Signal Correction Research Articles

Design and implementation of a physical layer optical fiber security communication system based on a ZUC stream cipher

In this work, we have proposed a new physical layer encryption scheme to improve the security of optical fiber communication systems. The secrecy scheme is based on disturbance of the data cluster under the ZUC algorithm (named for Zu Chongzhi, a famous mathematician in ancient China), which is generated by optical XOR using a dual-drive Mach–Zehnder modulator (DD-MZM). A base scrambling pseudo random binary sequence (PRBS) generated by the ZUC stream cipher is introduced, which results in a better scrambling effect and randomness in the key stream of the block encryption structure. A simulation setup conducted in an encrypted transmission of the 64×64 grayscale image over 80 km of optical fiber shows that the authorized user can successfully decrypt the received signal, while the eavesdroppers cannot derive useful information with a bit error rate (BER) at approximately 0.5. In the simulation, after setting the delay compensation to 3.125 ps, a correct signal with a Q-factor as high as 11.3642 and a BER as low as 3.14295−30 can be obtained.

Vibration signal acquisition and extraction for cutter wear monitoring

Abstract The cutter wear state is key in manufacturing. During the machining, the vibration signal of the tool spindle will change with the increase of cutter wear, which provides an effective method for cutter wear monitoring. However, due to the popularity of machining centers, the machining trajectory of a cutter is extremely complex in practical production. The complexity includes a single process containing multiple work steps, a step containing random and long non-cutting time, etc., making it difficult to acquire and extract the specific step signal as the effective signal for cutter wear monitoring. Therefore, this paper proposes a system that can synchronously acquire machine parameters and tool spindle vibration signals. Then, two algorithms—the cutting signal recognition algorithm and the step signal similarity detection algorithm—are proposed. The former algorithm is based on sliding window energy, which can accurately identify the instant the cutter begins to cut the workpiece. The step signal similarity detection algorithm based on the Edit Distance on Real (EDR) sequence ensures the correctness of the step extraction. The experimental results show that the system has efficient acquisition of vibration signals and correct step signal extraction, providing a data basis for cutter wear monitoring.

Use of Constant-Analyte Samples in Updating Quantitative Near-Infrared Calibration Models for Physiological Levels of Glucose

Updating procedures were investigated for multivariate calibration models for physiological levels of glucose in aqueous samples based on near-infrared spectroscopy. The methodology evaluated used samples with constant glucose concentrations collected on the prediction day to augment previously collected calibration spectra for the purpose of computing an updated model for use only on that day. Models were based on partial-least squares (PLS) regression in conjunction with spectral preprocessing using direct orthogonal signal correction (DOSC). The use of constant-analyte samples allowed the procedure to be compatible with samples collected at the beginning of a chemical reaction or from a fasting patient in a blood glucose monitoring application. The methodology was tested with nine sets of prediction spectra collected from 368 to 617 days after the collection of the calibration data. The updated PLS models based on four augmented samples exhibited an approximately 50% improvement in prediction accuracy relative to models that were based solely on the original calibration data. The use of DOSC in conjunction with updating was found to be beneficial overall, but only led on average to less than 10% improvement in prediction performance.

Design and experiment of non-destructive testing system for moisture content of in-situ maize ear kernels based on VIS-NIR

To facilitate rapid, non-destructive, cost-effective continuous detection of Moisture Content in corn kernels, a Near-infrared (VIS-NIR) spectroscopy based in-situ maize ear moisture detection device was developed, utilizing machine learning for predictive modeling. Field experiments (30–35 °C) assessed three preprocessing algorithms: z-score normalization (ZS), Orthogonal Signal Correction (OSC), and a ZS-OSC combination, with ZS-OSC selected for its superior performance (R2 ≥ 0.90, RMSE ≤ 2.12 %, RPD＞2.9). Spectral imaging from 410 to 940 nm was used to develop moisture prediction models via Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM), where PLSR is suited for single variety (R2 ≥ 0.82, RMSE ≤ 2.62 %, RPD ≥ 2.2) and SVM for both single and mixed varieties. Additionally, grain temperature's impact on model performance was analyzed, showing decreased accuracy across temperatures of 30–35 °C, 35–40 °C, and 40–45 °C. The final device and models excelled in 30–35 °C field tests, achieving R2 ≥ 0.88, RPD＞2.5, RMSE ≤ 0.901 %, with less than 1.82 % deviation between predicted and actual values, and all classification indices over 84.38 %. The device is proven accurate and effective for corn grain moisture detection, offering valuable insights for in-situ maize moisture content analysis.

A Baseline Drift-Elimination Algorithm for Strain Measurement-System Signals Based on the Transformer Model

Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model’s ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications.

Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering

Introduction. The use of non-equidistant pulse sequences as probing radar signals makes it possible to eliminate blind spots in speed and range. However, the implementation of multi-channel Doppler filtering (MDF) based on the classical fast Fourier transform (FFT) algorithm of non-equidistant signal samples in the signal detection problem is associated with energy losses. The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. To eliminate this drawback, the authors previously proposed using combined classical and modified FFT algorithms. However, the use of the combined method does not lead to an optimal solution in terms of MDF efficiency.Aim. Optimization of weight processing of non-equidistant signals to improve the efficiency of MDF.Materials and methods. An MDF synthesis was carried out using optimization procedures, and the effectiveness of the algorithms was assessed using computer calculations.Results. The results show that the Kaiser Bessel window with a window parameter of 4.42 provides the highest signal-(clutter+noise) ratio improvement coefficient averaged over frequency channels equal to 30.06 dB and the highest probability of correct signal detection averaged over MDF channels equal to 0.5 at processing of non-equidistant pulse sequences. Optimization of the weight processing of MDF under the specified conditions increased the average efficiency characteristics used of up to 53.18 dB and 0.92, respectively.Conclusion. Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. However, these advantages are achieved at the cost of not using the FFT, i.e., implemented within the framework of the discrete Fourier transform (DFT) algorithm.

Personalized screening before subcutaneous cardioverter-defibrillator implantation: Usefulness and outcomes in clinical practice—the S-ICD screening SIS prospective study

BackgroundElectrocardiographic screening before subcutaneous implantable cardioverter-defibrillator (S-ICD) implantation is unsuccessful in around 10% of cases. A personalized screening method, by slightly moving the electrodes, to obtain a better R/T ratio has been described to overcome traditional screening failure. ObjectiveThe objectives of the SIS study were to assess to what extent a personalized screening method improves eligibility for S-ICD implantation and to evaluate the inappropriate shock rate after such screening success. MethodsAll consecutive patients eligible for an S-ICD implantation were prospectively recruited across 20 French centers between December 2019 and January 2022. In case of traditional screening failure, patients received a second personalized screening. If at least 1 vector was positive, the personalized screening was considered successful, and the patient was eligible for implantation. ResultsThe study included 474 patients (mean age, 50.4 ± 14.1 years; 77.4% men). Traditional screening was successful in 456 (96.2%) cases. This figure rose to 98.3% (n = 466; P = .002) when personalized screening was performed. All patients implanted after successful personalized screening had correct signal detection on initial device interrogation. Nevertheless, after 1-year follow-up, 3 of the 7 patients (43%) implanted with personalized screening experienced inappropriate shock vs 18 of the 427 patients (4.2%) with traditional screening and S-ICD implantation (P = .003). ConclusionTraditional S-ICD screening was successful in our study in a high proportion of patients. Considering the small improvement in success of screening and a higher rate of inappropriate shock, a strategy of personalized screening cannot be routinely recommended.ClinicalTrials.gov identifier: NCT04101253

B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T.

CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 μT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach. B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices. The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

Advancing 2D reaction rate measurements in BNCT: Validation of the indirect neutron radiography method

The precise characterization of neutron beams is a cornerstone of Boron Neutron Capture Therapy (BNCT). While Instrumental Neutron Activation Analysis (INAA) is the standard technique for neutron flux measurement, it is limited in its ability to capture two-dimensional (2D) reaction rate distributions. This study aims to validate the Indirect Neutron Radiography (INR) method for 2D reaction rate quantification, addressing critical variables such as temperature sensitivity and signal fading. We designed and constructed an optimized INR platform comprising an Imaging Plate (IP), readout device, activation detectors (copper foils), and real-time temperature monitoring. Comprehensive experiments were conducted to investigate the impact of ambient temperature and fading time on IP signal reliability. A robust calibration curve was formulated, linking IP signals to dose deposition metrics, thereby enabling precise reaction rate assessments. The study found that IP signals are minimally sensitive to temperature variations (less than 0.1% change per 1 °C), but are subjected to linear fading over time, necessitating stringent temperature control and time-dependent signal corrections. A mathematical relationship between IP signals and dose deposition was established, represented by Y = 20,500 × x0.01803-21103. Application of the INR method revealed that the depth-dependent reaction rates in copper strips closely aligned with those acquired through INAA, exhibiting a relative deviation of less than 5% within a 4cm depth range inside the phantom. Our findings demonstrate that the INR method offers a robust alternative to traditional INAA for capturing 2D reaction rates, effectively addressing complexities like temperature sensitivity and signal fading. While challenges persist, particularly in the realm of measurement errors, this study lays the groundwork for further methodological refinements and broadens the scope for future research in BNCT neutron beam characterization.

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

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

In Situ Nondestructive Detection of Nitrogen Content in Soybean Leaves Based on Hyperspectral Imaging Technology

In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

Quantitative analysis of three ingredients in Salvia miltiorrhiza by near infrared spectroscopy combined with hybrid variable selection strategy

Rosmarinic acid (RA), Tanshinone IIA (Tan IIA), and Salvianolic acid B (Sal B) are crucial compounds found in Salvia miltiorrhiza. Quickly predicting these components can aid in ensuring the quality of S. miltiorrhiza. Spectral preprocessing and variable selection are essential processes in quantitative analysis using near infrared spectroscopy (NIR). A novel hybrid variable selection approach utilizing iVISSA was employed in this study to enhance the quantitative measurement of RA, Tan IIA, and Sal B contents in S. miltiorrhiza. The spectra underwent 108 preprocessing approaches, with the optimal method being determined as orthogonal signal correction (OSC). iVISSA was utilized to identify the intervals (feature bands) that were most pertinent to the target chemical. Various methods such as bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable combination population analysis (VCPA), successive projections algorithm (SPA), iteratively variable subset optimization (IVSO), and iteratively retained informative variables (IRIV) were used to identify significant feature variables. PLSR models were created for comparison using the given variables. The results fully demonstrated that iVISSA-SPA calibration model had the best comprehensive performance for Tan IIA, and iVISSA-BOSS had the best comprehensive performance for RA and Sal B, and correlation coefficients of cross-validation (R2cv), root mean square errors of cross-validation (RMSECV), correlation coefficients of prediction (R2p), and root mean square errors of prediction (RMSEP) were 0.9970, 0.0054, 0.9990 and 0.0033, 0.9992, 0.0016, 0.9961 and 0.0034, 0.9998, 0.0138, 0.9875 and 0.1090, respectively. The results suggest that NIR spectroscopy, along with PLSR and a hybrid variable selection method using iVISSA, can be a valuable tool for quickly quantifying RA, Sal B, and Tan IIA in S. miltiorrhiza.

A novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series

The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.

基于可见/近红外光谱的鲜枣可溶性固形物在线检测

Soluble solid content (SSC) is one of the important evaluation indexes of the internal quality and taste of fresh jujube. In order to realize the online nondestructive detection of SSC of fresh jujube, this paper took Huping jujube as the research object, adopted self-constructed nondestructive online testing system to collect the spectral information of jujubes (350~2500 nm), and studied the influence of the rotational speed of 4 r/min on the online prediction model of SSC of jujube. Kennard-Stone (KS) algorithm was used to divide the sample into correction set and prediction set. Six commonly used preprocessing methods such as SG smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate (SNV), orthogonal signal correction (OSC), first derivative (FD), and second derivative (SD) were applied to the spectral data, and the regression coefficient (RC) algorithm and the successive projections algorithm (SPA) were utilized to select informative wavelengths, and a quantitative prediction model for the SSC of Huping jujube was established using partial least squares regression (PLSR). The results indicate that the PLSR prediction model established by preprocessing the original spectrum with OSC and combining it with RC algorithm to select characteristic wavelengths was optimal. Therefore, when predicting the SSC of Huping jujube, the optimal model was OSC-RC-PLSR, and the correlation coefficients of the correction set and prediction set were 0.846 and 0.782, respectively, and the corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP) were 1.962 and 2.247, respectively. The results show that non-destructive detection of soluble solid content of jujube can be achieved by combining visible-near-infrared spectroscopy and appropriate regression model, which provides an innovative way for online sorting and identifying fresh jujube.

Analysis and Simulation of Current Balancer Circuit for Phase-Gain Correction of Unbalanced Differential Signals

The phase and gain imbalance of a balun output can be adjusted by a differential current balancer (DCB) circuit. The performance of DCB circuit, for correcting the phase (gain) imbalance, is analyzed for a wide range of input signal level, and the accuracy is verified with circuit simulation. To illustrate the phase-error/gain-error (PE/GE) correction, a 30–40[Formula: see text]GHz DCB circuit is designed and simulated in a 180-nm CMOS process. The DCB is examined for input PE, [Formula: see text], of [Formula: see text] and input GE, GA, of [Formula: see text][Formula: see text]dB. Analysis and simulation illustrate an output phase error [Formula: see text] of [Formula: see text] and output gain error [Formula: see text] of [Formula: see text][Formula: see text]dB, over frequency range of 20–50[Formula: see text]GHz. The results of DCB circuit for PE (GE) compensation are compared to that of phase-correction technique (PCT) circuit, illustrating the superior phase (gain) imbalance correction of the DCB circuit with lower NF and DC power consumption.

Effects of through-thickness dielectric sensor on carbon fibre epoxy cure monitoring

Dielectric sensors are an appealing solution for in-situ cure monitoring of thermoset polymers and thermoset composites. Analysis techniques have been shown to produce highly accurate and repeatable insight into cure state metrics both during and after cure. However, most dielectric sensors only report data on the surface of the material that the sensor is in direct contact with, neglecting the remainder of the thickness of the component. This study evaluates a novel dielectric sensor which is designed with a 20 mm penetration depth to monitor through the thickness of the composite part. While the prototype sensor design was shown to influence the raw data signal, a correction factor was successfully applied, and signals were analysed in accordance with the standard set of dielectric methods. The corrected signal had good accuracy and repeatability across laminates from 2 to 20 mm thick, demonstrating a non-invasive, through-thickness monitoring for a range of part designs.

Quantitative analysis of the hexamethylenetetramine concentration in a hexamethylenetetramine–acetic acid solution using near infrared spectroscopy: A comprehensive study on preprocessing methods and variable selection techniques

Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA–acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction, first derivative, second derivative, and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination, competitive adaptive reweighted sampling, and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x–y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.

Abstract 960: Improved cfDNA methylation profiling through correction of misrepaired jagged-ends

Abstract Background: Profiling cfDNA methylation enables early cancer detection and classification, but challenges arise from "jagged ends" (JEs) for ~90% of cfDNA molecules, as well as for FFPE samples and partially degraded genomic DNA. Repair of these jagged ends introduces erroneous methylation signals, especially in shorter cfDNA fragments enriched for tumor derived molecules. Separately, DNA conversions during 5mC profiling hamper accurate simultaneous ctDNA genotyping of somatic mutations. To meet these needs, we developed a method preserving double-stranded cfDNA molecules without error-prone end-repair at jagged ends. This enables superior methylation profiling including hemimethylation detection, and simultaneous ctDNA SNV genotyping. Methods: We present JEEPERS (Jagged-End Error Polishing of Enzymatically misRepaired Sequences), a novel method for correcting errors in double-stranded library preparation for methylation profiling. JEEPERS detects and quantifies enzymatic repair errors at JEs, correcting them in silico. Leveraging duplex UMIs, it utilizes complementary strands and sibling reads from cfDNA families without jagged ends. The method exploits the 5' to 3' polarity of ER-associated errors and JE length profiles for correction. JEEPERS introduces anchor CpGs for precise correction of rare signals and enables accurate SNV calling at non CpG sites, addressing the challenge of differentiating true SNVs from conversion artifacts leading to C>T/G>A changes in sequencing data. Results: Analyzing 248 cfDNA samples (139 NSCLC patients, 109 healthy controls) by methylation profiling (EM-Seq) and ctDNA mutation genotyping (CAPP-Seq), JEEPERS correction efficiently addressed distortions, even at low tumor concentrations. Limiting dilution experiments demonstrated JEEPERS achieving a LOD95 of ~0.2% with 95% specificity (n=300). Despite ~50% lower molecular depth, JEEPERS-corrected cfDNA EM-Seq enabled accurate simultaneous methylation profiling and ctDNA genotyping, correlating strongly with CAPP-Seq genotypes (R=0.998, n=30). As expected, mutant ctDNA molecules were shorter than wild-type in both CAPP-Seq and EM-Seq data. cfDNA methylation patterns varied by oncogene vs. tumor suppressor status; EGFR mutants showed increased methylation (p<0.01), while TP53 mutants had lower methylation levels (p<0.05). Conclusions: Our method effectively corrects cfDNA methylation data, even at low tumor concentration, ensuring enhanced methylation signal accuracy and proficient SNV calling capabilities. This integrated approach offers a powerful means for investigating the link between SNVs and DNA methylation, significantly enhancing the accuracy of cancer detection using cfDNA, especially for early-stage tumors. This paves the way for a deeper understanding of the molecular mechanisms driving cancer initiation and progression. Citation Format: Rui Wang, Emily G. Hamilton, Diego Almanza, Angela Hui, Maximilian Diehn, Ash A. Alizadeh. Improved cfDNA methylation profiling through correction of misrepaired jagged-ends [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 960.

ИССЛЕДОВАНИЕ АЛГОРИТМОВ СИНТЕЗА САМОПРОВЕРЯЕМЫХ ЦИФРОВЫХ УСТРОЙСТВ НА ОСНОВЕ ЛОГИЧЕСКОЙ КОРРЕКЦИИ СИГНАЛОВ С ПРИМЕНЕНИЕМ ВЗВЕШЕННЫХ КОДОВ БОУЗА – ЛИНА

When synthesizing self-checking digital devices based on Boolean correction of signals, it is proposed to use weight-based Bose – Lin codes, the construction principles of which imply preliminary weighting of data symbols by natural numbers. Two “basic” structures are proposed for the synthesis of built-in control circuits for groups of six outputs of the diagnostic object. The structures are based on weight-based Bose – Lin codes with summation in the residue ring modulo M=4. There are 15 such noise-protected codes with the number of data symbols m=4, which allows to select the best option as a base code in the builtin control circuit according to various criteria, including achieving self-checking properties even in cases where this cannot be achieved using traditional approaches, including duplication. Two algorithms for the synthesis of built-in control circuits based on Boolean signal correction have been developed, allowing the use of correction of only two of the six functions in the basic structure. For basic structures, there are 720 ways to construct an integrated control circuit based on Boolean correction of signals using each weight-based Bose – Lin code, which makes it possible to choose the best way to implement a self-checking device, considering various indicators (structural redundancy, testability, etc.). The operation of the algorithms is demonstrated on simple examples. The results of experiments with test digital circuits from the MCNC Benchmars set confirming the efficiency of the developed algorithms are given. It is shown that with a large number of outputs, there is an astronomical number of ways to organize built-in control circuits, which makes it possible to build self-checking devices with various characteristics. The use of Boolean correction of signals using weight-based Bose – Lin codes can be used in the development and design of self-checking digital devices on various element bases.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.