Electromagnetic Wave Reradiation TRIZ Cases

Overlapping measurement windows keep the ADC active across successive LIDAR output windows, reducing idle time and energy use in data generation.

A time-of-flight 3D sensor and 2D camera share one cable for power and data, simplifying compact integration in hard-to-access spaces.

Threshold counting in a SPAD receiver filters disturbance-light noise, improving TOF distance accuracy for distance-image generation.

Simulated scenes with known ground truth automate LiDAR point-cloud labeling, reducing the cost and error of training-data preparation.

Traditional low-cost PM sensors can miss indoor variation; calibrated LiDAR/ToF reflections enable real-time particle-level estimates.

Continuity constraints unwrap modulo ToF distances to track fast-moving objects beyond the sensor’s unambiguous range without extra hardware.

Intensity thresholds and neighboring-point density help LiDAR remove rain and snow noise in real time while preserving environmental features.

Crossed optical paths can raise background noise in multi-channel detection; designed splitting and mixing topology reduces crosstalk.

Timed detector modes distinguish protective-window reflections from object echoes, reducing saturation and close-range interference.

Multiple beam-splitting and amplification stages help preserve per-channel light power as coherent lidar channel count grows.

Temperature and aging can shift laser timing in lidar; an optical-fiber loopback measures delay for real-time detection correction.

Differential timing across reference and measurement links removes temperature-sensitive delays from LiDAR time-of-flight results, improving accuracy.

Position-transform lidar ground truth boxes until they match radar point-cloud density, improving target-detection training accuracy.

Differentiate near and far lidar targets with identical speeds by assigning distinct optical frequencies to pulse cycles.

Synchronizing image capture with electromagnetic-wave radiation helps separate overlapping targets by range for accurate 3D positioning.

Precomputed probability functions filter Geiger-mode LiDAR noise in real time, reducing computation while improving signal-to-noise ratio.

Ambient-light polarization changes can disrupt LIDAR signal-to-noise ratio; adaptive reception filtering and transmission control stabilize distant-object detection.

LiDAR sensors and cameras let a robot build a 2D map, recognize tables and chairs, and choose obstacle-free arrival routes without manual mapping.

An interference filter on the detector attenuates undesired reflected light by angle, reducing optical cross-talk without added barriers.

A band-pass filter passes 800–1000 nm light while low visible-light transmittance suppresses stray light and improves distance accuracy.

Combining dToF time measurements with iToF phase analysis resolves wraparound limits while extending depth-map range and resolution.

Variable-power pulse sequences separate near- and far-field echoes, reducing detector saturation and LiDAR measurement blind zones.

A 3D aperture blocks unwanted reference-reflector light while a tilted bandpass filter limits cavity reflections and distance errors.

Variable integration periods create adaptive histogram bin widths, improving distance resolution without increasing storage space or total sensing time.

Retroreflector targets and baseline comparisons detect rain, mud, dust, or ice on lidar surfaces and support cleaning actions.

Visual screening directs LiDAR toward key targets for precise distance measurement and real-time 3D trajectory prediction.

A two-tap image sensor uses voltage-threshold overflow detection and a ground current path to limit ToF saturation errors.

Two aspheric lens elements improve receiving quality and distance identification while keeping the time-of-flight module compact.

A spiral MEMS mirror and cone-shaped top reflector give one LiDAR unit 360-degree horizontal coverage without multi-unit alignment.

A reconfigurable diffraction optical element creates phase-coded beams from one modulator, adding lidar channels without optical cross-talk.

ToF, mmWave, and computer vision sensors detect nearby or approaching objects so the treadmill can slow or stop its belt before collisions.

Combining pulsed coarse ToF with coherent phase detection balances fast acquisition, fine depth resolution, and lower walk error.

Environmental changes can distort weight-plate readings; dual laser sensors separate motion detection from reference tracking for clearer exercise feedback.

Electronics assign distinct frequency offsets to LIDAR channels, enabling concurrent data generation across more sample regions with fewer light sources.

Separating LiDAR transmission and detection chips uses SiN or SiO2 paths to reduce laser loss and raise output power.

A variable-size reference marking lets the scanner calculate optical-axis deviations from scan data and correct temperature-related measurement errors.

Waveform-width comparisons help distinguish real from virtual objects when saturated peak intensities limit conventional detection.

Motion-aware pseudo-labeling groups LiDAR points across scans, improving rare-class 3D detection while reducing manual annotation.

Weak TM-mode reflections can cause LiDAR detection failures; dual TE/TM channels use both returns to measure obstacle distance and speed.

Integrated charge variation guides the next frame’s exposure time, limiting underexposure and overexposure for more reliable distance measurement.

Traditional rangefinders lack multi-target tracking; wireless signals and GPS-based correction provide live icons and signal-loss alerts.

Galvanometer angle noise can distort 3D point clouds; a scanner dynamic model predicts angles from drive signals without low-pass filtering.

A preliminary LiDAR scan identifies a UAV zone, then a secondary scan measures velocity and distance for high-resolution identification.

Parallel detection across light-receiving elements builds time histograms for higher angular and time resolution without enlarging the LiDAR sensor.

FMCW modulation, circular polarization, and integrated interference couplers address EEL alignment cost while preserving compact, high-resolution LIDAR detection.

Moving parts and bulk lenses make conventional LiDAR bulky and unreliable; coherent pixels electronically steer beams through a grating stack.

Optics extend the beam beyond its target shape to capture specular reflections and improve dark-object detection in vehicles.

FMCW LiDAR uses AFMCW signaling and distance-based amplification to recover weak distant returns for fuller 3D surroundings.

This case derives velocity from position and velocity measurements to avoid preset-vector errors in sensor data association.

An ECR mirror between two scan mirrors addresses ladar size, pulse-energy, and field-of-view trade-offs.