Subsampling Process Research Articles

ACCURACY RESEARCH OF THE EFFECT OF LOCAL SURFACE MODELING METHODS ON THE GENERATION OF 3D MODELS OF HISTORICAL OBJECTS WITH ROCK-BASED BUILDING STRUCTURES USING OPEN-SOURCE SOFTWARE

Abstract. The paper describes the effect of the local surface modelling methods known as plane, quadric and triangulation on the generation of the 3D mesh models of the historical objects with rock-based building structures using Cloudcompare, an open-source software for point cloud data processing. The methodology begins with data collection of the 3D test objects using terrestrial laser scanning technology, the pre-processing of the point cloud data, the point cloud subsampling process, the process of generating of the local surface modelling data, the process of generating the 3D mesh models and finally, the analysis, which involves the 3D surface deviation analysis between the generated 3D mesh models. The overall results shows that there are no significant differences between the 3D mesh models that was generated from all the three local surface modelling methods. The histogram analysis shows that the plane and the quadric local surface modelling methods is the best methods to be used in the research where the 3D test object to be modelled contains curvy surfaces without sharp edges and corners.

Analog Lock-In Amplifier Design Using Subsampling for Accuracy Enhancement in GMI Sensor Applications.

A frequency downscaling technique for enhancing the accuracy of analog lock-in amplifier (LIA) architectures in giant magneto-impedance (GMI) sensor applications is presented in this paper. As a proof of concept, the proposed method is applied to two different LIA topologies using, respectively, analog and switching-based multiplication for phase-sensitive detection. Specifically, the operation frequency of both the input and the reference signals of the phase-sensitive detector (PSD) block of the LIA is reduced through a subsampling process using sample-and-hold (SH) circuits. A frequency downscaling from 200 kHz, which is the optimal operating frequency of the employed GMI sensor, to 1 kHz has been performed. In this way, the proposed technique exploits the inherent advantages of analog signal multiplication at low frequencies, while the principle of operation of the PSD remains unaltered. The circuits were assembled using discrete components, and the frequency downscaling proposal was experimentally validated by comparing the measurement accuracy with the equivalent conventional circuits. The experimental results revealed that the error in the signal magnitude measurements was reduced by a factor of 8 in the case of the analog multipliers and by a factor of 21 when a PSD based on switched multipliers was used. The error in-phase detection using a two-phase LIA was also reduced by more than 25%.

Drivers of Odonata flight timing revealed by natural history collection data.

Global change may cause widespread phenological shifts. But knowledge of the extent and generality of these shifts is limited by the availability of phenological records with sufficiently large spatiotemporal extents. Using North American odonates (damselflies and dragonflies) as a model system, we show how a combination of natural history museum and community science collections, beginning in 1901 and extending through 2020, can be leveraged to better understand phenology. We begin with an analysis of odonate functional traits. Principal coordinate analysis is used to place odonate genera within a three-dimensional trait ordination. From this, we identify seven distinct functional groups and select a single odonate genus to represent each group. Next, we pair the odonate records with a list of environmental covariates, including air temperature and degree days, photoperiod, precipitation, latitude and elevation. An iterative subsampling process is then used to mitigate spatiotemporal sampling bias within the odonate dataset. Finally, we use path analysis to quantify the direct effects of degree days, photoperiod and precipitation on odonate emergence timing, while accounting for indirect effects of latitude, elevation and year. Path models showed that degree days, photoperiod and precipitation each have a significant influence on odonate emergence timing, but degree days have the largest overall effect. Notably, the effect that each covariate has on emergence timing varied among functional groups, with positive relationships observed for some group representatives and negative relationships observed for others. For instance, Calopteryx sp. emerged earlier as degree days increased, while Sympetrum sp. emerged later. Previous studies have linked odonate emergence timing to temperature, photoperiod or precipitation. By using natural history museum and community science data to simultaneously examine all three influences, we show that systems-level understanding of odonate phenology may now be possible.

Low-Complexity Chroma Subsampling Using Optimal Lines of Subproblems of Pixel Distortion

Video compression is an important procedure for digital applications. A commonly used format for inputs to video coders is YUV420, which is a subsampled result from YUV444, that is transformed and demosaicked from a color filter array (CFA) format. The process of chroma subsampling is a crucial step for the reconstructed image quality. A state-of-the-art method utilizes the solution of a prior method to compute the starting point, and devises a search method to improve the results. However, the search method does not consider the optimality condition of the problem, and the overall computational complexity is high because of the execution of the prior method for the starting point. In the proposed work, the cost function of the optimization problem is analyzed and decomposed into 4 subchannels based on the CFA format. The optimal line of each subterm is studied in 3-dimensional space. The problem is then mathematically reduced to a 3-subchannel problem. Based on the combinations of the optimal lines, a triangle search area is formed and proved to contain the optimal solution. And because the derived triangle search area is small on average, the search for the optimal value can be considerably fast. Experimental results demonstrate that the proposed approach can produce reconstructed images with qualities that are exactly the same as those generated using the state-of-the-art method. Relative to the state-of-the-art method, the proposed approach can reduce the time complexity by 84.08%–84.91%, and reduce the number of search points by 40.24%–85.25%, for the image datasets Kodak and IMAX with different demosaicking methods.

Predicting Earthquake-Induced Soil Liquefaction Based on Machine Learning Classifiers: A Comparative Multi-Dataset Study

The study and prediction of soil liquefaction is an important and complex issue in geotechnical earthquake engineering. This paper attempts to compare the predictability of soil liquefaction potential between several machine learning classification models, which includes some tree-based classifiers, multilayer perceptron (MLP) neural networks, Support Vector Machine (SVM), some state-of-the-art ensemble methods, K nearest neighbors method, classical Naive Bayesian classifier and logistic regression. Three data sets covering shear-wave velocity measurements, cone penetration testing (CPT), and real historic earthquakes cases are employed to train and evaluate the machine learning classifiers. In order to make the best use of large varieties of statistical and machine learning classification algorithms, it is necessary to give a comparative evaluation of the model performance before model selection and offer advice on a unified stable model for all sorts of collected datasets. In the comparative study, data preprocessing is first performed to ensure the dataset into all machine models is of good quality. Then all three datasets with different input features are passed into the machine learning algorithms to obtain its confusion matrix and some evaluation indices. Reliable assessment of model performance is done through a repeated sub-sampling process. Experimental results are also supported by ROC curves. The results of this study indicated that although most machine learning methods are able to represent the complex relationship between seismic proper seismic properties of soils and corresponding liquefaction potential, ensemble learning has achieved more successful results in all three datasets test and can be a fairly promising approach on prediction of earthquake-induced soil liquefaction.

Compressive Sensing Magnetic Resonance Image Reconstruction and Denoising using Convolutional Neural Network

Restoration of high-quality brain Magnetic Resonance Image (MRI) from the sparse under-sampled complex k-space signal is a widely studied ill-posed inverse transform problem. A deep learning-based data-adaptive and data-driven convolutional technique has been proposed for high-quality MRI recovery from its under-sampled complex domain k-space signal. The uniform subsampling process is very slow in phase-encoding to generate high-resolution images. The longer scan times degrade the perceptual image quality. Various factors contribute to image degradation during data acquisition such as the inception of body motion artifacts, the thermal energy effects of the body, and random noise artifacts due to voltage fluctuations. Keeping in view the patient’s critical condition and comfort, longer scan times are not preferred in practice. To reduce the image acquisition time, noise levels, and motion artifacts in the MR images, Compressive Sensing (CS) provides an accelerated way to reconstructs the high-quality MR image from very limited signal measurements acquired much below the Nyquist rate. However, such data acquisition strategies require advanced computer algorithms for the reconstruction of high-quality MRI from the undersampled MRI data. An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms. The performance of the proposed algorithm is measured by image quality checking tools such as normalized-MSE, PSNR, and SSIM.

Masked Face Recognition Algorithm for a Contactless Distribution Cabinet

A contactless delivery cabinet is an important courier self-pickup device, for the reason that COVID-19 can be transmitted by human contact. During the pandemic period of COVID-19, wearing a mask to take delivery is a common application scenario, which makes the study of masked face recognition algorithm greatly significant. A masked face recognition algorithm based on attention mechanism is proposed in this paper in order to improve the recognition rate of masked face images. First, the masked face image is separated by the local constrained dictionary learning method, and the face image part is separated. Then, the dilated convolution is used to reduce the resolution reduction in the subsampling process. Finally, according to the important feature information of the face image, the attention mechanism neural network is used to reduce the information loss in the subsampling process and improve the face recognition rate. In the experimental part, the RMFRD and SMFRD databases of Wuhan University were selected to compare the recognition rate. The experimental results show that the proposed algorithm has a better recognition rate.

Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam

Soil erosion induced by rainfall under prevailing conditions is a prominent problem to farmers in tropical sloping lands of Northeast Vietnam. This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). Model evaluation employed a historical dataset consisting of ten explanatory variables and soil erosion featured four different land use managements on hillslopes in Northwest Vietnam. All 236 data samples representing soil erosion/nonerosion events were randomly prepared (80% for training and 20% for testing) to assess the robustness of the five models. This subsampling process was repeatedly carried out by 30 rounds to eliminate the issue of randomness in data selection. Classification accuracy rate (CAR) and area under receiver operating characteristic (AUC) were used to evaluate performance of the five models. Significant difference between different algorithms was verified by the Wilcoxon test. Results of the study showed that RVM model achieves the best outcomes in both training (CAR = 92.22% and AUC = 0.98) and testing phases (CAR = 91.94% and AUC = 0.97). Four other learning algorithms also demonstrated good performance as indicated by their CAR values surpassing 80% and AUC values greater than 0.9. Hence, these results strongly confirm the efficacy of applying machine learning models for soil erosion prediction.

Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study

Electroencephalography (EEG) is used in the diagnosis and prognosis of mental disorders because it provides brain biomarkers. However, only highly trained doctors can interpret EEG signals due to its complexity. Machine learning has been successfully trained with EEG signals for classifying mental disorders, but a time consuming and disorder-dependant feature engineering (FE) and subsampling process is required over raw EEG data. Deep Learning (DL) is positioned as a prominent research field to process EEG data because (i) it features automated FE by taking advantage of raw EEG signals improving results and (ii) it can be trained over the vast amount of data generated by EEG. In this work, a systematic mapping study has been performed with 46 carefully selected primary studies. Our goals were (i) to provide a clear view of which are the most prominent study topics in diagnosis and prognosis of mental disorders by using EEG with DL, and (ii) to give some recommendations for future works. Some results are: epilepsy was the predominant mental disorder present in around half of the studies, convolutional neural networks also appear in approximate 50% of the works. The main conclusions are (i) processing EEG with DL to detect mental disorders is a promising research field and (ii) to objectively compare performance between studies: public datasets, intra-subject validation, and standard metrics should be used. Additionally, we suggest to pay more attention to ease the reproducibility, and to use (when possible) an available framework to explain the results of the created DL models.

Close Encounters of the Binary Kind: Signal Reconstruction Guarantees for Compressive Hadamard Sampling With Haar Wavelet Basis

We investigate the problems of 1-D and 2-D signal recovery from subsampled Hadamard measurements using Haar wavelet as a sparsity inducing prior. These problems are of interest in, e.g., computational imaging applications relying on optical multiplexing or single-pixel imaging. However, the realization of such modalities is often hindered by the coherence between the Hadamard and Haar bases. The variable and multilevel density sampling strategies solve this issue by adjusting the subsampling process to the local and multilevel coherence, respectively, between the two bases; hence enabling successful signal recovery. In this work, we compute an explicit sample-complexity bound for Hadamard-Haar systems as well as uniform and non-uniform recovery guarantees; a seemingly missing result in the related literature. We explore the faithfulness of the numerical simulations to the theoretical results and show in a practically relevant instance, e.g., single-pixel camera, that the target signal can be recovered from a few Hadamard measurements.

A State Optimization Model Based on Kalman Filtering and Robust Estimation Theory for Fusion of Multi-Source Information in Highly Non-linear Systems.

Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.

Differentially Private Event-Triggered Sampling

This paper describes a differentially private event-triggered sampling mechanism to select measurement samples from a data sequence whose dynamics can be modelled by a stochastic linear system. The mechanism produces subsequences that can be used to reestimate the original sequence relatively accurately and the differential privacy constraint guarantees that these subsequences are insensitive to certain variations in the input sequence. The subsampling process can be motivated by the presence of communication bandwidth constraints, but also provides an additional tool to explore achievable privacy-utility tradeoffs in privacy-preserving signal processing and control. Event-triggered sampling can offer benefits over periodic subsampling by attempting to select the most useful samples, but the fact that it leaks information when no sampling occurs must be carefully taken into account to meet the differential privacy requirement. We propose a design using a stochastic sampling threshold, leveraging the "sparse vector technique" from differential privacy to incur a privacy loss only when samples are actually released. This design includes a suboptimal but tractable recursive finite-dimensional estimator that can also be used to re-estimate the original sequence from the differentially private noisy subsequence.

How to control for confounds in decoding analyses of neuroimaging data

Over the past decade, multivariate “decoding analyses” have become a popular alternative to traditional mass-univariate analyses in neuroimaging research. However, a fundamental limitation of using decoding analyses is that it remains ambiguous which source of information drives decoding performance, which becomes problematic when the to-be-decoded variable is confounded by variables that are not of primary interest. In this study, we use a comprehensive set of simulations as well as analyses of empirical data to evaluate two methods that were previously proposed and used to control for confounding variables in decoding analyses: post hoc counterbalancing and confound regression. In our empirical analyses, we attempt to decode gender from structural MRI data while controlling for the confound “brain size”. We show that both methods introduce strong biases in decoding performance: post hoc counterbalancing leads to better performance than expected (i.e., positive bias), which we show in our simulations is due to the subsampling process that tends to remove samples that are hard to classify or would be wrongly classified; confound regression, on the other hand, leads to worse performance than expected (i.e., negative bias), even resulting in significant below chance performance in some realistic scenarios. In our simulations, we show that below chance accuracy can be predicted by the variance of the distribution of correlations between the features and the target. Importantly, we show that this negative bias disappears in both the empirical analyses and simulations when the confound regression procedure is performed in every fold of the cross-validation routine, yielding plausible (above chance) model performance. We conclude that, from the various methods tested, cross-validated confound regression is the only method that appears to appropriately control for confounds which thus can be used to gain more insight into the exact source(s) of information driving one's decoding analysis.

Informed sub-sampling MCMC: approximate Bayesian inference for large datasets

This paper introduces a framework for speeding up Bayesian inference conducted in presence of large datasets. We design a Markov chain whose transition kernel uses an unknown fraction of fixed size of the available data that is randomly refreshed throughout the algorithm. Inspired by the Approximate Bayesian Computation literature, the subsampling process is guided by the fidelity to the observed data, as measured by summary statistics. The resulting algorithm, Informed Sub-Sampling MCMC, is a generic and flexible approach which, contrary to existing scalable methodologies, preserves the simplicity of the Metropolis–Hastings algorithm. Even though exactness is lost, i.e the chain distribution approximates the posterior, we study and quantify theoretically this bias and show on a diverse set of examples that it yields excellent performances when the computational budget is limited. If available and cheap to compute, we show that setting the summary statistics as the maximum likelihood estimator is supported by theoretical arguments.

Plane detection of polyhedral cultural heritage monuments: The case of tower of winds in Athens

This study introduces an efficient and easy to implement plane detection approach towards the extraction of high-level information from 3D point clouds associated with polyhedral cultural heritage monuments. An adapted version of the randomized Hough transform (RHT) called “adaptive point randomized Hough transform” (APRHT) and a multiscale framework in terms of Level of Detail 1 (LoD 1) and LoD 2 are proposed. A dense image matching point cloud of an octagonal tower called Tower of Winds, which is situated on the northern foot of the Acropolis hill in Athens was used. A pre-process is carried out to extract points associated with the vertical structural elements. Then a plane detection process is performed in terms of LoD 1 to calculate the plane parameters (θ, φ and ρ) of each of the eight planar surfaces using a coarse form of the entire monument, that is, a sparse point cloud extracted via subsampling process. A mask of one representative detected planar surface is used to clip the initial point cloud with the initial point density. Then, a second plane detection process in terms of LoD 2 at the clipped point cloud is implemented to calculate the corresponding accurate plane parameters. The results are useful for cultural heritage preservation purposes and illustrate the robustness, efficiency and the rapidity of the proposed framework.

Characterization of Processing Artifacts in High Dynamic Range, Wide Color Gamut Video

With higher resolution, wider color gamut, and extended dynamic range, the new ultrahigh-definition (UHD) TV standards promise a more immersive television experience. Although very little content exploiting the full range of the technology is currently available, artifacts associated with the compression of high dynamic range content have already been identified and reported by engineers working in this area. They note that the chroma subsampling process has been shown to cause disturbing artifacts for regions of the image possessing a certain color and luminance. This paper quantifies the distortion and identifies regions of the extended color volume where artifacts associated with standard image processing techniques are more likely to occur. In doing so, it argues that these problems will become greater as more content exploiting the full potential of UHD becomes available, requiring additional care and processing in content production and delivery. This paper suggests ways of addressing these issues.

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter‐Based Feature Extraction

This study establishes an artificial intelligence (AI) model for detecting pothole on asphalt pavement surface. Image processing methods including Gaussian filter, steerable filter, and integral projection are utilized for extracting features from digital images. A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS‐SVM) and the artificial neural network (ANN). Experimental results obtained from a repeated subsampling process with 20 runs show that both LS‐SVM and ANN are capable methods for pothole detection with classification accuracy rate larger than 85%. In addition, the LS‐SVM has achieved the highest classification accuracy rate (roughly 89%) and the area under the curve (0.96). Accordingly, the proposed AI approach used with LS‐SVM can be very potential to assist transportation agencies and road inspectors in the task of pavement pothole detection.

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study

Assessment of the earthquake-induced liquefaction potential is a critical concern in design processes of construction projects. This study proposes a novel soft computing model with a hierarchical structure for evaluating earthquake-induced soil liquefaction. The new approach, named KFDA-LSSVM, combines kernel Fisher discriminant analysis (KFDA) with a least squares support vector machine (LSSVM). Based on the original data set, KFDA is used as a first-level analysis to construct an additional feature that best represents the data structure with consideration of different class labels. In the next level of analysis, based on such additional features and the original features, LSSVM generalizes a classification boundary that separates the learning space into two decision domains: “liquefaction” and “non-liquefaction.” Three data sets of liquefaction records have been used to train and verify the proposed method. The model performance is reliably assessed via a repeated sub-sampling process. Experimental results supported by the Wilcoxon signed-rank test demonstrate significant improvements of the hybrid framework over other benchmark approaches.

The Influence of Mark-Recapture Sampling Effort on Estimates of Rock Lobster Survival.

Five annual capture-mark-recapture surveys on Jasus edwardsii were used to evaluate the effect of sample size and fishing effort on the precision of estimated survival probability. Datasets of different numbers of individual lobsters (ranging from 200 to 1,000 lobsters) were created by random subsampling from each annual survey. This process of random subsampling was also used to create 12 datasets of different levels of effort based on three levels of the number of traps (15, 30 and 50 traps per day) and four levels of the number of sampling-days (2, 4, 6 and 7 days). The most parsimonious Cormack-Jolly-Seber (CJS) model for estimating survival probability shifted from a constant model towards sex-dependent models with increasing sample size and effort. A sample of 500 lobsters or 50 traps used on four consecutive sampling-days was required for obtaining precise survival estimations for males and females, separately. Reduced sampling effort of 30 traps over four sampling days was sufficient if a survival estimate for both sexes combined was sufficient for management of the fishery.

Hadley cell dynamics in Japanese Reanalysis-55 dataset: evaluation using other reanalysis datasets and global radiosonde network observations

Hadley circulation (HC) is a planetary scale circulation spanning one-third of the globe from tropics to the sub-tropics. Recent changes in HC width and its temporal variability is a topic of paramount interest because of the climate implications it carry alongside. The present study attempts to bring out the subtropical climate change indications in the comparatively new Japanese Re-analysis (JRA55) dataset by means of the mean meridional stream function (MSF). The observed features of HC in JRA55 are found to be reproduced in NCEP, MERRA and ECMWF datasets, with notable differences in the magnitudes of MSF. The calculated annual cycle of HC edges, center and total width from this dataset closely resembles the annual cycle of the respective parameters derived from the rest of the datasets, with very less inter-annual variability. For the first time, MSF estimated using four reanalysis datasets (JRA55, NCEP, MERRA and ECMWF datasets) are verified with observations from integrated global radiosonde archive datasets, using the process of subsampling. The features so estimated show a high degree of similarity amongst each other as well as with observations. The monthly trend in the total width of the HC is quantified to show a maximum of expansion during the month of July, which is significant at the 95 % confidence interval for all datasets. The present paper also discusses the presence of a ‘minor circulation’ feature in the northern hemisphere which is centered on 34°N during the June and July months, but not in all years. The significance of the present study lies in evaluating the relatively new JRA55 datasets with widely used reanalysis data sets and radiosonde observations and revelation of a minor circulation not discussed hitherto in the context of HC dynamics.