A wireless monitoring unit transmits environmental sensor data to a remote computing unit for immediate analysis.
A holographic imaging system records interference patterns from scattered light waves to reconstruct aerosol particle images.
Integrating optoelectronic components into a single semiconductor substrate eliminates bulky discrete modules while maintaining measurement reliability.
A vacuum transducer monitors suction pressure to identify clogs, reducing system complexity by eliminating probe sensors.
Replacing micrometer actuators, a non-parallel lens rotates to shift the optical axis and center the image on a photodetector.
A particle analyzer combines magnetophoresis, dielectrophoresis, electrophoresis, photophoresis, and gravitational sedimentation within a single measurement cell to determine multiple physical properties.
A test system compares particle counts before and after a filter to determine retention efficiency.
Electrodes at the pipette tip aperture detect particles via electrical impedance, eliminating dead volume and preventing particle loss during analysis.
Stacked housing sections define distinct optical and fluid chambers, reducing mechanical alignment dependencies while minimizing optical signal interference.
A compact flue gas particle sensor uses multi-path light routing to measure scattered and unscattered signals via a single detector.
Glass drawing forms monolithic optical flow cells with polygonal cross-sections, eliminating assembly joins that cause optical aberrations.
A reflective laser particle detector uses optical interferometry to monitor vacuum contamination without breaking the seal.
Humidity sensors measure hygroscopicity to resolve measurement precision issues caused by optical scattering limitations.
A method derives effective observation volume from time-series particle trajectories to determine concentration without calibration.
Multi-wavelength and multi-angle measurements enable accurate smoke detection while reducing interference from dust particles without adding sensors.
A neural network model calculates erythrocyte sedimentation rate using aggregation curves and blood cell histograms.