Random Noise Component Research Articles

Estimation of the error in determining the motion parameters of maneuvering objects

The study contains the definition of errors in estimating the motion parameters of maneuvering objects that arise due to the discrepancy between the real model and the model used to build the filter. In the real model, along with noise and stochastic uncertainties, non-stochastic uncertainties act. The solution to the problem of estimation errors is based on the use of convex polyhedra. The statement of the research problem is given. It is shown that since the observer uses the motion model and the measurement equation, the source of estimation errors is not only the above, but also the fact that along with the random component of the measurement noise, there is also a non-stochastic component that is not taken into account by the observer. At the same time, the statistical characteristics of the latter do not exist or are unknown. It is shown that the use of a filter, for example, Kalman, will lead to errors. The conditions that determine the average filtering error and the total standard error of the estimation are described. It is noted that the obtained calculation results allow us to estimate the maximum permissible values used in the design of the filter for the most unfavorable case of evaluation. The relationship between the filtration quality and the parameters of the effects is determined. Expressions are obtained regarding the filtration quality for the worst and the best for object maintenance. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. The asymptotic properties of filtering errors are considered. It is shown that the "convergence" of the filter means its final "memory", since errors at the next step do not depend on errors from all previous steps, but only on steps from a certain value. The conditions under which the potential accuracy of the filter is achieved are also determined. The solution of extreme problems is described. You can determine the maximum filtering quality by iterating over the values on a set of corner points or using extremum conditions. Numerical simulation of extreme values of filtering errors is given. Expressions for the set of average values of filtering errors are found, asymptotic and extreme properties of errors and sets are considered. It is shown that the conducted analysis of the accuracy of the filtering algorithm is evaluative in nature and is designed for the worst case. If the accuracy is not satisfactory, it is necessary to either change the model or synthesize a minimax-stochastic filter.

Distributed Acoustic Sensing Signal Model Under Static Fiber Conditions

This paper presents a statistical model for distributed acoustic sensor interrogation units that utilizes laser pulses transmitted into fiber optics. Interactions within the fiber lead to localized acoustic energy, resulting in backscatter, which is a reflection of the light. Explicit equations were used to calculate the amplitudes and phases of backscattered signals. The proposed model accurately predicts the amplitude signal spectrum and autocorrelation, aligning well with experimental observations. This study also explores the phase signal characteristics relevant to optical time-domain reflectometry (OTDR) system sensing applications, demonstrating consistency with the experimental results. The experiments were conducted using Python coding, enabling the analysis of the individual components of the Distributed Acoustic Sensing (DAS) system. The assumptions of the model include the static condition of the fiber, implying the absence of external forces or vibrations. Consequently, no external acoustic disturbances were considered. The backscattered signal comprises a random noise component resulting from intrinsic fiber imperfections, and a coherent component arising from the interplay between the laser pulse and the fiber. Keywords: distributed acoustic sensing, fiber optics, optical time-domain reflectometry, Rayleigh scattering.

Research and Development of Noise-Resistant Algorithms for Calculating the Group Delay Time of a Model Digital Signal with a Random Noise Component

Research and Development of Noise-Resistant Algorithms for Calculating the Group Delay Time of a Model Digital Signal with a Random Noise Component

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

AbstractWind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

Noise Reduction of Cable Partial Discharge Vibration Signal based on EEMD-CIIT method

Abstract The cable partial discharge vibration signal is often polluted by noise. It is necessary to denoise the signal, improve the signal-to-noise ratio, and then realize the feature extraction of the vibration signal. Therefore, this paper builds a cable partial discharge test platform, collects the time-domain waveform of vibration signals under different working conditions, analyzes the noise reduction effect of ensemble empirical mode decomposition clearing repetition interval threshold noise reduction method under different threshold functions, and analyzes the difference and approximation degree between the simulated signal and the real signal after noise reduction by using signal-to-noise ratio and correlation, By comparing the difference of statistical characteristics in different defects before and after noise reduction, the wavelet threshold denoising method is established synchronously and compared with the proposed method. The results show that the high-frequency part of the vibration signal has obvious random noise components, and the effective frequency is mainly concentrated in the low-frequency band; The smoothing shear absolute deviation penalty threshold function has the smoothest curve after noise reduction and the highest approximation to the real signal. The high-frequency noise component is eliminated, and the low-frequency band frequency component is effectively retained. The method proposed in this paper can be extended to the noise reduction of vibration signals of other industrial equipment and provide technical support for the signal noise reduction of various industrial equipment.

Continuous monitoring of rolling element bearing health by nonlinear weighted squared envelope-based fuzzy entropy

Fuzzy entropy (FE) can be regarded as an effective measure for nonlinear characterization of rolling element bearing (REB) health condition by quantifying the complexity of vibration signals. However, during continuous monitoring operation under heavy noise, transient impulses corresponding to a REB fault get submerged under unnecessary random noise components. As a consequence, FE algorithm not only fails to detect a REB fault at the earliest point of inception but also performs poorly in monitoring the development of the incepted fault in an efficient manner. Aiming at solving the aforementioned limitations of FE in continuous monitoring of REB health, background noise associated with collected vibration signals is eliminated by weighting the corresponding square envelope signal. Due to the utilization of weighted squared envelope signal, the proposed measure is termed as weighted square envelope-based FE (WSEFE). One simulated case and two different run-to-failure experimental cases are used for validation. The comparison results demonstrate that the proposed WSEFE not only overcomes the limitations of original FE but also performs better than conventional permutation entropy and advanced FE-based measure multiscale FE (MFE) in continuous monitoring of REB health.

Two-dimensional variational mode decomposition for seismic record denoising

Abstract Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which $K$ and $\alpha $ are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate $K$ and $\alpha $ values and apply these values to the proposed method. First, for a 2D seismic signal, the 2D-VMD method can decompose it into $K$ modes with specific direction and vibration characteristics. Next, the PE value of each mode is calculated. Random noise components are eliminated according to the PE value. Finally, the signal components are reconstructed to acquire the denoised seismic signal. Experimental and simulation results indicate that the proposed method has remarkable denoising effect on synthetic and real seismic signals. We hope that this new method can inspire and help evaluate new ideas in this field.

Modeling and analysis of the Haldane genetic model under Brownian motion using stochastic differential equation

Heterozygote advantage as a natural consequence of adaptation in diploid organisms is an attractive mechanism by which two alleles are maintained in natural populations. It has significant effects on biodiversity conservation and plant and animal breeding programs. The mathematical modeling of this biological mechanism is important for eco-evolutionary dynamics studies and genetics investigations. In this paper, I aimed to formalize the changes of gene frequency in time v(t), and in time and space v(t,x) with additive effects in a birth and death process of the Haldane genetic model using Brownian motion under fluctuations of habitat. In addition, the gene-environment interactions were evaluated under the mechanism. The mathematical model was investigated in both deterministic and white noise forms. It was shown that if the environmental random processes in the Haldane genetic model changed quickly and smoothly, then the diffusion approximation of the allele frequencies could be modeled and analyzed by a stochastic partial differential equation. It was revealed that the mathematical model used in this paper belonged to a more general model. The mathematical model was analyzed and since the modeling by the Cauchy problem had not had a usual global solution, the qualitative behavior of the solutions was considered. Besides, the generalizations of ItÔ integral were defined as the integrals of Wick products of random parameters and noise components. It was found that if v(t,x) behaved like a super-Brownian motion and the fatal mutations took place, as a consequence a tiny group of alleles was quickly disappeared. The v(t,x) was unstable when it was close to one. The stationary phase appeared and v(t,x) tended to the stationary situation in the intermediate region under the stabilizing selection. This was a condition under additive gene effect, but with the presence of dominance gene effect, it might be ambidirectional without considering the epistatic effects. The emergence of the dominance and epistatic effects was due to the directional selection. Since Falconer and MacKay had already introduced a deterministic model to study the frequency of genes with no spatial spreading of the population and no stochastic processes, another model was explained to study their equation in the case of heterozygote intermediate for diffusion approximation of frequency of genes, including white noise. It was shown that if the rates of mutation and selection became very small, then the model would be more deterministic and predictable. On the other hand, if the rates of mutation and selection became large, then the model would be more stochastic, and more fluctuations occurred because of the strong effective noise strength. In this case, the stationary situation did not take place. The outlook can help to model the similar biological mechanisms in eco-evolutionary community genetics for studying the indirect genetic effects via the systems of stochastic partial differential equations, and white noise calculus.

Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia

This study estimates the technical efficiency measures of maize producing farm households in Ethiopia using stochastic frontier (SF) panel models that take different approaches to model firm heterogeneity. The efficiency measures are found to vary depending on how the estimation model treats both unobserved and observed firm heterogeneity. Estimates from the ‘true’ random effects (TRE) models that treat firm effects as heterogeneity are found to be identical to those from pooled SF models. Those results differ from the ones generated from the basic random effects (RE) models that treat firm effects as part of overall technical inefficiency. The more flexible generalised ‘true’ random effects (GTRE) model that splits the error term into firm effects, persistent inefficiency, transient inefficiency, and a random noise component indicates the presence of higher levels of persistent inefficiency than transient inefficiency. The basic truncated-normal RE model and heteroscedastic RE model yields similar efficiency estimates. The GTRE model predict persistent efficiency measures similar to those from the basic RE and flexible RE model with environmental variables incorporated in the variance function as well as in the deterministic production frontier. These results imply that the RE and GTRE panel models provide reliable efficiency estimates for our data compared to the TRE models. All the estimated SF models generate comparable production function parameters in terms of magnitude and sign. Overall, the results underscore the importance of scrutinising stochastic frontier models for their reliability of analytical results before drawing policy inferences.

Deep low-rank feature learning and encoding for cross-age face recognition

Cross-age face recognition (CAFR) is a challenging task, due to significant intra-personal variations. Furthermore, the training and testing data may contain random noise components. To address these issues, this paper proposes a deep low-rank feature learning and encoding method. Firstly, our method employs manifold learning in the low-rank optimization, which preserves the global and local structure of the data samples, while learning the clean low-rank features. Secondly, we encode the low-rank features using our locality-constrained feature encoding method, which learns an age-insensitive codebook from training data, and enables the intra-class samples to share the same local bases in a codebook. In the testing stage, the gallery and probe features are encoded by the learned codebook, which represents the images of the same identity by similar codewords for recognition. Furthermore, the periocular region of human faces is investigated for CAFR. Extensive experiments on five datasets demonstrate the effectiveness of our method.

Statistical modelling of rate gyros based on fully overlapping Allan variance

Angular random walk and rate random walk are two dominant random noise components which are inherent in almost gyroscopes. Therefore, modelling these noise components accurately is very important. Here, a modified algorithm is proposed for estimating the two random noise components accurately. Up to now, the statistical modelling of the two random noise components has been conducted by the best linear unbiased estimator, which is based on the formulae for mean, variance and covariance of non-overlapping Allan variance of them, that is, angular random walk and rate random walk. The modified algorithm is developed by using the best linear unbiased estimator, based on formulae for mean, variance and covariance of fully-overlapping Allan variance which are newly derived in this paper. Efficiency of the algorithm is evaluated by simulation and experiment.

IMK/IAA MIPAS temperature retrieval version 8: nominal measurements

Abstract. A new global set of atmospheric temperature profiles is retrieved from recalibrated radiance spectra recorded with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Changes with respect to previous data versions include a new radiometric calibration considering the time dependency of the detector nonlinearity and a more robust frequency calibration scheme. Temperature is retrieved using a smoothing constraint, while tangent altitude pointing information is constrained using optimal estimation. ECMWF ERA-Interim is used as a priori temperature below 43 km. Above, a priori data are based on data from the Whole Atmosphere Community Climate Model Version 4 (WACCM4). Bias-corrected fields from specified dynamics runs, sampled at the MIPAS times and locations, are used, blended with ERA-Interim between 43 and 53 km. Horizontal variability of temperature is considered by scaling an a priori 3D temperature field in the orbit plane in a way that the horizontal structure is provided by the a priori while the vertical structure comes from the measurements. Additional microwindows with better sensitivity at higher altitudes are used. The background continuum is jointly fitted with the target parameters up to 58 km altitude. The radiance offset correction is strongly regularized towards an empirically determined vertical offset profile. In order to avoid the propagation of uncertainties of O3 and H2O a priori assumptions, the abundances of these species are retrieved jointly with temperature. The retrieval is based on HITRAN 2016 spectroscopic data, with a few amendments. Temperature-adjusted climatologies of vibrational populations of CO2 states emitting in the 15 µm region are used in the radiative transfer modeling in order to account for non-local thermodynamic equilibrium. Numerical integration in the radiative transfer model is now performed at higher accuracy. The random component of the temperature uncertainty typically varies between 0.4 and 1 K, with occasional excursions up to 1.3 K above 60 km altitude. The leading sources of the random component of the temperature error are measurement noise, gain calibration uncertainty, spectral shift, and uncertain CO2 mixing ratios. The systematic error is caused by uncertainties in spectroscopic data and line shape uncertainties. It ranges from 0.2 K at 20 km altitude for northern midlatitude summer conditions to 2.3 K at 12 km for tropical conditions. The estimated total uncertainty amounts to values between 0.6 K at 20 km for midlatitude summer conditions to 2.5 K at 12–15 km for tropical conditions. The vertical resolution varies around 3 km for altitudes below 50 km. The long-term drift encountered in the previous temperature product has been largely reduced. The consistency between high spectral resolution results from 2002 to 2004 and the reduced spectral resolution results from 2005 to 2012 has been largely improved. As expected, most pronounced temperature differences between version 8 and previous data versions are found in elevated stratopause situations. The fact that the phase of temperature waves seen by MIPAS is not locked to the wave phase found in ECMWF analyses demonstrates that our retrieval provides independent information and does not merely reproduce the prior information.

New effective spectral matching measures for hyperspectral data analysis

ABSTRACT The successful implementation of Spectral Matching Measures (SMMs) often plays a crucial role in material discrimination and classification using hyperspectral dataset. The commonly exploited SMMs, such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and their hybrid, i.e., SIDSAMtan, show limited discrimination ability while discriminating spectrally similar materials. This study presents three new effective SMMs named Dice Spectral Similarity Coefficient (DSSC), Kumar–Johnson Spectral Similarity Coefficient (KJSSC), and a hybrid of DSSC and KJSSC, i.e., KJDSSCtan, for accurate discrimination of spectrally similar materials. A wide range of hyperspectral datasets of minerals and vegetation acquired under laboratory and real atmospheric conditions were used to compare and evaluate the performance of newly proposed and existing SMMs using Relative Spectral Discrimination Power (RSDPW) statistics. We also assessed the discrimination ability of the proposed and existing SMMs using spectra of selected minerals and vegetation species with an added component of random noise and linearly synthesized mixed spectra. An in-depth comparison and evaluation of different SMMs demonstrated that the discrimination power of the proposed SMMs is significantly higher than existing SMMs. The proposed SMMs also outperform existing SMMs when discriminating noisy and linearly synthesized mixed counterparts. The KJSSC and DSSC show similar efficacy in discriminating spectra of minerals and vegetation, whereas their hybrid measure, i.e., KJDSSCtan shows significantly higher spectral discrimination ability. Therefore, the newly proposed hybrid measure, i.e., KJDSSCtan is recommended over existing SMMs for successful material discrimination and classification using hyperspectral data.

Frequency-range enhanced delay locked loop based on varactor-loaded and current-controlled delay elements

In this paper, some building blocks of the conventional delay-locked-loops are modified to achieve a higher frequency range and smaller jitter on generated phases. A novel sensitive and dead-zone free phase detector is proposed with a reduced number of MOS transistors. A voltage-controlled capacitive load is added to the basic delay elements to expand the lower limit of the locking frequency range from 50 MHz to 10 MHz. Besides, the upper limit of the operating frequency is improved from 1 GHz to 1.5 GHz, by reducing the input capacitance of the delay cells via an auxiliary shortening path. Rigorous theoretical analysis is provided on the capacitance variations and the expanded frequency range. Post-layout simulation results confirm that RMS and peak-to-peak jitter of 0.83 pS and 6.85 pS are achieved at 1 GHz operating frequency when 1.8 V supply is subject to random noise components with peak-to-peak variations of 66 mV. The proposed strategy consumes the maximum power of 6.3 mW at TT conditions, within the frequency range of 10 MHz-to-1.5 GHz, and occupies 0.0036 µm2 active area. Simulation results are presented using the BSim3v3 model of MOS transistors in a 0.18 µm CMOS process.

Description of the UCAR/CU Soil Moisture Product

Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides.

Reaching Consensus in a Variable-Topology Multiagent System under Additive Random Noise

A discrete-time multiagent system with a fixed set of elements (particles) is studied. Particles synchronize with each other in accordance with a family of communication graphs. At each time instant (step), the system is described by a vector that updates iteratively: the state of each agent is linearly determined by the states of its neighbors and also by an additive random noise component; in addition, links between particles change over time. Thus, the system’s evolution can be modeled by an iterative process in which the state vector is multiplied by a certain stochastic matrix and added to a random vector. The goal of this paper is to analyze an index measuring the system’s closeness to a consensus. Some constraints on the family of communication graphs that are sufficient for obtaining an upper bound on this index are established. Also, a modified model in which the upper bound is satisfied under slightly weakened conditions on communication graphs is presented.

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

The paper develops a simultaneous equations stochastic frontier model (SFM) with dependent random noise and inefficiency components of individual equations as well as allowing dependence across all equations of the model using copula functions. First, feasibility of our developed model was verified via two simulation studies. Then the model was applied to assess cost efficiency and market power of the banking industry of China using a panel data of 37 banks covering the period 2013–2018. Results confirmed that our simultaneous SFM with dependent random noise and inefficiency components outperformed its predecessor, which is a simultaneous SFM with dependent composite errors but with independent random noise and inefficiency components of individual SFMs as well as the conventional single-equation SFM. Apart from the statistical and computational superiority of our developed model, we also see that Chinese banks in general have a high level of cost efficiency and that competition in the banking industry of China mainly exists in state-owned banks and joint stock banks. Presence of economies of scales as well as diseconomies of scales were found in different banks. Also, the state-owned banks embraced most sophisticated technologies thereby allowing them to operate with the highest level of cost efficiency.

Selective Sweep at a QTL in a Randomly Fluctuating Environment.

Adaptation is mediated by phenotypic traits that are often near continuous, and undergo selective pressures that may change with the environment. The dynamics of allelic frequencies at underlying quantitative trait loci (QTL) depend on their own phenotypic effects, but also possibly on other polymorphic loci affecting the same trait, and on environmental change driving phenotypic selection. Most environments include a substantial component of random noise, characterized both by its magnitude and its temporal autocorrelation, which sets the timescale of environmental predictability. I investigate the dynamics of a mutation affecting a quantitative trait in an autocorrelated stochastic environment that causes random fluctuations of an optimum phenotype. The trait under selection may also exhibit background polygenic variance caused by many polymorphic loci of small effects elsewhere in the genome. In addition, the mutation at the QTL may affect phenotypic plasticity, the phenotypic response of given genotype to its environment of development or expression. Stochastic environmental fluctuations increase the variance of the evolutionary process, with consequences for the probability of a complete sweep at the QTL. Background polygenic variation critically alters this process, by setting an upper limit to stochastic variance of population genetics at the QTL. For a plasticity QTL, stochastic fluctuations also influences the expected selection coefficient, and alleles with the same expected trajectory can have very different stochastic variances. Finally, a mutation may be favored through its effect on plasticity despite causing a systematic mismatch with optimum, which is compensated by evolution of the mean background phenotype.

CIM: Camera in Motion for Smartphone Authentication

The photo response non-uniformity of a smartphone camera is a reliable hardware fingerprint that can be used to authenticate the smartphone owner. This type of camera-based authentication is convenient and of low cost since it requires only pictures taken by a smartphone. However, as shown in this paper, existing camera-based authentication systems are either impractical or subject to fingerprint forgery attacks, in which an adversary intentionally submits forged pictures with fake fingerprints. We propose Camera in Motion (CIM), a practical and reliable camera-based smartphone authentication system. In CIM , a user is asked to move his/her smartphone along a specific route, take pictures of QR codes displayed on the verifier’s interface in burst mode , and submit particular burst pictures to the verifier for authentication. We find that, because burst images are captured in rapid succession, the random noise components of a captured image can be partially preserved across multiple images that are captured in a row. The preserved noise forms a forgery-sensitive noisechain embedded in burst images. We also find that there exists various correlations between the movement of the camera and the noise components of the captured images. The noisechain and these correlations are then explored for forgery detection. We performed extensive experiments with 22 smartphones of 5 different models. Our experiment results show that CIM can achieve 100% true acceptance rate at 0% false acceptance rate in both fingerprint matching and forgery detection.

HIRAS noise performance improvement based on principal component analysis.

Mirror jitters around a bias tilt angle can make noise performance degradation for a space-borne Michelson interferometer. A numerical model simulates the Hyperspectral Infrared Atmospheric Sounder (HIRAS) spectra affected by the mirror jitters. According to the simulation, mirror jitters mainly generate spectrally correlated noise, which can be estimated by subtracting the random noise component from the total noise. The random noise is estimated through a principal component analysis (PCA) technique. Applying the PCA noise estimator as a diagnostic tool to monitor the noise level in the process of bias tilt angle tuning, optimized HIRAS noise performance is achieved with the correlated noise component minimized.