Search

The Moho is the interface between crust and mantle, and accurate location of the Moho is important for both resource exploration and deep earth condition and structural change investigations. The theory of the traditional Parker‐Oldenburg (P‐O) method is quite simple and it is widely applied in the frequency domain of Moho depth inversion. However, Moho fluctuation simulations using the P‐O method are not reliable because of the lack of field geographic data constraints during the inversion process and the excessively smoothing of data details caused by using filters to correct the source data signals. To solve those problems, we propose an improved iteration P‐O method with a variable density contrast model, the iterative process is constrained by geological data in the inversion parameters, and the variable depth of the gravity interface is iterated using an equivalent form of upward continuation in the Fourier domain, which is more stable and convergent than downward continuation term in original P‐O method. Synthetic experiments indicate that improved method has the better consistency among the simulations than original method, and our improved method has the smallest root mean square (RMS) of 0.59 km. In a real case, we employed the improved method to invert for the Moho depth of the South China Sea (SCS), and the RMS between our Moho depth model and the seismological data is the smallest value of 2.27 km. The synthetic experiments and application of the model to the SCS further prove that our method is practical and efficient. Key Points: We developed an improved Parker‐Oldenburg method using an equivalent form of upward continuation in the Fourier domain instead of applying a low‐pass filter, which achieves a better stable performanceGravity inversion combines the actual density variations between layersMoho inversion in the South China Sea fits well with the seismic data, and our Moho depth model have the smallest root mean square (2.27 km) compared to other gravimetric Moho models [ABSTRACT FROM AUTHOR]

This paper proposes the usage of the fuzzy rule-based Bayesian algorithm to determine which residential appliances can be considered for the Demand Response program. In contrast with other related studies, this research recognizes both randomness and fuzziness in appliance usage. Moreover, the input data for usage prediction consists of nodal price values (which represent the actual power system conditions), appliance operation time, and time of day. The case study of residential power consumer behavior modeling was implemented to show the functionality of the proposed methodology. The results of applying the suggested algorithm are presented as colored 3D control surfaces. In addition, the performance of the model was verified using R squared coefficient and root mean square error. The conducted studies show that the proposed approach can be used to predict when the selected appliances can be used under specific circumstances. Research of this type may be useful for evaluation of the demand response programs and support residential load forecasting. [ABSTRACT FROM AUTHOR]

The new generation of Intrusion Detection Systems (IDS) progressively requires automatic and intellectual network ID strategies for managing security risks made by a rising amount of advanced attackers from the cyber environment. Specifically, there were huge demands for autonomous agent-related IDS solution which need some human intervention as possible whereas can progress and enhance themselves (by making suitable acts for a presented environment) and turns out to be highly powerful to effective threats that were not seen before (for instance, zero-day attacks). Recently, DRL methods were presented that can learns from the environment with uncontrollable massive amount of states for addressing the major drawbacks of prevailing RL methods. This article introduces a Binary Bat Algorithm-based Feature Selection with Deep Reinforcement Learning (BBAFS-DRL) system for IDSs. The major intention of the BBAFS-DRL approach is the recognition and classification of intrusion systems. In the projected BBAFS-DRL method, data pre-processed was primarily executed to change the data as suitable format. Furthermore, the BBAFS system can be utilized for the effectual feature selection. Next, deep Q-network is applied for the effectual identification and classification of intrusion. At last, the root means square propagation (RMSProp) optimizer is utilized for the effectual hyperparameter tuning procedure. The experimental analysis of the BBAFS-DRL algorithm can be tested by utilize of benchmark database and the outcome can be analyzed on many measures. The comparison outcome demonstrated the improvement of the BBAFS-DRL methodology over other existing approaches. [ABSTRACT FROM AUTHOR]

This paper introduces methods for parallelizing the algorithm to enhance the efficiency of event recovery in Spin Physics Detector (SPD) experiments at the Nuclotron-based Ion Collider Facility (NICA). The problem of eliminating false tracks during the particle trajectory detection process remains a crucial challenge in overcoming performance bottlenecks in processing collider data generated in high volumes and at a fast pace. In this paper, we propose and show fast parallel false track elimination methods based on the introduced criterion of a clustering-based thresholding approach with a chi-squared quality-of-fit metric. The proposed strategy achieves a good trade-off between the effectiveness of track reconstruction and the pace of execution on today's advanced multicore computers. To facilitate this, a quality benchmark for reconstruction is established, using the root mean square (rms) error of spiral and polynomial fitting for the datasets identified as the subsequent track candidate by the neural network. Choosing the right benchmark enables us to maintain the recall and precision indicators of the neural network track recognition performance at a level that is satisfactory to physicists, even though these metrics will inevitably decline as the data noise increases. Moreover, it has been possible to improve the processing speed of the complete program pipeline by 6 times through parallelization of the algorithm, achieving a rate of 2000 events per second, even when handling extremely noisy input data. [ABSTRACT FROM AUTHOR]

For a pilot, the angle of attack (AoA) estimation may represent a possibility to cross-check the traditional cockpit indicators and also to obtain information whose benefits are widely recognized in many inflight situations. This paper proposes an AoA algorithm—named ACF—capable of detecting fast changes in the actual signal, that is a useful stall prevention capability, for example. The solution is based on the complementary filtering methodology and does not involve any airdata sensor output, with consequent saving of weight and cost reduction. In particular, it does not use the airspeed measurement, easily subject to failure and has been identified as the root cause of several fatal accidents. Consequently, the ACF shows the same (high) reliability as the used inertial sensors. Furthermore, the ACF algorithm requires neither information on the aircraft aerodynamic coefficients nor forecast wind information to be preloaded onboard, thus being time and cost effective. The ACF is also characterized by a low computational load, only needing a few dozen of algebraic operations. In this work, the ACF performances have been assessed using both a detailed simulation environment and real flight data. In the former case, an airliner following a sample maneuver is assumed, and a mean root mean square (RMS) less than 3° is guaranteed under all possible conditions of wind, gust, and turbulence. In the latter case, data collected in two different general aviation flights are used, providing a mean RMS of less than 0.3°. During piloting, the angle of attack (AoA) readings may represent a possibility to cross-check the traditional indicators and to obtain information whose benefits are recognized in many inflight situations. Unfortunately, the AoA sensors are exposed to several possible failures, often common to the other airdata such as airspeed and altitude. In addition, especially for small aerial vehicles, the systems cannot accommodate direct sensors due to size, weight, and power constraints. For these reasons, having an AoA sensorless estimation would be extremely important. A software solution (ACF) is proposed to estimate the AoA without using any external sensor, saving weight and costs, and with a strong improvement of reliability since the angle vanes—and especially the air probes—are generally identified as the root cause of several inflight accidents. Due to the fact that the ACF algorithm relies on inertial measurements only, it could even be implemented in a pilot's personal device because it does not require any tuning or flight calibration. Furthermore, in spite of its simplicity, it can achieve good accuracy by detecting—in any weather and regardless of particular airplane or maneuver—fast changes in the actual AoA that may endanger the safety of the flight. [ABSTRACT FROM AUTHOR]

With the increasing number of available satellites from multi‐global navigation satellite systems (GNSS) and their applications in complicated environments, an increased number of faults in measurements are inevitable. Fault detection and exclusion (FDE) is an effective way to reject observation faults in order to guarantee the integrity of a GNSS positioning and navigation system. We propose a modified between‐receiver single‐differencing (SD)‐based FDE algorithm for real‐time kinematic (RTK) positioning. First, a modified between‐receiver SD model‐based FDE is proposed for testing the estimated float ambiguities. This is helpful in rejecting observations with unreliable float ambiguities and obtaining reliable double‐differencing (DD) integer ambiguities, denoted as the SD float model in the sequel. Second, DD ambiguities are resolved by the DD model and used to update the previous SD float ambiguity. After that, a modified SD fix model, taking the DD ambiguities as the known parameters, is further developed to detect any unreliable or fault‐resolved ambiguities. To verify the modified SD model‐based FDE algorithm, a kinematic vehicle test is conducted. The fault detection test statistics of the SD float model and the SD fix model, time‐to‐first‐fix, ambiguity‐fix rate, SD residuals, and positioning performances are examined. The results show that the modified FDE method can detect and exclude fault satellites accurately and effectively, which is verified by the SD residuals of fault satellites. The ambiguity‐fix rate of the proposed modified FDE algorithm is improved by approximately 3% with 70 more epochs of fixes. Regarding the 3D positioning performance, the modified proposed SD‐based FDE algorithm can achieve 54.11%, 57.78%, and 73.11% improvements for standard deviation, root mean square, and mean bias, respectively, when compared with the processing strategy without the use of the proposed FDE. [ABSTRACT FROM AUTHOR]

We present a simple iterative pre-distortion algorithm for achieving a rapid linear frequency sweep of semiconductor lasers. The algorithm achieves the desired frequency swept linearity with only four iterations. We derive a general formula for iterative pre-distortion by establishing the relationship between the laser output frequency and the drive current. The linear frequency-swept laser source obtained by this algorithm can be used in FMCW LiDAR systems. Experimentally, we investigated the algorithm using a 1550 nm distributed feedback (DFB) laser, achieving frequency swept excursion of 30.26 GHz, and frequency swept slope of 504 THz/s. We analyzed the linearity of the frequency swept results for the fourth iteration, achieving less than 5 MHz root mean square (RMS) value of frequency swept nonlinearity. [ABSTRACT FROM AUTHOR]

Due to factors such as indoor multipath effects and noise, the positioning signal becomes unstable, posing a challenge for visible light positioning systems relying on a single photodetector (PD) to achieve accurate position estimation. To enhance localization precision and ranging accuracy, a positioning algorithm based on spatial constraints of the PD array is introduced in this paper. By leveraging spatial information from the PD array and the channel state information (CSI) from multiple PDs, cross-matching among multiple PDs is performed to alleviate the limitation of unstable positioning information of a single PD during the positioning process. Moreover, the distances obtained through cross-matching are fused with those based on CSI, and the measured distance is iteratively updated to reduce the interference of reflection paths on ranging. This algorithm utilizes the spatial arrangement of the PD array to constrain positioning information received by multiple PDs, rather than relying on a single PD for positioning. This method can achieve joint optimization of positioning accuracy and ranging accuracy without requiring additional equipment or modifying the transceiver structure. Simulation results within a 4 m × 4 m × 3 m indoor space showcase the proposed method's effectiveness. The average positioning error has been reduced to 1.28 cm, and the root mean square error has been reduced to 1.51 cm. In comparison to the least squares (LS) algorithm based on CSI and the nonlinear LS algorithm based on CSI, the new approach reduces average localization errors by approximately 50.4% and 41.3%, respectively. Additionally, the algorithm attains a ranging accuracy of 3.86 cm, outperforming CSI-based distance estimation accuracy by 41.9%. • A high-accuracy positioning method based on spatial constraint of the PD array is proposed. • It visibly reduces positioning errors caused by unstable positioning information. • The proposed algorithm significantly reduces the impact of multipath effects on ranging. • The mean positioning error is 1.28 cm and 95% of the positioning errors are within 2.8 cm. [ABSTRACT FROM AUTHOR]

• Numerical methods used for the estimation of the RMS. • Simpson's 1/3 rule and Simpson's 3/8 rule modified for the purpose of general application. • Modified Simpson's rules do not necessitate more complex mathematical calculations than those used in the existing methods. • Modifications provide better measurement results for some a lower ratio of the frequency of sampling and the frequency of the signal. • Modified methods do not require the manufacturers of commercial measurement equipment to additionally invest in it. This paper demonstrates that direct changes in the algorithm for the estimation of the root mean square value of a voltage signal of an arbitrary waveform can lead to improved performances and lower measurement uncertainty of commercially available instruments without requiring any upgrade of their existing hardware. The research conducted and presented here is an original contribution to the development of estimation techniques and mathematical models for measurement oriented purposes regardless of the number of samples in the given period relying on mathematical calculation of the equal complexity as in the methods already in use. The theoretical approach examines the problem of numerical integration focusing on modified Simpson's 1/3 rule and modified Simpson's 3/8 rule used for the purpose of the estimation of the root mean square value when a small number of samples per period is available. It highlights the limitations of Simpson's 1/3 rule and Simpson's 3/8 rule, and shows that the newly proposed algorithm is optimal with respect to measurement accuracy and precision even in cases when the ratio of the sampling frequency and the signal's fundamental frequency is low. All theoretical results have been validated experimentally. [ABSTRACT FROM AUTHOR]

In this paper, a new fractional-order fuzzy controller named proportional fuzzy ا integral fuzzy + deferential fuzzy (FP + Fla + FDp) is proposed. The proposed controller is single gain, integrate, derivative and fractional order in its structure. The controller has a structure with two adjustable fractions and is simply designed. This controller performs the stability process in a short time. The response time can also be changed by adjusting the coefficient of time scale a and p, which makes it more flexible and efficient compared to the classic PID controller. On the other hand, in the design of the proposed controller, the optimal adjustment of the controller gain and fuzzy members has turned into an optimization problem, which is done by a hybrid algorithm based on the vcs and the HBC colony (ABC) based on a function in the time domain. In the proposed hybrid algorithm, attempts have been made to take advantage of local and final search as the strengths of these controllers to dramatically reduce the possibility of locating in local points. Investigations of different load variation scenarios, better performance of the proposed controller facing load disturbances from the root mean square, rise and fall time, will show the number of oscillations, and the fall time of the frequency changes. [ABSTRACT FROM AUTHOR]

Purpose: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics. Methods: A retrospective, single-institution analysis of patients with non-pseudocystic PCs, contrast-enhanced computed tomography scans within 1 year of resection, and available surgical pathology were included. A quantitative imaging software platform was used to extract radiomics. An extreme gradient boosting (XGBoost) machine learning algorithm was used to create mucinous classifiers using texture features only, or radiomic/radiologic and clinical combined models. Classifiers were compared using performance scoring metrics. Shapely additive explanation (SHAP) analyses were conducted to identify variables most important in model construction. Results: Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model. Conclusion: Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis. [ABSTRACT FROM AUTHOR]

A contra-rotating propfan consists of inner and outer propellers, having a dual inter-shaft bearing structure. The operating conditions are characterized by Slight Rotating Speed Differences (SRSDs) between the inner and outer rotors. These speed differences generate complex vibration transmission paths and harmonic cross-coupling between the dual rotors. Consequently, it becomes challenging to identify unbalanced vibration, leading to decreased low dynamic balance efficiency. Focusing on the prominent issues of low identification accuracy and poor stability in existing algorithms, mainly Least Square Method (LSM), Non-whole Beat Correlation Method (NWBCM), and Whole Beat Correlation Method (WBCM), when dealing with SRSDs under harmonic and noise interference, this paper introduces the Kalman Filter-Based Least Squares Identification Method for Filtering Harmonic Coupling (HLSM-KF). This method achieves precise, stable, and fast identification of unbalanced responses in contra-rotating rotors with SRSDs ranging from 1 to 30 rpm. Comparing the errors of the HLSM-KF algorithm to the other existing algorithms through simulations and experiments, the findings demonstrated that the identification accuracy and stability of the HLSM-KF algorithm are significantly dominant. Specifically, the Root Mean Square Error (RMSE) of the HLSM-KF algorithm is 47 % lower than that of the LSM algorithm. Therefore, a balancing experiment is conducted on the contra-rotating dual-rotor test rig, using the HLSM-KF algorithm. Consequently, the unbalanced vibrations of the inner and outer rotors are reduced by 86 % and 91 %, respectively, and the Root Mean Square (RMS) value of the whole system vibration is reduced by 87 %. [ABSTRACT FROM AUTHOR]

For GNSS coordinate time series analysis with missing data, we propose an improved comprehensive scheme without any pre-interpolation of missing data, and we perform a simulation experiment to evaluate the validity of this method. The coordinate series of more than 260 reference stations in the CMONOC (Crustal Movement Observation Network of China) from 2011 to 2018 are employed to verify this method further. First, the moving median method is applied to detect outliers and determine the occurrence times and amplitudes of all offsets. The results of the offsets indicate that the discontinuities caused by large earthquakes can be located and adjusted accurately. Second, a method based on MTLS (Multivariate Total Least Squares) and ILSP (Improved Lomb–Scargle Periodogram) is applied in a weighted iterative procedure. Using the ILSP-based algorithm, not only can the influence of both observation noise and the mutual modulation between different frequency signals be eliminated, but the periods of all actual periodic signals can also be accurately determined even if there are false spectral peaks. Analysis of the velocity results shows that the crustal movement in the eastern region of mainland China is weaker than that in the west, where the most intense crustal activities usually happened (such as Tibet, Sichuan, and Yunnan). For the periodic signals in the coordinate series of each station, the annual (365.25 d) and semi-annual (182.26 d) signals are dominant, followed by other quad-annual (91.3 d) signals, and the amplitude of each periodic signal is associated with specific geographical or hydrological causes. The reliability of the MTLS + ILSP method is further verified by a comparative analysis of three sets of velocity field products, where the RMS (Root Mean Square) of the residual velocity is reduced by more than 70% on average, and high precision velocity field is conducive to realize and maintain regional reference frame. [ABSTRACT FROM AUTHOR]

The geeSEBAL application estimates and displays evapotranspiration maps and times series based on Landsat images and global meteorological data from ERA5 Land reanalysis. Codes and applications are available at https://github.com/et-brasil/geesebal and https://etbrasil.org/geesebal , respectively. [Display omitted] Accurate estimation of evapotranspiration (ET) is essential for several applications in water resources management. ET models using remote sensing data have flourished in recent years allowing spatial and temporal assessments at unprecedented resolutions. This study presents geeSEBAL, a new tool for automated estimation of ET , based on the Surface Energy Balance Algorithm for Land (SEBAL) and a simplified version of the CIMEC (Calibration using Inverse Modeling at Extreme Conditions) process for the endmembers selection, developed within the Google Earth Engine (GEE) environment. The tool framework is introduced, and case studies across multiple biomes in Brazil are presented by comparing daily ET estimates with eddy covariance (EC) data from 10 flux towers. Based on 224 Landsat images using ERA5 Land as meteorological inputs, daily ET estimates of geeSEBAL yielded an average root mean squared difference (RMSD) of 0.67 mm day−1 when compared to EC data corrected for the energy balance closure. Additional analyses indicate a low geeSEBAL sensitivity to meteorological inputs, yielding an average RMSD of 0.71 mm day−1 when driven by in situ meteorological measurements. On the other hand, we found a higher sensitivity of the automated CIMEC algorithm to the selection of endmembers for internal calibration. For instance, by adjusting the endmembers percentiles to tropical biomes we found an error that was 36% lower compared to the standard CIMEC percentiles. Finally, we assessed the long-term effects (1984–2020) of land cover changes on surface energy fluxes and water use in agriculture for key areas in Brazil, from deforested areas in the Amazon to irrigated crops in the Pampas and Cerrado biomes. A comparison with a land surface temperature-based (SSEBop) and a vegetation-based (MOD16) model was also performed to assess relative advantages and disadvantages. This analysis showed that geeSEBAL has a significant potential for long-term assessment of ET in data-scarce areas, due to its lower sensitivity to meteorological inputs. geeSEBAL codes are written in Python and JavaScript and are freely available on GitHub (https://github.com/et-brasil/geesebal). geeSEBAL also includes a graphical user interface (https://etbrasil.org/geesebal), allowing important advances in water resources management at regional scales. [ABSTRACT FROM AUTHOR]

Breast cancer is the only type of cancer which is affecting the woman on a large scale because the tumors which are developed in the early stage of cancer or the origin of cancer these tumor cells or lumps are divided into two different kinds and both are dangerous in their own away as one of the lump is cancerous and other is not but both of them are one of the main reason behind the increasing numbers of death among the women. The purpose of the proposed research is control the rising tally of death day by day all over the world because there no efficient way of predicting or diagnosing the tumor of breast cancer whether the lumps developed in the patients are cancerous or not so, the objective of the proposed research is to overcome this major problem by using machine learning and deep learning as machine learning (ML) can provide certain tools which can be used in addition with hyper parametric for diagnosing the tumor in an efficient way that is the main reason behind the use of six supervised Machine Learning(ML) algorithm involving. The KNN, SVM, DT, and DP methods like Adam Gradient learning were utilized due to their adaptive gradient algorithm and root mean square propagation benefits; as in each model is has been proved that it shows more accuracy individually in the models or on comparing them with each other as the accuracy shown by these method was 98.50%. as described in result and discussion section. [ABSTRACT FROM AUTHOR]

In this study, we used the ARIMA time series model, the fuzzy-neural inference network, multi-layer perceptron artificial neural network, and ARIMA-ANN, ARIMA-ANFIS hybrid models for the modeling and prediction of the daily electrical conductivity parameter of daily teleZang hydrometric station over the statistical period of 49 years. For this purpose, the daily data for the 1996-2004 period were used for model training and data for the 1996-2006 period were applied for testing. In order to verify the validity of the fitted ARIMA models, the residual autocorrelation and partial autocorrelation functions and Port Manteau statistics were used. PMI algorithm were then used to model and predict electrical conductivity for selecting the effective input parameter of the neural fuzzy inference network and the artificial neural network. The daily parameters of magnesium (with two days delay) and sodium (with one day delay), heat (with one day delay), flow rate (with two months delay), and acidity (with one day delay) were obtained with the lowest values of Akaike and highest values of hempel statistics as the input of the neural fuzzy inference network and the artificial neural network for modelling daily electric conductivity predictions; then predictions were made. Also, models evaluation criteria confirmed the superiority of the ARIMA-ANFIS hybrid model with the trapezoidal membership function and with two membership numbers, as compared to other models with a coefficient of determination of 0.86 and the root mean square of 29 dS / m. Also, the Arima model had the weakest performance. So, it could be applied to modeling and forecasting the daily quality parameter of the tele Zang hydrometer station. [ABSTRACT FROM AUTHOR]

We optimize Hockney and Eastwood’s particle-particle particle-mesh algorithm to achieve maximal accuracy in the electrostatic energies (instead of forces) in three-dimensional periodic charged systems. To this end we construct an optimal influence function that minimizes the root-mean-square (rms) errors of the energies. As a by-product we derive a new real-space cutoff correction term, give a transparent derivation of the systematic errors in terms of Madelung energies, and provide an accurate analytical estimate for the rms error of the energies. This error estimate is a useful indicator of the accuracy of the computed energies and allows an easy and precise determination of the optimal values of the various parameters in the algorithm (Ewald splitting parameter, mesh size, and charge assignment order). [ABSTRACT FROM AUTHOR]

This paper presents a method for estimating the land surface temperature (LST) from Himawari-8 data. The Advanced Himawari Imager onboard Himawari-8 has three thermal infrared bands in the spectral range of 10 - 12.5 µm. We developed a nonlinear three-band algorithm (NTB) that makes the best use of these bands to estimate the LST. The formula of the algorithm includes 10 coefficients. The optimum values of these coefficients were derived using a statistical regression method from the simulated data, as obtained by a radiative transfer model. The simulated data sets correspond to a variety of values of LST, as well as surface emissivity, type and season of temperature and water vapor profiles. Viewing zenith angles (VZAs) from 0° to 60° were considered. For the coefficients obtained in this way, we verified the root-mean-square error (RMSE) in terms of the VZA, LST and precipitable water dependence. We showed that the NTB can accurately estimate the LST with an RMSE less than 0.9 K compared with the nonlinear split-window algorithm developed by Sobrino and Romaguera (2004). Moreover, we evaluated the sensitivities of the LST algorithms to the uncertainties in input data by using the dataset independent of the dataset used to obtain coefficients. Consequently, we showed that the NTB has the highest robustness against the uncertainties in input data. Finally, the stepwise LST retrieval method was constructed. This method includes a simple cloud mask procedure and the land surface emissivity estimation. The LST product was evaluated using in-situ data over the Tibetan Plateau, and the validity was confirmed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m2 and the root-mean-square error (RMSE) is 242 W/m2. The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes. [ABSTRACT FROM AUTHOR]

Purpose To develop and implement an efficient reconstruction technique to improve accelerated multi-channel multi-contrast MRI. Theory and Methods The vectorial total generalized variation (TGV) operator is used as a regularizer for the sensitivity encoding (SENSE) technique to improve image quality of multi-channel multi-contrast MRI. The alternating direction method of multipliers (ADMM) is used to efficiently reconstruct the data. The performance of the proposed method (MC-TGV-SENSE) is assessed on two healthy volunteers at several acceleration factors. Results As demonstrated on the in vivo results, MC-TGV-SENSE had the lowest root-mean-square error (RMSE), highest structural similarity index, and best visual quality at all acceleration factors, compared to other methods under consideration. MC-TGV-SENSE yielded up to 17.3% relative RMSE reduction compared to the widely used total variation regularized SENSE. Furthermore, we observed that the reconstruction time of MC-TGV-SENSE is reduced by approximately a factor of two with comparable RMSEs by using the proposed ADMM-based algorithm as opposed to the more commonly used Chambolle–Pock primal-dual algorithm for the TGV-based reconstruction. Conclusion MC-TGV-SENSE is a better alternative than the existing reconstruction methods for accelerated multi-channel multi-contrast MRI. The proposed method exploits shared information among the images (MC), mitigates staircasing artifacts (TGV), and uses the encoding power of multiple receiver coils (SENSE). [ABSTRACT FROM AUTHOR]

The global monthly equivalent water height (EWH) anomaly (ΔEWH), derived from the Gravity Recovery and Climate Experiment (GRACE) level-2 data, helps understand the terrestrial water storage variation. Unfortunately, the ΔEWH data consist of stripes or noise. Numerous de-noising approaches have been studied. However, the following issues exist, possible overly smoothed results, two-step (de-striping and Gaussian-based smoothing) procedures introducing additional uncertainty and error propagation, requiring multi-temporal datasets or prior knowledge, and possible computational inefficiency. Thus, after analyzing the spectrum of the ΔEWH data, a novel low-pass-filtering algorithm is proposed to remove the noise and resolve the issues. Furthermore, without the global in situ measurements, an alternative assessment method is studied based on the additive characteristics of the signal and noise in the GRACE data. The method consists of the residual analysis and root mean square (RMS) value of the de-noised signal. Then, the proposed algorithm was applied to de-noising the ΔEWH datasets between 2002 and 2015. De-noised results are satisfactory qualitatively and quantitatively. Compared with well-known two-step de-noising methods, data blurring does not occur after the proposed algorithm. The algorithm removes noise the most assessed by the residual analysis and preserves the signal the most evaluated by large RMS values. • Significantly removing the stripes and noise without Gaussian-like smoothening. • Requiring no prior information or the time series. • The time complexity is noticeably low. • Developing a novel performance analysis method to assess global GRACE data. [ABSTRACT FROM AUTHOR]

• We propose a more aggressive algorithm for generating adversarial examples, namely, the accelerated gradient iterative fast gradient sign method(AI-FGSM), and we prove that AI-FGSM can improve the transferability of adversarial examples. • From the perspective of the deep neural network training optimization process, we introduce the gradient update method of the adaptive sub-gradient and root mean square prop into the iterative fast gradient sign method to obtain AG-FGSM and RMS-FGSM, respectively. We analyse the attack results of AG-FGSM and RMS-FGSM, and the results show that they are better than the traditional iterative gradient sign method. • We introduce invariance of image scaling into a deep neural network. With this property,we can replace the training processes of a series of models by scaling the images in different proportions. We introduce this property into our methods and obtain SAI-FGSM, SAG-FGSM and SRMS-FGSM. Experimental results show that the integrated combat training model with strong defense ability is also vulnerable to blackbox attacks. Most existing deep neural networks are susceptible to the influence of adversarial examples, which may cause them to output incorrect prediction results. An adversarial example is the addition of small noise disturbances to the input data samples, which will deceive the classification. Generally, these modifications are very small noise disturbances, which are not easily noticed by the human eye. However, most existing adversarial attacks achieve only low success rates in typical black-box settings, where the attackers have no prior knowledge about the model structures and/or model parameters. To tackle this problem, we propose an iterative algorithm based on an acceleration gradient to enhance the adversary attack. Our method accumulates the gradient of the loss function in each iteration and uses the next gradient information to influence the future gradient. We also introduce the scaling invariance principle of a deep neural network to optimize the input images for black-box attacks. In addition, to handle the drawbacks of a traditional iterative fast gradient sign method, we further present two gradient optimization methods. Experimental results on the ImageNet dataset show that our attack methods can achieve good transferability. In addition, those models obtained by integrated adversarial training with strong defense capability are also quite vulnerable to our black-box attack. [ABSTRACT FROM AUTHOR]

This paper introduces the impact localization algorithm on a composite stiffened plate using an array of four multiplexed fiber Bragg grating (FBG) sensors. For this algorithm, the impact signals are required as the reference data on the baseline points. In order to localize the impact event, the similarities between the obtained and each reference signal have to be evaluated. In this study, such similarities were assessed by calculating the root mean squared (RMS) values between the signals. In the first step, four RMS matrices from each sensor are obtained using the RMS values about each reference location on the test section as the elements. Then, the elements of each matrix are processed to extremely low value excepting a few number of elements with values near the minimum RMS values. From the element-by-element multiplications of these processed matrices, the final matrix could be obtained. Finally, the impact location could be predicted from the location of the high valued element in that matrix. This principle was validated from the localizations of non-baseline points. Also, to check the robustness of this algorithm to dynamic loading conditions, experimental investigations were performed. As a result, this suggested algorithm showed a reasonable localization performance. [ABSTRACT FROM AUTHOR]

The deviation-angle variance technique (DAV-T), which was introduced in the North Atlantic basin for tropical cyclone (TC) intensity estimation, is adapted for use in the North Pacific Ocean using the 'best-track center' application of the DAV. The adaptations include changes in preprocessing for different data sources [Geostationary Operational Environmental Satellite-East (GOES-E) in the Atlantic, stitched GOES-E-Geostationary Operational Environmental Satellite-West (GOES-W) in the eastern North Pacific, and the Multifunctional Transport Satellite (MTSAT) in the western North Pacific], and retraining the algorithm parameters for different basins. Over the 2007-11 period, DAV-T intensity estimation in the western North Pacific results in a root-mean-square intensity error (RMSE, as measured by the maximum sustained surface winds) of 14.3 kt (1 kt ≈ 0.51 m s−1) when compared to the Joint Typhoon Warning Center best track, utilizing all TCs to train and test the algorithm. The RMSE obtained when testing on an individual year and training with the remaining set lies between 12.9 and 15.1 kt. In the eastern North Pacific the DAV-T produces an RMSE of 13.4 kt utilizing all TCs in 2005-11 when compared with the National Hurricane Center best track. The RMSE for individual years lies between 9.4 and 16.9 kt. The complex environment in the western North Pacific led to an extension to the DAV-T that includes two different radii of computation, producing a parametric surface that relates TC axisymmetry to intensity. The overall RMSE is reduced by an average of 1.3 kt in the western North Pacific and 0.8 kt in the eastern North Pacific. These results for the North Pacific are comparable with previously reported results using the DAV for the North Atlantic basin. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning is a promising technique for automatic measurements of tree stems. The objectives of the study were (1) to develop and validate a new method for the detection, classification and measurements of tree stems and canopies using the Hough transformation and the RANSAC algorithm and (2) assess the influence of distance to the scanner on the measurement accuracy. Tree detection and stem diameter estimates were validated for 16 circular plots with 20 m radius. The three dominating tree species were Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.) and birch (Betula spp.). The proportion of detected trees decreased as the distance to the scanner increased and followed the trend of decreasing visible area. Within 10 m from the scanner, the proportion of detected trees was 87% on average for the plots and the diameter at breast height was estimated with a relative root-mean-square-error (RMSE) of 14%. The most accurate diameter measurements were obtained for pine, which had a RMSE of 7% for all the full 20 m radius plots. The RANSAC algorithm reduced noise and made it possible to obtain reliable estimates. [ABSTRACT FROM AUTHOR]

This paper presents a method for automatic extraction of ground control points (GCPs) of fully polarimetric synthetic aperture radar (SAR) (PolSAR) images obtained from various satellites with different viewing angles. The scale-invariant feature transform (SIFT) algorithm is applied to extract candidate GCPs, where two-way keypoint matching eliminates improbable correspondence keypoints. Minimizing the root-mean-square error (rmse) also removes matching points with large rmse through a pseudoaffine transformation. In addition, information entropy and spatial dispersion quality constraints enable quantification of the spatial distribution of the GCPs. In accordance with full polarization, applying the SIFT-OCT algorithm (SIFT algorithm with the first scale-space octave skipped) to PolSAR data is examined. The total power (TP) image represents a combination of the characteristics of all four polarization images [horizontal transmitting and horizontal receiving (HH), horizontal transmitting and vertical receiving (HV), vertical transmitting and horizontal receiving (VH), and vertical transmitting and vertical receiving (VV)]. Therefore, GCP extraction using a TP image rather than each polarization image is proposed in order to maximize the accuracy of GCP extraction for all of the polarization data, as the TP image generates the highest signal-to-noise ratio (SNR) value. The SNR in conjunction with the matching correlation surface is used as an indicator of the reliability and accuracy of GCP extraction. After successfully applying the method to Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar and Japanese Earth Resources Satellite-1 SAR images, the GCP matching accuracy is further improved by using geometric calibration. [ABSTRACT FROM AUTHOR]

A physical retrieval algorithm has been developed for deriving the legacy atmospheric profile (LAP) product from infrared radiances of the Advanced Baseline Imager (ABI) on board the next-generation Geostationary Operational Environmental Satellite (GOES-R) series. In this study, the GOES-R ABI LAP retrieval algorithm is applied to the GOES-13 sounder radiance measurements (termed the GOES-13 LAP retrieval algorithm in this study) for its validation as well as for potential transition of the GOES-13 LAP retrieval algorithm for the operational processing of GOES sounder data. The GOES-13 LAP retrievals are compared with five different truth measurements: radiosonde observation (raob) and microwave radiometer-measured total precipitable water (TPW) at the Atmospheric Radiation Measurement Cloud and Radiation Testbed site, conventional raob, TPW measurements from the global positioning system-integrated precipitable water NOAA network, and TPW measurements from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The results show that with the GOES-R ABI LAP retrieval algorithm, the GOES-13 sounder provides better water vapor profiles than the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) forecast fields at the levels between 300 and 700 hPa. The root-mean-square error (RMSE) and standard deviation (STD) of the GOES-13 sounder TPW are consistently reduced from those of the GFS forecast no matter which measurements are used as the truth. These substantial improvements indicate that the GOES-R ABI LAP retrieval algorithm is well prepared to provide continuity of quality to some of the current GOES sounder products, and the algorithm can be transferred to process the current GOES sounder measurements for operational product generation. [ABSTRACT FROM AUTHOR]

We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms. [ABSTRACT FROM AUTHOR]

A new passive microwave rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that relies on an a priori database derived from matchups between TMI brightness temperatures and precipitation radar (PR)-derived surface rain rates has been developed. In addition to implementing a fairly conventional Bayesian approach to precipitation estimation, it exploits a dimensional reduction technique designed to increase the effective sample density in the database and also to improve the detectability of precipitation over problem surface types. The details of the algorithm itself are described in a companion paper. In this paper, the algorithm is validated against independent PR-TMI matchups from calendar year 2002. The validation results are benchmarked against results obtained for the same scenes from the current standard (version 7) 2A12 rainfall product for TRMM. Validation statistics considered include the biases, correlation coefficients, and root-mean-square (RMS) differences for annual precipitation totals on a 1° grid as well as two-threshold Heidke skill scores (HSS) for instantaneous (pixel level) retrievals, determined separately for each of seven surface classes, including ocean, coast, and five other basic land surface types as well as for cold (<275 K) and warm surface skin temperatures. Overall, the University of Wisconsin (UW) algorithm exhibits markedly reduced RMS error and bias in the annual total rainfall and markedly improved instantaneous skill at delineating light rain rates, especially over land and the coast. To ensure that the improved results were not due to both the training and validation data having been taken from the same calendar year, the validation of the UW algorithm is repeated using 2005 matchup data. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta (YRD) is one of the regions with the most intensive human activities. The eutrophication of lakes in this area is becoming increasingly serious with consequent negative impacts on the water supply of the surrounding cities. But the spatial-temporal characteristics and driving factors of the trophic state of the lake in this region are still not clearly addressed. In this study, a semi-analytical algorithm for estimating the trophic index (TSI) using particle absorption at 645 nm based on MODIS images is proposed to monitor and evaluate the trophic state of 41 large lakes (larger than 10 km2) in the YRD from 2002 to 2020. The performance of the proposed algorithm is evaluated using an independent dataset. Results showed that the root-mean-square error (RMSE) of the algorithm is less than 6 and the mean absolute percentage error (MAPE) does not exceed 8%, indicating that it can be applied for remotely deriving the TSI in the YRD. The spatial-temporal patterns revealed that there were significantly more lakes with moderate eutrophication in the Lower Yangtze River (LYR) than in the Lower Huaihe River (LHR). The overall average value of the TSI reaches a maximum in summer and a minimum in winter. The TSI value in the YRD over the period 2002–2020 showed a downward trend, especially after 2013. Individually, 33 lakes showed a downward trend and 8 lakes showed an upward trend. Furthermore, marked seasonal and interannual temporal variations can be clearly observed in the LYR and LHR and the sum of the variance contributions of seasonal and interannual components is more than 50%. Multiple linear regression analysis showed that human activities can explain 65% of the variation in the lake TSI in the YRD. [ABSTRACT FROM AUTHOR]

Stable gaze scanning control technology is the key to realize the integration of stable imaging, search, and tracking on airborne platforms. We investigate the control technology of stable gaze scanning on an airborne platform. The working principle of a stable staring scanning system is analyzed, and a servo system control algorithm is modeled. The position loop, velocity loop, and acceleration loop are designed. A stability model of the space with inclination angle is established. The coordinate transformation relationship between inertial coordinate system and airborne axis coordinate system is obtained. The designed control model and algorithm are used to control the single-lens reflex mirror mounted at an angle of 15 deg for spatial gaze pointing and step scanning. A test system is built for performance verification. Results show that the root-mean-square accuracy of the proposed algorithm is higher than 10 μrad in a dynamic staring step scan with ±30 deg and a step length of 2.16 deg. The single-step time is <52 ms, the stabilization time is >53 ms, and the servo system overshoot is small. The system step accuracy root mean square is better than 52 μrad. These findings confirm the effectiveness of the integrated search and follow technology under an airborne platform. [ABSTRACT FROM AUTHOR]

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatial-resolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (R rs (λ)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate R rs (λ) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr−1 and 16.2%, respectively. In contrast, the R rs (λ) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as ~0.0018 sr−1 and ~38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is ~0.0062 sr−1 and MAPD is ~228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain. • An algorithm is developed to process high-resolution coastal turbid waters. • The algorithm uses multiple pixels to constrain aerosol properties. • The algorithm requires at least two bands in the NIR or SWIR domain. • Excellent agreements are found between Landsat-8 products and field measurements. [ABSTRACT FROM AUTHOR]

Here we test the capability of the Broadcast Ephemeris Message, in both its GPS-like (Keplerian ellipse with secular and periodic perturbations) and Glonass-like (numerical integration of a 9D state vector) formats, to reproduce a corresponding precise ephemeris. We start from a daily Rinex 3.04 navigation file for multiple GNSS and the corresponding SP3 precise orbits computed by CNES (Centre National d'Etudes Spatiales) for GPS, Glonass, Galileo and CODE (Center for Orbit Determination in Europe) for Beidou, and compute broadcast ECEF coordinates and clocks. The pre-fit discrepancies are converted by least squares to corrections to the broadcast ephemeris parameters in two-hour consecutive arcs (for GPS, Galileo and Beidou) and to a set of seven Helmert parameters for the entire day, to align in origin, orientation and scale to the common GNSS IGS14 Reference Frame. The test cases suggest that the Broadcast Ephemeris Message, complemented with Reference Frame information, can reproduce the precise ephemeris and clocks with centimetric accuracy for intervals at least equal to the respective validity times, typically 2 h. The broadcast ephemeris of Glonass consists of three initial positions and velocities at epoch, three constant Lunisolar accelerations for the satellite position, and of three polynomial coefficients for the satellite clock. The 9D vector of state is numerically integrated to generate position and velocity data within the validity time (0.5 h) of the message. To test the capability of this model to reproduce the corresponding values of a precise ephemeris, the 9D vector of state and clock polynomials are adjusted until the rms (root mean squared spread) of the post-fit residuals relative to a precise orbit (CNES's in our case) is minimum. We show in one example (one satellite for one day) that the Glonass type of message can reproduce a precise ephemeris and clock with a rms spread of 0.025 m over one-hour arcs. Volume computations on one month of data with all available satellites confirm the test results. For GPS, Glonass, Galileo and Beidou, the best fitting clock values predicted by our second order polynomials, based on a 15 min sampling, are shown to fit the corresponding high rate clocks (30 s sampling) of MGEX with zero bias and a rms spread of 0.062 ns (GPS G01), 0.023 ns (Galileo E01), 0.43 ns (Glonass R01), 0.086 ns (Beidou C07) and 0.086 ns (Beidou C12). Modifications to the GPS-like message structure and Glonass algorithm are proposed to increase the validity time by including the effect of the 3rd zonal harmonic of the Earth's gravity field. The potential of the RTCM messages for broadcasting the improved navigation message is reviewed. [ABSTRACT FROM AUTHOR]

Abstract: The study of the amplitude of respiratory muscle mechanomyographic (MMG) signals could be useful in clinical practice as an alternative non-invasive technique to assess respiratory muscle strength. The MMG signal is stochastic in nature, and its amplitude is usually estimated by means of the average rectified value (ARV) or the root mean square (RMS) of the signal. Both parameters can be used to estimate MMG activity, as they correlate well with muscle force. These estimations are, however, greatly affected by the presence of structured impulsive noise that overlaps in frequency with the MMG signal. In this paper, we present a method for assessing muscle activity based on the Lempel–Ziv algorithm: the Multistate Lempel–Ziv (MLZ) index. The behaviour of the MLZ index was tested with synthesised signals, with various amplitude distributions and degrees of complexity, and with recorded diaphragm MMG signals. We found that this index, like the ARV and RMS parameters, is positively correlated with changes in amplitude of the diaphragm MMG components, but is less affected by components that have non-random behaviour (like structured impulsive noise). Therefore, the MLZ index could provide more information to assess the MMG–force relationship. [Copyright &y& Elsevier]

Abstract: Accurate battery State of Charge (SOC) estimation is of great significance for safe and efficient energy utilization for electric vehicles. This paper presents a comparison between a novel robust extended Kalman filter (REKF) and a standard extended Kalman filter (EKF) for Li-ion battery SOC indication. The REKF-based method is formulated to explicitly compensate for the battery modeling uncertainty and linearization error often involved in EKF, as well as to provide robustness against the battery system noise to some extent. Evaluation results indicate that both filters have a good average performance, given appropriate noise covariances, owing to a small average modeling error. However, in contrast, the REKF-based SOC estimation method possesses slightly smaller root-mean-square (RMS) error. In the worst case, the robustness characteristics of the REKF result in an obviously smaller error bound (around by 1%). Additionally, the REKF-based approach shows superior robustness against the noise statistics, leading to a better tolerance to inappropriate tuning of the process and measurement noise covariances. [Copyright &y& Elsevier]

Environmental monitoring through the method of traditional ship sampling is time consuming and requires a high survey cost. This study was carried out to investigate the relationship between total suspended solids (TSS) and satellite THEOS data by using a calibrated algorithm. The study area is the seawater region around Penang Island, Malaysia. Water-quality TSS values were collected simultaneously during the acquisition of the satellite imagery and later analyzed in the laboratory. The digital numbers of the corresponding TSS measurements were extracted and converted into reflectance values for algorithm regression. The two-band algorithm used here is based on the reflectance model, which is a function of the inherent optical properties of water, and this in turn can be related to the concentration of its constituents. The developed algorithm was used to correlate the digital signal and the TSS values. The generated algorithm was developed for two visible wavelengths, red and blue for this study. The digital numbers corresponding to the sea truth locations were extracted for algorithm calibration. Based on the values of the correlation coefficient squares (R2) and root-mean-square deviation (RMS), the proposed algorithm is considered superior. A water-quality image was generated using the multispectral data set and the proposed calibrated TSS algorithm. Finally, the generated TSS map was geometrically corrected to produce a geocoded map. Filtering was performed to the generated map to remove random noise, and it was color-coded for visual interpretation. This study indicates that the TSS algorithm developed from optical properties is a promising TSS model for high-accuracy TSS mapping using satellite data. [ABSTRACT FROM AUTHOR]

Abstract: Currently, ground-based Global Navigation Satellite System (GNSS) stations of the International GNSS Service (IGS) are distributed unevenly around the world. Most of them are located on the mainland, while only a small part of them are scattered on some islands in the oceans. As a consequence, many unreasonable zero values (in fact negative values) appear in Vertical Total Electron Content (VTEC) of European Space Agency (ESA) and Center for Orbit Determination in Europe (CODE) IONEX products, especially in 2008 and 2009 when the solar activities were rather quiet. To improve this situation, we directly implement non-negative physical constraints of ionosphere for global ionosphere maps (GIM) with spherical harmonic functions. Mathematically, we propose an inequality-constrained least squares method by imposing non-negative inequality constraints in the areas where negative VTEC values may occur to reconstruct GIM models. We then apply the new method to process the IGS data in 2008. The results have shown that the new algorithm efficiently eliminates the unwanted behavior of negative VTEC values, which could otherwise often be seen in the current CODE and ESA GIM products in both middle and high latitude areas of the Southern Hemisphere (45°S∼90°S) and the Northern Hemisphere (50°N∼90°N). About 64% of GPS receivers’ DCBs have been significantly improved. Finally, we compare the GIM results between with and without the inequality constraints, which has clearly shown that the GIM result with inequality constraints is significantly better than that without the inequality constraints. The inequality-constrained GIM result is also highly consistent with the final IGS products in terms of root mean squared (RMS) and mean VTEC. [Copyright &y& Elsevier]

This article deals with the problem of mean square stability of discrete-time networked control systems (NCSs) over a communication channel subject to packet dropout. By introducing a parameter-independent slack variable with lower triangular structure, the existence of a static output feedback controller is formulated in the form of linear matrix inequalities. Neither equality constraint nor iterative algorithm is involved through the derivation of the controller. Furthermore, a new algorithm is proposed to obtain the admissible packet dropout probability bound for the given controller gain. This is particularly important in the co-design of controller and scheduling for NCSs. The simplicity of the methods is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Abstract: White matter fiber bundles of the brain can be delineated by tractography utilizing multiple regions-of-interest (MROI) defined by anatomical landmarks. These MROI can be used to specify regions in which to seed, select, or reject tractography fibers. Manual identification of anatomical MROI enables the delineation of white matter fiber bundles, but requires considerable training to develop expertise, considerable time to carry out and suffers from unwanted inter- and intra-rater variability. In a study of 20 healthy volunteers, we compared three methodologies for automated delineation of the white matter fiber bundles. Using these methodologies, fiber bundle MROI for each volunteer were automatically generated. We assessed three strategies for inferring the automatic MROI utilizing nonrigid alignment of reference images and projection of template MROI. We assessed the bundle delineation error associated with alignment utilizing T1-weighted MRI, fractional anisotropy images, and full tensor images. We confirmed the smallest delineation error was achieved using the full tensor images. We then assessed three projection strategies for automatic determination of MROI in each volunteer. Quantitative comparisons were made using the root-mean-squared error observed between streamline density images constructed from fiber bundles identified automatically and by manually drawn MROI in the same subjects. We demonstrate that a multiple template consensus label fusion algorithm generated fiber bundles most consistent with the manual reference standard. [Copyright &y& Elsevier]

Abstract: A new method of image projection based on fractional Fourier transformation is presented. This method can project an image at any distance after a lens plane. We use a modified Gerschberg–Saxton (GS) iteration algorithm to compute a phase-only hologram. The amplitude distributions both on the hologram plane and image plane are restricted while allowing their phase distributions to drift into an optimum value. The quality of the image projected by fractional Fourier hologram is close to the image projected by Fourier hologram. The RMS error between the projected image and the constrained image is computed in our experiment. A comparison in flexibility of the two projection methods is also discussed. [Copyright &y& Elsevier]

Abstract: The present paper considers the problem of partitioning a dataset into a known number of clusters using the sum of squared errors criterion (SSE). A new clustering method, called DE-KM, which combines differential evolution algorithm (DE) with the well known K-means procedure is described. In the method, the K-means algorithm is used to fine-tune each candidate solution obtained by mutation and crossover operators of DE. Additionally, a reordering procedure which allows the evolutionary algorithm to tackle the redundant representation problem is proposed. The performance of the DE-KM clustering method is compared to the performance of differential evolution, global K-means method, genetic K-means algorithm and two variants of the K-means algorithm. The experimental results show that if the number of clusters K is sufficiently large, DE-KM obtains solutions with lower SSE values than the other five algorithms. [Copyright &y& Elsevier]

We propose a path-tracking algorithm that is developed using an iterative learning control (ILC) technique and use the algorithm to control an omni-directional mobile robot. The proposed algorithm can be categorized as an open-closed PD-type ILC; it generates robot velocity commands by a PD-type ILC update rule using both previous and current information. When applied to the omni-directional mobile robot, it can decrease position errors and track the desired trajectory. Under the general problem setting that includes a mobile robot, we show that the proposed algorithm guarantees that the system states, outputs and control inputs converge to within small error bounds around the desired ones even under state disturbances, measurement noises and initial state errors. By using simulation and experimental tests, we demonstrate that the proposed algorithm converges fast to the desired path, and results in small root-mean-square (r.m.s.) position error under various surface conditions. The proposed algorithm shows better path-tracking performance than the conventional PID algorithm and achieves faster convergence and lower r.m.s. error than the existing two ILC algorithms. [ABSTRACT FROM AUTHOR]

The effect of shortcuts on the task completion landscape in parallel discrete-event simulation (PDES) is investigated. The morphology of the task completion landscape in PDES is known to be described well by the Langevin-type equation for nonequillibrium interface growth phenomena, such as the Kardar-Parisi-Zhang equation. From the numerical simulations, we find that the root-mean-squared fluctuation of task completion landscape, W(t,N), scales as W(t → ∞, N) ∼ N when the number of shortcuts, l, is finite. Here N is the number of nodes. This behavior can be understood from the mean-field type argument with effective defects when t is finite. We also study the behavior of W(t , N) when £ increases as N increases and provide a criterion to design an optimal topology to achieve a better synchronizability in PDES. [ABSTRACT FROM AUTHOR]

Abstract: We studied sea surface temperature (SST) retrieval algorithms for Sendai Bay, using output from the thermal-infrared channels of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board Terra. While the highest resolutions of other satellite SST products are about 1km, the ASTER thermal-infrared channels provide 90-m spatial resolution. To develop the ASTER algorithm, we employed statistical methods in which SSTs retrieved from the thermal-infrared measurements were tuned against the Moderate Resolution Imaging Spectroradiometer (MODIS) SST product with a 1-km spatial resolution. Terra also carries a MODIS sensor, which observed the same area as the ASTER sensor at the same time. The MODIS SST was validated around Sendai Bay, revealing a bias of −0.15°C and root mean-square difference (RMSD) of 0.67°C against in situ SSTs. Taking into account the spatial-resolution difference between ASTER and MODIS, match-up was generated only if the variability of ASTER brightness temperatures (T 13) was small in a pixel of MODIS SST (MP). The T 13 within one MP was about 121pixels. The standard deviation (σ 13) of T 13 was calculated for each cloud-free MP, and the threshold of σ 13 for choosing match-up MPs was decided by analyzing the σ 13 histogram of one ASTER image. The 15 synchronous pairs of ASTER/MODIS images are separated into two groups of 8 pairs called set (A) and 7 pairs called set (B). Using the common procedure, the match-ups are generated for set (A) and set (B). The former is used for developing the ASTER Multi-Channel SST (MCSST) algorithm, and the latter for validation of the developed ASTER SST. Analysis of the whole 15 pairs indicated that ASTER SST does not depend on the satellite zenith angle. We concluded that, using Akaike''s information criterion with set (A) match-ups, the multiple regression formula with all five thermal-infrared channels was adequate for the ASTER SST retrieval. Validation of ASTER SST using match-up set (B) indicated a bias of 0.101°C and RMSD of 0.455°C. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a noise removal algorithm for digital images. This algorithm is based on hypergraph model of image, which enables us to distinguish noisy pixels in the image from the noise-free ones. Hence, our algorithm obviates the need for denoising all the pixels, thereby preserving as much image details as possible. The identified noisy pixels are denoised through Root Mean Square (RMS) approximation. The performance of our algorithm, based on peak-signal-to-noise-ratio (PSNR) and mean-absolute-error (MAE), was studied on various benchmark images, and found to be superior to that of other traditional filters and other hypergraph based denoising algorithms. [Copyright &y& Elsevier]

Abstract: Quantifying the geometries (defined here as width, height and depth of burial) of archeological structures within resistivity models produced as a result of the regularization constraints used in most inversion algorithms is difficult, especially when structures are closely spaced. Here we apply the watershed by simulated immersion method of boundary detection to smooth 2D resistivity images generated for synthetic and field data over 3D targets. The synthetic studies include a single cavity model, a model for two widely spaced cavities (spacing≫unit electrode spacing) and a model for two closely spaced cavities (spacing