Distortion Of Curve Research Articles

Los vehículos eléctricos y su impacto en el sistema eléctrico de distribución: Caso de estudio “Alimentador urbano de la ciudad de Portoviejo”

In Portoviejo, the current use of electric vehicles (EVs) is limited compared to conventional vehicles. However, due to the implementation of laws, regulations, and policies promoting electric mobility in Ecuador, a significant increase in the integration of EVs into the city's electrical system is anticipated in the coming years. To anticipate the impact on the electrical infrastructure, a simulation is conducted using CYMDIST software on an electrical distribution feeder operated by the Public Company Corporación Nacional de Electricidad (CNEL EP), Manabí Business Unit (Portoviejo). The simulation considers three scenarios projected for 2030: 1. Baseline scenario without EV integration, 2. Unrestrained EV integration, and 3. Managed EV integration. This research aims to simulate the integration of up to 230 EVs into the network to provide benchmark data for understanding the potential impacts on the feeder as EV adoption increases, with vehicles being charged over extended periods. The investigation will highlight the importance of demand management with EV integration, demonstratingsignificant effects on the demand curve, voltage profile, and total harmonic distortion rate (THD%) of a 13.8 kV distribution feeder.

Nitric oxide signal is required for glutathione-induced enhancement of photosynthesis in salt-stressed Solanum lycopersicum L.

Reduced glutathione (γ-glutamyl-cysteinyl-glycine, GSH), the primary non-protein sulfhydryl group in organisms, plays a pivotal role in the plant salt stress response. This study aimed to explore the impact of GSH on the photosynthetic apparatus, and carbon assimilation in tomato plants under salt stress, and then investigate the role of nitric oxide (NO) in this process. The investigation involved foliar application of 5 mM GSH, 0.1% (w/v) hemoglobin (Hb, a nitric oxide scavenger), and GSH+Hb on the endogenous NO levels, rapid chlorophyll fluorescence, enzyme activities, and gene expression related to the Calvin cycle in tomato seedlings (Solanum lycopersicum L. cv. 'Zhongshu No. 4') subjected short-term salt stress (100 mM NaCl) for 24, 48 and 72 hours. GSH treatment notably boosted nitrate reductase (NR) and NO synthase (NOS) activities, elevating endogenous NO signaling in salt-stressed tomato seedling leaves. It also mitigated chlorophyll fluorescence (OJIP) curve distortion and damage to the oxygen-evolving complex (OEC) induced by salt stress. Furthermore, GSH improved photosystem II (PSII) electron transfer efficiency, reduced QA - accumulation, and countered salt stress effects on photosystem I (PSI) redox properties, enhancing the light energy absorption index (PIabs). Additionally, GSH enhanced key enzyme activities in the Calvin cycle and upregulated their genes. Exogenous GSH optimized PSII energy utilization via endogenous NO, safeguarded the photosynthetic reaction center, improved photochemical and energy efficiency, and boosted carbon assimilation, ultimately enhancing net photosynthetic efficiency (Pn) in salt-stressed tomato seedling leaves. Conversely, Hb hindered Pn reduction and NO signaling under salt stress and weakened the positive effects of GSH on NO levels, photosynthetic apparatus, and carbon assimilation in tomato plants. Thus, the positive regulation of photosynthesis in tomato seedlings under salt stress by GSH requires the involvement of NO.

Online two-dimensional filter for anti-interference aging features extraction to accurately predict the battery health

Reliable and precise extraction of features plays a critical role in predicting the State of Health (SOH) of lithium batteries. Nevertheless, noise-tainted measurement signal can cause the distortion of differential curves, consequently compromising the accuracy of feature extraction. This paper develops an online two-dimensional filtering framework by fusing the Adaptive Taylor Kalman filter (ATKF) and the Improved Gaussian filter (IGF), applying it to both incremental capacity analysis and differential thermal voltammetry methods. Firstly, the Taylor series is harnessed to construct a state model for the noise-affected measurement signal. Then, an adaptive algorithm is introduced to diminish the dependence on human judgment when configuring variances. Finally, the IGF filters the curve from the two-dimensions of time and period. Experimental results demonstrate that the two-dimensional filtering method exhibits robust noise mitigation capabilities, enhancing the stability of feature extraction while maintaining system causality. Furthermore, this method has strong robustness to battery inconsistency and temperature uncertainty. When applying the proposed filter to extract aging features from battery data provided by CALCE and Oxford datasets, the correlation coefficient between the extracted aging features and SOH increases by 34.75% and 10.71%, respectively. Additionally, the RMSE of the SOH prediction results decreases by 60.57% and 59.46%, respectively.

Collective behaviors of fractional-order FithzHugh–Nagumo network

Brain connection is the mechanism of determining the brain function and cognition, and the small world network is widely investigated among these connections. In this paper, the small world complex brain network is constructed by the fractional-order neurons, and the fractional-order memristor is used to connect these neurons. Compared with the integer-order counterpart, the fractional-order memristor describes precisely the distortion of hysteresis curves, and it exhibits more abundant dynamical behaviors to function as the synapse. In addition, the neurons with different fractional orders represent the individual differences during the cell differentiation. For the two coupled neurons, the phase lock is achieved in the bursting and spiking neurons, and it denotes a time delay between the two firing process, whereas the chaotic neurons cannot synchronize. In small world network, the collective behaviors of neurons tend to synchronize with the increasing of the coupling intensity. These results promote the understanding of information coding and transformation in complex brain network.

Modeling non-linear loads for low-voltage electric grids

The authors performed the computer modeling of a power distribution grid section comprising a significant non-linear and asymmetric household and office load. The modeling was performed using the SimInTech software. The developed model was used to study the impacts that non-linear single-phase consumers (e.g. computers, printers, TV sets, microwave ovens, etc) have on the distortion of current and voltage curves in the grid and the formation of current in the transformer neutral. The authors demonstrate the dependency between the higher harmonic values of the phase and transformer neutral currents and the total non-linear consumer power, their share in the overall power substation load, and the distribution of non-linear consumer load across the substation feeders. To reduce the level of harmonic current components, the authors suggest using narrow-band passive higher harmonic filters. A computer model was developed for the simultaneous operation of a single-phase non-linear load and a four-link filter compensating device that comprises the filters set for the frequencies of the third, fifth, seventh, and ninth harmonics. The analysis of the non-sinusoidal operating modes of the grids with -harmonic filters connected showed that there is practically no current in the neutral while the distortion factors of the phase currents and grid voltage curves are at the minimum.

Methods to maximize detector count rates on small-angle neutron scattering diffractometers at reactor sources: I. Optimizing wavelength selection

Methods to determine the optimal neutron wavelength to maximize detector count rates on small angle neutron scattering (SANS) diffractometers at reactor sources is presented. Three experimental methods are used to determine the choice of optimal wavelength and collimation combination that maximizes the detector count rate. The wavelength optimization methods are applied to two different SANS diffractometers at the NCNR, and all methods are found to confirm the optimal wavelength of approximately 9.5 Å ± 0.5 Å for optically thin samples. The optimum wavelength is shifted by the wavelength-dependence of the sample transmission to 8.5 Å ± 0.5 Å for thicker absorbing samples or 6.5 Å ± 0.5 Å if the amount of multiple scattering needs to be minimized to limit scattering curve distortions. The same optimization methods can be applied to SANS diffractometers at other reactor facilities.

A denoising method based on deep learning for proton radiograph using energy resolved dose function

Objective. Proton radiograph has been broadly applied in proton radiotherapy which is affected by scattered protons which result in the lower spatial resolution of proton radiographs than that of x-ray images. Traditional image denoising method may lead to the change of water equivalent path length (WEPL) resulting in the lower WEPL measurement accuracy. In this study, we proposed a new denoising method of proton radiographs based on energy resolved dose function curves. Approach. Firstly, the corresponding relationship between the distortion of WEPL characteristic curve, and energy and proportion of scattered protons was established. Then, to improve the accuracy of proton radiographs, deep learning technique was used to remove scattered protons and correct deviated WEPL values. Experiments on a calibration phantom to prove the effectiveness and feasibility of this method were performed. In addition, an anthropomorphic head phantom was selected to demonstrate the clinical relevance of this technology and the denoising effect was analyzed. Main results. The curves of WEPL profiles of proton radiographs became smoother and deviated WEPL values were corrected. For the calibration phantom proton radiograph, the average absolute error of WEPL values decreased from 2.23 to 1.72, the mean percentage difference of all materials of relative stopping power decreased from 1.24 to 0.39, and the average relative WEPL corrected due to the denoising process was 1.06%. In addition, WEPL values correcting were also observed on the proton radiograph for anthropomorphic head phantom due to this denoising process. Significance. The experiments showed that this new method was effective for proton radiograph denoising and had greater advantages than end-to-end image denoising methods, laying the foundation for the implementation of precise proton radiotherapy.

Unmanned aerial vehicle for monitoring transport infrastructure

We consider the unmanned aerial vehicle of a multicopter type, in which the on-board battery is recharged using the energy of an external electromagnetic field created by the currents flowing through the wires of the contact network of an electrified railway transport or an overhead power line. This process is carried out without interrupting the flight of the aerial vehicle. The process of transferring this energy to the board of the multicopter is carried out by inductive method using an electric winding placed on it. This winding is connected to the battery. We constructed the finite element mathematical model. We performed the calculations of the EMF induced in the winding at the different trajectories of the aerial vehicle near the contact wire of the railway. It is revealed that the highest EMF values in the winding are induced when flying under the alternating current contact wire. When the winding moves under the direct current wire the frequency and amplitude of the induced EMF depend on the period of deviations of the aerial vehicle from the direction of the axis of its straightline movement. The EMF curve is non-sinusoidal. The degree of distortion of the EMF curve depends on the amplitude of the deviation of the winding from the axis of the direction of movement. At the alternating current through the contact wire the amplitude of the induced EMF mainly depends on the amplitude and frequency of the external magnetic field created by this current. The movement parameters of the aerial vehicle have a negligible effect on the EMF value.

Loss compensated fiber loop ring-down system driven by random distributed multi-pulse laser

The sensing characteristics of loss compensated fiber loop ring-down (FLRD) system driven by random distributed multi-pulse have been analyzed. We used a random distributed multi-pulse laser to drive a fiber loop with loss compensation of erbium-doped fiber amplifier (EDFA) and analyzed the distortion of exponential attenuation curves, sensitivity and baseline stability of the system. The performance is compared with those of loss compensated FLRD system driven by single pulse and chaotic laser. The loss compensated FLRD theoretical model is discussed by the transmission equation of EDFA and finite element analysis. The performance of loss compensated FLRD system driven by random distributed multi-pulse under different injection optical powers is analyzed under the influence of gain saturation effect of EDF and amplified spontaneous emission (ASE). The results show that the random distributed multi-pulse system has higher R-square of exponential attenuation curves and better baseline stability than the single-pulse system. The increase of injection optical power has the least influence on the sensitivity reduction of random distributed multi-pulse system. The random distributed multi-pulse laser has better performance than single-pulse laser and chaotic laser in loss compensated FLRD system.

Experimental study on various factors contributing to poor microtremor data processing results using ESAC method

In practical applications, microtremor data often encounter contamination from various sources of noise, leading to suboptimal processing outcomes. This study aimed to provide a comprehensive analysis of the impact of noise on the microtremor method. The focus was on presenting explicit and quantitative evidence to demonstrate the extent to which these noises influence the accuracy and reliability of the method. Initially, a ten-station triangular array was employed to gather a sequence of high-precision microtremor data. Next, various influencing factors, such as high-amplitude random noise, were generated and incorporated into the meticulously collected real data in order to simulate the actual perturbation process. Based on both the raw and the artificially contaminated data, a series of dispersion curves of the fundamental-mode Rayleigh waves were obtained by applying the extended spatial autocorrelation method (ESAC). Based on the obtained results, several valuable conclusions have been derived that can effectively inform the procedures for acquiring and processing microtremor data. If the amplitude and duration of sudden onset vibrations are considerable, more data should be collected to reduce the Negative impact. If non-random noise exhibits similar frequency characteristics as microtremor data, and the duration and energy of the noise are relatively small, it can be ignored; otherwise, more data should be collected. The dispersion curve, determined by the principle of the ESAC method, remains unaffected by any changes in the amplitude of one or more traces. The likelihood of frequency dispersion curve distortion increases as the impact of entire data anomalies on the coherence curve becomes greater. In general, when the collection positions of abnormal data are more widely spaced, the impact on the frequency dispersion curve tends to decrease. It is important to note that, in a nested-triangular array configuration, the central geophone plays a crucial role in ensuring accurate and dependable outcomes.

An Intelligent Method for Predicting the Pressure Coefficient Curve of Airfoil-Based Conditional Generative Adversarial Networks.

Recently, extensive studies have focused on analyzing aerodynamic performance due to its important impact on aircraft design. Most of these works compute the aerodynamic coefficient of the airfoil through computational fluid dynamics (CFD) simulation, which is too time-consuming. To reduce the computational time required, some intelligence-based methods have been presented. However, these methods also suffer from certain issues. First, most of them directly implement existing machine learning methods used to predict the aerodynamic coefficient without adding any improvements. Second, some methods convert the airfoil shape and aerodynamic curves into images, which may lead to curve distortion and the introduction of noise. Third, some methods learn the relationship between the airfoil shape and aerodynamic coefficients but ignore the influence of initial inflow conditions. Accordingly, to address these issues, we propose an intelligent method for predicting the pressure coefficients (Cp) of airfoil based on a conditional generative adversarial network (cGAN). More specifically, we first present a two-step data augmentation strategy designed to expand the original airfoil dataset. Subsequently, we design a novel cGAN-based neural network to predict the Cp curve. To the best of our knowledge, this is the first work to apply generative adversarial network (GAN) to aerodynamic coefficient prediction. Moreover, we design a new loss function to train our network. Extensive experimental results demonstrate that the Cp curve predicted by our method is very close to that generated via CFD simulation. More importantly, our method achieves a speedup close to 1000x compared with CFD simulation.

Efficiency Improvement Methods of Independent Wind Electric Installation with Programmed Control

Relevance justification of effective automated control method of independent wind electric installation on the basis of voltage feedback and parameters of inductance x1, reduced inductance x′2, phase resistance of starter winding r1, reduced phase resistance of starter winding r′2 is made under different conditions of electric installation operation to minimize the time of phase voltage regulation transient process of wind turbine generator, that promotes quality improvement of self-excitation and reliability indicator improvement of energy supply of the basic control under insufficient information of meteorological and electric power conditions significaltly changing in time. The review of basic ways to control independent wind electric installations which has shown their negative sides is made, that revealed their negative sides like low execution speed and reliability, lack required voltage control quality, idle excitation current distortion leading to voltage sine curve distortion of the asynchronous generator, and providing no relay protection or the automatic switch and automation operating mode control. The differential equation of wind turbine rotor angular speed is composed. Transfer functions of dynamic links for generator phase voltage automatic control of wind turbine taking into account wind speed, rotor winding current, inductance, rotor winding phase resistance, reduced stator winding phase resistance are plotted. The block diagram imitating model is constructed and schedules of transient processes with zero and non-zero delay were plotted providing delay negative effect development decision making on stability of wind turbine generator phase voltage and to establish method of relevant system preparation to external disturbances vanishing above mentioned disadvantage.

Research on the Magnetostrictive Characteristics of Transformers under DC Bias

Direct current (DC) bias leads to increased vibration and noise in transformers. One of the main causes is the magnetostrictive effect of the transformer core. To address this phenomenon of magnetostriction, firstly, a transmission line model (TLM) of a single-phase transformer under DC bias is developed using transmission line theory and Jiles–Atherton (J–A) ferromagnetic hysteresis theory, taking into account the winding copper loss, core eddy current loss, and leakage effect. Secondly, the time-domain simulation of the single-phase transformer based on the Newton–Raphson iterative method is carried out, and the magnetostriction characteristics of the transformer under different DC and its variation law are analyzed. Finally, the results show that the DC bias results in magnetostrictive distortion and vibration acceleration curve distortion, the left and right wings of the magnetostrictive butterfly curve are no longer symmetrical, the slope of the vibration acceleration image increases significantly, and the degree of distortion is positively correlated with the magnitude of the DC. In addition, the peak values of the magnetostrictive deformation and vibration acceleration become larger under DC bias, leading to an increase in the vibration and noise of the transformer. The research object of this paper is the single-phase transformer, and the research method can also be applied to the study of three-phase transformers.

Large amplitude oscillatory shear stress (LAOStress) analysis for an acid-curd Spanish cheese: Afuega'l Pitu atroncau blancu and roxu (PDO)

Large amplitude oscillatory shear stress (LAOStress) was used to study the nonlinear rheological properties of Afuega'l Pitu (PDO) cheese atroncau blancu and roxu, a Spanish acid-curd cheese made from cow's milk, from nine manufacturers. Composition, physicochemical and lipolytic parameters were determined. The moisture content varied between (23.60 ± 0.75)% and (45.84 ± 0.01)% and the percentage of salt-in-moisture was <(8.17 ± 0.86)%. Stress amplitude sweeps were conducted at 20 °C, 50 °C and 75 °C at frequencies of 0.628 rad/s, 6.28 rad/s and 31.4 rad/s, for each temperature. Experiments were analyzed using the MITlaos software modified to extract LAOStress quantitative material functions. The large number of experiments and the high dimensionality of the LAOS signals were carefully analyzed and reduced to understand the material response and extract the relevant properties. All cheeses showed a distinct signature that motivates us to introduce the concept of “pseudo-linear LAOS” where nonlinear rheology is evident in only some measures while others minimally change from the linear regime. We geometrically interpret these different LAOS measures as rotation and distortion of Lissajous curves; all cheeses showed pseudo-linear LAOS regimes where rotation is strong and distortion is minimal, i.e. first-harmonics change dramatically in a regime where higher-harmonics are small enough that Lissajous curves are nearly elliptical. The pseudo-linear behavior enabled low-dimensional data reduction and three metrics were discovered to be the most relevant: linear elastic modulus (G′), critical stress (σcrit) and strain (γcrit) amplitudes. All Afuega'l Pitu cheese samples softened as stress amplitude increased while maintaining a solid-like behavior (tanδ^1∼ 0.3), regardless of frequency and temperature. Moreover, the difference between blancu and roxu was statistically insignificant compared to difference between manufacturers. Linear G′ and nonlinear σcrit decreased with increasing temperature, while the nonlinear γcrit maintained a constant value at 50 °C and 75 °C. Although G′, σcrit and γcrit metrics establish comparison between cheeses, more metrics are still required to fully connect rheology to cheese texture and sensory profiles.

A Single Phase Hybrid Multiport Microinverter for Photovoltaic Energy Controlled by Exact Linearization

Solar energy can be captured by Photovoltaics (PV) and converted into electrical power. This electrical power can then be connected to grids by power electronic converters, which can also operate the PV panels at around their maximum power point (MPP), maximizing the harvested energy. When the PV panels are connected in an array, the partial shading effect generates a distortion of the power curve can result in a power loss since tracking algorithms might not be able to detect the MPP. To solve this issue microinverters have been proposed, with a small power electronic converter being connected to each PV panel, this arrangement enables independence for maximum power point tracking and electrical isolation. This paper presents a double output multiport microinverter capable of feeding DC and AC loads or providing connection to a single-phase AC grid and energy storage. The proposed structure can be operated in grid-connected and islanded modes. This paper describes the microinverter controlled with the exact linearization technique on both the DC and AC sides, obtaining a lineal transfer function representation of the nonlinear and coupled power electronic converters. The operation of the proposed topology and its control strategy is validated using a laboratory proof-of-concept prototype.

Phenology Alignment-Based PolSAR Crop Classification Considering Polarimetric Statistical and Time-Varying Curve Characteristics

The uncertainty of crop phenological cycle is an important issue in crop classification with time series PolSAR data. The time series alignment algorithm represented by dynamic time warping (DTW) can supply a potential solution, which realigns curves based on shape matching, dealing with the distortion of feature curves caused by uncertain crop phenological development. However, previous studies mainly focused on shape characteristics of time-varying feature curves, which is hard to comprehensively evaluate the similarity degree of crop phenological cycles. Furthermore, it ignored the differences in scattering signal and polarimetric statistical distribution of crops, which limited the accuracy of crop classification. In this letter, a novel crop classification method based on phenology alignment is proposed. Firstly, the dual-branch time series alignment method is proposed, including the time-weighted dynamic time warping (TWDTW) alignment and the Wishart distance-based TWDTW (WD-TWDTW) alignment, which combines the feature curve characteristics and the polarimetric statistical information to correctly describe the similarity degree of phenological cycles. Secondly, a multi-similarity measure (including shape similarity, feature similarity and polarimetric similarity) is defined to improve capacity of crop discrimination. The multi-similarity measure can describe the differences of crop types from three aspects, including crop growth trend, growth status, and statistical distribution. The proposed method is evaluated with time series full-polarization Radarsat-2 data in Flevoland area. The results show that our method is superior to traditional method with single TWDTW alignment and shape similarity, and the corresponding overall accuracy is improved by 6%.

Atmospheric icing process measurement utilizing impedance spectroscopy and thin film structure

To solve the detection problem in icing process, a thin film impedance sensor for atmospheric icing environment is proposed and evaluated. The sensor identifies rapid icing process and static ice layer as different conditions. A theoretical model of temperature-dependent impedance icing detection is built to describe the polarization effect. And the finite element simulation in the case of water film covering ice layer is carried out. The impedance curve distortion caused by phase mixing and temperature gradient during atmospheric icing is analyzed, and 9 related features are extracted. The evaluation experiments of the sensor involve 5 surface conditions, and totally 3143 impedance curves are obtained. The classification results of various models indicate that support vector machine achieves the highest recognition accuracy, reaching 93.1 %. And in atmospheric icing conditions, the RMSE of ice thickness measurement using neural network reaches 0.25 mm in the laboratory tests and 0.51 mm in the wind tunnel tests.

Luminous Optical and X-Ray Flaring of the Putative Redback Millisecond Pulsar 1FGL J0523.5–2529

Several redback and black widow millisecond pulsar binaries have episodes of flaring in X-rays and optical. We initially detected such behavior from the Fermi selected redback candidate 1FGL J0523.5–2529 during optical time-series monitoring. Triggered observations with the Neil Gehrels Swift Observatory over the next ≈100 days showed episodic flaring in X-rays with luminosity up to 8 × 1033 erg s−1 (∼100 times the minimum), and a comparable luminosity in the optical/UV, with similar power-law spectra of f ν ∝ ν −0.7. These are the most luminous flares seen in any nonaccreting “spider” pulsar system, which may be related to the large size of the companion through the fraction of the pulsar wind that it or its ablated wind intercepts. Simultaneously with an optical flare, we see Balmer line and He i emission, not previously known in this object, which is evidence of a stellar wind that may also inhibit detection of radio pulsations. The quiescent optical light curves, while dominated by ellipsoidal modulation, show evidence of variable nonuniform temperature that could be due either to large starspots or asymmetric heating of the companion by the pulsar. This may explain a previous measurement of unusual nonzero orbital eccentricity as, alternatively, distortion of the radial-velocity curve by the surface temperature distribution of the large companion.

Using High Field Model to Interpret Potentiodynamic Polarization Curves of AA7075 at High Scan Rates

The drastic distortion of potentiodynamic polarization curves measured at high potential scan rates prevents the extraction of accurate kinetic parameters. By measuring potentiodynamic polarization curves of AA7075 at scan rates ranging from 0.167 mV/s to 100 mV/s, in an acidic solution of 0.62 M NaH2PO4 and a near-neutral 3.5 wt% NaCl solution, changes in potentiodynamic polarization curves are observed not only at different scan rates and electrolytes but also between replicated experiments. Contrary to what was reported in previous studies, the disturbance of charging current associated with high scan rates does not satisfactorily explain the potentiodynamic polarization shape. Instead, the high field model that incorporates the kinetics of anodic oxide growth successfully captures the features of experimental potentiodynamic polarization curves. Compared to Tafel’s theory, the high field model explains remarkably both the changing kinetics with scan rates, electrolytes, and the variance between the measurement performed at different sites. Figure 1

Diffuse Correlation Spectroscopy Beyond the Water Peak Enabled by Cross-Correlation of the Signals From InGaAs/InP Single Photon Detectors.

Diffuse correlation spectroscopy (DCS) is an optical technique that allows for the non-invasive measurement of blood flow. Recent work has shown that utilizing longer wavelengths beyond the traditional NIR range provides a significant improvement to signal-to-noise ratio (SNR). However, current detectors both sensitive to longer wavelengths and suitable for clinical applications (InGaAs/InP SPADs) suffer from suboptimal afterpulsing and dark noise characteristics. To overcome these barriers, we introduce a cross correlation method to more accurately recover blood flow information using InGaAs/InP SPADs. Two InGaAs/InP SPAD detectors were used for during in vitro and in vivo DCS measurements. Cross correlation of the photon streams from each detector was performed to calculate the correlation function. Detector operating parameters were varied to determine parameters which maximized measurement SNR.State-space modeling was performed to determine the detector characteristics at each operating point. Evaluation of detector characteristics was performed across the range of operating conditions. Modeling the effects of the detector noise on the correlation function provided a method to correct the distortion of the correlation curve, yielding accurate recovery of flow information as confirmed by a reference detector. Through a combination of cross-correlation of the signals from two detectors, model-based characterization of detector response, and optimization of detector operating parameters, the method allows for the accurate estimation of the true blood flow index. This work presents a method by which DCS can be performed at longer NIR wavelengths with existing detector technology, taking advantage of the increased SNR.