Spectral Instrument Research Articles

Winter wheat identification in China through FY-3D NDVI&EVI satellite data and DNN Fusion

ABSTRACT Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China’s main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R 2 ) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas.

Contribution of Spectral and Temporal Cues in Timbre Recognition among Normal-Hearing and Hearing- Impaired People

Background and Aim: Timbre perception is a multidimensional feature of music perception and hearing loss may deteriorate timbre and instrument recognition. Deficits in timbre recognition may originate from poor temporal or spectral coding of musical notes subsequent to hearing loss However, it is not well understood which of these features are more dominant in instrument recognition with normal hearing and hearing loss. This study aimed to evaluate the relative importance of temporal and spectral cues instrument recognition in people with and without nearing loss. Methods: Two groups of adults with normal-hearing and hearing loss were tested with the timbre subtest of the Clinical Assessment of Music Perception (CAMP). Originally, a series of notes is played with eight musical instruments and participants were asked to identify the played instrument among them. In two other conditions, either the rise time or spectral cues of the same notes were masked using a special masking technique. Among eight instruments. Results: Instrument recognition scores were not significantly different between the original and spectral-manipulated conditions but were lower at the temporal-manipulated condition. The difference between the two groups was significant (p<0.05), where the normal-hearing group recognized the musical instruments significantly better than participants with hearing loss group (p<0.05). Conclusion: Temporal cues may have greater importance on timbre recognition regardless of hearing status. This suggests that the interventions and assistive devices which are designed to improve timbre recognition for people with hearing loss should prioritize improvement in temporal coding. Keywords: Timbre recognition; hearing loss; music; musical instrument; temporal; spectral

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.

Non-invasive exploration of malting barley (Hordeum vulgare) in vitro germination and varietal effects using short wave infrared spectral imaging and ANOVA simultaneous component analysis

ANOVA-simultaneous component analysis (ASCA) was applied to short-wave infrared spectral fingerprints of 5 malting barley varieties collected using a hyperspectral imaging system to determine the effect of germination, the influence of time and the influence of barley by means of a full factorial experimental design. ASCA indicated that there was a significant (p < 0.0001) effect of the germination status, the germination time and interaction on the spectral data for all varieties. The biochemical and physiological modification of the samples were characterised by visualisation of the longitudinal scores obtained from simultaneous component analysis for the germination time factor. This resulted in the visualisation and explanation of biochemical change over the course of barley germination as a factor of time. The relevant loadings indicated a significant change to the proteome, lipid and starch structure as driven by the uptake of water over time. The ASCA model were extrapolated to include the effect of barley variety to the already mentioned germination status and germination time factors, resulting once again in all the effects being significant (p < 0.0001). Here it was shown that all the barley varieties are significantly different from one another pre- and post-modification, based on the molecular vibrations observed in the short wave-infrared (SWIR) spectra, suggesting that the detection of biotic stress factors, such as pre-harvest germination, also differ for each variety, by indicating that the germination profile of each barley variety varies as a function of germination time. Thus, also the malting performance, germinative energy and chemical profile of each barley variety tested will vary before, during and after imbibition and germination – indicating the importance of malting commercial barley malt true to variety. These results indicate that (SWIR) spectral imaging instrumentation can possibly be used to monitor controlled germination of barley grain. Due to the shown ability of SWIR spectral imaging to detect small biochemical changes over time of barley grain during germination.

Automatic control method of spherical wave exposure interference field based on the Moiré alignment principle

Aberration-corrected gratings are widely applied in spectral analysis owing to their dispersion and convergence properties. However, the phase distribution error of the exposure interference field reduces the accuracy of the groove density distribution, making it difficult to satisfy the needs of high-precision spectral instruments. Therefore, this paper establishes an error model for the phase distribution of the spherical wave exposure interference field, describing the relationship between the phase distribution error and the recording parameter error. This model is used to propose a method of automatically controlling a spherical wave exposure interference field based on Moiré alignment principle. This method automatically measures the phase of the interference field by extracting the phase from the Moiré fringes generated by the superposition of the interference field and the reference grating, and then inversely calculates the recording parameters. The measurement results are then fed back to the automatic calibration mechanism for compensation, thereby achieving automatic control of the exposure interference field. Applying this method to calibrate the exposure interference field reduces the average relative error of the groove density of the produced plane aberration-corrected grating by two orders of magnitude compared with that of the traditional control method. This method significantly enhances the control accuracy for the spherical wave exposure interference field, improving the distribution accuracy of the groove density of the aberration-corrected grating, thereby supporting spectral analysis.

Spectral line confocal sensor signal analysis and correction: Unlocking reflectance difference sample measurements

Spectral line confocal imaging (LCI) technology enables high-resolution, rapid 3D surface morphology analysis of transparent materials and other samples, positioning it as a leading optical measurement tool. Nevertheless, where the sensor measures samples with significant variations in surface reflectance, the peak signals of the acquired images exhibit low signal-to-noise ratios or become distorted, hampering the peak extraction algorithm from accurately locating their peak positions. Consequently, it's arduous to reconstruct their precise surface topography. To enhance the sensor's dynamic measurement range and adaptability, this paper introduces an adaptive correction approach. It utilizes a regularized non-negative matrix to decompose images with various grey levels. This is followed by weighted frequency filtering and Gaussian enhancement of all the grey layers. Eventually, these layers are fused into one image with high contrast and uniformity. Using this approach, the sensor is capable of achieving precise 3D reconstruction with high-quality peak signals. Additionally, this study experimentally demonstrates the efficacy of the suggested approach by measuring the interdigital electrode and printed circuit boards (PCB). It should be noted that the technique presented in this paper is not limited to spectral line confocal instruments and is also effective for line laser and line structured light instruments.

Mass spectrometry based proteomics: Changing the impact of protein analysis in forensic science

AbstractMass spectrometry‐based proteomics, born from the invention of soft ionization, has revolutionized protein analysis. In‐depth characterization of complex and variable mixtures of proteins, such as that found at crime‐scenes, is now possible. This capability continues to expand with advancements in mass spectral instrumentation and computational power, allowing for the development of dynamic data acquisition approaches and sophisticated bioinformatics tools for data interpretation. Proteins are the major component of all biological material, as a result they have been of interest to forensic practitioners for centuries. While DNA is the gold standard for identification, proteins are able to provide contextual information about biological material not available through genomic approaches alone. Mass spectrometry‐based proteomics has expanded the breadth and ease of forensic protein analysis with several emerging applications that promise to become common place as investigative tools, particularly in forensic serology and toxicology. This includes the targeted analysis of protein biomarkers for the classification of biological tissue and fluids, the detection of protein toxins such as ricin and identification of peptide hormones such as insulin. The ability of protein analysis to leverage information from the genome has been demonstrated to achieve individual identification from biological material, an emerging technology that can be used in the absence of extractable DNA. In recent years, there has been considerable scrutiny of forensic techniques that lack objectivity and a statistical basis, an alternative molecular approach for forensic analysis that utilizes the high specificity of mass spectrometry, is a welcome addition with many potential future applications.This article is categorized under: Forensic Chemistry and Trace Evidence &gt; Trace Evidence

Consideration of the influence of aberrations in the operation diffraction prism spectral device

Spectral analysis is one of the most important methods of physico-technical research for solving many scientific and practical problems. The key parameter of a spectral instrument is its resolving power. Instruments based on diffraction gratings face a fundamental limitation: their resolving power is limited to operation in the first two orders of diffraction. The limitation is due to the fact that the intensity of diffraction maxima drops sharply with increasing diffraction order. As a result, such devices are unable to distinguish closely spaced spectral lines. Increasing the resolving power of spectral priors is an important and urgent task for spectroscopy. The aim of the research was to develop an optical scheme of a spectral instrument based on a high-resolution prism for the detection of nearby spectral lines with minimal influence of aberrations. The optical scheme of the diffraction prism spectral instrument was developed. The possibility of detecting nearby spectral lines is shown. Also a comprehensive study of the influence of aberrations and the development of methods of their minimisation to achieve the optimum quality of the spectral instrument and accuracy of measurements is carried out. The obtained results confirm the possibility of increasing the resolving power of the spectral instrument for the detection of closely spaced spectral lines. Taking into account the influence of aberrations in the diffraction prism spectral instrument will improve the quality of its work and measurement accuracy. The obtained results open new possibilities for studying complex spectra with high detail.

Pulpwood holocellulose model transfer based on screening with consistent and stable signals algorithm.

In this study, in order to realize the sharing of the near-infrared analysis model of holocellulose between three spectral instruments of the same type, 84 pulp samples and their content of holocellulose were taken as the research objects. The effects of 10 pre-processing methods, such as 1st derivative (D1st), 2nd derivative (D2nd), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), autoscaling, normalization, mean centering and pairwise combination, on the transfer effect of the stable wavelength selected by screening wavelengths with consistent and stable signals (SWCSS) were discussed. The results showed that the model established by the wavelength selected by the SWCSS algorithm after the autoscaling pre-processing method had the best analysis effect on the two target samples. Root mean square error of prediction (RMSEP) decreased from 2.4769 and 2.3119 before the model transfer to 1.2563 and 1.2384, respectively. Compared with the full-spectrum model, the value of AIC decreased from 3209.83 to 942.82. Therefore, the autoscaling pre-processing method combined with SWCSS algorithm can significantly improve the accuracy and efficiency of model transfer and provide help for the application of SWCSS algorithm in the rapid determination of pulp properties by near-infrared spectroscopy (NIRS).

Daily disposable soft lens sagittal depth and other lens parameter changes measured at room temperature (20 °C) and on-eye temperature (34 °C)

PurposeTo investigate the differences in contact lens sagittal depth (CL-SAG), base curve (BC), and overall lens diameter (OAD) of daily disposable (DD) SCLs at room temperature (20 °C) and on-eye (34 °C) temperature. MethodsTwelve different spherical daily SCLs were analyzed (labeled power −3.00 D) at 20 °C and 34 °C. The posterior CL-SAG, BC, and OAD were measured directly using a spectral domain OCT-based instrument (Optimec is830). ResultsThe CL-SAG range for the daily disposable lenses measured at 20 °C was 3468–4016 µm. At 34 °C, the DD CL-SAG of all lenses individually decreased compared to 20 °C (t = 7.78, p < 0.001), ranging from 3385 to 3913 µm. On average at the higher temperature, the measured overall lens diameter was smaller (t = 8.18, p < 0.001) and the BC was marginally steeper (t = 2.11, p = 0.058). ConclusionsSoft lens parameters change when equilibrated from room temperature (20 °C) to on-eye temperature (34 °C). All lenses shrink when going to on-eye temperature (both lens diameter and CL-SAG decreases), compared to lenses measured at room temperature by ISO standards. Higher water content materials tend to have greater changes to the lens parameters compared to lower water content material lenses.

Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

Abstract 7427: Automated, real-time acquisition and quantification of peak, plateau-phase in vivo bioluminescent data

Abstract It is widely recognized that plateau-phase bioluminescent (BL) kinetic curve data should be used when reporting on BLI results from substrate-injection BLI model systems. The basis for this understanding is that plateau-phase BL data is uniquely suited in several ways: (i) it will optimize the detection of target, luciferase-expressing cells (especially when they are low in copy number or deep within the animal model), (ii) it will optimize BLI data reproducibility (with there being no change in signal values across the plateau phase), and (iii) it will also provide BLI data values that correlate consistently with the number of viable target cells in a given BLI model. Given this critical value of plateau-phase BL data, there is, nevertheless, a simple practical issue to be addressed: The overall manual process of generating BL kinetic curves, and then identifying and quantifying plateau-phase data is not insignificant—it takes time!In this poster, we review the performance of “Kinetics,” a new feature in the Aura software platform from Spectral Instruments Imaging, LLC. Kinetics is designed to collect, present, and analyze BL kinetic curve data for up to 10 mice, in a completely automated and real-time fashion. Data outputs include a live graph that simultaneously presents individual mouse, whole body ROI (Total photon/sec) values vs. time post-luciferase injection, and analogous mean BL values per mouse group. Here in, we present an evaluation of the performance of Kinetics under various testing conditions. We initially evaluated Kinetics’ ability to acquire, present and analyze the BL kinetic curves of 5 phantoms. The photon output rates of these phantoms (Total photons/sec) were programmed to mimic the rise, plateau, and fall sequence typically seen BLI studies. In an analogous fashion, we then tested Kinetics’ ability to acquire, present, and analyze the BL kinetic curves of 5 to 10 live mice from several oncology models. Furthermore, we used Kinetics in oncology efficacy studies to monitor for expected changes in BL kinetic curve shapes (i.e., for changes in the onset times and durations of the rise, plateau and fall phases in BL kinetic curves) between different treatment groups (e.g., between positive controls groups and one or more treatment groups). In the same studies, we also used Kinetics to check for changes in BL kinetic curve shapes that might occur for a given treatment group across several time points, after pathogen challenge. Results from this set of experiments consistently and clearly illustrated the capability of Kinetics to acquire, present and analyze individual and mean BL kinetic curve data for up to 10 mice at a time, in an automated and real-time fashion. We believe that Kinetics from Spectral Instruments Imaging, LLC, has the potential to revolutionize the ease with which plateau-phase, BL kinetic curve data can be collected, presented, and used in a wide range of preclinical BLI studies. Citation Format: Andrew Van Praagh, Bo Nelson, Paul Ballieu, Melanie Smith, Mike Rule. Automated, real-time acquisition and quantification of peak, plateau-phase in vivo bioluminescent data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7427.

Investigation of Slagging Condition in a Zhundong Coal-Fired Boiler via In Situ Optical Measurement of Gaseous Sodium.

In this study, a portable spectral analysis instrument based on spontaneous emission spectroscopy (SES) was developed for the in situ, non-intrusive, and quantitative measurement of gaseous Na inside ZD coal-fired boilers, which is mainly applied for predicting slagging in furnaces. This technology is needed urgently because the problem of fouling and slagging caused by high alkali metals in ZD coal restricts the rational utilization of this coal. The relative extended uncertainty for the measurement of gaseous Na concentration is Urel = 10%, k = 2, which indicates that measurement data are reliable under working conditions. It was found that there is a clear linear relationship between the concentration of gaseous Na and fouling in high-alkali coal boilers. Therefore, a fast and efficient method for predicting the slagging and fouling of high-alkali coal boilers can be established by using this in situ online real-time optical measurement.

Optical design of sub-angular second spatially resolved solar extreme ultraviolet broadband imaging spectrometer

The slit imaging spectrometer is one of the important tools for solar extreme ultraviolet (EUV) spectral imaging detection. However, at present, there is no such instrument load in China. The research of solar physics and space weather in the field of EUV spectral diagnosis mainly depends on foreign instrument data, which seriously restricts the development of related disciplines. The spectral imaging instruments that have been launched internationally have only a spatial resolution of &lt;inline-formula&gt;&lt;tex-math id="Z-20240130160605"&gt;\begin{document}$2''$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231481_Z-20240130160605.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="3-20231481_Z-20240130160605.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, and it is difficult to observe the core characteristics of the plasma related to the coronal heating mechanism predicted by the theoretical model. In order to better understand the coupling process between different layers of the sun’s atmosphere, solar physics research requires the observed data with wider spectral coverage. In light of this, we propose and design a sub-angular second spatially resolved solar extreme ultraviolet broadband imaging spectrometer operating in a band range of 62–80 nm and 92–110 nm. Compared with the existing instruments, the system can achieve high spatial resolution and spectral resolution, and wide spectral range coverage. Performance evaluation results indicate that the imaging spectrometer’s spatial resolutions in both bands are better than 0.4&lt;i&gt;''&lt;/i&gt;, and their spectral resolutions are both better than 0.007 nm, with spectral imaging quality approaching the diffraction limit. The system designed in this work holds significant reference value for developing the first Chinese space-based solar EUV spectroscopic instrument in the future.

Evaluation of Spectral Indices for Mapping Burned Areas using Unsupervised Classification in Different Ecosystems using Spectral Indices from Sentinel-2 images

Fire is one of the natural agents that has the greatest impact on the terrestrial ecosystem and plays an important ecological function in a huge portion of the Earth's surface. Remote sensing is an important source for mapping and monitoring forests as well as environmental damage to the landscape caused by fire.To estimate the severity of the fire, the temporal distinction between the spectral indices before and after the fire is critical. This study examines the performance of spectral indices derived from Sentinel-2 Multi Spectral Instrument (MSI) bands for the detection of fire-affected areas in forest fires in regions with different ecosystems located in Brazil, the United States, and Portugal. Separability Index (M) and the Reed-Xiaoli Detection (RXD) Anomaly automatic classifier allowed us to assess the spectral separability and the thematic accuracy of the burned area for the different spectral indices. The analysis parameters were based on spatial dispersion with validation data, Commission Errors (CE), Omission Errors (OE), and Sorensen–Dice Coefficient (DC). The results indicated that the indices based exclusively on the longer shortwave infrared (SWIR1), shorter SWIR (SWIR2), and red-edge bands showed a high degree of separability and were more suitable for detecting the burned areas, although it was observed that the diversity of land use affects the performance of the indices. The evaluation of the performance of the spectral indices based on Sentinel-2 data was important in the analyzes of potentialities and limitations in the detection of burned areas in the face of the different global biomes.

A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network

Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data.

Premotor function in interpersonal bimanual coordination: Neural responses to varying frequencies and spatio–temporal relationships

BackgroundInterpersonal movement coordination is an important aspect of daily life. Behavioral studies have found that rhythmic bimanual coordination of movement is mainly influenced by two factors, spatio–temporal relationship and frequency of movements. How these factors affect action coordination at the neural level needs further exploration. The current study used a factor design to investigate the brain basis of movement coordination under various spatiotemporal relationships and frequencies, as well as their intricate interaction. MethodsParticipants were asked to perform symmetric or alternating hand movements under conditions of different spatio–temporal relationships (symmetric, alternating) and frequencies. A multi–channel, continuous wave, functional near–infrared spectral (fNIRS) imaging instrument was used to monitor hemodynamic activity while 16 pairs of volunteers performed the task. ResultsBehaviorally, as indexed by phase locking value, movements were more stable in symmetric mode than in alternate mode. With increasing frequency, symmetric mode became more unstable; in contrast, alternating mode became more stable at higher frequencies, suggesting phase transition. Activation in brain regions of interest was much stronger in symmetric mode as compared with alternate mode. In alternate mode, but not symmetric mode, [HbO] varied with frequency. ConclusionInterpersonal bimanual coordination involves activity in premotor areas (premotor cortex, supplementary motor area, and frontal eye fields). More oxygen is consumed in these regions in alternating mode than in symmetric mode.

Deuterated PET: The new verification target of the Raman spectrometer for the MMX mission to explore Phobos

AbstractJAXA's upcoming MMX (Martian Moons eXploration) mission will deliver a small rover to Phobos, to explore its surface complementary to the MMX orbiter and the sample return lander. On board this rover is a Raman spectrometer, the RAX Instrument (RAman Spectrometer for MMX), a payload to science. RAX has been designed for being capable to analyze the mineral composition of the Phobos' surface, in order to help reveal the nature and distribution of materials on the surface and ultimately the origin and evolution of the moon. Attached to MMX spacecraft, with direct view from the RAX instrument, a polyethylene terephthalate (PET) target will be placed for on‐ground and during cruise, spectral instrument performances verification, before the MMX rover is released and dropped to the Phobos surface for developing its scientific tasks. A novel deuterated PET material was proposed as verification target (VT) to check the spectral instrument performances. INTA‐CAB developed, manufactured and qualified for the MMX‐RAX space mission, 13 mm diameter and 2‐mm thick pellets of this material, which incorporates two relevant improvement characteristics: reduction in fluorescence and additional Raman bands with regard to ordinary PET used with this purpose. The material was selected as RAX VT and delivered to DLR for performance tests and later integration into the MMX rover together with the RAX flight model. The RAX VT will be critical to check the instrument performances during the several months cruise phase after launch and just before its landing on Phobos surface. This paper reports the development and characterization of the deuterated PET.

Retracted: Infrared blind spectral deconvolution with low-rank sparse regularization for small object tracking

Infrared spectral signal often exists the random noise and peak overlap problems, which is limited its widely applications. To address those issues, we proposed a novel blind spectral deconvolution model with low-rank sparse regularization (LSR) for small object tracking. Specifically, a new infrared spectral deconvolution (ISD) model integrating the rank of ground-truth spectral matrix is constructed to regularize the rank of degraded infrared spectral lines by L1 norm. In addition, a method based on alternating solutions between the latent spectral line and instrument function (INF) is used to optimize the ISD model. Finally, the experimental results are compared with other decomposition methods. The proposed LSR method not only recovers spectral structural details but also suppresses noise effectively. The restored infrared spectrum is more accurate in the application of IR spectral feature extraction and recognition.

Multi-sensor spectral fusion to model grape composition using deep learning

Spectral instruments can be useful for the rapid assessment of chemical compounds in different targets, and their use have been already reported for the modeling of grape composition comparing two spectral ranges. Still, with the increased easiness of acquiring data with several sensors, it would be valuable to explore spectral fusion techniques for the modeling with deep learning, seeking to obtain improved performance. Therefore, the objective of this work was to develop multi-sensor spectral fusion approaches for the deep learning modeling of grape composition. From 128 grape samples, two spectra per sample were acquired from two different ranges using two sensors (visible and shortwave near infrared, 570–1000 nm; and wider NIR 1100–2100 nm). From each sample, 15 grape nitrogen compounds were analyzed by wet chemistry. Three different data fusion approaches are defined using neural networks and deep learning, testing several ways of structuring and merging the input spectra. Statistical analyses supported that (i) the proposed deep learning fusion architectures performed better than single spectral range models, and (ii) neural networks have better modeling capabilities than partial least squares in spectral fusion. The results demonstrate the potential of deep learning for spectral data fusion in grape nitrogen composition regression, and potentially other traits in food and agriculture spectroscopy.