Low Sampling Frequency Research Articles

The quantification of NO<sub><i>x</i></sub> and SO<sub>2</sub> point source emission flux errors of mobile differential optical absorption spectroscopy on the basis of the Gaussian dispersion model: a simulation study

Abstract. Mobile differential optical absorption spectroscopy (mobile DOAS) has become an important tool for the quantification of emission sources, including point sources (e.g., individual power plants) and area emitters (e.g., entire cities). In this study, we focused on the error budget of mobile DOAS measurements from point sources, and we also offered recommendations for the optimum settings of such measurements via a simulation with a modified Gaussian plume model. Following the analysis, we conclude that (1) the proper sampling resolution should be between 5 and 50 m. (2) When measuring far from the source, undetectable flux (measured slant column densities (SCDs) are under the detection limit) resulting from wind dispersion is the main error source. The threshold for the undetectable flux can be lowered by larger integration time. When measuring close to the source, low sampling frequency results in large errors, and wind field uncertainty becomes the main error source of SO2 flux (for NOx this error also increases, but other error sources dominate). More measurement times can lower the flux error that results from wind field uncertainty. The proper wind speed for mobile DOAS measurements is between 1 and 4 m s−1. (3) The remaining errors by [NOx] ∕ [NO2] ratio correction can be significant when measuring very close. To minimize the [NOx] ∕ [NO2] ratio correction error, we recommend minimum distances from the source, at which 5 % of the NO2 maximum reaction rate is reached and thus NOx steady state can be assumed. (4) Our study suggests that emission rates < 30 g s−1 for NOx and < 50 g s−1 for SO2 are not recommended for mobile DOAS measurements. Based on the model simulations, our study indicates that mobile DOAS measurements are a very well-suited tool to quantify point source emissions. The results of our sensitivity studies are important to make optimum use of such measurements.

Reliability of low electrocardiogram sampling frequencies for short-term heart rate variability analysis to estimate transient psychophysiological stress induced by car driving

Abstract Background Heart rate variability analysis (HRVa) is increasingly used to evaluate stress-induced adaptation of the autonomic nervous system (ANS), basing on electrocardiogram (ECG) recorded with novel wearable wireless devices (WWD) for physiological monitoring. However, in order to transfer ECG data to mobile phones efficiently, WWD use low sampling frequency (SF), usually ∼100 Hz, thus much lower than the 0.5–1 kHz, previously suggested to optimize R–R intervals detection, precise enough for HRVa in the time-domain (TD), frequency-domain (FD) and with nonlinear (NL) methods. Moreover, the latter are sensitive to SF as a function of data length. Aim Aim of this study was to quantify the relative error (RE%) of HRVa, when calculated from ECG downsampled at 100 Hz, compared with that digitized at 1 kHz, considering different data lengths. Methods ECG of four healthy professional pilots was continuously recorded with a WWD (Vi-grade, Udine), at 1 kHz SF, while driving a professional simulator, inducing different degree of psychophysiological stress by changing suddenly the vehicle's behavior without advising the driver. TD, FD and NL HRV parameters were calculated (Kubios 3.0.2, Finland), from time intervals of 300, 120, 60 and 30 seconds, of ECG downsampled at 100 Hz SF from original recordings acquired at 1 kHz SF (assumed as goldstandard). The RE% and the intraclass correlation coefficient (ICC) were calculated. Results A good correlation (ICC ≥0.79–0.84) was found for the majority of HRV parameters, in both driving conditions, for all selected intervals lengths. The average RE% ranged between zero and 3%, increasing if the length of the interval selected for HRVa was lower than 60 second (Table 1). However higher RE was occasionally found. In Figure 1, an example of HRVa calculated from 2 minutes tachogram's segments, at 100 Hz (left) and at 1 kHz SF (right) is shown. Conclusions Compared to 1 kHz SF, downsampling ECG at 100 Hz doesn't affect significantly HRVa for data lengths between 5 and 1 minutes. However, for shorter time intervals the RE increases. This must be taken into account if HRVa is used to track transient short-lasting changes of ASN modulation induced by acute stress. Figure 1 Funding Acknowledgement Type of funding source: None

Optimization of sampling frequency for coastal seawater quality monitoring

The selection of an appropriate sampling frequency is very important in monitoring coastal seawater quality. The present study sought to optimize the sampling frequency for coastal seawater quality monitoring. We combined mathematical models with natural and human activities by the analytic hierarchy process method. The study was designed to consider historical monitoring data, regulated seawater quality standard, tide, land use, and ship activities in optimizing coastal seawater sampling frequency in Ha Long Bay, Vietnam. The study results indicated that monitoring activities should focus in points having high human activities as well as high concentration of TSS, ammonium, and oil. Points S1-S8 should have high sampling frequency (16-30 samples/year), whereas points S23-S28 can have low sampling frequency (3-5 samples/year). In a year, monitoring activities should be focused in June, July, and August with the number of samples of 42, 54, and 44 pear year (weighting values 0.120, 0.161, and 0.112), respectively. It should consider the mathematical aspects associated with natural and human factors when calculating sampling frequency for coastal seawater quality monitoring. Proposed optimization method can be applied for other coastal regions.

Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models

BackgroundAnimal movement expressed through home ranges or space-use can offer insights into spatial and habitat requirements. However, different classes of estimation methods are currently instinctively applied to answer home range, space-use or movement-based research questions regardless of their widely varying outputs, directly impacting conclusions. Recent technological advances in animal tracking (GPS and satellite tags), have enabled new methods to quantify animal space-use and movement pathways, but so far have primarily targeted mammal and avian species.MethodsMost reptile spatial ecology studies only make use of two older home range estimation methods: Minimum Convex Polygons (MCP) and Kernel Density Estimators (KDE), particularly with the Least Squares Cross Validation (LSCV) and reference (href) bandwidth selection algorithms. These methods are frequently applied to answer space-use and movement-based questions. Reptile movement patterns are unique (e.g., low movement frequency, long stop-over periods), prompting investigation into whether newer movement-based methods –such as dynamic Brownian Bridge Movement Models (dBBMMs)– apply to Very High Frequency (VHF) radio-telemetry tracking data. We simulated movement data for three archetypical reptile species: a highly mobile active hunter, an ambush predator with long-distance moves and long-term sheltering periods, and an ambush predator with short-distance moves and short-term sheltering periods. We compared traditionally used estimators, MCP and KDE, with dBBMMs, across eight feasible VHF field sampling regimes for reptiles, varying from one data point every four daylight hours, to once per month.ResultsAlthough originally designed for GPS tracking studies, dBBMMs outperformed MCPs and KDE href across all tracking regimes in accurately revealing movement pathways, with only KDE LSCV performing comparably at some higher frequency sampling regimes. However, the LSCV algorithm failed to converge with these high-frequency regimes due to high site fidelity, and was unstable across sampling regimes, making its use problematic for species exhibiting long-term sheltering behaviours. We found that dBBMMs minimized the effect of individual variation, maintained low error rates balanced between omission (false negative) and commission (false positive), and performed comparatively well even under low frequency sampling regimes (e.g., once a month).ConclusionsWe recommend dBBMMs as a valuable alternative to MCP and KDE methods for reptile VHF telemetry data, for research questions associated with space-use and movement behaviours within the study period: they work under contemporary tracking protocols and provide more stable estimates. We demonstrate for the first time that dBBMMs can be applied confidently to low-resolution tracking data, while improving comparisons across regimes, individuals, and species.

Meteotsunamis in Brazil: an overview of known occurrences from 1977 to 2020

From 1977 to March 2020, eight atmospherically induced tsunami-like events have been observed in Brazil. Six of them occurred in the southern region of the country, where the atmosphere tends to be more unstable due to the passage of low-pressure systems, which often move from the south to the north along the coast. This trajectory combined with the shallow continental shelf in the region can favor the occurrence of resonance and, consequently, the amplification of the waves. Most events resulted only in material losses, sometimes severe, but at least one fatal victim was registered. On the vast majority of the occasions, seven in eight, the tsunami-like waves happened on the open coast, and half of them under bad-weather situations. The sparse tide gauge network and its low sampling frequency surely prevented them from being recorded, and mostly the only references about these events came only from the media.

Evidence on time-varying inflation synchronization

Most studies on global inflation are conducted on homogeneous, advanced-economy, low-frequency samples and present evidence favouring the global inflation paradigm. I challenge this consensus view by quantifying price co-movements across a large, heterogeneous sample of countries, while accounting for volatility clustering in monthly inflation data. Estimation results broadly validate the global dimension of inflation but reveal that the strength of the link between global and domestic inflation is time-varying. Price co-movements have continued to be strongest for advanced economies and have increased considerably in emerging economies in recent years. However, they have remained feeble for low-income countries in the last two decades. Inflation synchronization tends to increase due to oil price shocks affecting most economies in a similar way, global economic expansions or recessions spilling over across economies and owing to more coordinated monetary policy of major central banks. Thus, marked price co-movements indicate the prevalence of common factors affecting inflation across countries.

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System.

In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

The dissolved organic carbon flux from the UK – A new Bayesian approach to flux calculation

The study of regional and national-scale fluxes of biogeochemically relevant elements (e.g. carbon) has been hampered by inconsistency of long-term sampling. In this study we solve the issue of sporadic, low-density and low-frequency sampling for nutrients by using Bayesian hierarchical generalised linear modelling. The Bayesian approach means that it is possible to estimate a distribution of the expected value of the nutrient of interest for each location within a survey, no matter whether it was surveyed in a particular year, as long as that site was sampled sometime during the entire study period and that somewhere within the wider survey was sampled in the particular year, i.e. there is no minimum number of samples for a site to be included save that the site was sampled during the entire study period. The method developed was applied to problem of understanding the long-term flux of dissolved organic carbon (DOC) for the UK. The method was applied to 251 monitoring sites across the UK from 1974 to 2019 and the approach was similarly applied to river discharges so as to infill gaps in flow records. The results showed that:I.The annual expected value of the DOC concentration at the UK tidal limit sites has significantly declined over the period from 1974 to 2019.II.Statistically significant changes in DOC concentration were observed over the period from 1974 to 2018 with DOC concentrations rising at the majority sites until the late 1990 s, before declining to a minimum in 2007 with modest increases to 2018.III.The DOC flux from the UK peaked in the year 2000 at 1103 ± 141 ktonnes C/yr (4.5 tonnes C/km2/yr) and reached a minimum of 503 ± 63 ktonnes C/yr (2.1 tonnes C/km2/yr) in 2018.IV.The spatial pattern of DOC export shows large declines in the south and east of the UK where urban centres dominate while catchments in the north of the country show continuing high exports (up to 21 tonnes C/km2/yr) but exports as low as 0.16 tonnes C/km2/yr in eastern England.The flux method developed here makes maximal use of the available data allowing for calculation of long time series, even with sporadic sampling, and gives results with a coherent and consistent measure of their uncertainty.

Rainfall and conduit drainage combine to accelerate nitrate loss from a karst agroecosystem: Insights from stable isotope tracing and high-frequency nitrate sensing

Understanding where nitrate is mobilized from and under what conditions is required to reduce nitrate loss and protect water quality. Low frequency sampling may inadequately capture hydrological and biogeochemical processes that will influence nitrate behavior. We used high-frequency isotope sampling and in-situ nitrate sensing to explore nitrate export and transformation in a karst critical zone. Nitrate was mobilised during light rainfall, and transferred from soil layers to the karst matrix, where some nitrate was retained and denitrified. Nitrate isotopic composition changed rapidly during the rising limb of events and slowly during the falling limb. The main nitrate source was synthetic fertiliser (up to 80% during event flow), transported by conduit flow following high rainfall events, and this contribution increased significantly as discharge increased. Soil organic nitrogen contribution remained constant indicating at baseflow this is the primary source. Isotope source appointment of nitrate export revealed that synthetic fertilizer accounted for more than half of the total nitrate export, which is double that of the secondary source (soil organic nitrogen), providing valuable information to inform catchment management to reduce nitrate losses and fluvial loading. Careful land management and fertilizer use are necessary to avoid nitrate pollution in the karst agroecosystem, for example by timing fertilizer applications to allow for plant uptake of nitrate before rainfall can flush it from the soils into the karst and ultimately into catchment drainage.

Research on signal processing technology in φ-OTDR system based on non-uniform sampling

In the optical cable communication system, because the optical cable often has a long span, the sampling frequency is limited when φ-OTDR is used as the monitoring method. When the system adopts the traditional uniform sampling method, the sampling frequency must be greater than 2 times of the signal frequency, otherwise spectrum aliasing will occur. How to get the frequency information of high frequency signal with lower sampling frequency, and how to solve the problem that the highest response frequency of the system decreases when the sensing distance of φ-OTDR system is extended, is a major technical problem for the monitoring of optical cable communication system. In this paper, through the analysis of the common methods of broadening the response frequency band of Φ-OTDR system, a non-uniform sampling method is selected to analyze the optimal value of some parameters of Φ-OTDR system based on non-uniform sampling, and a method of using the mean square change value to locate the vibration source is proposed. The simulation results show that this method is suitable for monitoring vibration signals with sparse characteristics in frequency domain, and the location SNR is better than the traditional method, and has good anti noise performance.

Successful implementations of a real-time and intelligent early warning system for loess landslides on the Heifangtai terrace, China

Real-time monitoring and intelligent early warning system are crucial and significant to take mitigation measures and reduce casualties and property losses related to landslides. It is difficult to obtain entire monitoring data in the accelerated deformation phase in a landslide event, and hard to issue early warning information using a traditional monitoring approach with fixed and low sampling frequency. Displacement increments of loess landslides induced by agriculture irrigation on the Heifangtai terrace could be sudden and extremely rapid. Typical landslide types include loess flowslides and loess falls. It is of practical significance to develop a self-adaptive data acquisition monitoring technique and establish a real-time landslide early warning system (LEWS) to meet the needs for risk mitigation of rapid sliding slopes on the Heifangtai terrace. The monitoring technique can wirelessly transmit displacement data and the LEWS was devised using the new artificial intelligence. The LEWS could automatically release the warning information in advance of the event once the early warning parameters exceed default thresholds. In this study, the early warning procedures, real-time monitoring approach, intelligent LEWS, a multiple criteria warning model, warning release and emergency mitigation measures, and performance are introduced in detail. Six loess landslides at Heifangtai and eight landslides in other regions of China have been successfully warned since its implementation in 2012. This study proposed an effective and practical solution for the early warning of loess landslides at Heifangtai. Two typical loess landslides that had successful early warnings at Heifangtai were presented. The successful implementation could serve as a reference for global rapid slope failure cases, considering the complex nature of landslide behaviors and failure mechanisms.

Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Vision‐aided intelligent vehicle sideslip angle estimation based on a dynamic model

The vehicle sideslip angle is an important state for vehicle dynamic control, which needs to be estimated as it could not be obtained directly by the vehicle. To improve the estimation accuracy of the sideslip angle based on the intelligent vehicle platform, this study proposes a novel vehicle sideslip angle estimation algorithm with the fusion of dynamic model and vision information. Firstly, to further improve the model accuracy of the vehicle during lateral acceleration conditions, a vehicle dynamic model is established considering the acceleration error compensation with the assistance of attitude information. In addition, based on the lane line information obtained from the equipped camera in intelligent vehicles, a visual geometric model is established. Owing to the measurement delay and low sampling frequency of the camera, a multi-rate sideslip angle observer with delay compensation is designed to coordinate with the inter-frequency signal of the vehicle chassis. Finally, the effectiveness of the algorithm is verified by the slalom test.

Theory and experiments of a low sampling frequency broadband digital predistortion with cyclostationarity

SummaryTo reduce the sampling rate from analog to digital converter for the feedback loop of digital predistortion (DPD) in a microwave power amplifier (MPA), a model was proposed and used to obtain the formulae for the cyclostationarity of the digital modulation signal, which shows that the nonlinear parameter of the MPA model is only determined by the correlation matrix of the signal and can be estimated with the under‐sampled output signal. The output signal of the MPA, similar to a high sampling rate, can be recovered by combining the nonlinear parameter with the input signal. Then the DPD of the MPA is implemented through indirect learning architecture to realize its linearization. A Ka‐band traveling wave tube amplifier is used to the circuit for performing a low sampling rate DPD process to validate the rationality of the proposed model and formulae.

End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning

With the steady rise of home and building automation management system, it is becoming paramount to gain access to information that reflects consumption patterns with device-level granularity. Various application-level services can then makes use of this data for monitoring and controlling purposes in an efficient manner. In this paper we report on the design and development of an Internet of Things (IoT) end-to-end solution for electric appliance recognition that can operate in real-time and entails low hardware cost.For the task of identifying various appliance signatures we also provide a comparative analysis, where on the one hand, we investigate the suitability of several machine learning approaches given publicly available datasets, that generally provide months worth of data with a relatively low sampling frequency. On the other hand, we proceed to evaluate their discriminative effectiveness for our particular scenario, where the goal is to provide rapid identification of the appliance signature in real-time based on a reduced training dataset (few-shot learning). This is particularly important in the context of appliance recognition, where due to the high variance in consumption patterns within each class, in order to achieve high accuracy, data points often need to be collected for each individual appliance or device that would need to be later identified. Clearly, this data collection process is often expensive and difficult to perform, especially in large-scale settings, hence few-shot learning is key. Besides presenting our end-to-end IoT solution that meets the abovementioned desiderata, the paper also provides an analysis of the computational demand of such an approach with regard to cost and real-time performance, which is often critical to low-powered IoT solutions.

An Online Parameters Monitoring Method for Output Capacitor of Buck Converter Based on Large-Signal Load Transient Trajectory Analysis

Aluminum electrolytic capacitor (AEC) is one of the weakest components in power electronic converters. As the degradation of AEC happens, its equivalent series resistance (ESR) increases and the capacitance ( C) decreases. Therefore, the online monitoring of ESR and C to predict AEC's life has great significance for ensuring safe and reliable operation of converters. In this article, an online monitoring scheme is proposed for buck converters, aiming to estimate the ESR and C of AEC at the output side. The proposed scheme utilizes large-signal load transient trajectories to estimate the AEC parameters and has a relatively low sampling frequency. By analyzing the relationship between the transient trajectory and capacitor parameters, the ESR is directly calculated using the voltage and current step values at the initial instant of the transient. Furthermore, C is calculated utilizing a calculation model derived from the output-voltage load transient trajectory. Corresponding simulation analysis and an online monitoring system implementation are provided. Furthermore, a 48-12-V buck converter with a digital proportional-integral (PI) controller and an analog V <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller is built to verify the proposed online estimation method. The experimental results of the estimated ESR and C are consistent with the results measured by an LCR meter, and the estimation error is less than 10%.

Gene regulatory network inference from sparsely sampled noisy data

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life.

Single‐end protection algorithm for HVDC transmission lines based on the current difference

This study presents a single-end protection algorithm for current source converter (CSC) high-voltage direct current (HVDC) transmission lines to solve shortcomings such as inadequate sensitivity, low speed and the need for communication link of conventional protection schemes. The proposed algorithm uses the average of DC line current at the rectifier terminal, sampled at 4 kHz within a 5 ms window, for simultaneous fault detection and faulted pole selection. For fault discrimination, the algorithm uses the average of second-order difference of the sampled current data. The algorithm is verified by extensive simulation studies using a CSC-HVDC test system modelled in PSCAD, as well as field data. The simulation results show fast and reliable protection of CSC-HVDC lines under different fault conditions including high-fault resistances. Moreover, the novel protection algorithm is stable against resistive–inductive faults, power flow changes, change of data window length, lightning interference and size of smoothing reactor. The algorithm does not require a communication link as used by numerous previous methods. It has a low computational burden, low sampling frequency and can be easily implemented in practical CSC-HVDC systems without extra arrangements as the required signals are readily available in the existing measuring hardware platforms.

Synthetic aperture with high lateral sampling frequency for ultrasound elastography.

Ultrasound elastography is a non-invasive technique for detecting pathological alterations in tissue. It is known that pathological alteration of tissue often has a direct impact on its elastic modulus, which can be revealed using elastography. For estimating elastic modulus, we need to estimate both axial and lateral displacement accurately. Current state of the art elastography techniques provide a substantially less accurate lateral displacement field as compared to the axial displacement field. One of the most important factors in poor lateral estimation is a low sampling frequency in the lateral direction. In this paper, we use synthetic aperture beamforming to benefit from its capability of high sampling frequency in the lateral direction. We compare highly sampled data and focused line per line beam formed data by feeding them to our recently published elastography method, OVERWIND [1]. According to simulation and phantom experiments, not only the lateral displacement estimation is substantially improved, but also the axial displacement estimation is improved.

An innovative hybrid approach for detection of pacemaker pulses at low sampling frequency.

Accurate assessment of pacemaker function or malfunction is essential to make clinical interpretations on pacemaker therapy and patient symptoms. This article presents an innovative approach for detecting pacemaker pulses at sampling frequency as low as 125Hz. The proposed method is validated in wide range of simulated clinical ECG conditions such as arrhythmia (sinus rhythms, supraventricular rhythms, and AV blocks), pulse amplitudes (~100µV to ~3mV), pulse durations (~100µs to ~2ms), pacemaker modes and types (fixed-rate or on-demand single chamber, dual chamber, and bi-ventricular pacing), and physiological noise (tremor). The proposed algorithm demonstrates clinically acceptable detection accuracies with sensitivity and PPV of 98.1 ± 4.4 % and 100 %, respectively. In conclusion, the approach is well suited for integration in long-term wearable ECG sensor devices operating at a low sample frequency to monitor pacemaker function.Clinical Relevance- The proposed system enables real-time long-term continuous assessment of the proper functioning of implanted pacemaker and progression of treatment for cardiac conditions using battery-powered wearable ECG monitors.