Spectral Signal-to-noise Ratio Research Articles

The hyperplane of early-type galaxies: using stellar population properties to increase the precision and accuracy of the fundamental plane as a distance indicator

ABSTRACT We use deep spectroscopy from the SAMI (Sydney-AAO Multi-object Integral) Galaxy Survey to explore the precision of the fundamental plane (FP) of early-type galaxies as a distance indicator for future single-fibre spectroscopy surveys. We study the optimal trade-off between sample size and signal-to-noise ratio (SNR), and investigate which additional observables can be used to construct hyperplanes with smaller intrinsic scatter than the FP. We add increasing levels of random noise (parametrized as effective exposure time) to the SAMI spectra to study the effect of increasing measurement uncertainties on the FP- and hyperplane-inferred distances. We find that, using direct-fit methods, the values of the FP and hyperplane best-fitting coefficients depend on the spectral SNR, and reach asymptotic values for a mean $\langle \mathrm{ SNR} \rangle =40\, \mathrm{\mathring{\rm A}}^{-1}$. As additional variables for the FP we consider three stellar-population observables: light-weighted age, stellar mass-to-light ratio, and a novel combination of Lick indices ($I_\mathrm{age}$). For an $\langle \mathrm{ SNR} \rangle =45~\mathrm{\mathring{\rm A}}^{-1}$ (equivalent to 1-h exposure on a 4-m telescope), all three hyperplanes outperform the FP as distance indicators. Being an empirical spectral index, $I_\mathrm{age}$ avoids the model-dependent uncertainties and bias underlying age and mass-to-light ratio measurements, yet yields a 10 per cent reduction of the median distance uncertainty compared to the FP. We also find that, as a by-product, the $I_\mathrm{age}$ hyperplane removes most of the reported environment bias of the FP. After accounting for the different SNR, these conclusions also apply to a 50 times larger sample from SDSS-III (Sloan Digital Sky Survey). However, in this case, only $\mathrm{ age}$ removes the environment bias.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI).

This work introduces an open-sourced graphical user interface (GUI) software enabling the combination of multi-channel magnetic resonance spectroscopy data with different literature-based methods for the improvement of the quality and reliability of combined spectra. The multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI) is a MATLAB-based spectroscopy processing GUI equipped to load multi-channel MRS data, pre-process, combine, and export combined data for evaluation with open-source quantification software (jMRUI). A literature-based, decision-tree process was incorporated into the combination type selection to serve as a guide to minimize spectral distortion in selecting between weighting methods. Multi-channel, simulated spectra were combined with the different combination techniques and evaluated for spectral distortion to validate the code. The incorporation of the combination methods into a single processing software enables multi-channel magnetic resonance spectroscopy (MRS) data to be combined and compared for improved spectral quality with little user knowledge of combination techniques. Through the spectral peak distortion simulation of the combination methods, combined signal-to-noise ratio (SNR) values from the literature were verified. The spectral peak distortion simulation provides a secondary tool for researchers to estimate the spectral SNR levels when spectral distortion could occur and use this knowledge to further guide the selection of their combination technique. The McMRSGUI provides a software toolkit for evaluating multi-channel MRS data and their combination. Simulations evaluating spectral distortion at different noise levels were performed for each combination method to validate the GUI and demonstrate a method for researchers to assess the combined SNR levels at which they could be introducing spectral distortion.

Enhancing the accuracy of rotational velocity measurement for vortex beams within the optimal ability of phase retrieval algorithm

The measurement of the rotational velocity using the rotational Doppler effect (RDE) of a vortex beam is easily affected by atmospheric turbulence, leading to dispersed orbital angular momentum (OAM), and reduced measurement accuracy. This study investigates the optimal ability of the Gerchberg-Saxton (GS) phase retrieval algorithm to compensate for the optical field and enhance the velocity measurement accuracy within the optimal range of intrinsic parameters, such as the number of GS iterations, and extrinsic parameters, such as the atmospheric turbulence intensity and beam properties. Through detailed theoretical and simulation analyses, we demonstrate the outstanding effectiveness of the GS algorithm in improving the velocity measurement accuracy. Simulations conducted for a system-target distance of zS-T = 500 m show a 29.88% improvement in the velocity measurement accuracy and a 1.03-fold increase in the spectral signal-to-noise ratio (SSNR) within the optimal range. It showcases advantages that set it apart from other methods. This study reveals the threshold of the ability of GS algorithm to significantly enhance the rotational velocity measurement accuracy, providing valuable insights to precision measurements of rotational velocities in free-space applications.

Impact of Spectral Resolution and Signal-to-Noise Ratio in Vis–NIR Spectrometry on Soil Organic Matter Estimation

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

Comparing Phantom and Animal Metrics Applied in the Determination of Focused Ultrasound Stable and Inertial Cavitation Levels.

The opening of the blood-brain barrier (BBB) to allow therapeutic drug passage can be achieved by inducing microbubble cavitation using focused ultrasound (FUS). This approach can be monitored through analysis of the received signal to distinguish between stable cavitation associated with safe BBB opening and inertial cavitation associated with blood vessel damage. In this study, FUS phantom and animal studies were used to evaluate the experimental conditions that generate several cross-consistent metrics having the potential to be combined for the reliable, automatic control of cavitation levels. Typical metrics for cavitation monitoring involve observing changes in the spectrum generated by applying the discrete Fourier transform (DFT) to the time domain signal detected using a hydrophone during FUS. A confocal hydrophone was used to capture emissions during a 10 ms FUS burst, sampled at 32 ns intervals, to produce 321,500 points and a high-resolution spectrum when transformed. The FUS spectra were analyzed to show the impact that equipment-transients and well-known DFT-related distortions had on the metrics used for cavitation control. A new approach, physical sparsification (PH-SP), was introduced to sharpen FUS spectral peaks and minimize the effect of these distortions. It was demonstrated that the general spectral signal-to-noise ratio (SNR) could be improved by removing the initial noisy phantom hydrophone signal transient. Minor changes in the transient length digitally removed from the sampled values significantly changed the spectral bandwidths of all the harmonically related FUS signals. We evaluated signal processing techniques to minimize the impact these DFT-related distortions on area-under-the-curve (AUC) metric calculations, and we identified the advantages of using PH-SP and proposed new metrics when characterizing FUS spectral properties. The results show many second, third and sub-harmonic metrics provide cross-consistent evidence of changes between stable and inertial cavitation levels. Removing the first harmonic signal component with a hardware low-pass filter allowed the hydrophone gain to be boosted without introducing distortion, leading to an improved analysis of the sub-harmonic signal orders of magnitude smaller in intensity. Metrics that optimized the energy in the real component of the complex-valued PH-SP spectra provided a 32% increase in the sub-harmonic sensitivity compared to standard metrics. A preliminary investigation of existing and proposed metrics showed that system noise could be large enough to mask the transition between stable and inertial cavitation. Strong narrowing of sub-harmonic peak shapes on applying physical sparsification (PH-SP) were seen in both phantom and animal studies. However, validating equivalent trends of the metrics with pressure were limited by the increased system noise level in the animal study combined with the natural variability between subjects studied. The combined use of hardware low-pass filters and physical sparsification to selectively removing distortions in the spectrum allowed the optimization of metrics for cavitation monitoring by improving the sub-harmonic sensitivity.

Greenhouse Gases Monitoring Instrument on GaoFen-5 Satellite-II: Optical Design and Evaluation

The Greenhouse gases Monitoring Instrument on GaoFen-5 satellite-II (GMI-II) uses spatial heterodyne spectroscopy (SHS) for quantitative monitoring of atmospheric greenhouse gases (GHG). Unlike the traditional SHS, the interferometer component of the GMI-II was designed with zero optical path difference offset, effectively improving spectral resolution while maintaining the same detector specifications. The secondary imaging system with non-isometric scaling of spatial and spectral dimensions was designed to decrease the integration time of a frame image or improve the spectral signal-to-noise ratio (SNR) under the same integration time. This paper introduces the design, manufacture, adjustment methods, and test results of the main performance indexes of the GMI-II that indicate that the spectral resolution of the O2 A-band detection channel is better than 0.6 cm−1 and other channels are better than 0.27 cm−1. Under the typical radiance of other carbon monitors’ on-orbit statistics, the spectral SNR of the GMI-II is more than 300. These test results demonstrate that the GMI-II can be well adapted to quantitative remote sensing monitoring of atmospheric GHG.

Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging.

Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems. Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI. MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods. Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid ( ). Removing spectral channels with reduced tissue classification accuracy. The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.

Rapid coherent Raman hyperspectral imaging based on delay-spectral focusing dual-comb method and deep learning algorithm.

Rapid coherent Raman hyperspectral imaging shows great promise for applications in sensing, medical diagnostics, and dynamic metabolism monitoring. However, the spectral acquisition speed of current multiplex coherent anti-Stokes Raman scattering (CARS) microscopy is generally limited by the spectrometer integration time, and as the detection speed increases, the signal-to-noise ratio (SNR) of single spectrum will decrease, leading to a terrible imaging quality. In this Letter, we report a dual-comb coherent Raman hyperspectral microscopy imaging system developed by integrating two approaches, a rapid delay-spectral focusing method and deep learning. The spectral refresh rate is exploited by focusing the relative delay scanning in the effective Raman excitation region, enabling a spectral acquisition speed of 36 kHz, ≈4 frames/s, for a pixel resolution of 95 × 95 pixels and a spectral bandwidth no less than 200 cm-1. To improve the spectral SNR and imaging quality, the deep learning models are designed for spectral preprocessing and automatic unsupervised feature extraction. In addition, by changing the relative delay focusing region of the comb pairs, the detected spectral wavenumber region can be flexibly tuned to the high SNR region of the spectrum.

Research on Spectral Restoration and Gas Concentration Inversion Accuracy Based on Quasi-Trapezoidal Window

The Fourier transform is a popular method for analyzing and processing interference data in which spectrum leakage occurs. Generally, window function (also called apodization function) weighting is employed to limit spectrum leakage. A rectangular window with optimal main-lobe performance and the Rife-Vincent (R-V) window were introduced to improve the window function performance, resulting in the establishment of a quasi-trapezoidal window function. Based on the experimental interference data, the quasi-trapezoidal window function was used in the spectral restoration process. The experimental results show that when the apodization degree of the quasi-trapezoidal window was 1.06, the spectral resolution was improved by 17.46% compared with that of the Hanning window; when the apodization degree was 2.71, the spectral signal-to-noise ratio (SNR) was improved by 130.09% compared with that of the Blackman-Harris window function. In the propane (C3H8) and ethylene (C2H4) gas concentration inversion experiment, when the apodization degree was increased from 1.06 to 2.58, the inversion precision was increased by 6.94% for C3H8 gas and 23.93% for C2H4 gas. Through the parameter adjustment, the quasi-trapezoidal window may achieve a high SNR or high-resolution spectral restoration, which can improve the accuracy of gas concentration inversion to some extent.

Dual-comb spectroscopy using free-running mechanical sharing dual-comb fiber lasers

We demonstrate balanced-detection dual-comb spectroscopy using two free-running mechanical sharing dual-comb fiber lasers assisted by an all-computational digital phase correction algorithm. The mutual coherence between the combs allows us perform mode-resolved spectroscopy of gaseous hydrogen cyanide by digitally compensating residual timing and offset frequency fluctuations of the dual-comb signal. Setting the repetition rate difference between the combs to 500 Hz (1.5 kHz) yields more than 2000 resolved radio frequency comb lines after phase correction in a 3-dB bandwidth centered at 1560 nm of wavelength. Through coadding the corrected interferograms, we obtain a single time-domain trace with a signal-to-noise ratio (SNR) of 6378 (13 960) and 12.64 (13.77) bits of dynamic range in 1 s of averaging. The spectral SNR of the coadded trace reaches 529 (585), corresponding to a figure of merit of SNR of 1.3 × 106 (1.4 × 106). The measured absorption spectrum of hydrogen cyanide agrees well with the HITRAN database.

Bessel Beam Coherent Anti-Stokes Raman Scattering Spectroscopy for Turbulent Flow Diagnosis.

Coherent anti-Stokes Raman scattering (CARS) spectroscopy plays an important role in chemical analysis for transient flow dynamics. Due to the turbulent ambient conditions, the CARS spectrum often suffers from a poor signal-to-noise ratio (SNR) and cannot provide a convincing measurement. Here, we report on a CARS spectroscopic method using a Bessel beam to enhance the spectral fidelity and SNR in a quasi-turbulent environment. Compared with traditional CARS, the measurement accuracy is significantly improved by taking advantage of the anti-scattering and self-healing characteristics of the Bessel beam. Our preliminary results indicate that Bessel beam CARS could be a promising method for high precision turbulent flow measurement fields.

Establishment of online deep learning model for insect-affected pests in "Yali" pears based on visible-near-infrared spectroscopy.

Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like "Yali" pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.

The progressive spectral signal-to-noise ratio of cryo-electron micrograph movies as a tool to assess quality and radiation damage

Background and ObjectiveThe quality of a cryoEM reconstruction is fundamentally a function of the signal-to-noise ratio (SNR) of the original micrographs. The SNR embodies multiple aspects of image formation, including microscope details and alignment, specimen composition and thickness, how it is recorded, and how the specimen degrades during imaging. With the advent of direct electron detectors and the recording of a series of images for each micrograph (a movie), we have an opportunity to count every electron and derive fully quantitative results. After alignment of the movie frames of a micrograph, we can calculate the SNR, or its spatial frequency equivalent, the spectral SNR (SSNR). This SSNR reflects residual movement between frames and the progressive effect of radiation damage. The goal is to develop a quantitative analysis of the SSNR and radiation damage to assess and improve the quality of micrographs. MethodsSeveral test cases were selected from the EMPIAR database and ten micrograph movies downloaded for each case. The movie frames were aligned as rigid bodies to compensate for stage and support movement. The SSNR for subsets of frames was then calculated to assess the effect of residual movement. The progressive SSNR (PSSNR) was subsequently calculated to determine the decrease in signal accumulation as a result of radiation damage. ResultsIn all cases the alignment of the movie frames compensated for global movement to the extent that the effect on the SSNR is negligible. The subset SSNR can be used as a tool to further confirm the extent of residual movement. The progressive SSNR indicates an increase in value up to an asymptote, consistent with the theory for radiation damage. Fitting these curves gives the inherent SNR before exposure, and the critical dose, which decreases with spatial frequency with an exponential parameter roughly between one and two. ConclusionsThe implementation of the PSSNR for movie frames provides a tool for assessing micrograph quality and progression of radiation damage. The estimation of the critical dose further quantifies radiation damage and may shed some light on the mechanisms of damage. These are likely both a function of the specimen composition and the imaging parameters used.

Tradeoffs in the Spatial and Spectral Resolution of Airborne Hyperspectral Imaging Systems: A Crop Identification Case Study

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

Dependent factors for improving the spectral resolution and signal-to-noise ratio in a spatially modulated Fourier transform spectrometer comprised of a Wollaston prism

We studied the dependent factors for improving spectral resolution and signal-to-noise ratio (SNR) in a spatially modulated Fourier transform spectrometer comprised of a Wollaston prism. The obtainable spectral resolution was investigated for the factors such as the ratio of overlapped to nonoverlapped sectors in a focal plane, SNR, and the maximum optical path difference. The accurate application of signal padding for the interferogram was effective in obtaining better spectral resolution and SNR in the spatially modulated Fourier transform spectrometer based on the Wollaston prism, for which it is difficult to have adequate optical path difference. We investigated the change of spectral resolution and shape for the measured and signal padded interferograms of the 6382 cm − 1 center wavenumber light source. The best obtainable spectral resolution was drastically improved to 27 cm − 1, whereas the SNR was improved up to 12 times compared to that of the not signal padded.

An oversampling software-triggering interferogram method for Fourier-transform infrared spectrometers

Fourier-transform spectrometers (FTSs) are widely used for spectroscopic analysis in various fields. Hardware-triggering interferogram sampling is commonly used in commercial FTSs, which performs interferogram acquisition at equal increments of optical path difference (OPD). However, due to the OPD of FTSs over time is non-uniform, the stability of hardware-triggering sampling method strongly suffers from the variation of sampling frequency in the time domain. In this paper, we propose an oversampling software-triggering interferogram method based on the OPD iterative algorithm and the non-uniform discrete Fourier transform (NUDFT). The non-uniform OPD variable can be accurately calculated, which is significant to spectral recovery using the NUDFT. This oversampling method has been effectively adopted in our home-made near-infrared FTS, which features a rotating interferometer. Moreover, it is found that the spectral signal-to-noise ratio (SNR) can be increased by ~ 5.3 times, benefitting from the oversampling software-triggering interferogram acquisition.Published by AIP Publishing.

Achieving Precise Spectral Analysis and Imaging Simultaneously with a Mode-Resolved Dual-Comb Interferometer.

In this paper, we report a scheme providing precise spectral analysis and surface imaging, simultaneously, based on a high-coherence dual-comb interferometer. With two tightly phase-locking frequency combs, we demonstrate a high-coherence dual-comb interferometer (DCI) covering 188 to 195 THz (1538.5 to 1595.7 nm) with comb-tooth resolution and a max spectral signal-to-noise ratio (SNR) of 159.7. The combination of the high-coherence dual-comb spectrometer and a reference arm simultaneously enables gas absorption spectroscopy and for the absolute distance information to be obtained in one measurement. As a demonstration, we measure the spectrum of CO2 and CO. From the same interferograms, we demonstrate that distance measurement, by time-of-flight (TOF), can be resolved with an rms precision of 0.53 μm after averaging 140 images and a measurement time of 1 s. Finally, we demonstrate that non-contact surface imaging, using 2D mechanical scanning, reaches lateral resolution of 40 μm. The longitudinal precision is 0.68 μm with a measurement time of 0.5 s. It verifies that DCS has the potential to be applied in standoff detection, environmental pollution monitors, and remote sensing.

Multi-Band Elastic Optical Networks: Inter-Channel Stimulated Raman Scattering-Aware Routing, Modulation Level and Spectrum Assignment

Recently, multi-band elastic optical networks (MB-EONs) have been introduced to increase the transmission capacity of optical networks by expanding the transmission frequency range beyond the conventional C-band (e.g., O-, E-, S-, and L-band). Thus, by utilizing the idle frequency bands of the existing standard single mode fibers (SMFs), the capacity improvement in MB-EONs is achieved with lower capital expenditure compared to other techniques, such as space division multiplexing (SDM) in multi-core fibers (MCFs). The routing, modulation level and spectrum assignment (RMLSA) problem in MB-EONs is more challenging than that of the conventional single band EONs, and the inter-channel stimulated Raman scattering (ISRS) process should be considered in signal-to-noise ratio (SNR) analysis. In this article, ISRS-aware RMLSA problem in MB-EONs is studied. We obtain the optimal solution of this problem by formulating a path-based integer linear programming (ILP) model. Since the number of slots and demands are too large in a multi-band system, the ILP problem is too complex to be solved in polynomial time. Therefore, we propose heuristic algorithms to solve the ISRS-aware RMLSA problem. Then, performance of the proposed schemes is evaluated through exhaustive simulations. The results reveal that among all the proposed heuristic algorithms, the algorithm named as enhanced in-process estimation (EIPE) has the best performance in saving more frequency slots for future demands, while achieving higher spectral efficiency and SNR estimation accuracy. Moreover, for a special case of small-scale scenario, the performance of EIPE is close to the results of ILP formulation. Finally, our results indicate that by ignoring ISRS process in the SNR estimation, outage occurs for some of lightpaths, where the outage percentage depends on the launch power.

Dual-comb Spectroscopy for Laminar Premixed Flames with a Free-running Fiber Laser

ABSTRACT Dual-comb spectroscopy (DCS) has emerged as an important new technique for high-resolution molecular spectroscopy. However, this powerful tool has not been widely utilized in combustion diagnostics due to the difficulty of maintaining the mutual coherence between the two comb sources. In this work, we demonstrated a free-running fiber-laser-based DCS for in situ and non-intrusive H2O and C2H2 measurements in laminar premixed flames. This picometer-resolution dual-comb spectroscopic system was developed by using a single erbium-doped fiber laser at 1.5 μm along with a multipass optical configuration for the enhanced absorption measurement. Because of the mutual coherence of the two comb pulse trains emitted from the same laser cavity, DCS could be performed without using any phase-locking systems. Absorption spectra of CH4/O2/air flames over the spectral range 6503 cm−1 – 6535 cm−1 were measured at different reactant flow rates and heights above the burner, showing a good agreement with the spectral simulation of high-temperature H2O using the HITEMP database. A noise equivalent (1σ) absorption coefficient of 9.5 × 10−5 cm−1 was achieved using the current DCS system. Besides, the dual-comb measurement of a C2H4/air sooting flame was also performed at the equivalence ratio Ф = 2.94 and compared with the spectral simulation of high-temperature H2O and C2H2. All the spectral data were retrieved by 1500 averages of interferograms to increase the spectral signal-to-noise ratio (SNR), but a single interferogram could be acquired at 0.83 ms using the current free-running dual-wavelength fiber laser.