Signal Correction Research Articles

Position‐sensitivity security transmission with orbital angular momentum directional modulation based on frequency diverse array

AbstractThe position‐sensitivity security communication scheme with orbital angular momentum (OAM) directional modulation (DM) beam pattern generated by the uniform circular frequency diverse array (FDA) is proposed. The transmitter employs FDA antennas to generate dual‐mode range‐dependent OAM beam and the direction information of OAM beam is modulated into the signal. The designed signal with random frequency offset excites the element of the uniform circular FDA to generate orthogonal OAM signals in the desired position. In the undesired position, the intensity pattern and the phase front of the radio beam vary randomly with the digital sequence transmission, which can make eavesdropping difficult. Since the modulation waveform has position‐dependent, the signal can be purposely distorted in undesired positions to provide transmission security for the legitimate user. The composite dual‐mode OAM signal makes it more difficult for eavesdroppers to demodulate correct messages. The receiver with a single antenna employs the phase compensation and helical phase factor to restore the correct digital signal in the desired position. Numerical results show that the space position‐sensitivity OAM‐DM technology based on FDA offers a security transmission scheme.

Synaptic Plasticity Dysfunctions in the Pathophysiology of 22q11 Deletion Syndrome: Is There a Role for Astrocytes?

The 22q11 deletion syndrome (DS) is the most common microdeletion syndrome in humans and gives a high probability of developing psychiatric disorders. Synaptic and neuronal malfunctions appear to be at the core of the symptoms presented by patients. In fact, it has long been suggested that the behavioural and cognitive impairments observed in 22q11DS are probably due to alterations in the mechanisms regulating synaptic function and plasticity. Often, synaptic changes are related to structural and functional changes observed in patients with cognitive dysfunctions, therefore suggesting that synaptic plasticity has a crucial role in the pathophysiology of the syndrome. Most interestingly, among the genes deleted in 22q11DS, six encode for mitochondrial proteins that, in mouse models, are highly expressed just after birth, when active synaptogenesis occurs, therefore indicating that mitochondrial processes are strictly related to synapse formation and maintenance of a correct synaptic signalling. Because correct synaptic functioning, not only requires correct neuronal function and metabolism, but also needs the active contribution of astrocytes, we summarize in this review recent studies showing the involvement of synaptic plasticity in the pathophysiology of 22q11DS and we discuss the relevance of mitochondria in these processes and the possible involvement of astrocytes.

Coordinated Distributed Voltage Control Methods for Standalone Microgrids

A microgrid is a small-scale power grid comprising distributed generators (DGs), distributed storage systems, and loads. It will lose contribution from the main grid if it shifts to islanded mode due to pre-planned or unforeseen disturbances. To restore the terminal voltages of all the distributed generators to the reference value, this paper presents three coordinated secondary control strategies. First, motivated by the synchronization control theory of multiagent systems, a distributed control technique is developed where each of the DGs is considered an agent and they exchange information via a communication network. second, a two-level control technique is designed in which a global controller is employed to monitor the overall performance of the DGs by transmitting corrective signals to the local controllers of the agents. In this technique, all the communication is between the global controller and the local controllers without any direct communication between the agents. Third, decentralized control is provided in which each DG is separately controlled by its local controller that operates based on the local feedback measurements. Simulations are carried out on an islanded microgrid consisting of four DGs to illustrate our design approach.

A surface-enhanced Raman scattering aptasensor for ratiometric detection of aflatoxin B1 based on graphene oxide-Au@Ag core-shell nanoparticles complex

Aflatoxin B1 (AFB1) has the strongest carcinogenesis and mutagenesis effects. Herein, a novel ratiometric surface-enhanced Raman scattering (SERS) aptasensor based on graphene oxide (GO) as a corrective internal standard (IS) and signal amplifier was constructed for reliable detection of AFB1. 4-Mercaptobenzoic acid as Raman reporter embedded in [email protected] core-shell nanoparticles and AFB1aptamer conjugated to the Ag shell acted as the signal probes ([email protected] NPs-AFB1apt). AFB1apt was attached to the surface of GO with Raman signals modified Au NPs/Indium tin oxide glass (GO/Au NPs/ITO) via π–π stacking interaction, facilitating further amplification of the signal. With an increase of AFB1 concentration (0.0001 ng/mL–100 ng/mL), the probes were released owing to the affinity of aptamers, causing the decreased 4-MBA intensity (I4-MBA) and unchanged GO intensity (IGO). Therefore, the quantitative test was achieved based on the ratiometric value (I4-MBA/IGO). A good negative correlation (R2 = 0.992) was achieved and the limit of detection (LOD) was as low as 0.1 pg/mL, proving the proposed sensor can become an efficient analytical tool and shows a bright application prospect in the food detection field.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error () was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent

The field-oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI-type controllers. In addition to their low complexity (an advantage for real-time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of id and iq currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor-load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC-type strategy, both when using simple PI-type controllers or in the case of complex SMC or synergetic-type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC-type strategy, both in the case of simple PI-type controllers and complex SMC or synergetic-type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL-DDPG.

Displacement damage dose analysis of the output characteristics of In0.5Ga0.5P and Cu(In,Ga)(S,Se)2 solar cells irradiated with alpha ray simulated helium ions

To investigate the applicability of radiation-hard indium–gallium–phosphide (InGaP) and copper–indium–gallium–sulfide–selenide (CIGS) solar cells to dosimeter devices without any modification, we irradiated high-energy He+ ions, which were simulated α-ray particles, to an InGaP and a CIGS solar cell. We found that both types of solar cells have sufficient resistance to He+ ions. By using displacement damage dose (DDD) analysis, the obtained He+ ion-induced degradation trends were compared with those induced by high-energy electrons and protons, and we found that the degradation trends due to He+-ions, electrons, and protons aligned on the same curve when we plotted the data as a function of a modified DDD conversion equation, which originally was applied to space solar cells. The obtained DDD formulas enable us to predict the device lifetime or correction of an output signal for degradation when such solar cells are employed as a dosimeter.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.

Data-driven power system small-signal stability assessment and correction control model based on XGBoost

The influence of small-signal stability on the safety and stability of the power system is becoming more prominent. A mapping model based on steady-state operation information is established using the sample learning method, which provides a new technical path for the rapid assessment and correction of significant power grid oscillation characteristics. This paper establishes a small signal stability assessment and correction control model based on the Extreme Gradient Boosting (XGBoost) algorithm. Firstly, the XGBoost model is obtained by analyzing the mapping relationship between generator power, node power, branch power, and minimum damping ratio. Then, the sensitivity of the generator damping ratio is calculated, and the objective is to minimize the active power adjustment amount of the generator. The stability constraint and power balance are the constraint conditions to establish the optimization correction model, obtain the optimal adjustment amount, correct the minimum damping ratio, and improve the system’s stability. Finally, the minimum damping ratio after correction is obtained, and the modified damping ratio is estimated by XGBoost algorithm. The performance of the proposed model is verified in IEEE 3-machine 9-node and 10-machine 39-node systems.

Correction of Signals in a Microbolometric Array Raising the Validity of Measuring Objects’ Temperature. Part 2

Correction of Signals in a Microbolometric Array Raising the Validity of Measuring Objects’ Temperature. Part 2

ИССЛЕДОВАНИЕ АЛГОРИТМА СНИЖЕНИЯ ВЗАИМОНАГРУЖЕНИЯ ДВУХ ПРИВОДОВ, ОДНОВРЕМЕННО УПРАВЛЯЮЩИХ АЭРОДИНАМИЧЕСКИМ РУЛЕМ МАГИСТРАЛЬНОГО САМОЛЕТА

Some civil aircraft assume two actuators simultaneously turning aside the flight control surface(rudder, in some cases aileron or elevator). Thus resulting in onset of the force fighting (mutualloading force). There are several ways to reduce the mutual loading force. One of them is to insertspecific force equalization algorithm in actuators steering. This article deals with effectiveness offorce equalization algorithm as well as its influence on system “2 active actuators - elevator”. Mathematic model of two actuators on elevator has been formed and tested by the developed testing methodthat reproduces the most severe operation conditions. There are several nonlinearities which iscontained in mathematic model: sleeve-travel physical limits, nonlinear effects of fluid foil, rod-travelphysical limits and attachment rigidity. Force equalization algorithm comprises a pressure differentialfeedback loop with PID regulator. The results show that force equalization algorithm is highlyefficient (mutual loading force has been reduced over than 90%) in mode test without external load.However, in test mode with external load it is necessary to introduce a limitation on the magnitude ofthe corrective signal so as decrease the rod drop to level specified in terms of reference. Algorithmhas no effect on stability margin of the system. In conclusion, results of this study could also be usefulto expand the test program for two actuators simultaneously turning aside the flight control surfacesto further simplify the implementation of the force equalization algorithm.

Exploring the application of blue and red thermoluminescence for dating volcanic glasses

Tephras are significant markers in the stratigraphic record and play a key role in establishing paleoenvironmental and paleoclimate histories worldwide. Despite burgeoning research focused on tephra characterization and correlation techniques, there are still few techniques that allow for the direct dating of tephra, particularly below the lower age limit of K/Ar and Ar/Ar dating methods. In this study, we test different thermoluminescence (TL) dating approaches on the 4–11 μm volcanic glass constituents of three different independently different tephras. By comparing against independent age control, we demonstrate the utility of both blue (320–450 nm) and red (587–651 nm) TL emissions for dating volcanic glasses using single aliquot regenerative (SAR) dose techniques. We find that both blue and red TL emissions from the volcanic glass shards are dim but reproducible and show no evidence for significant sensitivity changes occurring between the natural TL and the first test dose during the SAR protocol. Fading tests on the blue TL signal show that g-values range from 1.6 ± 1.0 to 2.9 ± 1.1%/decade and are statistically indistinguishable with zero at 2σ for the red TL. Bleaching experiments show that both blue and red TL signals are sensitive to light exposure, with sensitivity corrected signals declining by ∼40% over a 2-h period. For all three tephras, both the fading-corrected blue and red SAR-TL ages are consistent with age expectation. These successful results demonstrate the effectiveness of TL techniques for determining the eruption ages of tephra deposits in primary position between ∼1 and at least 30 ka.

Implementation of tracking system correction methods in the Simulink package from MATLAB CAD

CNC equipment is flexible, versatile, and has high performance. The use of such equipment significantly changes the nature of production and makes it mobile, meeting the requirements for continuous improvement and updating of mechanical engineering products. However, CNC machines are quite complex, and their acquisition is associated with large financial costs, therefore, the problem of efficient use of CNC machines is a priority for most mechanical engineering enterprises and is inextricably linked with the need to write various programs. Variants of the implementation of sequential, parallel correction and feedback correction methods in the Simulink package from MATLAB CAD are proposed. When performing the work, customizable library blocks are used, which are available for students to access on the official website of the software manufacturer. Schemes for each correction method with a description of the included blocks are presented. The output of the results and the comparison of the obtained data is carried out using a software oscilloscope built into the package, which allows clearly understanding the difference between the schemes. A comparison is made on the example of various types of signals for the most objective assessment of the quality of signal correction by one method or another. The practical significance of the work is familiarization with the MATLAB CAD Simulink package and consideration of possible ways to implement these correction schemes, as well as availability of the same schemes for self-construction in this CAD. This work can be used not only for teaching students of higher educational institutions in the field of development, debugging and coding of electronic and radio-electronic devices in terms of describing the algorithm of their work, but also for organizing laboratory work on courses of disciplines related to this topic, as well as when creating and designing real devices in production. Familiarization and study of this programming language are conducted within the walls of one of the leading engineering universities of the Russian Federation, the Bauman Moscow State Technical University.

Glyco-nanotechnology: A biomedical perspective

Glycans govern cellular signaling through glycan–protein and glycan–glycan crosstalk. Disruption in the crosstalk initiates ‘rogue’ signaling and pathology. Nanomaterials supply platforms for multivalent displays of glycans, mediate ‘rogue’ signal correction, and provide disease treatment modalities (therapeutics). The decorated glycans also target overexpressed lectins on unhealthy cells and direct metal nanoparticles such as gold, iron oxide, and quantum dots to the site of infection. The nanoparticles inform us about the state of the disease (diagnosis) through their distinct optical, magnetic, and electronic properties. Glyco-nanoparticles can sense disease biomarkers, report changes in protein–glycan interactions, and safeguard quality control (analysis). Here we review the current state of glyco-nanotechnology focusing on diagnosis, therapeutics, and analysis of human diseases. We highlight how glyco-nanotechnology could aid in improving diagnostic methods for the detection of disease biomarkers with magnetic resonance imaging (MRI) and fluorescence imaging (FLI), enhance therapeutics such as anti-adhesive treatment of cancer and vaccines against pneumonia, and advance analysis such as the rapid detection of pharmaceutical heparin contaminant and recombinant SARS-COV-2 spike protein. We illustrate these progressions and outline future potentials of glyco-nanotechnology in advancing human health.

10-Bit 5 MS/s Successive Approximation Register Analog-to-Digital Converter with a Phase-Locked Loop and Modified Bootstrapped Switch for a BLDC Motor Drive

In this paper, we present a successive approximation register (SAR) analog-to-digital converter (ADC) with a charge-pump (CP) phase-locked loop (PLL) and a bootstrapped switch, also called PLL-SAR ADC. To meet system-on-chip (SOC) and industrial requirements, the proposed SAR ADC and the control circuits of electric vehicles must be integrated into a single chip and be fabricated using the TSMC 0.25-μm 1P3M complementary metal oxide semiconductor (CMOS) high-voltage process. It is difficult to implement a high-speed SAR ADC with the TSMC 0.25-μm CMOS high-voltage process because it includes an N-type buried layer, which shorts all p-type metal oxide semiconductor field-effect transistor (PMOSFET) bodies together to withstand high voltages. In the proposed PLL-SAR ADC, two clock signals, an external clock signal and an internal clock signal from the CP-PLL, are provided to guarantee that a correct clock signal is fed. This design improves the robustness of the designed system. A monotonic capacitor-switching procedure is considered to reduce power consumption. Furthermore, a bootstrapped switch was added along with a dummy switch and a dummy transistor to eliminate disturbances in the input voltages and to improve the device’s anti-noise capability. Moreover, a two-stage dynamic comparator was used to prevent kickback noise induced by the parasitic capacitors. The measurements indicate that the signal-to-noise-and-distortion ratio, effective number of bits, power consumption, and chip area are 53.82 dB, 8.65 bits, 1.256 mW, and 1.261 × 0.975 mm2, respectively. The FoM is approximately 0.625 pJ/conv-step at 1.256 mW, 8.65 bits, and 5 MS/s. The high sampling rate of 5 MS/s and high accuracy of 8.65 bits are the main advantages of the proposed PLL-SAR ADC.

DYNAMIC NONLINEAR CORRECTION PROCESSING OF PRESSURE SENSOR TEST SIGNAL IN INTERNET OF THINGS SYSTEM

In order to further explore the intelligent research of the pressure sensor in the pipeline, based on the existing pipeline detection methods of the Internet of Things (IoT), the signal processing method and correction research in the wireless sensor network of the IoT are explored. A multi-pressure sensor detection method of negative pressure wave is proposed. Scientific and intelligent means are used to explore the pipeline remote intelligent detection. The specific research results are as follows. In the simulation study, when the working condition of pump station 1# changes, the pressure wave first reaches A1 and B2 sensors, and finally reaches C1 and C2 sensors. If there is leakage in the pipeline, the simulation results show that when the pressure of pump station rises to 5[Formula: see text]MPa, the pressure of B1 and B2 test points decreases to about 4.9[Formula: see text]MPa, and the pressure of A1, A2, C1 and C2 test points is about 3[Formula: see text]MPa; when the pressure of the pump station drops to 3[Formula: see text]MPa, the pressure of all test points decreases. Generally speaking, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of the back end increases when the pressure increases; when the pressure decreases, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of all the back-end pressure sensor decreases. After correction, the signal data is obviously smooth and the error is obviously reduced. This study can provide scientific and effective reference for the pressure monitoring of IoT sensor network.

Contributed Session I: Towards clinically friendly optoretinography (ORG) using 100 kHz swept-source OCT without adaptive optics (AO)

Optoretinography measures the stimulus-evoked response of the photoreceptors using light. Current systems use adaptive optics (AO) to obtain cellular-level resolution and fast volume acquisitions, to be able to monitor a single cone's response with sufficient sampling over time. The systems are complex to operate and therefore are not ideal in the clinical setting, where ease-of-use and high throughput are desirable. Here, we set out to explore if a less complex research-grade imaging system without AO could detect light-evoked changes in the retina when stimulus is applied. The imaging system uses 100 kHz swept source for OCT and tracking laser scanning ophthalmoscope is coupled into the sample arm for active eye-tracking. This provides a correction signal for the OCT to minimize spurious phase noise from eye motion. The stimulus is done with 555 nm LED (single flash and 10 Hz flicker) during OCT imaging. Obviously, the resolution is not the same without AO and signals are a collective response from tens of photoreceptors resulting in measurements of phase shift between scattering speckle fields rather than well-defined single reflections from within a photoreceptor. Improper eye tracking can also negatively affect phase-sensitive imaging needed to observe ORG signals. The ability to observe any stimulus-evoked responses using standard OCT would make optoretinography more accessible to study human vision as well as novel functional biomarkers of retinal diseases.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.

Contemporary N2 and SF6 multiple breath washout in infants and toddlers with cystic fibrosis.

Multiple breath washout (MBW) is used for early detection of cystic fibrosis (CF) lung disease, with SF6 MBW commonly viewed as the reference method. The use of N2 MBW in infants and toddlers has been questioned for technical and physiological reasons, but a new correction of the N2 signal has minimized the technical part. The present study aimed to assess the remaining differences and the contributing mechanisms for the differences between SF6 and N2 MBW,corrected-such as tidal volume reduction during N2 washout with pure O2 . This was a longitudinal multicenter cohort study. SF6 MBW and N2 MBW were performed prospectively at three CF centers in the same visits on 154 test occasions across 62 children with CF (mean age: 22.7 months). Offline analysis using identical algorithms to the commercially available program provided outcomes of N2,original and N2,corrected for comparison with SF6 MBW. Mean functional residual capacity, FRCN2,corrected was 14.3% lower than FRCN2, original , and 1.0% different from FRCSF6 . Lung clearance index, LCIN2,corrected was 25.2% lower than LCIN2,original , and 7.3% higher than LCISF6 . Mean (SD) tidal volume decreased significantly during N2 MBWcorrected , compared to SF6 MBW (-13.1 ml [-30.7; 4.6], p < 0.0001, equal to -12.0% [-25.7; 1.73]), but this tidal volume reduction did not correlate to the differences between LCIN2,corrected and LCISF6 . The absolute differences in LCI increased significantly with higher LCISF6 (0.63/LCISF6 ) and (0.23/LCISF6 ), respectively, for N2,original and N2,corrected , but the relative differences were stable across disease severity for N2,corrected , but not for N2,original . Only minor residual differences between FRCN2,corrected and FRCSF6 remained to show that the two methods measure gas volumes very similar in this age range. Small differences in LCI were found. Tidal volume reduction during N2 MBW did not affect differences. The corrected N2 MBW can now be used with confidence in young children with CF, although not interchangeably with SF6 .