We present a novel, to the best of our knowledge, form of polarization microscopy capable of producing quantitative optic-axis and phase retardation maps of transparent and anisotropic materials. The proposed method operates on differential phase-contrast (DPC) microscopy that produces a phase image of a thin specimen using multi-axis intensity measurements. For polarization-sensitive imaging, patterned illumination light is circularly polarized to illuminate a specimen. The light transmitted through a specimen is split into two orthogonal polarization states and measured by an image sensor. Subsequent DPC computation based on the illumination patterns, acquired images, and the imaging model enables the retrieval of polarization-dependent quantitative phase images, which are utilized to reconstruct the orientation and retardation of the specimen. We demonstrate the validity of the proposed method by measuring the optic-axis and phase retardation maps of calibrated and various anisotropic samples.

Although researchers are actively investigating methods to improve fire detector performance, few studies have investigated fire detectors that detect the type of fire. Fire type detection serves a key role in quickly extinguishing fires and preventing their spread. We present a non-dispersive infrared (NDIR)-based dual-channel mid-infrared (mid-IR) method that can detect and classify aerosol particles and gases. 4.2 μm and 4.7 μm mid-IR light emitting diodes (LEDs) light sources with strong absorption for CO2 and CO are employed. and, and the mid-IR LEDs are modulated with 900 Hz and 1,000 Hz, respectively to increase the signal-to-noise ratio and reduce interference between the light sources. The modulated lights pass through the lenses and sample, and are acquired by a photodetector. The transmittances of the 4.2 μm and 4.7 μm lights are measured to detect the aerosol particles and gases, and the aerosol particles and gases are classified via hierarchical clustering using the measured transmittances and the ratio between the measured transmittances. Various aerosol particles and gases are detected by measuring the transmittance, and the aerosol particles and gases are classified by calculating the distance between clusters. Spectral transmittances analysis of different wavelength bands will enable the detection of various aerosol particles and gases, and further improve the classification accuracy. Furthermore, this method can be applied to fire detection to develop a highly useful technique that can detect and classify fire smoke and rapidly detect the type of fire.

Photoelectric smoke detectors, which operate by reacting to the scattering of light caused by particles entering the light path, are widely used and extremely sensitive. Owing to higher standards imposed by Underwriters Laboratories, researchers have begun analyzing the properties of smoke particles. In particular, several wavelengths are used to classify particles by their scattering reactivity. The performances of actual smoke detectors are limited by their hardware and price. Therefore, properties that can distinguish particle types in these limited conditions must be determined. In addition, algorithms for extracting valid data intervals from unstable scattering data must be developed. In this study, scattering intensity ratios for three wavelengths are derived via simulations of light scattering by particles. An upper cumulative sum is defined for the three wavelengths, and an index for the start of particle inflow is extracted. In addition, valid intervals are extracted based on the scattering intensity ratios and the moving variance of adjacent wavelengths, and the properties of each particle are defined using the extracted indexes. For verification, a data acquisition device that can obtain data using the three selected wavelengths (470, 525, and 850 nm) from two sensors is designed. Five types of fire sources and non-fire alarm sources are selected and used in a test chamber designed to generate particle data. After applying the algorithm, the data in the valid data intervals can be used to derive a sample mean scattering intensity ratio that is more constant than that of the overall data or the data processed using the CUSUM index. In addition, the fire sources have a higher sample mean scattering intensity ratio than water vapor, which is a non-fire alarm source. The scattering intensity ratios for smoke particles can be extracted in real time via a comparison with experimental results obtained from the selected sensors.

Recently, installing smoke detectors has become crucial owing to the risk of fatal human damage that may be caused by inhaling smoke during a fire. Smoke detectors have been reported as highly efficient in detecting smoke particles from fire; however, they may generate false alarms because of their limitation in distinguishing the fire smoke from the smoke generated by daily activities. Despite the frequent occurrence of these false alarms, research on predicting the types of sources through smoke particles remains insufficient. This study involved the development process of an intelligent smoke detector for false alarm reduction that aims to predict the occurrence and type of fire and the evaluation of its performance using the light-scattering characteristics for fire/non-fire sources. First, a previous experimental dataset of fire-related conditions was collected from three fire sources and three non-fire sources to train the model with the light-scattering characteristics of the smoke generated from each source. In addition, to reduce the computing power, data preprocessing was performed on the collected dataset using the median and RobustScaler. Finally, we evaluated the prediction performance of the three deep learning models using three networks: RNN, LSTM, and CNN-LSTM. As a result, we confirmed that the scattering intensity of smoke particles has unique characteristics for each source. When the data preprocessing and prediction models were applied, all three models achieved an accuracy of 0.90 or higher. However, some errors occurred that appeared at similar scattering intensities. The proposed method differs from existing methods in that it presents the possibility of predicting fire and non-fire sources and can be used as an alternative for improving false alarms in the future.