Differences In Sampling Rates Research Articles

Validity of GPS data in driving cycles

AbstractThere is continuous research into driving cycles (DCs) as researchers across the globe seek to define driving characteristics, energy consumption, and emissions in a local context. For decades, data collection for the development of DCs has been conducted in three ways: chase car, instrumented vehicle, or a combination of both. Many studies have moved on to cheap and easily available global positioning system (GPS) technology, while others record vehicle data directly through the on‐board diagnostics (OBD) port. However, there are major limitations to GPS data collection such as frequent inaccuracies and loss of coverage in urban environments. For this reason, both OBD and GPS vehicle speed data have been collected. Then, the recorded data has been analysed to capture any differences in sampling rates and dropping data. Finally, basic DCs were created from smoothed GPS and OBD data and compared. DCs were developed with a microtrip‐based method, and a relative error term was used to compare candidate DCs to the collected data. DCs were compared based on kinematic characteristic parameters that are most used in the field. The results of this study could be used to assess the validity of GPS‐based DCs compared to OBD cycles using low‐cost devices.

Revealing Inconsistencies in ROTI Index Using Multi‐GNSS Constellation Measurements: Impact of Sampling Rates and Time Window

AbstractUnderstanding ionospheric irregularities and their dynamics is crucial, with the rate of change of the total electron content index (ROTI) serving as a significant metric for this purpose. However, inconsistencies in ROTI magnitudes have been noted when sampled at one‐second interval across various Global Navigation Satellite System (GNSS) receivers. This study presents a detailed statistical analysis to investigate inconsistencies in ROTI using multi‐GNSS observations in conjunction with four distinct GNSS receiver types. Various factors affecting the ROTI inconsistencies among receivers were examined, including differences in sampling rates (1, 5, 15, and 30 s), and varying time window widths (1, 2, and 5 min). By examination of data obtained from GNSS receivers with zero or short baselines on a global scale, the analysis uncovered substantial variations in multi‐GNSS ROTI values across the four assessed receiver types. The main findings suggest that reducing the sampling rate reduces the inconsistencies in the magnitude of ROTI, particularly at lower sampling rates. This reduction can be attributed to the exclusion of high‐frequency components in the ROTI spectrum. Interestingly, the width of the time window is found to have minimal impact on the ROTI magnitude. The study also shows a direct correlation between a larger magnitude of ROTI and the increased noise in the signals tracked by receivers. These results emphasize the importance of considering sampling rates and GNSS receiver types when utilizing ROTI to investigate ionospheric irregularities.

Correlation between surface air temperature and lightning events in Colombia

This paper presents findings on the correlation between surface air temperature and lightning activity in Colombia. The air temperature data spans several decades, while the lightning data covers 17 years. The temperature records come from nine cities, and the lightning data covers about 50% of the territory. Despite differences in sampling rates and dataset sizes, the findings show a positive correlation between surface air temperature and lightning activity, suggesting a possible relationship between warmer conditions and increased lightning activity.

Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method.

Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite

Spliced optical satellite cameras suffering from low stitching accuracy are influenced by various factors which can greatly restrict their applications. Most previous studies have focused on the geometric precision of stitched images, which is influenced by the stitching consistency and the relationships between different inner CCD (Charge-Coupled Device) images. Therefore, the stitching accuracy is of great significance in multiple CCD image production. Traditionally, the line-time normalization method has been applied for inner CCD image stitching based on designed line-times with the assumption of uniform sampling during imaging. However, the misalignment of the designed and actual line-time affected by various factors can lead to image distortion. Therefore, this paper investigates the performance of different normalization methods to produce stitched images with higher geometric performance using the actual line-time. First, the geometric distortions caused by misalignments between the designed and actual line-time are analyzed to show the differences in sampling rate and step-points. To overcome the distortions introduced by the fitting error of the designed line-time, three fine normalization methods based on the actual line-time, respectively called scene-based, block-based, and line-based line-time normalization methods, are introduced and compared with the traditional method. The scene-based and block-based line-time normalization methods fit the actual line-time section-by-section, while the line-based method builds the relationships between adjacent inner CCD images line-by-line. Images obtained from the Sustainable Development Goals Satellite 1 (SDGSAT-1) satellite are used for verification of different methods. The performance of the designed line-time normalization method and three fine actual line-time normalization methods is compared; the stitching accuracy can reach about 0.8, 0.56, 0.5, and 0.45 pixels, respectively. The time consumption of these four compared methods is about 5.5 s, 4.9 s, 5.4 s, and 58.9 s, respectively. Therefore, the block-based actual line-time normalization method utilized in practice can provide a good balance between running time and accuracy. In the future, we intend to find a new way to improve the efficiency of line-based line-time normalization methods to produce stitched images with higher geometric consistency and accuracy.

Travel-mode classification based on GPS-trajectory data and geographic information using an XGBoost classifier

Massive Global Positioning System (GPS) trajectory datasets are being produced owing to the advances in mobile sensors, the Internet, and GPS devices. Accurately inferring travel modes from GPS trajectory data can be helpful in transportation planning and modeling, infrastructure design, etc. However, adverse factors such as data noise, differences in sampling rate, and inadequate features have a negative impact on the results of travel mode classification. In this paper, to address such issues, we first propose a preprocessing workflow, which includes data cleaning, segmentation, and resampling, to preprocess raw trajectories. Then, we add new features related to the road and bus stop information for travel mode classification using an XGBoost (eXtreme Gradient Boosting) classifier, along with various basic features of the trajectories. We conducted a set of experiments on the GeoLife dataset using a group of state-of-the-art methods. The results showed that the proposed methods can improve the classification accuracy by using all the classifiers we compared and the classification accuracy using the XGBoost classifier can reach a maximum of 90.41%.

Performance of the organic-diffusive gradients in thin-films passive sampler for measurement of target and suspect wastewater contaminants

Although passive sampling is widely accepted as an excellent tool for environmental monitoring, their integration with suspect or non-targeted screening by high-resolution mass spectrometry has been limited. This study describes the application of the organic-diffusive gradients in thin-films (o-DGT) passive sampler as a tool for accurate measurement of both targeted and suspect polar organic contaminants (primarily pharmaceuticals) in wastewater. First, performance of o-DGT was assessed alongside the polar organic chemical integrative sampler (POCIS) and active sampling at two wastewater treatment facilities using targeted analyses. Overall, water concentrations measured by o-DGT, POCIS, and 24-hr integrative active samples were in good agreement with each other. There were exceptions, including a systematic difference between o-DGT and POCIS at certain sites that we propose was a result of site-specific conditions and a difference in sampling rates between the two techniques. The second component of this work involved suspect screening of the o-DGT extracts using high-resolution, high mass accuracy quadrupole time-of-flight mass spectrometry (QTOF). Lamotrigine, venlafaxine, and des-methylvenlafaxine were three suspect compounds identified and selected as proof-of-concept case studies to determine the feasibility and accuracy of o-DGT for estimating water concentrations based upon predicted sampling rates using a previously validated o-DGT diffusion model. Semi-quantification of the suspect compounds was conducting using an average surrogate response factor based on the suite of compounds measured by the targeted analyses. This, combined with the modelled sampling rates provided time-weighted average wastewater concentrations of the identified suspects within a factor of 2 of the true value, confirmed by isotope dilution with mass labelled internal surrogates. To the knowledge of the authors, this work is the first to demonstrate the utility of the o-DGT passive sampler as a potential environmental screening tool that can be integrated into the rapidly advancing field of non-targeted high resolution mass spectrometry.

Utilization of Smartphone Data for Driving Cycle Synthesis Based on Electric Two-Wheelers in Shanghai

Driving cycles play an important role in analysis, design, and optimization of vehicles. The use of smartphones as data acquisition devices leads to easier access to a large test group, but results might be less accurate than with professional test devices. In this work, a systematic comparison of smartphone-based data acquisition and data recorded by a professional device is performed regarding the simplicity of data acquisition and the accuracy of energy estimation. Challenges and opportunities of smartphone-based driving cycle synthesis are described. A frequency analysis presents differences in sampling rates and energy characteristics. It is shown that a sampling rate of 1Hz is sufficient to cover relevant dynamics for low-power electric two-wheelers (E2Ws). The driving behavior of E2Ws in Shanghai is used as a case study and analyzed by means of a data collection campaign. Based on a localized E2W driving cycle in Shanghai, a data collection strategy for improving driving cycles is proposed. In particular, the inclusion of data collection by smartphone and an offline data fusion with IMU data to improve energy predictions are presented. The derived method can help manufacturers to estimate energy consumption and improve the design process of localized driving cycles.

Less is more: On-board lossy compression of accelerometer data increases biologging capacity.

GPS‐tracking devices have been used in combination with a wide range of additional sensors to study animal behaviour, physiology and interaction with their environment. Tri‐axial accelerometers allow researchers to remotely infer the behaviour of individuals, at all places and times. Collection of accelerometer data is relatively cheap in terms of energy usage, but the amount of raw data collected generally requires much storage space and is particularly demanding in terms of energy needed for data transmission.Here, we propose compressing the raw accelerometer (ACC) data into summary statistics within the tracking device (before transmission) to reduce data size, as a means to overcome limitations in storage and energy capacity.We explored this type of lossy data compression in the accelerometer data of tagged Bewick's swans Cygnus columbianus bewickii collected in spring 2017. Using software settings in which bouts of 2 s of both raw ACC data and summary statistics were collected in parallel but with different bout intervals to keep total data size comparable, we created the opportunity for a direct comparison of time budgets derived by the two data collection methods.We found that the data compression in our case yielded a six times reduction in data size per bout, and concurrent, similar decreases in storage and energy use of the device. We show that with the same accuracy of the behavioural classification, the freed memory and energy of the device can be used to increase the monitoring effort, resulting in a more detailed representation of the individuals’ time budget. Rare and/or short behaviours, such as daily roost flights, were picked up significantly more when collecting summary statistics instead of raw ACC data (but note differences in sampling rate). Such level of detail can be of essential importance, for instance to make a reliable estimate of the energy budgets of individuals.In conclusion, we argue that this type of lossy data compression can be a well‐considered choice in study situations where limitations in energy and storage space of the device pose a problem. Ultimately, these developments can allow for long‐term and nearly continuous remote monitoring of the behaviour of free‐ranging animals.

Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children

Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mgs), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15 s and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n = 29) wore a hip- and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15 s were computed for each axis and as vector magnitude. Main results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15 s (mean bias 4–43 counts/15 s across axes) for both the hip- and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r = 0.46–0.63; wrist: r = 0.26–0.55) and were greater for hip- versus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4%–99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate.

Temporal correlations in human locomotion: Recommendations for sampling rate and foot strike detection

Human stride intervals exhibit statistically persistent fluctuations over a wide range of timescales. Numerous studies have examined stride interval correlations during overground and treadmill walking. However, the practices and procedures for pre-preprocessing gait data vary considerably. This paper examined how differences in sampling rate and gait event detection affected stride interval correlations measured using detrended fluctuation analysis. Sampling rates ranged from 30–360 Hz. Three common kinematic event detection algorithms were compared to a force-based reference. Lower sampling frequencies failed to capture subtle variations in gait cycle timing, which reduced the strength of stride interval correlations. The kinematic event detection algorithms produced slightly higher, but comparable, estimates of stride interval correlations relative to the force-based reference. Guidelines and considerations for assessing stride interval correlations and the implications of these findings are discussed.

Challenges of ERAU’s First Suborbital Flight Aboard Blue Origin’s New Shepard M7 for the Cell Research Experiment In Microgravity (CRExIM)

Abstract Cell Research Experiment In Microgravity (CRExIM) was launched aboard Blue Origin’s New Shepard suborbital vehicle on Tuesday, December 12, 2017, from the West Texas Launch Site in Van Horn, Texas. One of the aims of this science experiment was to assess the effects of microgravity on murine T-cells during suborbital flight. These cells were placed in a NanoLab with a data logger that sensed the acceleration, temperature, and relative humidity during preflight, flight, and postflight operations. Some discrepancies in sensor measurement were noticed, and these errors were attributed partly to the difference in sampling rates and partly to the different locations of the sensors, which made it difficult to obtain highly accurate measurements of the accelerations and to correlate both sets of data. This paper discusses the setbacks and lessons learned, which made our team find new alternatives while meeting all milestones as mandated by NanoRacks and Blue Origin. This manuscript highlights these alternatives that led to the success of the mission and gives recommendations that will enable customers to alleviate some of these challenges in future flights.

Co-ordinated detection of microparticles using tunable resistive pulse sensing and fluorescence spectroscopy.

Tunable resistive pulse sensing (TRPS) has emerged as a useful tool for particle-by-particle detection and analysis of microparticles and nanoparticles as they pass through a pore in a thin stretchable membrane. We have adapted a TRPS device in order to conduct simultaneous optical measurements of particles passing through the pore. High-resolution fluorescence emission spectra have been recorded for individual 1.9 μm diameter particles at a sampling period of 4.3 ms. These spectra are time-correlated with RPS pulses in a current trace sampled every 20 μs. The flow rate through the pore, controlled by altering the hydrostatic pressure, determines the rate of particle detection. At pressures below 1 kPa, more than 90% of fluorescence and RPS events were matching. At higher pressures, some peaks were missed by the fluorescence technique due to the difference in sampling rates. This technique enhances the particle-by-particle specificity of conventional RPS measurements and could be useful for a range of particle characterization and bioanalysis applications.

Assessment of central haemomodynamics from a brachial cuff in a community setting.

BackgroundLarge artery stiffening and wave reflections are independent predictors of adverse events. To date, their assessment has been limited to specialised techniques and settings. A new, more practical method allowing assessment of central blood pressure from waveforms recorded using a conventional automated oscillometric monitor has recently been validated in laboratory settings. However, the feasibility of this method in a community based setting has not been assessed.MethodsOne-off peripheral and central haemodynamic (systolic and diastolic blood pressure (BP) and pulse pressure) and wave reflection parameters (augmentation pressure (AP) and index, AIx) were obtained from 1,903 volunteers in an Austrian community setting using a transfer-function like method (ARCSolver algorithm) and from waveforms recorded with a regular oscillometric cuff. We assessed these parameters for known differences and associations according to gender and age deciles from <30 years to >80 years in the whole population and a subset with a systolic BP < 140 mmHg.ResultsWe obtained 1,793 measures of peripheral and central BP, PP and augmentation parameters. Age and gender associations with central haemodynamic and augmentation parameters reflected those previously established from reference standard non-invasive techniques under specialised settings. Findings were the same for patients with a systolic BP below 140 mmHg (i.e. normotensive). Lower values for AIx in the current study are possibly due to differences in sampling rates, detection frequency and/or averaging procedures and to lower numbers of volunteers in younger age groups.ConclusionA novel transfer-function like algorithm, using brachial cuff-based waveform recordings, provides robust and feasible estimates of central systolic pressure and augmentation in community-based settings.

A novel approach for monitoring of cyanobacterial toxins: development and evaluation of the passive sampler for microcystins

We have investigated the ability of an integrative sampler for polar organic chemicals to sequestrate a group of common and highly hazardous cyanobacterial toxins-microcystins. In a pilot experiment, commercially available passive samplers were shown to effectively accumulate microcystins after 7 days' exposure in the field. To find the most efficient configuration for sequestration of microcystins, four different porous membranes (polycarbonate, polyester, polyethersulfone and nylon) and two sorbents (Oasis HLB and Bondesil-LMS) were evaluated in the laboratory experiments, where samplers of different configuration were exposed to microcystins (microcystin-RR and microcystin-LR) for 14 days under steady conditions. We observed differences in sampling rates and amounts of accumulated microcystins depending on the sampler configurations. The samplers constructed with the polycarbonate membrane and Oasis HLB sorbent (2.75 mg/cm2) provided the highest sampling rates (0.022 L/day for microcystin-RR and 0.017 L/day for microcystin-LR). To the best of our knowledge, the present study is the first reporting application of passive samplers for microcystins, and our results demonstrate the suitability of this tool for monitoring cyanotoxins in water.

Investigation of hydrophobic contaminants in an urban slough system using passive sampling – insights from sampling rate calculations

Semipermeable membrane devices (SPMDs) were deployed in the Columbia Slough, near Portland, Oregon, on three separate occasions to measure the spatial and seasonal distribution of dissolved polycyclic aromatic hydrocarbons (PAHs) and organochlorine compounds (OCs) in the slough. Concentrations of PAHs and OCs in SPMDs showed spatial and seasonal differences among sites and indicated that unusually high flows in the spring of 2006 diluted the concentrations of many of the target contaminants. However, the same PAHs - pyrene, fluoranthene, and the alkylated homologues of phenanthrene, anthracene, and fluorene - and OCs - polychlorinated biphenyls, pentachloroanisole, chlorpyrifos, dieldrin, and the metabolites of dichlorodiphenyltrichloroethane (DDT) - predominated throughout the system during all three deployment periods. The data suggest that storm washoff may be a predominant source of PAHs in the slough but that OCs are ubiquitous, entering the slough by a variety of pathways. Comparison of SPMDs deployed on the stream bed with SPMDs deployed in the overlying water column suggests that even for the very hydrophobic compounds investigated, bed sediments may not be a predominant source in this system. Perdeuterated phenanthrene (phenanthrene-d (10)). spiked at a rate of 2 microg per SPMD, was shown to be a reliable performance reference compound (PRC) under the conditions of these deployments. Post-deployment concentrations of the PRC revealed differences in sampling conditions among sites and between seasons, but indicate that for SPMDs deployed throughout the main slough channel, differences in sampling rates were small enough to make site-to-site comparisons of SPMD concentrations straightforward.

Using Passive Air Samplers To Assess Urban−Rural Trends for Persistent Organic Pollutants and Polycyclic Aromatic Hydrocarbons. 2. Seasonal Trends for PAHs, PCBs, and Organochlorine Pesticides

This is the second of two papers demonstrating the feasibility of using passive air samplers to investigate persistent organic pollutants along an urban-rural transect in Toronto. The first paper investigated spatial trends for polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). This second paper investigates the seasonality of air concentrations for polycyclic aromatic hydrocarbons (PAHs), PCBs, and OCPs along this transect. Air samplers, consisting of polyurethane foam (PUF) disks housed in stainless steel domed chambers, were deployed for three 4-month integration periods from June 2000 to July 2001. The seasonal variations of derived air concentrations for PAHs, PCBs, and OCPs reflected the different source characteristics for these compounds. PAHs showed a strong urban-rural gradient with maximum concentrations at urban sites during the summer period (July-October). These high summer values in Toronto were attributed to increases in evaporative emissions from petroleum products such as asphalt. PCBs also exhibited a strong urban-rural gradient with maximum air concentrations (approximately 2-3 times higher) during the spring period (April-June). This was attributed to increased surface-air exchange of PCBs that had accumulated in the surface layer over the winter. alpha-HCH was fairly uniformly distributed, spatially and temporally, as expected. This pattern and the derived air concentration of approximately 35 to approximately 100 pg m(-3) agreed well with high volume air data from this region, adding confidence to the operation of the passive samplers and showing that site-to-site differences in sampling rates was not an issue. For other OCPs, highest concentrations were observed during the spring period. This was associated with either (i) their local and/or regional application (gamma-HCH, endosulfan) and (ii) their revolatilization (chlordanes, DDT isomers, dieldrin, and toxaphene). Principal component analysis resulted in clusters for the different target chemicals according to their chemical class/source type. The results of this study demonstrate how such a simple sampling technique can provide both spatial and seasonal information. These data, integrated over seasons, can be used to evaluate contaminant trends and the potential role of large urban centers as sources of some semivolatile compounds to the regional environment, including the Great Lakes ecosystem.

Effects of Misclassification in Epidemiologic Studies

A FREQUENTLY used procedure in epidemiology and other disciplines involves the comparison of disease rates in two populations, communities, or groups. While epidemiologists desire, of course, to knolw the true population rates, they must, o,f necessity, be content with observed rates, often derived from samples of the populations. These observed rates may differ from the true rates of the samples as a result of error introduced by misclas'sification according to disease status. In other words, some persons with the disease will be erroneously classified by the study procedure as being without the disease and some persons without the disease will be classified, in error, as having the disease. As well described by Rubin and coworkers (1), the apparent difference in sample rates is related to the true difference in sample rates by the formula