave component.

Component errors prevent linear photonic circuits from being scaled to large sizes. These errors can be compensated by programming the components in an order corresponding to nulling operations on a target matrix X through Givens rotations X.fwdarw.T.sup.†X, X.fwdarw.XT.sup.†. Nulling is implemented on hardware through measurements with feedback, in a way that builds up the target matrix even in the presence of hardware errors. This programming works with unknown errors and without internal sources or detectors in the circuit. Modifying the photonic circuit architecture can reduce the effect of errors still further, in some cases even rendering the hardware asymptotically perfect in the large-size limit. These modifications include adding a third directional coupler or crossing after each Mach-Zehnder interferometer in the circuit and a photonic implementation of the generalized FFT fractal. The configured photonic circuit can be used for machine learning, quantum photonics, prototyping, optical switching/multicast networks, microwave photonics, or signal processing.

A method and device for measuring the profile of the surface of a flat object of unknown materials, including an interferometry measuring system, ellipsometry measuring system, beam splitter for splitting a light beam of a light source into an interferometry light beam and an ellipsometry light beam, and an analysis unit designed to ascertain the profile height in the measured region on the object surface from an analysis beam analyzed in a detector unit of the interferometry measuring system and a sensor beam received in an ellipsometry sensor. The interferometry measuring system includes a beam divider, reference mirror, and the detector unit, and the ellipsometry measuring system includes a polarizer for polarizing an ellipsometry light beam and transmitting same onto the measuring region on the object surface as well as the ellipsometry sensor, which includes a polarization filter in order to determine the polarization state of a received sensor beam.

System and methods for Light Detecting and Ranging (LIDAR) are disclosed. The LIDAR system includes a light source that is configured project a beam at various wavelengths toward a wavelength dispersive element. The wavelength dispersive element is configured to receive the beam and direct at least a portion of the beam into a field of view (FOV) at an angle dependent on frequency. The system also includes a detector that is positioned to receive portions of the beam reflected from an object within the FOV and a processor that is configured to control the light source and determine a velocity of the object.

The present disclosure provides an external parameter calibration method for robot sensors as well as an apparatus, robot and storage medium with the same. The method includes: obtaining first sensor data and second sensor data obtained through a first sensor and a second sensor of the robot by collecting position information of a calibration reference object and converting to a same coordinate system to obtain corresponding first converted sensor data and second converted sensor data, thereby determining a first coordinate and a second coordinate of a reference point of the calibration reference object; using the first coordinate and the second coordinate are as a set of coordinate data; repeating the above-mentioned steps to obtain N sets of the coordinate data to calculate the external parameter between the first sensor and the second sensor in response to a relative positional relationship between the robot and the calibration reference object being changed.

A light detection and ranging (LIDAR) system includes a LIDAR measurement unit, a reference measurement unit, and a phase cancellation unit. The LIDAR measurement unit estimates a time for which a laser beam travels. The reference measurement unit determines a phase of a laser source. The phase cancellation unit identifies phase noise and cancels the phase noise from the laser beam, at least partially based on the phase of the laser source and the time for which the laser beam travels. The denoised signal is used to determine the range between a laser source and a target.

An opto-electronic dual-comb interferometric distance measuring method and device wherein a signal comb is chromatically divided into a target signal comb and a non-target signal comb at a emission position, preferably by an optical interleaver in a measurement probe of the device. Only the target signal comb serves as a free beam emitted to the target. The non-target signal comb serves for generation of additional or compensation internal phase differences. Thus, the distance to the target is based on first, target related phase differences and on the second, internal compensation phase differences.