Optimal Data Acquisition Research Articles

Exploring the application of synchronous analysis of physiological signals and facial expressions in emotion recognition

In today's increasingly frequent human-computer interaction, the accurate recognition and response of intelligent systems to human emotions has become the key to improving the user experience. This paper aims to explore the application of physiological signals and facial expression synchronization analysis in emotion recognition. This paper adopts the method of literature review, and summarizes the acquisition techniques of physiological signals and facial expressions, synchronous analysis methods, and their application cases in emotion recognition. At the same time, the limitations and challenges of the existing technology are discussed. The review shows that the synchronous analysis of physiological signals and facial expressions has important application value in the field of emotion recognition, especially in mental health monitoring, educational feedback, and customer service automation. However, the practical application of the technology still faces many challenges, including the real-time nature of data collection, the adaptability of individual differences, and other identification problems. Therefore, future research needs to further optimize data acquisition technology and develop more accurate and personalized analysis algorithms.

Automatic assignment of optimal data acquisition protocols for MRI examinations in Japanese

Automatic assignment of optimal data acquisition protocols for MRI examinations in Japanese

Combining Gyroscope and Electromyogram Analysis for the Detection of Resting Tremor and Muscle Activity in Parkinson's Disease

In this paper, we focus on designing and implementing a wearable device for detecting Parkinson's disease (PD) symptoms by analyzing resting tremors and abnormal muscle activity which contribute to PD combining gyroscope and electromyogram(EMG) analysis. Using advanced sensor technology, real-time data about movement and muscle activity is captured by the device. Here, we outline a hardware framework for optimizing data acquisition by identifying sensors to be used, their placement and integration strategies. In order to analyze data, machine learning algorithms are used to distinguish between tremors and muscle activity that are specific to Parkinson's disease and normal movements using classification technique. By enabling proactive healthcare interventions and customized patient management strategies, the proposed device represents a promising tool for the detection of early-stage Parkinson's disease.

Enhancing Subsurface Imaging: Optimization of Data Acquisition and Processing Parameters in Passive Roadside MASW Surveys

Enhancing Subsurface Imaging: Optimization of Data Acquisition and Processing Parameters in Passive Roadside MASW Surveys

Newcomer’s Guide into Optimal Data Acquisition for Electron Ptychography

Newcomer’s Guide into Optimal Data Acquisition for Electron Ptychography

Colloidal Probe Technique Optimization for Determination of Young's Modulus of Soft Adhesive Hydrogels.

Atomic force microscopy (AFM) is a valuable tool for determining the Young's modulus of a wide range of materials. However, it faces challenges, particularly when assessing adhesive materials like soft poly(N-isopropylacrylamide) (pNIPAM) hydrogels. This study focuses on enhancing the consistency and reliability of AFM measurements by functionally modifying AFM spherical tip cantilevers to address substrate adhesion issues with these hydrogels. Specifically, hydrophobic functionalization with 1H,1H,2H,2H-perfluorooctyltrichlorosilane (PFOCTS) emerged as the most effective approach, yielding consistent and reliable Young's modulus data across various pNIPAM hydrogel samples. This work highlights the importance of optimizing data acquisition in AFM, rather than relying on postprocessing, to reduce inconsistencies in Young's modulus assessment.

Optimizing data acquisition: a Bayesian approach for efficient machine learning model training

Acquiring a substantial number of data points for training accurate machine learning (ML) models is a big challenge in scientific fields where data collection is resource-intensive. Here, we propose a novel approach for constructing a minimal yet highly informative database for training ML models in complex multi-dimensional parameter spaces. To achieve this, we mimic the underlying relation between the output and input parameters using Gaussian process regression (GPR). Using a set of known data, GPR provides predictive means and standard deviation for the unknown data. Given the predicted standard deviation by GPR, we select data points using Bayesian optimization to obtain an efficient database for training ML models. We compare the performance of ML models trained on databases obtained through this method, with databases obtained using traditional approaches. Our results demonstrate that the ML models trained on the database obtained using Bayesian optimization approach consistently outperform the other two databases, achieving high accuracy with a significantly smaller number of data points. Our work contributes to the resource-efficient collection of data in high-dimensional complex parameter spaces, to achieve high precision ML predictions.

Rapid response to pressure variations in water distribution networks through machine learning-enhanced data acquisition

ABSTRACT This study investigates rapid dynamic pressure variations in water distribution networks due to critical incidents such as pipe bursts and valve operations. We developed and implemented a machine learning (ML)-based methodology that surpasses traditional slow cycles of pressure data acquisition, facilitating the efficient capture of transient phenomena. Employing the Orion ML library, which features advanced algorithms including long short-term memory dynamic threshold, autoencoder with regression, and time series anomaly detection using generative adversarial networks, we engineered a system that dynamically adjusts data acquisition frequencies to enhance the detection and analysis of anomalies indicative of system failures. The system's performance was extensively tested using a pilot-scale water distribution network across diverse operational conditions, yielding significant enhancements in detecting leaks, blockages, and other anomalies. The effectiveness of this approach was further confirmed in real-world settings, demonstrating its operational feasibility and potential for integration into existing water distribution infrastructures. By optimizing data acquisition based on learned data patterns and detected anomalies, our approach introduces a novel solution to the conventionally resource-intensive practice of high-frequency monitoring. This study underscores the critical role of advanced ML techniques in water network management and explores future possibilities for adaptive monitoring systems across various infrastructural applications.

Accessing self-diffusion on nanosecond time and nanometre length scales with minute kinetic resolution.

Neutron spectroscopy uniquely and non-destructively accesses diffusive dynamics in soft and biological matter, including for instance proteins in hydrated powders or in solution, and more generally dynamic properties of condensed matter on the molecular level. Given the limited neutron flux resulting in long counting times, it is important to optimize data acquisition for the specific question, in particular for time-resolved (kinetic) studies. The required acquisition time was recently significantly reduced by measurements of discrete energy transfers rather than quasi-continuous neutron scattering spectra on neutron backscattering spectrometers. Besides this reduction in acquisition times, smaller amounts of samples can be measured with better statistics, and most importantly, kinetically changing samples, such as aggregating or crystallizing samples, can be followed. However, given the small number of discrete energy transfers probed in this mode, established analysis frameworks for full spectra can break down. Presented here are new approaches to analyze measurements of diffusive dynamics recorded within fixed windows in energy transfer, and these are compared with the analysis of full spectra. The new approaches are tested by both modeled scattering functions and a comparative analysis of fixed energy window data and full spectra on well understood reference samples. This new approach can be employed successfully for kinetic studies of the dynamics focusing on the short-time apparent center-of-mass diffusion.

Optimizing Data Acquisition to Enhance Machine Learning Performance

In this paper, we study how to acquire labeled data points from a large data pool to enrich a training set for enhancing supervised machine learning (ML) performance. The state-of-the-art solution is the clustering-based training set selection (CTS) algorithm, which initially clusters the data points in a data pool and subsequently selects new data points from clusters. The efficiency of CTS is constrained by its frequent retraining of the target ML model, and the effectiveness is limited by the selection criteria, which represent the state of data points within each cluster and impose a restriction of selecting only one cluster in each iteration. To overcome these limitations, we propose a new algorithm, called CTS with incremental estimation of adaptive score (IAS). IAS employs online learning, enabling incremental model updates by using new data, and eliminating the need to fully retrain the target model, and hence improves the efficiency. To enhance the effectiveness of IAS, we introduce adaptive score estimation, which serves as novel selection criteria to identify clusters and select new data points by balancing trade-offs between exploitation and exploration during data acquisition. To further enhance the effectiveness of IAS, we introduce a new adaptive mini-batch selection method that, in each iteration, selects data points from multiple clusters rather than a single cluster, hence eliminating the potential bias due to using only one cluster. By integrating this method into the IAS algorithm, we propose a novel algorithm termed IAS with adaptive mini-batch selection (IAS-AMS). Experimental results highlight the superior effectiveness of IAS-AMS, with IAS also outperforming other competing algorithms. In terms of efficiency, IAS takes the lead, while the efficiency of IAS-AMS is on par with that of the existing CTS algorithm.

Optimal data acquisition in tomography.

In tomography, three-dimensional images of a medium are reconstructed from a set of two-dimensional projections. Each projection is the result of a measurement made by the scanner via radiating some form of energy and collecting the scattered field after interacting with the medium. The information content of these measurements is not equal, and one projection can be more informative than others. By choosing the most informative measurement at every step of scanning, an optimal tomography system can maximize the speed of data acquisition and temporal resolution of acquired images, reducing the operation cost and exposure to possible harmful radiations. The aim of this paper is to introduce mathematical algorithms that can be used to design measurements with optimal information content when imaging static or dynamically evolving objects.

Application of Electromagnetic Methods for Reservoir Monitoring with Emphasis on Carbon Capture, Utilization, and Storage

The Controlled-Source ElectroMagnetic (CSEM) method provides crucial information about reservoir fluids and their spatial distribution. Carbon dioxide (CO2) storage, enhanced oil recovery (EOR), geothermal exploration, and lithium exploration are ideal applications for the CSEM method. The versatility of CSEM permits its customization to specific reservoir objectives by selecting the appropriate components of a multi-component system. To effectively tailor the CSEM approach, it is essential to determine whether the primary target reservoir is resistive or conductive. This task is relatively straightforward in CO2 monitoring, where the injected fluid is resistive. However, for scenarios involving brine-saturated (water-wet) or oil-wet (carbon capture, utilization, and storage—CCUS) reservoirs, consideration must also be given to conductive reservoir components. The optimization of data acquisition before the survey involves analyzing target parameters and the sensitivity of multi-component CSEM. This optimization process typically includes on-site noise measurements and 3D anisotropic modeling. Based on our experience, subsequent surveys tend to proceed smoothly, yielding robust measurements that align with scientific objectives. Other critical aspects to be considered are using magnetotelluric (MT) measurements to define the overall background resistivities and integrating real-time quality assurance during data acquisition with 3D modeling. This integration allows the fine tuning of acquisition parameters such as acquisition time and necessary repeats. As a result, data can be examined in real-time to assess subsurface information content while the acquisition is ongoing. Consequently, high-quality data sets are usually obtained for subsequent processing and initial interpretation with minimal user intervention. The implementation of sensitivity analysis during the inversion process plays a pivotal role in ensuring that the acquired data accurately respond to the target reservoirs’ expected depth range. To elucidate these concepts, we present an illustrative example from a CO2 storage site in North Dakota, USA, wherein the long-offset transient electromagnetic method (LOTEM), a variation of the CSEM method, and the MT method were utilized. This example showcases how surface measurements attain appropriately upscaled log-scale sensitivity. Furthermore, the sensitivity of the CSEM and MT methods was examined in other case histories, where the target reservoirs exhibited conductive properties, such as those encountered in enhanced oil recovery (EOR), geothermal, and lithium exploration applications. The same equipment specifications were utilized for CSEM and MT surveys across all case studies.

Optimal timing of satellite data acquisition for estimating and modeling soil salinity in cotton fields of the Mingbulak District, Uzbekistan

Agriculture is frequently hampered by soil salinity, which has a negative impact on crop growth and yield. This study aims to identify the optimal timing of satellite data acquisition to predict soil salinity levels indirectly using satellite images in cotton growth fields as a basis. Data was collected in the Mingbulak district of Uzbekistan, where soil electrical conductivity (EC) was measured in a laboratory using soil samples collected from various fields with similar management practices. In this research, we present a linear regression model that uses satellite data and the Normalized Difference Salinity Index (NDSI) to forecast soil salinity levels indirectly. The results of the linear regression analysis showed a positive correlation between the soil electrical conductivity values and the NDSI values for each month, with August having the highest correlation (R2 = 0.70). The study found that the cotton growth stages and the process of soil salinity formation in the study area were the main factors affecting the correlation between electrical conductivity and NDSI. The model developed in this study has R2 value of 0.70. This suggests a moderate to strong relationship between the two variables, which is promising for the indirect assessment of soil salinity using the NDSI index. The study discovered a positive relationship between soil electrical conductivity and NDSI values, which were highest in pre-flowering and flowering stages of cotton. Our findings show that satellite-based estimation and modeling with NDSI can be used to indirectly assess cotton field soil salinity, especially during the pre-flowering and flowering stages. This study contributes to the development of optimal satellite data acquisition timing, which can improve soil salinity predictions and agricultural productivity.

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms

The data for many machine learning tasks are owned by individuals who are typically concerned about privacy. Here, the authors study the optimal design of a data acquisition mechanism aimed at learning the mean of a population. This data acquisition scheme includes the design of a payment rule to compensate users for their privacy loss. It also involves selecting an estimator that minimizes estimation error while simultaneously providing privacy guarantees to users in line with their privacy preferences. The authors formulate this problem as a Bayesian mechanism design problem and propose approximately optimal data acquisition mechanisms.

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS.

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.

Optimizing data acquisition for MLDS: when is it valid to take a short-cut?

Optimizing data acquisition for MLDS: when is it valid to take a short-cut?

An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging With Distributed Pressure and Temperature Measurements

Summary Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emissions. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning–based workflow that can efficiently visualize CO2 plume in near real time. Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure, and temperature, as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: First, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower-dimensional latent variables that compress high-dimensional output images. The “onset time” is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning–based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently, the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real time. The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning–based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottomhole pressure (BHP) data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning–based framework. The novelty of this work is the development and application of a unique and efficient deep learning–based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.

Use of scanners for colorimetric analysis of microfluidic paper-based analytical devices (µPADs): A practical guide

Office style scanners are often used by researchers for the colorimetric detection in the development of microfluidic paper-based analytical devices (µPADs). However, little attention has been paid to nuances in calibration curve responses caused by varying settings of a single image-capturing device. Hence, in this study, the effect of parameters, such as the scanner operation conditions, the method of data analysis used (e.g., choice of colour space system, suitable channel), and the influence of the physical characteristics of the µPAD (e.g., template colour, type of lamination pouches used) on the colorimetric detection were evaluated for the first time.Combinations of the scanning parameters were thoroughly examined and their impact on the quality of a mock calibration curve assessed. The most sensitive calibration curve was achieved only by the selection of the optimal scanning conditions combined with an appropriate choice of colorimetric system. A flatbed scanner, used in this study, showed greater variation of signal with varying scanning setup, whereas a portable scanner provided nearly identical responses under diverse scanning conditions. Gamma correction, scanning resolution, region of interest (ROI) surrounding colour (i.e., template colour), bit-depth, image file type, and the number of analysed pixels were found not to markedly affect the dynamics of the calibration curve when colour in the ROI is homogeneous. Parameters that had an effect on the colorimetric detection were the scanner settings, colour of the blank, image acquisition mode (colour vs greyscale), and µPAD covering (e.g., lamination). Based on these findings, generic guidelines for optimal data acquisition and µPAD manipulation with respect to µPAD calibration have been formulated.

Optimizing data acquisition in undersampled magnetic resonance imaging (MRI) using two alternative forced choice (2-AFC) and search tasks.

Undersampling in the frequency domain (k-space) in MRI accelerates the data acquisition. Typically, a fraction of the low frequencies is fully collected and the rest are equally undersampled. We used a fixed 1D undersampling factor of 5x where 20% of the k-space lines are collected but varied the fraction of the low k-space frequencies that are fully sampled. We used a range of fully acquired low k-space frequencies from 0% where the primary artifact is aliasing to 20% where the primary artifact is blurring in the undersampling direction. Small lesions were placed in the coil k-space data for fluid-attenuated inversion recovery (FLAIR) brain images from the fastMRI database. The images were reconstructed using a multi-coil SENSE reconstruction with no regularization. We conducted a human observer two-alternative forced choice (2-AFC) study with a signal known exactly and a search task with variable backgrounds for each of the acquisitions. We found that for the 2-AFC task, the average human observer did better with more of the low frequencies being fully sampled. For the search task, we found that after an initial improvement from having none of the low frequencies fully sampled to just 2.5%, the performance remained fairly constant. We found that the performance in the two tasks had a different relationship to the acquired data. We also found that the search task was more consistent with common practice in MRI where a range of frequencies between 5% and 10% of the low frequencies are fully sampled.

Optimization of hydrogen-deuterium exchange ensemble reweighting (HDXer) to aid the biophysical characterization of proteins.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments provide unique insights into native protein structure and dynamics in solution but have limited spatial resolution. Molecular dynamics (MD) simulations, in contrast, allow for the exploration of protein conformational landscapes with an atomistic level of resolution. However, forcefield inaccuracies, crystallographic biases, and timescale limitations may compromise the validity of the final structural ensemble. Hydrogen Deuterium eXchange Ensemble Reweighting (HDXer) is a maximum entropy approach that was recently developed to (1) quantitatively predict hydrogen-deuterium exchange from a modeled conformational ensemble and (2) refine the candidate ensemble to best fit an experimental HDX-MS dataset. Here, we compare how various computational and experimental parameters impact the performance of HDXer, using extracellular regulated kinase 2 (ERK2) and hen egg white lysozyme (HEWL) as model proteins. Conventional molecular dynamics simulations of ERK2 and HEWL were performed in triplicate using the CHARMM36m, AMBER FF19SB, and Drude forcefields. After predicting the HDX rates of each candidate ensemble, HDXer was used to fit the simulated structures to the corresponding HDX-MS datasets. To assess the influence of simulation length, truncated and strided copies of the full-length simulations were generated. Other factors, including peptide charge state selection, ion mobility mass spectrometry, and independent HDX-MS data acquisitions, were also tested. We find that while some variables (e.g., high vs. low peptide charge states) do not have a significant effect, other parameters (e.g., early vs. late timepoint selection) directly correlate with the disagreement between calculated and experimental deuterium uptake. The results from this benchmarking inform the development of a recommended HDXer workflow and best practices for optimal data acquisition.