Increasing Data Density Research Articles

Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding

Aiming at the problem of sparse measurement points due to test conditions in engineering, a smoothing method based on zero-padding in the wavenumber domain is proposed to increase data density. Firstly, the principle of data extension and smoothing is introduced. The core idea of this principle is to extend the discrete data series by zero-padding in the wavenumber domain. The conversion between the spatial and wavenumber domains is achieved using the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). Then, two sets of two-dimensional discrete random data are extended and smoothed, respectively, and the results verify the effectiveness and feasibility of the algorithm. The method can effectively increase the density of test data in engineering tests, achieve smoothing and extend the application to areas related to data processing.

Trends among Flux, Luminosity, and Color Indices in High Redshift QSOs

The following study discusses the relationship of high redshift(z) Quasi Stellar Objects’(QSOs) flux, luminosity, and color indices. The paper analyzes observable trends among these 3 variables and uses them to understand the distribution of QSOs across space-time. While many such studies have been conducted in redshift ranges of 1<z<4, the following research or study has been done in the relatively unexplored date range of 2 <z<7. To answer this question, we used the Sloan Digital Sky Survey to obtain samples, curate the data, and analyze it using Python Data Science tools including Pandas and Matplotlib. The results suggest a general trend of increasing data density with the progression of distance and convergence of QSO Magnitude distributions about the median. This research provides insight into the evolution of QSOs over space-time and why they occurred.

An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study.

Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.

Programmable chips for 5G applications

Abstract5G deployment is facing significant challenges with the need for novel technologies able to increase data density in current networks. These include antennas and radio access network (RAN) hardware, which are driven by the growing bandwidth requirements in access networks.

Multichannel High‐Efficiency Metasurfaces Based on Tri‐Band Single‐Cell Meta‐Atoms with Independent Complex‐Amplitude Modulations

Achieving multitasking wavefront manipulations at multiple frequencies within a planar meta‐device is pivotal for increasing data density and functionality diversity yet still challenging in integrated devices. Considering both high efficiency and compactness, herein, a high‐efficiency reflective metasurface based on single‐cell meta‐atoms with independent complex‐amplitude modulations at three distinct frequencies under the circularly polarized incidence is demonstrated, which can facilitate the formation of three mutually orthogonal channels with three distinctive functionalities. As a proof‐of‐concept demonstration, a tri‐channel metasurface is designed and verified through both numerical simulation and experiment, where channels 1, 2, and 3 implement a dual‐vortex‐beam generator, a quad‐focus metalens, and a meta‐hologram at three different frequencies, respectively. The simulated results and the measured results have very good agreements, validating the proposed tri‐band single‐cell metasurface design strategy. The proposed method can open new opportunities to improve the information density and has promising prospects in image displays, communication systems, and optical storage.

Geostatistical interpolation of streambed hydrologic attributes with addition of left censored data and anisotropy

Spatial geostatistical interpolation of point measurements of streambed attributes in the hyporheic zone may be constrained by the streambed anisotropy, and data density and spatial distribution may significantly impact the results. Spatial clustering and low spatial data density can be caused by bedrock outcropping at the streambed limiting installation of in-stream piezometers. This study examines parameter error variability of the geostatistical interpolation using anisotropic interpolation methods and increasing the data density by adding left censored values (i.e., data below measurement limit) to locations where measurements were limited by exposed bedrock lining the streambed. The reduction in relative standard error of the interpolation was determined for the spatial distributions of streambed attributes including hydraulic conductivity, seepage flux, and mercury solute flux measured in two different years along a study reach in East Fork Poplar Creek, Tennessee, USA. Two methods to impute the left censored values were compared including the conventional half the detection limit substitution method, and the Stochastic Approximation of Expectation-Maximization (SAEM) algorithm, which both had comparable results. Imputing left censored data increased the data density to recommended ranges, reduced data clustering, increased the spatial dependence for some attributes, and reduced the standard error for each of the three attributes. For the reach considered herein, addition of the left censored values resulted in a larger error reduction than the consideration of anisotropy within the interpolation, which confirms the benefit of data addition to increase data density within data-limited river corridors.

DATA COMPRESSION IN BLACK-GRAY-WHITE BARCODING

Context. In this paper the authors propose a method for data compression to be used for presenting information in the form of 2D matrix barcode. The proposed method is based on both a structural-logical approach and using three colors in a barcode instead of two colors as it is in standard black-and-white barcodes. This approach allows to increase data density keeping the same area as bicolor barcodes take. In the paper authors present the data compression method and demonstrate the barcoding technology.Objective. The goal of the work is to develop a method of data barcoding that would allow to encode more information in the form of 2D matrix barcode.Method. The method of tricolor matrix barcoding with compression is proposed. The main idea of the method is to compress input textual information on the stage of alphanumeric sequence transformation into a set of barcode patterns, which will form a resulting barcode symbol. It is possible due to intermediate transformation of input characters from initial notation, which is determined by cardinality of an input alphabet, to a notation defined by cardinality of barcode patterns alphabet. Choice of the input alphabet influences overall compression, and it is an important step of the method to choose the initial alphabets for the textual information to be encoded. Use of three colors over standard two colors is also an important component for creating a barcode symbol with increased informational density. As ternary notation is used, the second transformation from the intermediate notation to the ternary one provides more compression. The proposed method allows to represent more textual data in a single barcode symbol than bicolor barcoding approaches do.Results. The method of tricolor matrix barcoding with compression has been developed and described. Authors provided an example of the method implementation on test data that had been barcoded using the method.Conclusions. The experiments conducted for this research have confirmed that the proposed method provides more informational density as compared with black-and-white matrix barcodes. The prospects for further research might include studying noise immunity issue in order to guarantee error-free scanning and increased reliability of the barcode, and extending the barcoding software to be used in any alphabet.

A Memristive Element Based on an Electrically Controlled Single‐Molecule Reaction

AbstractThe exponential proliferation of data during the information age has required the continuous exploration of novel storage paradigms, materials, and devices with increasing data density. As a step toward the ultimate limits in data density, the development of an electrically controllable single‐molecule memristive element is reported. In this device, digital information is encoded through switching between two isomer states by applying a voltage signal to the molecular junction, and the information is read out by monitoring the electrical conductance of each isomer. The two states are cycled using an electrically controllable local‐heating mechanism for the forward reaction and catalyzed by a single charge‐transfer process for the reverse switching. This single‐molecule device can be modulated in situ, is fully reversible, and does not display stochastic switching. The I–V curves of this single‐molecule system also exhibit memristive character. These features suggest a new approach for the development of molecular switching systems and storage‐class memories.

Electronic mechanism for resistance drift in phase-change memory materials: link to persistent photoconductivity

‘Phase-change’ memory materials, such as the canonical composition Ge2Sb2Te5, are being actively researched for non-volatile resistive random-access memory applications. In these devices, ultra-rapid reversible transformations between metastable highly electrically conducting (degenerate-semiconducting) crystalline and more electrically resistive (semiconducting) glassy phases are produced by the application of appropriate voltage pulses. Multilevel programming, wherein more than two metastable resistance states can be stored in the memory material as different proportions of partially glassy/crystalline regions, allows more than one bit to be stored per memory cell. However, this route to increasing data density, without recourse to device-size down-scaling, is threatened by the phenomenon of ‘resistance drift’, wherein the electrical resistance of the glassy phase slowly increases with time, following a weak power-law dependence, after being written with a voltage pulse. In this paper, we propose an intrinsic electronic mechanism for the resistance drift, particularly valid at ambient temperature and below, by identifying it with the phenomenon of persistent photoconductivity that is commonly observed in a wide range of disordered semiconductors. We develop a model for it in terms of the long-time, deep-trap release and subsequent recombination of charge carriers, akin to that which is believed to be responsible for the long-time photocurrent decay in amorphous semiconductors, such as hydrogenated amorphous silicon. In this case, the parameters controlling the resistance drift are the widths of the (localized) valence- and conduction-band tails in the vicinity of the bandgap. Hence, there is the potential for mitigating resistance drift in the amorphous state of phase-change memory materials by suitable material engineering (e.g. via compositional or fabricational control) to control the extent of band-tailing, thereby facilitating the future introduction of multistate memory.

A protocol for studying structural dynamics of proteins by quantitative crosslinking mass spectrometry and data-independent acquisition

Quantitative crosslinking mass spectrometry (QCLMS) reveals structural details of protein conformations in solution. QCLMS can benefit from data-independent acquisition (DIA), which maximises accuracy, reproducibility and throughput of the approach. This DIA-QCLMS protocol comprises of three main sections: sample preparation, spectral library generation and quantitation. The DIA-QCLMS workflow supports isotope-labelling as well as label-free quantitation strategies, uses xiSEARCH for crosslink identification, and xiDIA-Library to create a spectral library for a peptide-centric quantitative approach. We integrated Spectronaut, a leading quantitation software, to analyse DIA data. Spectronaut supports DIA-QCLMS data to quantify crosslinks. It can be used to reveal the structural dynamics of proteins and protein complexes, even against a complex background. In combination with photoactivatable crosslinkers (photo-DIA-QCLMS), the workflow can increase data density and better capture protein dynamics due to short reaction times. Additionally, this can reveal conformational changes caused by environmental influences that would otherwise affect crosslinking itself, such as changing pH conditions. SignificanceThis protocol is an detailed step-by-step description on how to implement our previously published DIA-QCLMS workflow (Müller et al. Mol Cell Proteomics. 2019 Apr;18(4):786–795). It includes sample preparation for QCLMS, Optimization of DIA strategies, implementation of the Spectronaut software and required python scripts and guideline on how to analyse quantitative crosslinking data. The DIA-QCLMS workflow widen the scope for a range of new crosslinking applications and this step-by-step protocol enhances the accessibility to a broad scientific user base.

Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction.

During image guided liver surgery, soft tissue deformation can cause considerable error when attempting to achieve accurate localization of the surgical anatomy through image-to-physical registration. In this paper, a linearized iterative boundary reconstruction technique is proposed to account for these deformations. The approach leverages a superposed formulation of boundary conditions to rapidly and accurately estimate the deformation applied to a preoperative model of the organ given sparse intraoperative data of surface and subsurface features. With this method, tracked intraoperative ultrasound (iUS) is investigated as a potential data source for augmenting registration accuracy beyond the capacity of conventional organ surface registration. In an expansive simulated dataset, features including vessel contours, vessel centerlines, and the posterior liver surface are extracted from iUS planes. Registration accuracy is compared across increasing data density to establish how iUS can be best employed to improve target registration error (TRE). From a baseline average TRE of 11.4 ± 2.2 mm using sparse surface data only, incorporating additional sparse features from three iUS planes improved average TRE to 6.4 ± 1.0 mm. Furthermore, increasing the sparse coverage to 16 tracked iUS planes improved average TRE to 3.9 ± 0.7 mm, exceeding the accuracy of registration based on complete surface data available with more cumbersome intraoperative CT without contrast. Additionally, the approach was applied to three clinical cases where on average error improved 67% over rigid registration and 56% over deformable surface registration when incorporating additional features from one independent tracked iUS plane.

Improving Coal Quality Estimations with Geostatistics and Geophysical Logs

In most modern coal mines, there are many coal quality parameters that are measured on samples taken from boreholes. These data are used to generate spatial models of the coal quality parameters, typically using inverse distance as an interpolation method. At the same time, downhole geophysical logging of numerous additional boreholes is used to measure various physical properties but no coal quality samples are taken. The work presented in this paper uses two of the most important coal quality variables-ash and volatile matter-and assesses the efficacy of using a number of geostatistical interpolation methods to improve the accuracy of the interpolated models, including the use of auxiliary variables from geophysical logs. A multivariate spatial statistical analysis of ash, volatile matter and several auxiliary variables is used to establish a co-regionalization model that relates all of the variables as manifestations of an underlying geological characteristic. A case study of a coal mine in Queensland, Australia, is used to compare the interpolation methods of inverse distance to ordinary kriging, universal kriging, co-kriging, regression kriging and kriging with an external drift. The relative merits of these six methods are compared using the mean error and the root mean square error as measures of bias and accuracy. The study demonstrates that there is significant opportunity to improve the estimations of coal quality when using kriging with an external drift. The results show that when using the depth of a sample as an external drift variable there is a significant improvement in the accuracy of estimation for volatile matter, and when using wireline density logs as the drift variable there is improvement in the estimation of the in situ ash. The economic benefit of these findings is that cheaper proxies for coal quality parameters can significantly increase data density and the quality of estimations.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

INTEGRATED APPROACH FOR INNOVATIVE MONITORING STRATEGIES OF RESERVOIRS AND LAKES

An innovative strategy significantly increasing data density by introducing a flexible, problem-orientated, and cost-effective water quality monitoring approach is presented. Most current monitoring strategies produce water quality data based on fixed stations conducted on fixed dates throughout a defined period of time and, thus, often give a biased and insufficient picture of the water quality. Establishing a refined picture of water quality while not increasing monitoring costs clearly needs a change in monitoring strategy. The complexity of social-economic needs, environmental aspects and evolving legislative guideline values makes the design of a suitable innovative strategy challenging. The combination of investigative and risk-based monitoring with real-time monitoring of proxies (e. g., electrical conductivity (EC)) is a vital asset within this here proposed innovative strategy. For the former, organic micropollutants (e. g., pesticides, pharmaceuticals) are suggested in this article to be a powerful tool for source apportionment as they allow to determine and quantify the cause and impact of water quality impairments. This strategy was tested in a field campaign in which an area of elevated EC was investigated at Lake Garda, Italy. A radio-controlled boat was used for EC mapping and sampling. As no chemical indicators for significant anthropogenic sources could be detected, the elevated EC could be assigned to natural sources.

Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research.

ObjectivesConditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the “conditional random slope” (CRS) model, based on a linear‐mixed effects model that incorporates a baseline‐time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM.METHODSIn two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years.RESULTSRTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation (r = 0.65–0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38–0.78). Estimates of the velocity‐stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics.CONCLUSIONThe CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses.

Genomic resources and their influence on the detection of the signal of positive selection in genome scans.

Genome scans represent powerful approaches to investigate the action of natural selection on the genetic variation of natural populations and to better understand local adaptation. This is very useful, for example, in the field of conservation biology and evolutionary biology. Thanks to Next Generation Sequencing, genomic resources are growing exponentially, improving genome scan analyses in non-model species. Thousands of SNPs called using Reduced Representation Sequencing are increasingly used in genome scans. Besides, genome sequences are also becoming increasingly available, allowing better processing of short-read data, offering physical localization of variants, and improving haplotype reconstruction and data imputation. Ultimately, genome sequences are also becoming the raw material for selection inferences. Here, we discuss how the increasing availability of such genomic resources, notably genome sequences, influences the detection of signals of selection. Mainly, increasing data density and having the information of physical linkage data expand genome scans by (i) improving the overall quality of the data, (ii) helping the reconstruction of demographic history for the population studied to decrease false-positive rates and (iii) improving the statistical power of methods to detect the signal of selection. Of particular importance, the availability of a high-quality reference genome can improve the detection of the signal of selection by (i) allowing matching the potential candidate loci to linked coding regions under selection, (ii) rapidly moving the investigation to the gene and function and (iii) ensuring that the highly variable regions of the genomes that include functional genes are also investigated. For all those reasons, using reference genomes in genome scan analyses is highly recommended.

<i>L</i>1<sub>0</sub>-CoPt Bit Patterned Media with Tilted Easy Axis for Ultrahigh Areal Density over 2.5 Tb/in<sup>2</sup>

Ultrahigh areal density is the key target of hard disk drive technology. Hence, writing field strength from head and switching field, Hsw, of media should be improved. In this work, we propose the one of alternative method to increase data density and reduce Hsw of the media by using tilted easy axis technology for bit patterned media (BPM) at areal density beyond 2.5 Tb/in2. Moreover, transition noise and superparamagnetic limit have been eliminated owing to characteristics of BPM. The effect of exchange coupled between adjacent bits, Adot, of tilted easy axis of BPM is analyzed by micromagnetic simulation software - the object oriented micromagnetic framework based on Landau-Lifshitz-Gilbert equation. The BPM with tilted easy axis perform clearly the reduction of Hsw below perpendicular media and available writing head field. The Adot of BPM has no effect on decreasing Hsw. Anisotropy and Zeeman energy density of BPM with tilted easy axis are higher and lower than perpendicular BPM, respectively. Thereby, BPM with tilted easy axis have high potentiality to improve Hsw of media at ultrahigh data density.

LLB simulation of the temperature dependent switching critical curve of a Stoner–Wohlfarth macrospin in the presence of a polarized current

An extension of Landau–Lifshitz–Bloch (LLB) equation is used to describe the behavior of single-domain particles under the influence of magnetic fields and polarized currents at elevated temperatures. We prove that using such a model, which takes into account the longitudinal magnetization relaxation, together with the consideration of the quartic crystalline anisotropy term, a number of recent experimental results can be explained concerning the free layer characteristic critical curves of spin valves commonly used in spin electronics. These results are of paramount importance for heat assisted magnetic recording (HAMR) processes in hard-drives or in new memory systems like the spin-transfer-torque magnetic random access memory (HA-STTMRAM) with the aim of increasing data density writing while avoiding superparamagnetic limit.

Investigation on Magnetic Properties of <i>L</i>1<sub>0</sub>-FePt/Fe Graded Media Multilayer

To improve writing capability of high magnetic anisotropy medium - L10-FePt, graded medium multilayer is one key candidate. Therefore, investigation of magnetic material properties of graded media multilayer is essential. In this work, we focus on magnetic properties of L10-FePt/Fe graded media multilayer such as hysteresis loop, magnetic energy and magnetic domain by the micromagnetic simulation the object oriented micromagnetic framework software based on the Landau - Lifshitz - Gilbert equation. The graded media multilayer can achieve reducing the switching field, Hsw, below available writing head field with high thermal stability. The anisotropy energy in graded media is higher than single layer and bilayer media that results in unstable magnetization in preferred direction. The proposed graded (f) and (g) structures are high grading magnetocrystalline anisotropy constant, Ku, that can reduce Hsw below single layer, bilayer and available writing head field. The hysteresis loop of low grading Ku structures has different steps; however, it disappear with high grading Ku structures of graded (e) - (g) structures. They have narrow loop compared with single layer. The results from this work lead to improve magnetic trilemma issue and increase data density.

Three-Dimensional Neurophenotyping of Adult Zebrafish Behavior

The use of adult zebrafish (Danio rerio) in neurobehavioral research is rapidly expanding. The present large-scale study applied the newest video-tracking and data-mining technologies to further examine zebrafish anxiety-like phenotypes. Here, we generated temporal and spatial three-dimensional (3D) reconstructions of zebrafish locomotion, globally assessed behavioral profiles evoked by several anxiogenic and anxiolytic manipulations, mapped individual endpoints to 3D reconstructions, and performed cluster analysis to reconfirm behavioral correlates of high- and low-anxiety states. The application of 3D swim path reconstructions consolidates behavioral data (while increasing data density) and provides a novel way to examine and represent zebrafish behavior. It also enables rapid optimization of video tracking settings to improve quantification of automated parameters, and suggests that spatiotemporal organization of zebrafish swimming activity can be affected by various experimental manipulations in a manner predicted by their anxiolytic or anxiogenic nature. Our approach markedly enhances the power of zebrafish behavioral analyses, providing innovative framework for high-throughput 3D phenotyping of adult zebrafish behavior.