PCF-based cavity enhanced spectroscopic sensors for simultaneous multicomponent trace gas analysis

Denoising Method for Passive Photon Counting Images Based on Block-Matching 3D Filter and Non-Subsampled Contourlet Transform

Multi-pixel photon counting detectors can produce images in low-light environments based on passive photon counting technology. However, the resulting images suffer from problems such as low contrast, low brightness, and some unknown noise distribution. To achieve a better visual effect, this paper describes a denoising and enhancement method based on a block-matching 3D filter and a non-subsampled contourlet transform (NSCT). First, the NSCT was applied to the original image and histogram-equalized image to obtain the sub-band low- and high-frequency coefficients. Regional energy and scale correlation rules were used to determine the respective coefficients. Adaptive single-scale retinex enhancement was applied to the low-frequency components to improve the image quality. The high-frequency sub-bands whose line features were best preserved were selected and processed using a symbol function and the Bayes-shrink threshold. After applying the inverse transform, the fused photon counting image was subjected to an improved block-matching 3D filter, significantly reducing the operation time. The final result from the proposed method was superior to those of comparative methods in terms of several objective evaluation indices and exhibited good visual effects and details from the objective impression.

The combination of gas-phase fluorophore technology and automation to enable high-throughput analysis of plant respiration

BACKGROUND

Mitochondrial respiration in the dark (Rdark) is a critical plant physiological process, and hence a reliable, efficient and high-throughput method of measuring variation in rates of Rdark is essential for agronomic and ecological studies. However, currently methods used to measure Rdark in plant tissues are typically low throughput. We assessed a high-throughput automated fluorophore system of detecting multiple O2 consumption rates. The fluorophore technique was compared with O2-electrodes, infrared gas analysers (IRGA), and membrane inlet mass spectrometry, to determine accuracy and speed of detecting respiratory fluxes.

RESULTS

The high-throughput fluorophore system provided stable measurements of Rdark in detached leaf and root tissues over many hours. High-throughput potential was evident in that the fluorophore system was 10 to 26-fold faster per sample measurement than other conventional methods. The versatility of the technique was evident in its enabling: (1) rapid screening of Rdark in 138 genotypes of wheat; and, (2) quantification of rarely-assessed whole-plant Rdark through dissection and simultaneous measurements of above- and below-ground organs.

DISCUSSION

Variation in absolute Rdark was observed between techniques, likely due to variation in sample conditions (i.e. liquid vs. gas-phase, open vs. closed systems), indicating that comparisons between studies using different measuring apparatus may not be feasible. However, the high-throughput protocol we present provided similar values of Rdark to the most commonly used IRGA instrument currently employed by plant scientists. Together with the greater than tenfold increase in sample processing speed, we conclude that the high-throughput protocol enables reliable, stable and reproducible measurements of Rdark on multiple samples simultaneously, irrespective of plant or tissue type.

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

The combination of gas-phase fluorophore technology and automation to enable high-throughput analysis of plant respiration

BACKGROUND

Mitochondrial respiration in the dark (R_dark) is a critical plant physiological process, and hence a reliable, efficient and high-throughput method of measuring variation in rates of R_dark is essential for agronomic and ecological studies. However, currently methods used to measure R_dark in plant tissues are typically low throughput. We assessed a high-throughput automated fluorophore system of detecting multiple O₂ consumption rates. The fluorophore technique was compared with O₂-electrodes, infrared gas analysers (IRGA), and membrane inlet mass spectrometry, to determine accuracy and speed of detecting respiratory fluxes.

RESULTS

The high-throughput fluorophore system provided stable measurements of R_dark in detached leaf and root tissues over many hours. High-throughput potential was evident in that the fluorophore system was 10 to 26-fold faster per sample measurement than other conventional methods. The versatility of the technique was evident in its enabling: (1) rapid screening of R_dark in 138 genotypes of wheat; and, (2) quantification of rarely-assessed whole-plant R_dark through dissection and simultaneous measurements of above- and below-ground organs.

DISCUSSION

Variation in absolute R_dark was observed between techniques, likely due to variation in sample conditions (i.e. liquid vs. gas-phase, open vs. closed systems), indicating that comparisons between studies using different measuring apparatus may not be feasible. However, the high-throughput protocol we present provided similar values of R_dark to the most commonly used IRGA instrument currently employed by plant scientists. Together with the greater than tenfold increase in sample processing speed, we conclude that the high-throughput protocol enables reliable, stable and reproducible measurements of R_dark on multiple samples simultaneously, irrespective of plant or tissue type.

Study on the Sensing Coating of the Optical Fibre CO₂ Sensor

Optical fibre carbon dioxide (CO₂) sensors are reported in this article. The principle of operation of the sensors relies on the absorption of light transmitted through the fibre by a silica gel coating containing active dyes, including methyl red, thymol blue and phenol red. Stability of the sensor has been investigated for the first time for an absorption based CO₂ optical fiber sensor. Influence of the silica gel coating thickness on the sensitivity and response time has also been studied. The impact of temperature and humidity on the sensor performance has been examined too. Response times of reported sensors are very short and reach 2-3 s, whereas the sensitivity of the sensor ranges from 3 to 10 for different coating thicknesses. Reported parameters make the sensor suitable for indoor and industrial use.

Baseline correction for stray light in log-ratio diode laser absorption measurements

Log-ratio detection is a convenient technique for making temperature and concentration measurements using sensors based on tunable diode laser absorption spectroscopy. In many practical sensing applications, it is difficult to avoid stray light falling on the signal photodiode of the sensor. This stray light acts as noncommon-mode interference and introduces a systematic error in absorption measurements, which is not removed by baseline subtraction. This paper analyzes the factors that determine this systematic error and also presents a calibration method that can correct for it. This correction method is verified using a simple experiment.