Low-pass Research Articles

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.

Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( FDG, L-DOPA, DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal values of the unfiltered images with a low mean ± SD difference of = (-3.9 ± 5.2)% and = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar values in the vast majority of cases with an overall average difference of = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals.

The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts. The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners. To identify unique EEG patterns, we used many well-known classifiers, including Naïve Bayes (NB), Random Forest, Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost and Linear Discriminant Analysis (LDA), to identify distinct EEG patterns. Input features comprised EEG data from nineteen channels, individually and in combination. Study indicates that EEG-based categorization can differentiate between individuals with ADHD and healthy individuals with accuracy of 84%. The RF classifier achieved a maximum accuracy of 0.84 when particular region combinations were used. Evaluation of classification performance utilizing hemisphere-specific EEG data yielded promising outcomes, particularly in the right hemisphere channels. The study goes beyond traditional methodologies by investigating the effect of regional data on categorization results. The contributions of various brain regions to these classifications are being extensively researched. Understanding the role of different brain regions in ADHD can lead to better diagnosis and treatment options for individuals with ADHD. The study of categorization ability, utilizing EEG data specific to each hemisphere, particularly channels in the right hemisphere region, provides further granularity to the findings.

Dual Imaging Technique for a Real-time Inspection System of Foreign Object Detection in Fresh-Cut Vegetables

Fresh-cut vegetables are a food product susceptible to contamination by foreign materials (FMs). To detect a range of potential FMs in fresh-cut vegetables, a dual imaging technique (fluorescence and color imaging) with a simple and effective image processing algorithm in a user-friendly software interface was developed for a real-time inspection system. The inspection system consisted of feeding and sensing units, including two cameras positioned in parallel, illuminations (white LED and UV light), and a conveyor unit. A camera equipped with a long-pass filter was used to collect fluorescence images. Another camera collected color images of fresh-cut vegetables and FMs. The feeding unit fed FMs mixed with fresh-cut vegetables onto a conveyor belt. Two cameras synchronized programmatically in the software interface simultaneously collected fluorescence and color image samples based on the region of interest as they moved through the conveyor belt. Using simple image processing algorithms, FMs could be detected and depicted in two different image windows. The results demonstrated that the dual imaging technique can effectively detect potential FMs in two types of fresh-cut vegetables (cabbage and green onion), as indicated by the combined fluorescence and color imaging accuracy. The test results showed that the real-time inspection system could detect FMs measuring 0.5 mm in fresh-cut vegetables. The results showed that the combined detection accuracy of FMs in the cabbage (95.77%) sample was superior to that of green onion samples (87.89%). Therefore, the inspection system was more effective at detecting FMs in cabbage samples than in green onion samples.

Lean Blowout Proximity Sensing in a Low Emission Liquid-Fueled Combustor

ABSTRACT Advanced aircraft engine combustor designs, intended for achieving low NOx emissions, necessitate lean combustion with small operating margins above the LBO boundary, posing a high risk of exhibiting lean blowout (LBO) problems. Employing active LBO controllers receiving input from LBO proximity sensors can allow for narrower margins, thereby yielding multiple performance gains. Although several previous studies investigated LBO proximity sensing, they predominantly focused on premixed gas-fueled combustors. The application of proximity sensing approaches to liquid-fueled combustor designs, relevant to aircraft engines have not received adequate attention. Therefore, this study investigates LBO proximity sensing in a low emission lean direct injection (LDI) liquid-fueled combustor design developed by NASA. The combustor operates at atmospheric pressure with preheated air. The proximity sensing is examined by analyzing both OH* and CH* chemiluminescence signals, with a focus on assessing their relative performances. The signal analysis approaches include spectral, partial extinction event (LBO precursor) detection, signal RMS, and the CH*/OH* ratio, with an emphasis on the first two methods. Both steady-state and transient operating conditions have been examined. The combustor did not exhibit large-scale flame partial extinction and re-ignition events near LBO, as commonly observed in gas-fueled combustors. However, an increase in flame unsteadiness and partial extinction events with very subtle signatures in optical signals, have been observed. Low-pass filtering has been observed to considerably enhance the precursor signatures enabling their improved detection. With a reduction in LBO margin, an increase in signal low-frequency power, RMS, and precursor event occurrence rate have been observed, which were utilized for providing LBO proximity measures. A key finding from this study is that CH* signals provide significantly superior performance compared to OH* for the proximity sensing in all the approaches, and the potential reasons are discussed.

Probe-guided endoscopic system for 5-aminolevulinic acid-based photodynamic diagnosis in cholangiocarcinoma

Background and AimThe diagnostic accuracy for cholangiocarcinoma (CCA) is inadequate, necessitating the exploration of novel diagnostic approaches. Protoporphyrin IX (Pp IX), a metabolic product of 5-aminolevulinic acid (5-ALA), emits red fluorescence upon blue light exposure. Because it accumulates selectively in cancer cells, photodynamic diagnosis using 5-ALA (5-ALA-PDD) has been integrated into clinical practice for diverse cancer types. Nevertheless, there is currently no device capable of capturing Pp IX-derived fluorescence for real-time 5-ALA-PDD within the biliary tract, largely due to challenges in device miniaturization. MethodsTo investigate the feasibility of real-time 5ALA-PDD in CCA, we developed two essential components of the cholangioscopy system: a small-diameter flexible camera and a light guide for emitting blue light. We evaluated the detectability of Pp IX fluorescence using these devices in experimental gels and animal models. ResultsOur camera and light guide were smoothly inserted into the lumen of existing cholangioscopes. Incorporating a long-pass filter at the camera tip enabled efficient detection of red fluorescence without significantly impacting white-light observation. The integration of these devices facilitated clear visualization of red fluorescence from gels containing Pp IX at concentrations of 5 μM or higher. Additionally, when observing subcutaneous human CCA tumor models in nude mice treated with 5-ALA, we successfully demonstrated distinct red fluorescence from Pp IX accumulation in tumors compared to peritumoral subcutaneous areas. ConclusionThe integration of our device combination holds promise for real-time 5-ALA-PDD in human CCA, potentially enhancing the diagnostic accuracy for this complex condition.

Garden of forking paths in ERP research - Effects of varying pre-processing and analysis steps in an N400 experiment.

This study tackles the Garden of Forking Paths, as a challenge for replicability and reproducibility of ERP studies. Here, we applied a multiverse analysis to a sample ERP N400 dataset, donated by an independent research team. We analyzed this dataset using 14 pipelines selected to showcase the full range of methodological variability found in the N400 literature using systematic review approach. The selected pipelines were compared in depth by looking into statistical test outcomes, descriptive statistics, effect size, data quality, and statistical power. In this way we provide a worked example of how analytic flexibility can impact results in research fields with high dimensionality such as ERP, when analyzed using standard null-hypothesis significance testing. Out of the methodological decisions that were varied, high-pass filter cut-off, artifact removal method, baseline duration, reference, measurement latency and locations, and amplitude measure (peak vs. mean) were all shown to affect at least some of the study outcome measures. Low-pass filtering was the only step which did not notably influence any of these measures. This study shows that even some of the seemingly minor procedural deviations can influence the conclusions of an ERP study. We demonstrate the power of multiverse analysis in both identifying the most reliable effects in a given study, and for providing insights into consequences of methodological decisions.

P-272 Cannabis negatively impacts female fertility: an in vitro and clinical investigation

Abstract Study question How does tetrahydrocannabinol (THC) impacts human oocyte maturation and female fertility? Summary answer THC alters the oocyte’s RNA profile and affects meiotic spindle formation resulting in an increased rate of aneuploidy in the oocyte and in the blastocyst. What is known already In the past decades, cannabis consumption and legalization has been increasing globally. It is the third most used substance by reproductive aged people and our group demonstrated that tetrahydrocannabinol (THC) reaches the ovarian follicle. THC is known to impact male fertility, but due to the challenges associated with investigating the female gamete in humans, there is no study about the human oocyte. Animal studies have shown that THC impacts oocyte maturation in a concentration dependent manner, but between models, the results are conflicting. Study design, size, duration For the in vitro exposure assessment, GV oocytes naïve to THC were collected from consenting patients undergoing IVF-ICSI from July 2022 to June 2024 (IRB: Veritas #16518). Per patient, oocytes were split into three groups: Control, THC1 (physiological concentration of THC, based on previous human studies) and THC2 (supra-physiological concentration, based on animal studies), and cultured for 24h in our standard clinical in vitro maturation media. Participants/materials, setting, methods Single-cell RNAseq and pathway analysis were conducted to identify key pathways altered by THC treatments (n = 86 from 24 patients). Spindle morphology was assessed using confocal microscopy. Polar body biopsy was performed and ploidy assessed using our clinical low pass whole genome sequencing pipeline. Also, we conducted a multi-year retrospective study (n = 1059 patients) by measuring the concentration of THC in their follicular fluid by mass spectrometry and collecting and comparing IVF outcomes between cases and controls. Main results and the role of chance In vitro, THC treatment marginally increased the maturation rate (46% for Ctrl (n = 103), 51% for THC1 (n = 103) and 58% for THC2 (n = 102)). Using time-lapse imaging, no difference was observed between key developmental stages of oocyte maturation including GVBD and polar body extrusion. Single-cell RNAseq revealed significant alterations to the transcriptome profile of THC treated oocytes. When treated with THC1 (physiological concentration), 89 upregulated and 227 downregulated genes were identified, this was exaggerated when treated with THC2 (supraphysiologic concentration) 402 upregulated and 62 downregulated genes were identified. Pathway analysis showed that differentially expressed genes in THC1 group were associated with immune system and synaptic transmission, while THC2 group showed genes enriched in meiotic spindle formation and apoptosis. Moreover, preliminary results demonstrated abnormal spindle configuration and misaligned chromosomes in THC-treated groups and a concurrent decrease in the number of euploid oocytes. Furthermore, retrospective analysis of clinical IVF outcomes revealed that patients whose FF was positive for THC (62 patients, 6%) also had a slight increased maturation rate (77% vs 73%, p=ns), but a significant reduction of their embryo euploid rate when compared to control patients (59% vs 70%, p < 0.01). Limitations, reasons for caution The in vitro exposure to THC utilized GV oocytes that did not mature after ovarian stimulation, so they might have a reduced capacity of becoming MII. The THC measurement was assessed only on the day of retrieval and we do not know the route, dose, or duration of THC consumption. Wider implications of the findings This is the first study investigating the impact of cannabis on human oocytes. Our data suggests that cannabis consumption may reduce the number of euploid embryos available for transfer. With cannabis legalization increasing worldwide, further investigation into its consequences is critical for clinical consultation and legalization guidelines. Trial registration number Not applicable

P-451 Improvement and validation of a cost effective PGT-A test for the detection of aneuploidy, triploidy and mosaicism

Abstract Study question This study aims to validate a PGT-A platform to simultaneously sequence and genotype embryos to detect triploidy as well as high and low frequency mosaicism. Summary answer Previda Plus platform validation establishes the capability to concurrently identify aneuploidy, triploidy and mosaicism with high and low frequencies at a significantly reduced cost. What is known already Low pass sequencing platforms can identify with great accuracy monosomy and trisomy karyotypes from embryo biopsies by comparing DNA quantities across chromosomes, however, these platforms exhibit limitations in identifying triploidy since all chromosomes exhibit uniform concentration. Triploidy detection necessitates genotyping involving the detection of highly variable SNPs to distinguish different haplotypes. Intermediate copy number may represent putative mosaicism. Recent publications show that embryos with low-level mosaicism demonstrate comparable implantation rates to euploid embryos, therefore validating cut offs to differentiate between low-level and high-level mosaics from euploid and aneuploid embryos is necessary. Study design, size, duration To validate triploidy detection, we measured allele frequencies (AF) using a high frequency SNP panel. The SNP frequency profile of a diploid exhibits a mix of AF of 100% and 50%, while a triploid exhibits AF of 100%, 66%, and 33%. To validate mosaicism, cell lines that were diploid, trisomic, or monosomic for a specific chromosome were mixed in pairs in increments of 10% (From 0%:100% line-1:line-2, to 100%:0%) with multiple iterations of each combination. Participants/materials, setting, methods Fifteen cell lines (triploid or diploid) underwent amplification using a 384 highly variable SNP panel. Pooled SNP and PGT-A products underwent processing in a single sequencing workflow. Ploidy Scores (PS) were derived from the total number of SNPs, 66% and 33%, and cutoff values were established to discriminate between diploid and triploid. For mosaicism validation, thirteen different trisomic cell lines were mixed in 10% ratio increments with 15 different monosomic cell lines. Main results and the role of chance Amplified samples exhibited distinct separation in ploidy scores between diploidy (PS of 0.91 to 1.85), and triploidy samples (PS of 0.28 to 0.61) with no overlap of boxplots. Specificity, sensitivity, and accuracy were 100%. For mosaicism validation, mixing different ratios of cell lines featuring monosomy and cell lines for a trisomy for different chromosomes resulted in ratios of monosomy: disomy for one chromosome and trisomy: disomy for the other. We assessed 32 combinations of cell lines, each one in 10% increments of each cell line from 0:100% to 100%:0%. Mixes with 40% trisomy mosaicism exhibited a 2.35 median ploidy, while 50%, 60%, 70% and 80% mixes show 2.45, 2.50, 2.60, and 2.70, respectively. Mixes with 40% monosomy mosaicism exhibited a median ploidy of 1.6, while 50%, 60%, 70% and 80% mixes showed 1.50, 1.45, 1.35, and 1.25, respectively. The literature recommends considering <40% mosaicism as euploid and 80-100% as aneuploid, therefore, ploidy scores below 2.35 are deemed euploid, ploidy scores at or above 2.35, but below 2.6 (40%-60%), are classified as low level mosaic, and ploidy scores equal to or above 2.6, but below 2.7, are classified as high mosaic. Embryos equal to or above 2.8 (80%+) are considered aneuploid. Limitations, reasons for caution This test is not designed to screen for tetraploidy (92 XXYY) and has not undergone validation for it nor haploidy. Validation procedures solely utilized cell lines. Reanalysis of the remainder of embryos deemed triploid or high-rate mosaic using this validation results are currently underway. Wider implications of the findings This Previda Plus platform combined with proprietary chemistry provides a rapid, cost effective, and a streamlined workflow for aneuploidy, triploidy, and mosaicism for use in PGT-A of blastocysts biopsies. Trial registration number not applicable

ADAPTATION OF THE FEEDBACK TRANSFER FUNCTION FOR OSICILATION BASED TESTING OF SECOND-ORDER ACTIVE RC FILTERS

This paper, explores possible solutions for the feedback transfer functions applied in active RC filters for oscillation-based testing (OBT). Three types of filter transfer functions, namely low-pass (LP), high-pass (HP) and band-pass (BP) are examined. Active filters are realized with the realistic operational amplifier model targeting the 180nm CMOS technology. To confirm the theoretical insights, three realizations of LP, HP, BP second-order filterers with proposed feedbacks are designed and simulated. One of the solutions, namely the BP filter, is further examined by inserting defects in the circuit. Based on the time domain simulations, the key parameters are extracted from the oscillating filter’s output signal. The fault dictionary has been created and appropriate classification of the defects is performed.

FLK: A filter with learned kinematics for real-time 3D human pose estimation

There is a growing interest in adopting 3D human pose estimation in safety-critical systems, from healthcare to Industry 5.0. Nevertheless, when applied in such settings, these neural networks may suffer from estimation inaccuracy. Besides imprecise or inconsistent annotations in the training dataset, the inaccuracy is caused by poor image quality, rare poses, dropped frames, or heavy occlusions in the scene. In addition, these scenarios often require the software results to have temporal constraints, such as real-time and zero- or low-latency, which make many of the filtering solutions proposed in the literature inapplicable. This paper proposes FLK, a Filter with Learned Kinematics, to refine 3D human motion data in real-time and at zero/low latency. The temporal core combines a Kalman filter and a low-pass filter, which learns the motion model through a recurrent neural network. The spatial core takes advantage of the biomechanical constraints of the human body to provide spatial coherency between keypoints. The combination of the cores allows the filter to adequately address different types of noise, from jittering to dropped frames. We test the filter on motion data from multiple datasets and seven 3D human pose estimation backbones, improving accuracy up to 140 mm with non-Gaussian noise and 53 mm with missing information.

Frequency control enhancement for hybrid microgrid using multi-terminal multi-function inverter

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

Алгоритм аппроксимации сигналов для классификации электроэнцефалограмм человека

The article examines the problem of classification of electroencephalograms (EEG), where noise in the signals, caused by various factors, prevents effective analysis and interpretation of the data. The main goal of the study is to analyze the effectiveness of a signal approximation algorithm using a wavelet technique with the next piece-wise approximation in order to effectively remove noise and subsequently solve the problem of signal classification using a convolutional neural network. The classification accuracy of the proposed algorithm with a low-pass filter is compared at different cutoff frequencies. One of the key findings of the study is a two-step approach to signal processing. In the first stage, the model is trained on the raw data, and then the trained convolution kernel with the highest variance is applied to the original signals. This solves the problem of choosing the mother function. This approach aims to enhance the informative components in the signal. The second processing step involves applying the proposed approximation algorithm after the convolution, which complements the first step to create a comprehensive method. This method not only effectively reduces noise in the data, but also has high potential to improve the overall accuracy in solving EEG signal classification problems. Thus, the results of the study provide important practical and theoretical implications, highlighting the prospects for applying the proposed method in the field of signal analysis.

Graph Aggregating-Repelling Network: Do Not Trust All Neighbors in Heterophilic Graphs

Graph neural networks (GNNs) have demonstrated exceptional performance in processing various types of graph data, such as citation networks and social networks, etc. Although many of these GNNs prove their superiority in handling homophilic graphs, they often overlook the other kind of widespread heterophilic graphs, in which adjacent nodes tend to have different classes or dissimilar features. Recent methods attempt to address heterophilic graphs from the graph spatial domain, which try to aggregate more similar nodes or prevent dissimilar nodes with negative weights. However, they may neglect valuable heterophilic information or extract heterophilic information ineffectively, which could cause poor performance of downstream tasks on heterophilic graphs, including node classification and graph classification, etc. Hence, a novel framework named GARN is proposed to effectively extract both homophilic and heterophilic information. First, we analyze the shortcomings of most GNNs in tackling heterophilic graphs from the perspective of graph spectral and spatial theory. Then, motivated by these analyses, a Graph Aggregating-Repelling Convolution (GARC) mechanism is designed with the objective of fusing both low-pass and high-pass graph filters. Technically, it learns positive attention weights as a low-pass filter to aggregate similar adjacent nodes, and learns negative attention weights as a high-pass filter to repel dissimilar adjacent nodes. A learnable integration weight is used to adaptively fuse these two filters and balance the proportion of the learned positive and negative weights, which could control our GARC to evolve into different types of graph filters and prevent it from over-relying on high intra-class similarity. Finally, a framework named GARN is established by simply stacking several layers of GARC to evaluate its graph representation learning ability on both the node classification and image-converted graph classification tasks. Extensive experiments conducted on multiple homophilic and heterophilic graphs and complex real-world image-converted graphs indicate the effectiveness of our proposed framework and mechanism over several representative GNN baselines.

Simulating a numerical UV completion of quartic Galileons

The Galileon theory is a prototypical effective field theory that incorporates the Vainshtein screening mechanism—a feature that arises in some extensions of general relativity, such as massive gravity. The Vainshtein effect requires that the theory contain higher order derivative interactions, which results in Galileons, and theories like them, failing to be technically well posed. While this is not a fundamental issue when the theory is correctly treated as an effective field theory, it nevertheless poses significant practical problems when numerically simulating this model. These problems can be tamed using a number of different approaches: introducing an active low-pass filter and/or constructing a UV completion at the level of the equations of motion, which controls the high momentum modes. These methods have been tested on cubic Galileon interactions, and have been shown to reproduce the correct low-energy behavior. Here we show how the numerical UV-completion method can be applied to quartic Galileon interactions, and present the first simulations of the quartic Galileon model using this technique. We demonstrate that our approach can probe physics in the regime of the effective field theory in which the quartic term dominates, while successfully reproducing the known results for cubic interactions. Published by the American Physical Society 2024

A broadband diplexer-integrated double-pole single-throw switch for FDD/TDD operations

ABSTRACT A broadband diplexer-integrated double-pole single-throw (DPST) switch is proposed to realize frequency division duplexing (FDD) and time division duplexing (TDD). It consists of a lowpass filter (LPF) and a bandpass filter (BPF). The series LC resonators are used in both channels to create transmission zeros (TZs) in the stopband to ensure high selectivity. The diplexer is realized by inter-digital capacitors and microstrip section inductors, which can achieve low cost and compact size. Besides, this diplexer does not require a T-junction, achieving a compact size. For demonstration, a diplexer operating in DC-1.78/3.29–4.42 GHz has been designed, fabricated, and measured. The diplexer has four operating modes. And it has a compact size of 24.67 mm × 21.4 mm. The measured results show that the diplexer exhibits broadband, low insertion losses, high isolation, and high selectivity.

Analysis of Moiré pattern for parallel binary gratings based on AND and OR operators

Abstract A method for analyzing the Moiré pattern of a parallel binary grating based on the high-frequency component of the omitted pattern is proposed. The analysis aims to investigate the signal components that influence the dark-light band formation of the Moiré pattern. The analytical technique begins with the consideration that both grating patterns are a set of binary rectangular signals with two signal level values, “0” and “1.” Subsequently, Fourier series expansion of these two sets of signals was performed considering the frequency, initial phase, and duty cycle of the signals. The superposition of both input signals relies on algebraic equations equivalent to AND and OR operators. The final step was to eliminate the effect of the high-frequency component, which is analogous to the low-pass filter of the electronic circuit (based on averaging the signal intensity per image length). If the frequencies of the two gratings are equal, the analysis based on both operators provides an output signal depending on the initial phase (placement position) and duty cycle values. If the frequencies of the two gratings are not equal, the analysis results show that the output signal has a frequency equal to the frequency difference between the two signals. Moreover, if the two input gratings had a duty cycle of 0.5, the overlapping resulted in a triangular shape. The results from the computer program to simulate the AND and OR operators were consistently compared to the plots of the analytical equations. In addition, they are compatible with image processing results.

Torque Ripple Suppression in the 6/4 Variable Flux Reluctance Machine with Open Winding Configuration by Using Harmonic Injection

High torque ripple can be observed with a 6/4 variable flux reluctance machine (VFRM). In order to minimize the torque ripple in VFRMs, this paper presents a harmonic injection method for 6/4 VFRMs with an open-winding configuration. By analyzing the impact of harmonics on VFRMs, the method involves detecting the third harmonic using a first-order low-pass filter (FLPF). Subsequently, the extracted harmonics are controlled and shifted to counteract the voltage harmonics in both inverters without inducing phase imbalance or overvoltage. With the proposed method, the torque ripple can be significantly reduced by about 50% under load conditions. The effectiveness of the harmonic injection method is validated through a prototype VFRM.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Hanning FIR window filtering analysis for EEG signals

The window function is a mathematical function in signal processing that is multiplied by a signal to lessen or eliminate the impact of undesired characteristics such as spectral leakage and edge effects that might arise while analyzing finite-duration signals. The Hanning window is just one of several window functionalities available. It is designed to gradually taper the endpoints of a signal to zero, reducing spectral leakage in the frequency domain. Filtering is commonly employed in Electroencephalography (EEG) signal processing to remove noise, artifacts, or undesired frequency components while retaining the desired information. Filtering techniques include band pass, low pass, and high pass and band reject filtering. Hanning window technique using low pass, high pass, band pass, and band reject is used for filtering of EEG signal. The response time analysis for the Hanning window is calculated using low, high, band pass, and band-reject filters for different types of EEG waves alpha, beta gamma, theta, and delta using the signal processing simulation tool MATLAB 2023. The response time for delta wave using a low pass filter has a low response time of 14.125 sec which is comparatively less than another filter. For theta wave high pass filter has a low response time of 11.965 seconds. For alpha wave low pass filter has a low response time of 16.706 seconds whereas for beta wave low pass filter has a low response time of 15.202 seconds. For gamma waves high pass filter has a low response time of 11.349 seconds. Hanning band-reject filter has proven low response time for delta, theta, and gamma waveforms of 16.475 seconds, 16.182 seconds, and 17.743 seconds respectively. The novelty of the work is that the Hanning window improves frequency resolution by balancing the main lobe width with side lobe attenuation Hanning band-reject filter is suited best to figure out the widest main-lobe peak and highest peak for different EEG waveforms.

Novel liquid biopsy assay to distinguish benign from malignant pelvic masses.

e17563 Background: Despite not ranking among the top ten most common cancers in women in the United States, ovarian cancer stands as the fifth leading cause of cancer-related deaths in this population. According to the American Cancer Society, only 20% of ovarian cancers are found at stages I-II, when 94% of patients are expected to live longer than five years after diagnosis. Instead, the majority of ovarian cancers are detected at stages III-IV, when long term survival is 20% or less. Early identification of ovarian cancer is challenging due to the wide range of symptoms with which a patient may present, most of which lack specificity for ovarian cancer. Even in cases where a physician may want to rule in or out ovarian cancer, the available methods for screening have a low positive predictive value. These methods include bimanual exams, transvaginal ultrasounds, and CA-125 blood tests. In a screening capacity, the sensitivity for a bimanual exam finding ovarian cancer is only about 44%, transvaginal ultrasound is approximately 85%, and CA-125 testing registers at 77%. The need for better tools to detect ovarian cancer is clear. Genece Health has developed an assay utilizing a proprietary algorithm that leverages deep learning to analyze cfDNA fragment size, end motif, and coverage patterns, along with other genomic features, to detect the presence of ctDNA in blood originating from ovarian cancer. The data presented herein demonstrate that the Genece Health assay has the ability to detect ctDNA in a single tube of blood using low pass whole genome sequencing in a cost-effective way to distinguish between malignant and benign pelvic masses. Methods: Blood was prospectively collected from 56 females with pelvic masses in advance of removal and histopathology (approximately 32 cancer and 24 benign masses) in Streck cfDNA BCT Devices. Additionally, healthy female donor blood from 50 individuals procured through the San Diego Blood Bank were collected in Streck cfDNA BCT Devices. cfDNA was extracted from double-spun plasma and low-pass whole genome sequencing (2X – 5X coverage) was performed. Data were analyzed with the Genece Health Algorithm. Results: The Genece Health Algorithm demonstrated the ability to distinguish between pelvic masses that were deemed benign versus malignant via histopathology. There was a significant (P<0.01) difference between the classifier assigned to the malignant and benign and presumed normal groups. Conclusions: The investigational assay described herein shows progress toward the ability to use non-invasive molecular testing to identify ovarian cancer in a blood sample. A blood test to differentiate between a benign and malignant pelvic mass will enhance the ability of physicians to detect ovarian cancer at earlier stage, improving clinical outcomes for women. The cost-effective low-pass whole genome sequencing approach taken by Genece will be further explored in expanded cohorts.