Combination Of Filters Research Articles

Revolutioning Lens-Based Systems Design: Showcasing the power of OptiMat

OptiMat, by Sagittal Optics, is a tool developed to generate spherical designs for color-corrected lens-based systems. OptiMat analytically achieves optimum optical materials selection, key to correcting chromatic aberration. However, this tool extends beyond material selection; it is also used to establish initial systems in the design process, which optical designers can further refine. By selecting from a catalog of materials and specifying the number of lenses in the system, optical designers receive ideal assemblies correcting chromaticity, spherical and field aberrations, together with a set of parameters to further improve the provided systems. Additionally, engineers can filter combinations based on different metrics, such as chromatic and monochromatic aberrations. By handling material selection and lens shaping at the project's outset, OptiMat significantly streamlines optical system design times.

Analysis on Improved Gaussian-Wiener filtering technique and GLCM based Feature Extraction for Breast Cancer Diagnosis

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis.

Thermoluminescence kinetics in beta-irradiated novel ZnGa2O4:Eu3+ phosphor produced via gel combustion synthesis

Eu3+ doped ZnGa2O4 phosphors with varying dopant concentrations were successfully synthesized using the combustion technique. X-ray diffraction analysis confirmed the consistency of both undoped and Eu3+ doped ZnGa2O4 samples with PDF#38–1240 card structure. The investigation of thermoluminescence (TL) characteristics involved studying TL glow curves in Eu3+ doped ZnGa2O4 samples with different dopant concentrations, employing various optical detection filter combinations. Among these, ZnGa2O4: 2%Eu3+ and the IRSL-TL wide blue band (wbb) proved to be the optimal sample and ideal detection filter pairing for identifying TL features. Following preheating experiments, the TL maximum was observed at around 190 °C, with a heating rate of 2 °Cs−1. The dose-response experiment for ZnGa2O4:2%Eu3+ exhibited satisfactory linearity, with a coefficient of b = 0.988 and an R2 value of 0.989 for doses up to 100 Gy. Moreover, for the dose range of 200–500 Gy, a sublinear relationship was maintained, with a coefficient of b = 0.31 and an R2 value of 0.988. Hoogenstraaten's method, along with the initial rise method and the Tm-Tstop experiment, was also employed in conjunction with a glow curve fitting package. This comprehensive approach facilitated the determination of the number of peaks, trap structure, and kinetic parameters within the thermoluminescence glow curve. Glow curve deconvolution using specialized software revealed the presence of five well-isolated and overlapping TL glow peaks, achieving a figure of merit (FOM) value of 1.39.

Interacting Multiple Model Estimators for Fault Detection in a Magnetorheological Damper.

This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is subject to a filtering process. This paper applies various model-based filters in combination with the IMM strategy. One such estimator employs the recently introduced extended sliding innovation filter (ESIF) known as the IMM-ESIF. The ESIF is an extension of the sliding innovation filter for nonlinear systems based on the sliding mode concept. In the presence of modeling uncertainties, the ESIF has been proven to be more robust compared to methods such as the extended Kalman filter (EKF). The novel IMM-ESIF strategy is also compared with the IMM strategy, which incorporates the unscented Kalman filter (UKF), referred to herein as IMM-UKF. While EKF uses Taylor series approximation to linearize the system model, the UKF uses sigma point to calculate the system's mean and covariance. The methods were applied to an experimental magnetorheological (MR) damper setup, which was designed for testing control and estimation theory. Magnetorheological dampers exhibit a diverse array of applications in the automotive and aerospace sectors, with particular relevance to attenuating vibrations through adaptive suspension systems. Applied to a magnetorheological (MR) damper with distinct operating modes determined by the damper's current, the results showcase the effectiveness of IMM-ESIF. In mixed operational conditions, IMM-ESIF demonstrates a notable 80% to 90% reduction in estimation error compared to its counterparts. Furthermore, it exhibits a 4% to 5% enhancement in correctly classifying operational modes, establishing IMM-ESIF as a promising and efficient alternative for adaptive estimation in electromechanical systems. The improved accuracy in estimating the system's behaviour, even amidst uncertainties and mixed operational scenarios, signifies the potential of IMM-ESIF to significantly enhance the overall robustness and efficiency of estimations.

The PENGARUH PENAMBAHAN EKSTRAK DAUN MANGGA MANALAGI TERHADAP MUTU FISIK DAN EFEKTIVITAS TABIR SURYA EMULGEL KOMBINASI OCTINOXATE-AVOBENZONE

Avobenzone and octinoxate are often used as a combination of organic UV filters in sunscreen. At high exposure to UV light, these UV filters can be degraded so that their sunscreen’s effectiveness is reduced. Several studies reported that the combination of plant extracts with synthetic UV filters could increase sunscreen's SPF. Mango leaves contain high levels of flavonoids and in several studies have been shown to have photoprotective activity with good Sun Protection Factor (SPF) values. The Manalagi mango plant is a type of mango that is widely planted in yards, so it's easy to get the raw materials. This study aims to determine the effect of manalagi mango leaf extract with a concentration of 1, 3, and 5% on the physical quality (pH, viscosity, spreadability) and sunscreen activity (SPF, %TE, %TP) of sunscreen emulgel containing avobenzone and octinoxate. This study showes that increasing the concentration of manalagi mango leaf extract reduced the pH and viscosity of the preparation, and significantly increased the spreadability of all formulas. Besides that, it can also increase the SPF value and decrease the %TE and %TP values. The emulgel formula that had the highest sunscreen activity was F3 with an extract concentration of 5%, had an SPF value of 30.597 ± 0.600, %TE 0.0001 ± 0.0001, and %TP 0.0038 ± 0.0016.

An efficient lung image classification and detection using spiral‐optimized Gabor filter with convolutional neural network

AbstractLung cancer has a high death rate of around seven million cases every year worldwide. A computed tomography (CT) scan provides certain essential data concerning lung diseases and their diagnosis. The main objective of this work is to classify various lung diseases such as Normal, Bronchiectasis, and Pleural Effusion. The proposed approach consists of three stages, namely pre‐processing, feature extraction, and classification. At first, CT lung images are collected from the dataset and pre‐processed. After pre‐processing, the important texture features are extracted from each image. For feature extraction, spiral‐optimized Gabor filter (SOGF) is utilized. The proposed SOGF is a combination of spiral optimization algorithm (SOA) and Gabor filter (GF). Then, the extracted features are given to the convolutional neural network (CNN) to classify different types of lung diseases. For comparison, we use different classifiers, namely artificial neural network (ANN), Random Tree, and the Naïve Bayes. The experimental results show that our proposed approach attained the maximum accuracy of 93.67% which is high compared to other methods.

A Digital Track Map-Assisted SINS/OD Fusion Algorithm for Onboard Train Localization

Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.

Prototype design and experimental study of fission neutron imaging at China Mianyang research reactor

Taking advantage of the special reaction principle between neutrons and elements, the neutron imaging method is better suited to testing the structure and the defects of light materials that are covered by heavy materials. In contrast to thermal or cold neutron imaging, the fast neutrons are best suited to testing the large samples composed of various materials. Fission neutrons emitted from the reactor are more stable than those from other neutron sources. Based on the beamline for thermal neutron imaging at China Mianyang Research Reactor (CMRR), a combination of filters could increase the proportion of the fission neutrons. Various fission neutron collimators were calculated, designed and measured. The flux of fission neutrons was close to 3 × 105 cm−2 s−1, when the collimation ratio was about 172. A fission neutron imaging system was built as well. The field of view (FOV) was as large as 400 mm × 400 mm, and the experimental spatial resolution was better than 0.5 mm. The fission neutron tomography was tested. The reconstruction results were comparable to the tomography performed in NECTAR at FRM-II. In addition, a serial time acquisition method was applied in the tomography to reduce the white noise. The ART reconstruction method may improve the quality of the reconstruction images.

Enhancing imaging capabilities with a high-sensitivity multichannel optical filter

This research introduces a high-sensitivity multichannel optical filter with combined filtering and imaging capabilities, enabling the separation of wavelengths from visible to near-infrared light into distinct colors. The device design comprises a metal-insulator-metal (MIM) straight waveguide coupled to six pairs of overlapping resonators. The transmittance properties and electromagnetic field distributions are analyzed by varying the geometrical sizes using the finite-element method. The designed structure effectively filters out four to fifteen color channels with a narrow full width at half maximum of approximately 2–6 nm. It provides precise multichannel transmittance wavelength selectivity with high color purity, spanning from visible to near-infrared. Additionally, this structure serves as a plasmonic refractive index sensor, demonstrating impressive sensitivity, figure of merit, quality factor, and dipping strength values of 1450 nm/RIU, 241.67 1/RIU, 259.11, and 81.89 %, respectively. These characteristics highlight the excellent sensing performance of the proposed structure, offering significantly higher resolution and smaller dimensions compared to conventional systems.

Whole-exome sequencing reveals novel variants of monogenic diabetes in Tunisia: impact on diagnosis and healthcare management.

Introduction: Monogenic diabetes (MD) accounts for 3%-6% of all cases of diabetes. This prevalence is underestimated due to its overlapping clinical features with type 1 and type 2 diabetes. Hence, genetic testing is the most appropriate tool for obtaining an accurate diagnosis. In Tunisia, few cohorts of MD have been investigated until now. The aim of this study is to search for pathogenic variants among 11 patients suspected of having MD in Tunisia using whole-exome sequencing (WES). Materials and methods: WES was performed in 11 diabetic patients recruited from a collaborating medical center. The pathogenicity of genetic variation was assessed using combined filtering and bioinformatics prediction tools. The online ORVAL tool was used to predict the likelihood of combinations of pathogenic variations. Then, Sanger sequencing was carried out to confirm likely pathogenic predicted variants among patients and to check for familial segregation. Finally, for some variants, we performed structural modeling to study their impact on protein function. Results: We identified novel variants related to MD in Tunisia. Pathogenic variants are located in several MODY and non-MODY genes. We highlighted the presence of syndromic forms of diabetes, including the Bardet-Biedl syndrome, Alström syndrome, and severe insulin resistance, as well as the presence of isolated diabetes with significantly reduced penetrance for Wolfram syndrome-related features. Idiopathic type 1 diabetes was also identified in one patient. Conclusion: In this study, we emphasized the importance of genetic screening for MD in patients with a familial history of diabetes, mainly among admixed and under-represented populations living in low- and middle-income countries. An accurate diagnosis with molecular investigation of MD may improve the therapeutic choice for better management of patients and their families. Additional research and rigorous investigations are required to better understand the physiopathological mechanisms of MD and implement efficient therapies that take into account genomic context and other related factors.

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

Road slope estimation for heavy-duty vehicles under the influence of multiple source factors in real complex road environments

Accurate road slope information is of paramount importance for optimizing vehicle control and enhancing energy efficiency. In the context of heavy-duty vehicles (HDVs), the precise estimation of road slope presents a formidable challenge. This challenge is rooted in the coupling between vehicle mass and road slope, the robustness of road slope estimation methods across diverse driving styles, and the generalizability of algorithms to real-world road scenarios. To address these challenges, this paper proposes an innovative road slope estimation scheme that comprehensively considers the influences of multiple source factors in a real-world HDV transportation scenario. Firstly, in order to solve the problem of road slope estimation error under complex road types and different driving styles, a complex road slope estimation method based on the nonlinear predictive filter (NPF) combined with the Cubature Kalman filter (CKF) is constructed. To eliminate the problem of vehicle power fluctuation in special traffic scenarios, we propose a special scenario road slope estimation method based on the combination of a low-pass filter (LPF) and the NPF_CKF algorithm. To address the error problem when using low-cost Inertial Measurement Unit (IMU) sensors, we designed a Kalman filter (KF)-based multi-sensor fusion correction method. Finally, we conduct real-world experiments on complex roads at the test site to verify the accuracy of the proposed estimation method and its insensitivity to model parameters. The robustness and applicability of the proposed method are also verified in free-driving experiments conducted in real-world road scenarios.

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

Exploring the impact of data curation criteria on the observed geographical distribution of mosses.

Biodiversity data records contain inaccuracies and biases. To overcome this limitation and establish robust geographic patterns, ecologists often curate records keeping those that are most suitable for their analyses. Yet, this choice is not straightforward and the outcome of the analysis may vary due to a trade-off between data quality and volume. This problem is particularly recurrent for less-studied groups with patchy sampling effort. The latitudinal pattern of mosses richness remains inconsistent across studies and these may emerge purely from sampling artefacts. Our main objective here is to assess the effect of different curation criteria on this spatial pattern in the Temperate Northern Hemisphere (above 20° latitude). We contrasted the geographical distribution of moss species records and the latitude-species richness relation obtained under different data curation scenarios. These scenarios comprehend five sources of taxonomical standardisations and eight data cleaning filters. The analyses are based on the selection of well-surveyed cells at 100 km cell resolution. The application of some 'data curation scenarios' severely affects the number of records selected for analysis and substantially changes the proportion of richness per cell. The sensitivity to data curation becomes detectable at regional and at the cell scales showing a large shift in the latitudinal richness peak in Europe, from 60° N to 45° N latitude, when only preserved specimens are selected and duplicates based on date of collection and coordinates are excluded. Our results stress the importance of justifying the criteria used for filtering biodiversity data retrieved from biodiversity databases to avoid detecting misleading patterns. Curating records under particular criteria compromises the information in some areas displaying different spatial information of mosses. This problem can be ameliorated if data filtering is combined with identifying well-surveyed cells, render relatively constant results under different combinations of filtering even for less well-known groups such as mosses.

BioDiscViz: A visualization support and consensus signature selector for BioDiscML results.

Machine learning (ML) algorithms are powerful tools to find complex patterns and biomarker signatures when conventional statistical methods fail to identify them. While the ML field made significant progress, state of the art methodologies to build efficient and non-overfitting models are not always applied in the literature. To this purpose, automatic programs, such as BioDiscML, were designed to identify biomarker signatures and correlated features while escaping overfitting using multiple evaluation strategies, such as cross validation, bootstrapping and repeated holdout. To further improve BioDiscML and reach a broader audience, better visualization support and flexibility in choosing the best models and signatures are needed. Thus, to provide researchers with an easily accessible and usable tool for in depth investigation of the results from BioDiscML outputs, we developed a visual interaction tool called BioDiscViz. This tool provides summaries, tables and graphics, in the form of Principal Component Analysis (PCA) plots, UMAP, t-SNE, heatmaps and boxplots for the best model and the correlated features. Furthermore, this tool also provides visual support to extract a consensus signature from BioDiscML models using a combination of filters. BioDiscViz will be a great visual support for research using ML, hence new opportunities in this field by opening it to a broader community.

A comparison of photographic and spectrometric methods to quantify the colours seen by animal eyes

Abstract Quantifying how colours stimulate animal eyes is an essential first step in many areas of biology, from behavioural and visual ecology to neuroethology. Although multiple methods exist, their relative accuracies remain untested, leaving researchers unsure which methods are most accurate and which might be unacceptably inaccurate. Here, I compare measurements from reflectance spectrometry and four camera‐based techniques to ground‐truth spectroradiometric measurements of radiance, using eight diverse visual systems. Because errors may compound through successive calculations, I calculate not only relative quantum catches but also chroma and colour contrasts. I find that filters mimicking photoreceptor spectral sensitivity curves, through either custom fabrication or weighted additive combinations of bandpass filters (‘computational filters’), as well as reflectance spectrometry, are the most accurate techniques, with most R2 > 0.98. Statistical filters trained on the library of reflectance spectra in the multispectral image calibration and analysis (MICA) toolbox lag behind in accuracy, particularly for dark colours, with R2 values dipping as low as 0.23. However, the performance of these filters is vastly improved (all R2 > 0.96) if one (a) uses the full UV–VIS range of camera and animal vision, even for animals lacking UV photoreceptors and (b) expands the library of training spectra to include simulated low‐reflectance spectra. Statistical filters trained on either a 24‐ or 45‐colour chart (an alternative method offered by the MICA toolbox) vary wildly in accuracy, with R2 values ranging from 0.11 to 0.94, and should not be trusted. For those requiring point measurements only, reflectance spectrometry is highly accurate within the constraints described in the main text. Those using photography and desiring the highest levels of accuracy should opt for custom‐fabricated or computational filters. Statistical filters trained on the MICA toolbox's library of natural spectra can be expected to be reasonably accurate for many combinations of visual systems and stimuli, but only if (a) the full UV–VIS spectrum is used and (b) the training library is expanded to include simulated low‐reflectance spectra. Statistical filters trained on a colour chart are highly unreliable and their use should be discontinued.

2526. Ex Vivo Assessment of Sulbactam-Durlobactam (SUL-DUR) Clearance during Continuous Renal Replacement Therapy (CRRT)

Abstract Background Optimal dosing of antibiotics in patients on CRRT is complicated by factors that can influence drug adsorption and clearance including filter type, CRRT mode, effluent rate (ER), and the drug’s properties itself. SUL-DUR is a novel combination antibiotic under development for management of Acinetobacter baumannii infections. We sought to characterize SUL and DUR adsorption and transmembrane clearance (CLTM) in an ex vivo CRRT model. Methods CLTM was determined in hemofiltration (CVVH) and hemodialysis (CVVHD) modes using the Prismaflex M100 and HF1400 hemofilter sets. One liter of heparinized bovine blood was allowed to circulate in the Prismaflex CRRT system for 10 minutes. SUL and DUR were then added to achieve a plasma concentration of 30 mg/L, the average maximum concentration achieved in humans following SUL-DUR 1g/1g q6h as a 3h infusion. Pre-filter blood, post-filter blood and effluent samples were collected at 0, 10, 30, and 60 minutes to determine SUL and DUR concentrations. Combinations of filters, modes, and replacement fluid were tested in triplicate at effluent rates of 1, 2 and 3 L/h. The sieving coefficient (SC) for CVVH and the saturation coefficient (SA) for CVVHD were calculated to determine CLTM. Adsorption was measured through a closed loop system bypassing effluent elimination. Multiple linear regression was used to determine SUL and DUR CLTM as a function of ER, filter, and mode. Results SUL and DUR adsorption was minimal at 10% for both drugs. Mean ± standard deviation initial SUL and DUR concentrations were 28.6 ± 2.5 and 27.4 ± 1.9 mg/L, respectively. The overall mean SC/SA across different modes, filters, and ERs was 1.0 and 0.9 for SUL and DUR, respectively. Multiple linear regression demonstrated that the ER was the primary driver (p< 0.001) of CLTM for both drugs based on the equations: SUL CLTM (L/h) = -0.0123 + (1 x ER), R2 = 0.998; DUR CLTM (L/h) = 0.0346 + (0.91 x ER), R2 = 0.958. Conclusion SUL and DUR were efficiently cleared by both CVVH and CVVHD through M100 and HF1400 filters. The clearance of both drugs during CRRT was dependent primarily on the ER. When incorporated into established population pharmacokinetic models, these data can be used to estimate CLTM and devise dosing recommendations for SUL-DUR for patients requiring CRRT. Disclosures Xiaoyi Ye, MD, Sanofi Genzyme: Honoraria David P. Nicolau, PharmD, Allergan: Advisor/Consultant|Allergan: Grant/Research Support|Cepheid: Advisor/Consultant|Cepheid: Grant/Research Support|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Tetraphase: Advisor/Consultant|Tetraphase: Grant/Research Support|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support|Wockhardt: Advisor/Consultant|Wockhardt: Grant/Research Support Joseph L. Kuti, PharmD, bioMeriuex Inc.: Grant/Research Support|Entasis Therapeutics: Grant/Research Support|Merck & Co, Inc: Grant/Research Support|Shionogi Inc: Advisor/Consultant|Shionogi Inc: Grant/Research Support|Shionogi Inc: Honoraria

Multiwavelength Observations of a B-class Flare Using XSM, AIA, and XRT

We present multiwavelength observations by Chandrayaan-2/Solar X-ray Monitor, Solar Dynamics Observatory/Atmospheric Imaging Assembly, and Hinode/X-Ray Telescope (XRT) of a B-class flare observed on 2021 February 25, originating from an active region (AR 12804) near the northwest limb. The microflare lasts for ∼30 minutes and is composed of hot loops reaching temperatures of 10 MK. We report excellent agreement (within 20%) for the average effective temperatures obtained at the flare peak from all the three instruments, which have different temperature sensitivities. The XRT filter combination of Be-thin and Be-med provides an excellent opportunity to measure the high temperatures in such microflare events. The elemental abundances during the evolution of the microflare are also studied and observed to drop toward photospheric values at the flare peak time, compared to coronal values during the rise and decay phase. This is consistent with previous XSM studies.

Theoretical Experiments on Road Profile Data Analysis using Filter Combinations

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel. The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing.

Interference filter based external-cavity diode laser with combined dual interference filters and largely adjustable feedback range

External-cavity diode lasers (ECDL) are widely used as light sources in laser spectroscopy, atomic physics, and quantum optics. This study demonstrated a home-made 852–nm ECDL with variable feedback, using the combined dual narrow-band interference filters (IFs) as the laser longitudinal mode selection element. The combination of narrow-band IFs, mainly for the current commercially available narrow-band IFs with the full width at half maximum (FWHM) of approximately 0.5 nm for 780-895 nm. We designed different narrow-band IFs combinations to achieve a narrower FWHM, and applied them in the ECDL. The combination of the dual narrow-band IFs further reduces the laser linewidth. We measured the laser linewidth using a high-finesse Fabry-Perot cavity with a linewidth of approximately 10 kHz. The laser linewidth is approximately 176 kHz for the Single-IF-ECDL and 96 kHz for the Dual-IF-ECDL with the same feedback. In addition, we have experimentally verified the results of narrower laser linewidth and larger tuning range with increase in the feedback. The developed Dual-IF-ECDL has the capability of narrow linewidth and wavelength tunability and can be applied to precision spectroscopy, cooling and trapping of neutral atoms.