Combination Of Filters Research Articles

A high precision vital signs detection method based on millimeter wave radar

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

Influence of X-ray spectrum and bowtie filter characterisation on the accuracy of Monte Carlo simulated organ doses: Validation in a whole-body CT scanning mode

PurposeFor patient-specific CT dosimetry, Monte Carlo dose simulations require an accurate description of the CT scanner. However, quantitative spectral information and information on the bowtie filter material and shape from the manufacturer is often not available. In this study, the influence of different X-ray spectra and bowtie filter characterisation methods on simulated CT organ doses is studied. MethodsUsing ImpactMC, organ doses of whole-body CTs were simulated in twenty adult whole-body voxel models, generated from PET/CT examinations previously conducted in these patients. Simulated CT organ doses based on the manufacturer X-ray spectra and bowtie filter data were compared with those obtained using alternative characterisation models, including spectrum generators and experimentally measured dose data. A total of four different X-ray spectra and one bowtie filter model were defined based on these data. ResultsFor all X-ray spectra and bowtie filter combinations, estimated CT organ doses are within 6% from those resulting from simulations with the CT characterisation models provided by the manufacturer. While varying the bowtie filter model results in CT organ dose differences smaller than 1%, dose differences up to 6% are observed when X-ray spectra are not based on the quantitative data from the manufacturer. ConclusionsEstimated organ doses slightly depend on the applied CT characterisation model. When manufacturer’s data are not available, half-value layer and dose measurements provide sufficient input to obtain equivalent X-ray spectra and bowtie filter profiles, respectively.

Synergistic enhancement of dewatering of montmorillonite-containing coal slime by novel combined filter aid: an experimental and simulation study

ABSTRACT The efficient dewatering of montmorillonite (Mt)-containing coal slime is still an urgent problem to be solved in coal preparation plant. In this study, the dewatering effect of poly(o-phenylenediamine)/Poly dimethyl diallyl ammonium chloride (POPD/PDADMAC) combined filter aid on Mt-containing coal slime was studied. The dewatering performance of combined filter aid was investigated by vacuum filtration test. The action mechanism of combined filter aid was revealed by contact angle, zeta potential, cake pore and atomic force microscope (AFM). The co-adsorption principle of the combined filter aid was elucidated based on density functional theory (DFT) and molecular dynamics simulation (MD). Compared to PDADMAC, the combined filter aid has better performance for dewatering of Mt-containing coal slime, and has the advantage of low reagent dosage. The combined filter aid can greatly reduce the Mt potential, increase the Mt contact angle, and increase the porosity of the filter cake. There was a co-adsorption effect between the combined filter aid. Specifically, there were ion–dipole interaction, cation–π interaction and Cl ions bridged electrostatic interaction between POPD and PDADMAC. This study has developed a novel combined filter aid, which provides theoretical and technical support for the high-efficiency pressure filtration dewatering of Mt-containing coal slime in coal preparation plants.

Optimizing gene selection for Alzheimer’s disease classification: A Bayesian approach to filter and embedded techniques

Alzheimer’s disease (AD) classification, which is crucial for identifying AD-associated genes, relies heavily on effective feature selection (FS) to tackle the curse of dimensionality. Traditional methods like filter, wrapper, and embedded techniques have their drawbacks, including ignoring feature independence, sensitivity to classifier choices, and high computational costs. Hybrid approaches combining these methods seek to harness their collective strengths but face challenges, particularly in selecting the optimal number of features from each method. This selection is typically manual or requires time-intensive k-fold cross-validation (KFCV), significantly increasing computational demands and complicating the process with the need for extensive parameter optimization across families, thereby escalating the complexity and resource requirements of model development. To overcome these challenges, this work proposes a framework for optimal FS and classification in AD using a combination of filter and embedded techniques, enhanced with hyperparameter tuning. Firstly, gene expression data (GED) from the AD Neuroimaging Initiative (ADNI) is preprocessed. Then, Chi-square filter selection is applied to decrease correlated features. Next, Logistic Regression with ElasticNet penalty (LREN) is employed to further refine the feature set. Finally, Bayesian Optimization (BO) is introduced to automatically determine the optimal number of features (k for Chi-square and max_features for LREN), iteratively evaluating different combinations to find the set that maximizes model accuracy. The selection process is used primarily in the function of BO to tune automatically the (k and max_features while minimizing the number of features and maximizing the accuracy of the Support Vector Machine (SVM). The SVM was used as a classifier to overcome the problem of embedded selection sensitivity to the model of selection. The tuned features are then used to select relevant features from the ADNI dataset and fitted to different models. We evaluated the performance of five classifiers—logistic regression (LR), SVM, Ridge Classifier (RC), stochastic gradient descent classifier (SGD), and Gaussian Naïve Bayes(GNB) across various metrics. Among these, SVM achieved 100% performance in all metrics. This approach significantly reduced the FS time and the number of initial features to 0.6% and 0.02%, respectively. Notably, it identified 6 out of 20 selected features as directly AD-related. Comparative analysis reveals that the proposed method outperforms existing approaches on the ADNI dataset and other datasets. Statistical tests were conducted to assess the significance of the results compared to other methods, confirming a significant improvement and underscoring the effectiveness of the proposed framework in optimal FS and biological relevance confirmation.

Electrical Tree Image De-Noising using Threshold Wavelet Transform and Wiener Filter

Electrical treeing occurred in solid dielectric materials, especially in electrical application with high voltage. The occurrence of electrical tree happens when high electric fields applied, causing tiny channels or paths to form. The main issue during the data collection process is the changes of lighting, making it difficult to study the tree's propagation length, fractal dimension, and growth rate due to corrupted images. This research aims to analyse electrical tree structure images in XLPE material using a CCD camera and develop image de-noising techniques to suppress noise on the electrical tree image. The performance was then analysed using the Otsu thresholding algorithm for accurate segmentation. The methodology was divided into four phases: sample preparation, experimental setup, image pre-processing in MATLAB, and testing four de-noising filters: Wiener, median, NLM, and Gaussian. The Wiener filter with higher PSNR, SNR, and RMSE was selected and using superimposed method, both threshold wavelet transforms and wiener was combined to eliminate the noise. Finally, the proposed method of superimposed was tested with the Otsu thresholding method to evaluate accuracy, sensitivity, and specificity of the combination filter. Based on the analysis of PSNR, SNR, and RMSE, the performance of the threshold wavelet and Wiener filter (TWWF) de-noising technique improves the image quality of the electrical tree structure. Thus, for the Otsu thresholding segmentation algorithm analysis, it also had the highest values in terms of accuracy, sensitivity, and specificity.

High-frequency Resonance Suppression of Railway Traction Power Supply System Based on the Combination of Single-tuned and C-type Filters

Background: Railroad transportation in the actual operation process, there are also many dangerous accidents caused by resonance, which greatly affect the safety of railroad transportation. A comprehensive examination of the operational dynamics within the power branch of a traction substation is imperative for sustaining system equilibrium. Discrepancies between these facets pose a potential threat to the safety of railway transport. Thus, a meticulous analysis of highfrequency resonance characteristics and the formulation of effective suppression techniques become paramount. Objective: Harmonics from grid-side locomotive traction braking are scrutinized under different operational scenarios and different train runs. The aim is to develop an effective harmonic management strategy to normalize the THD and thus maintain the traction power supply system to become more stable. Methods: This study investigates the high-frequency harmonic resonance characteristics using a control variable approach. Harmonics and negative sequences generated during locomotive traction braking under different operating conditions are investigated by means of extensive analysis. These challenges require the implementation of a harmonic management method. This method uses a combination of two monotonic filters and a C filter in parallel. This method applies to different operating conditions and can dynamically adapt harmonics to changes in the number of trains, which is related to the actual dynamics of the traction power system closely. Results: A comparative evaluation of seven operating conditions shows that the filter in this patent exceeds the efficiency of existing methods. The filter reduces the fluctuations during spectral changes, and the voltage distortion rate decreases from the previous 2.33% to 0.55%, making it more adaptable and promising effective harmonic management in high voltage and high current situations. Conclusion: Through multiple simulation tests, a synergistic configuration involving the C-type filter and two single-tuned parallel connections under regenerative braking conditions emerges. This refined, patented filter design not only mitigates the impact of negative sequence during the filtration process but also substantially diminishes high harmonics and THD.

Tectonic reevaluation of West Cameroon domain: Insights from high-resolution gravity models and advanced edge detection methods

The West Cameroon region, characterized by a diverse geomorphology of highlands and plains resulting from tectonic processes across different geological ages, has been extensively explored for natural resources. Recognizing the significance of its tectonic and magmatic features associated with seismic and volcanic activity, this study focuses on geodynamic investigations of the Cameroon Volcanic Line (CVL). Despite previous efforts, detailed structural geophysical studies of the West Cameroon domain have proven inconclusive, prompting a comprehensive structural reinterpretation. Utilizing the high-resolution SGG-UGM-2 satellite gravity model and innovative processing techniques, including the horizontal gradient of a modified tilt (HGSTDR), the balanced horizontal gradient (BHG), the tilt of the horizontal gradient (TAHG), the improvised horizontal gradient tilt angle (impTAHG), and the Tilt Depth method, our research aims to enhance the interpretational quality of tectonic lineaments. By separating regional and residual anomalies in the Bouguer gravity map and applying a combination of filters to delineate geological units, the BHG filter emerges as a robust tool that highlights subsurface edges without generating false features. This approach unveils previously undetected NNW-SSE-oriented lineaments, confirming the presence of deep fractures and faults in Bafoussam, Nkongsamba, and along the Benue Trough, corroborated by newly discovered NNW-SSW trending lineaments. The study suggests that the region’s topography is overcompensated by deep mountain roots and compressive tectonism. Digitizing the BHG filter produces a structural map, revealing predominant NE trends in identified geological margins, including NNE-SSW, N-S, NW-SE, E-W, and NE-SW directions. Geological contacts between granite and high-grade gneiss are indicated by NNW-SSE and NNE-SSW trending lineaments along the Benue Trough. These results contribute significantly to the understanding of the tectonic setting of the West Cameroon Domain.

VenSpec-H spectrometer on the ESA EnVision mission: Design, modeling, analysis

VenSpec is a spectrometer suite on board ESA’s EnVision mission to planet Venus, due for launch in November 2031. VenSpec consists of three spectrometers, VenSpec-M , VenSpec-U and VenSpec-H. VenSpec-H stands for Venus Spectrometer with High resolution. It operates in the near-infrared wavelength range between 1.15 and 2.5 μm and it aims at mapping the near surface atmosphere during the night and the atmosphere above the cloud deck during the day. More specific, VenSpec-H will measure gases related to volcanism and surface changes on Venus. It will perform its measurements by means of nadir observations.In this paper an overview is given of the main design requirements, followed by a description of the design activities performed during the feasibility study (phase A) and the preliminary definition (phase B1) of the instrument, including mathematical modeling and analysis, and prototyping. Focus is put on the optical working principle of the instrument, where an echelle grating, used as diffractive element, is combined with an inventive combination of filters for spectral band selection.The design and development of VenSpec-H is done in a consortium under Belgian management and with important contributions from Belgian, Swiss, Spanish, and Dutch research institutes, universities, and industrial partners.

The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands (Y, J, and H), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints

This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.

The JWST Resolved Stellar Populations Early Release Science Program. VI. Identifying Evolved Stars in Nearby Galaxies

We present an investigation of evolved stars in the nearby star-forming galaxy Wolf–Lundmark–Melotte (WLM), using Near-Infrared Camera (NIRCam) imaging from the JWST Resolved Stellar Populations Early Release Science program. We find that various combinations of the F090W, F150W, F250M, and F430M filters can effectively isolate red supergiants and thermally pulsing asymptotic giant branch (TP-AGB) stars from one another, while also providing a reasonable separation of the primary TP-AGB subtypes: carbon-rich C-type stars and oxygen-rich M-type stars. The classification scheme we present here agrees very well with the well-established Hubble Space Telescope (HST) medium-band filter technique. The ratio of C to M-type stars is 0.8 ± 0.1 for both the new JWST and the HST classifications, which is within 1σ of empirical predictions from optical narrowband CN and TiO filters. The evolved star colors show good agreement with the predictions from the PARSEC + COLIBRI stellar evolutionary models, and the models indicate a strong metallicity dependence that makes stellar identification even more effective at higher metallicity. However, the models also indicate that evolved star identification with NIRCam may be more difficult at lower metallicities. We test every combination of NIRCam filters using the models and present additional filters that are also useful for evolved star studies. We also find that ≈90% of the dusty evolved stars are carbon rich, suggesting that carbonaceous dust dominates the present-day dust production in WLM, similar to the findings in the Magellanic Clouds. These results demonstrate the usefulness of NIRCam in identifying and classifying dust-producing stars without the need for mid-infrared data.

Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters

The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these perturbations are difficult for humans to detect, they can easily mislead deep learning classifiers. Noise injection, as a defense mechanism, can offer a provable defense against adversarial attacks by reducing sensitivity to subtle input changes. However, these methods face issues of computational complexity and limited adaptability. We propose a multilayer filter defense model, drawing inspiration from filter-based image denoising techniques. This model inserts a filtering layer after the input layer and before the convolutional layer, and incorporates noise injection techniques during the training process. This model substantially enhances the resilience of image classification systems to adversarial attacks. We also investigated the impact of various filter combinations, filter area sizes, standard deviations, and filter layers on the effectiveness of defense. The experimental results indicate that, across the MNIST, CIFAR10, and CIFAR100 datasets, the multilayer filter defense model achieves the highest average accuracy when employing a double-layer Gaussian filter (filter area size of 3×3, standard deviation of 1). We compared our method with two filter-based defense models, and the experimental results demonstrated that our method attained an average accuracy of 71.9%, effectively enhancing the robustness of the image recognition classifier against adversarial attacks. This method not only performs well on small-scale datasets but also exhibits robustness on large-scale datasets (miniImageNet) and modern models (EfficientNet and WideResNet).

The rate and contribution of mergers to mass assembly from NIRCam observations of galaxy candidates up to 13.3 billion years ago

ABSTRACT We present an analysis of the galaxy merger rate in the redshift range $4.0\lt z\lt 9.0$ (i.e. about 1.5 to 0.5 Gyr after the big bang) based on visually identified galaxy mergers from morphological parameter analysis. Our data set is based on high-resolution NIRCam JWST data (a combination of F150W and F2000W broad-band filters) in the low-to-moderate magnification ($\mu \lt 2$) regions of the Abell 2744 cluster field. From a parent set of 675 galaxies $(M_{U}\in [-26.6,-17.9])$, we identify 64 merger candidates from the Gini, $M_{20}$ and asymmetry morphological parameters, leading to a merger fraction $f_m=0.11\pm 0.04$. There is no evidence of redshift evolution of $f_m$ even at the highest redshift considered, thus extending well into the epoch of reionization the constant trend seen previously at $z\lesssim 6$. Furthermore, we investigate any potential redshift-dependent differences in the specific star formation rates between mergers and non-mergers. Our analysis reveals no significant correlation in this regard, with deviations in the studied redshift range typically falling within $(1-1.5)\sigma$ from the null hypothesis that can be attributed to sample variance and measurement errors. Finally, we also demonstrate that the classification of a merging system is robust with respect to the observed (and equivalently rest frame) wavelength of the high-quality JWST broad-band images used. This preliminary study highlights the potential for progress in quantifying galaxy assembly through mergers during the epoch of reionization, with significant sample size growth expected from upcoming large JWST infrared imaging data sets.

Research on key circuits of high‐precision servo channel loading card for wing fatigue testing

AbstractThe key circuits of servo channel loading card with high‐precision data acquisition and output functions for wing fatigue testing are studied and improved in this paper. To solve the problem of large offset voltage in some op‐amps, non‐inverting combined amplifier circuit (NCAC) and inverting combination amplifier circuit (ICAC) are proposed by utilizing precision op‐amps combined with wide voltage and high‐power output op‐amps. In the force signal acquisition section, a non‐inverting combined zeroing filter circuit (NCZFC) is proposed to improve the precision of signal acquisition. In the excitation source output section, a non‐inverting combined excitation output circuit (NCEOC) is proposed to improve the precision of the positive excitation output; an inverting combined excitation output circuit (ICEOC) is also proposed, which improves the precision of the negative excitation output and also realizes the inversion of the input and output signals. In the servo drive output section, a non‐inverting combined voltage and current conversion circuit is proposed to improve the precision of voltage and current outputs, which has two modes of non‐inverting combined voltage output (NCVO) and non‐inverting combined current output (NCIO) modes. Simulation experiments in Multisim and actual tests have been carried out on the above improved circuits to verify the stability and precision.

Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.

Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and firm tissues, including cancerous nodules. The challenge of accurately detecting nodules close to critical lung structures such as blood vessels, bronchi, and the pleura highlights the necessity for more sophisticated methods to enhance diagnostic accuracy. This paper proposed combined processing filters for data preparation before using one of the modified Convolutional Neural Networks (CNNs) as the classifier. With refined filters, the nodule targets are solid, semi-solid, and ground glass, ranging from low-stage cancer (cancer screening data) to high-stage cancer. Furthermore, two additional works were added to address juxta-pleural nodules while the pre-processing end and classification are done in a 3-dimensional domain in opposition to the usual image classification. The accuracy output indicates that even using a simple Segmentation Network if modified correctly, can improve the classification result compared to the other eight models. The proposed sequence total accuracy reached 99.7%, with 99.71% cancer class accuracy and 99.82% non-cancer accuracy, much higher than any previous research, which can improve the detection efforts of the radiologist.

54142 Sebaceus secretion reduction analysis by visioscan and sebufix measures of an spf 50 sunscreen with an innovative combination of filters

54142 Sebaceus secretion reduction analysis by visioscan and sebufix measures of an spf 50 sunscreen with an innovative combination of filters

HASCH - A high-throughput amplicon-based SNP-platform for medicinal cannabis and industrial hemp genotyping applications

BackgroundCannabis sativa is seeing a global resurgence as a food, fiber and medicinal crop for industrial hemp and medicinal Cannabis industries respectively. However, a widespread moratorium on the use and research of C. sativa throughout most of the 20th century has seen the development of improved cultivars for specific end uses lag behind that of conventional crops. While C. sativa research and development has seen significant investments in the recent past, resulting in a suite of publicly available genomic resources and tools, a versatile and cost-effective mid-density genotyping platform for applied purposes in breeding and pre-breeding is lacking. Here we report on a first mid-density fixed-target SNP platform for C. sativa.ResultsThe High-throughput Amplicon-based SNP-platform for medicinal Cannabis and industrial Hemp (HASCH) was designed using a combination of filtering and Integer Linear Programming on publicly available whole-genome sequencing and RNA sequencing data, supplemented with in-house generated genotyping-by-sequencing (GBS) data. HASCH contains 1,504 genome-wide targets of high call rate (97% mean) and even distribution across the genome, designed to be highly informative (> 0.3 minor allele frequency) across both medicinal cannabis and industrial hemp gene pools. Average numbers of mismatch SNP between any two accessions were 251 for medicinal cannabis (N = 116) and 272 for industrial hemp (N = 87). Comparing HASCH data with corresponding GBS data on a collection of diverse C. sativa accessions demonstrated high concordance and resulted in comparable phylogenies and genetic distance matrices. Using HASCH on a segregating F2 population derived from a cross between a tetrahydrocannabinol (THC)-dominant and a cannabidiol (CBD)-dominant accession resulted in a genetic map consisting of 310 markers, comprising 10 linkage groups and a total size of 582.7 cM. Quantitative Trait Locus (QTL) mapping identified a major QTL for CBD content on chromosome 7, consistent with previous findings.ConclusionHASCH constitutes a versatile, easy to use and cost-effective genotyping solution for the rapidly growing Cannabis research community. It provides consistent genetic fingerprints of 1504 SNPs with wide applicability genetic resource management, quantitative genetics and breeding.

Radiomics Based Differentiation of Glioblastoma and Metastatic Disease: Impact of Different T1-Contrast Enhanced Sequences on Radiomic Features and Model Performance.

To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and brain metastases (BM) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences. T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 BM) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross validation. Performance was measured by mean AUC-ROC, Log-loss and Brier scores. A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features. The mean AUC across the top-ten pipelines ranged between 0.851-0.890 with T1-CE MPRAGE and between 0.869-0.907 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. For the same ML-feature reduction pipeline, model performances were comparable (AUC-ROC difference range: [-0.078, 0.046]). Radiomic features derived from T1-CE MPRAGE and VIBE sequences are strongly correlated and may have similar overall classification performance for differentiating GB from BM. BM: Brain metastases, GB: glioblastoma, T1-CE: T1 contrast enhanced sequence, MPRAGE: magnetization prepared rapid gradient echo, ML: machine learning, RF: random forest, VIBE: volumetric interpolated breath-hold examination.

Development of a correction coefficient for radiographic evaluation of the alveolar bone crest: a pilot study.

To analyze the differences in cusp height on radiographs, establishing proportional relationships between cusp and alveolar bone crest (ABC) measurements. The goal of this study was to develop a correction coefficient by considering this proportion. Twenty-one artificial teeth, molars and premolars, and bovine ribs were used. Interproximal radiographs were taken with the aid of a positioner. The vertical angles used were: 0°, + 5°, and + 10°, and processed using three spatial resolutions measured in line pairs per mm (lp/mm): 20, 25 and 40. The Perio filter was applied to each image, in addition to the original one. Combinations of angle, resolution, and filter were made. Eighteen images were analyzed by three specialists, resulting in 252 measurements for each evaluator, totaling 756 measurements. The overall variability of the measurements can be explained mainly by the variation in tooth anatomy. The 0° 25 lp/mm Perio filter method was the closest one to the actual clinical scenario for both cusps and ABC. The correction factor managed to explain 71.45% of the errors. The variation in vertical angulation interferes with cusp and ABC measurements, and the angulation at 0º and spatial resolution of 25 lp/mm showed better results. The use of correction coefficients allowed approaching actual measurement values. More accurate ABC height measurements are essential even in radiographic exams that do not meet the standard of excellence because the need to repeat radiographic exams is then eliminated.

Implementation of Massive Multiple-Input Multiple-Output (MIMO) 5G Communication System using Modified Least-Mean-Square (LMS) Adaptive Filters Algorithm

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.