Average Window Research Articles

Sensor to segment calibration for magnetic and inertial sensor based motion capture systems

This study aims to propose a novel sensor to segment calibration method for magnetic and inertial measurement unit (M-IMU) based human motion capture systems. The calibration procedure is designed to be easily performed by the subject, which can calibrate the whole body segments with only three predefined postures. Firstly, the orientation measurements for each body segment are collected during calibration process. Secondly, a segmentation procedure based on a moving average window algorithm and double-threshold technique is introduced to recognize and segment the calibration postures automatically. To suppress the shaking during the calibration process, an intrinsic average algorithm is presented to smooth the orientation measurements. Finally, an iteration hand-eye calibration approach is utilized to retrieve the sensor to segment calibration matrices. The error due to the ungraded calibration postures can be reduced observably, and we also define an indicator to evaluate the quality of the performed calibration postures. The calibration procedure has been validated on several subjects using a wearable M-IMU based motion capture system. Experiment results show that the proposed calibration method can suppress the shaking disturbances effectively, and has good repeatability.

In situ observed relationships between snow and ice surface skin temperatures and 2 m air temperatures in the Arctic

Abstract. To facilitate the construction of a satellite-derived 2 m air temperature (T2 m) product for the snow- and ice-covered regions in the Arctic, observations from weather stations are used to quantify the relationship between the T2 m and skin temperature (Tskin). Multiyear data records of simultaneous Tskin and T2 m from 29 different in situ sites have been analysed for five regions, covering the lower and upper ablation zone and the accumulation zone of the Greenland Ice Sheet (GrIS), sea ice in the Arctic Ocean, and seasonal snow-covered land in northern Alaska. The diurnal and seasonal temperature variabilities and the impacts from clouds and wind on the T2 m–Tskin differences are quantified. Tskin is often (85 % of the time, all sites weighted equally) lower than T2 m, with the largest differences occurring when the temperatures are well below 0 ∘C or when the surface is melting. Considering all regions, T2 m is on average 0.65–2.65 ∘C higher than Tskin, with the largest differences for the lower ablation area and smallest differences for the seasonal snow-covered sites. A negative net surface radiation balance generally cools the surface with respect to the atmosphere, resulting in a surface-driven surface air temperature inversion. However, Tskin and T2 m are often highly correlated, and the two temperatures can be almost identical (<0.5 ∘C difference), with the smallest T2–Tskin differences around noon and early afternoon during spring, autumn and summer during non-melting conditions. In general, the inversion strength increases with decreasing wind speeds, but for the sites on the GrIS the maximum inversion occurs at wind speeds of about 5 m s−1 due to the katabatic winds. Clouds tend to reduce the vertical temperature gradient, by warming the surface, resulting in a mean overcast T2 m–Tskin difference ranging from −0.08 to 1.63 ∘C, with the largest differences for the sites in the low-ablation zone and the smallest differences for the seasonal snow-covered sites. To assess the effect of using cloud-limited infrared satellite observations, the influence of clouds on temporally averaged Tskin has been studied by comparing averaged clear-sky Tskin with averaged all-sky Tskin. To this end, we test three different temporal averaging windows: 24 h, 72 h and 1 month. The largest clear-sky biases are generally found when 1-month averages are used and the smallest clear-sky biases are found for 24 h. In most cases, all-sky averages are warmer than clear-sky averages, with the smallest bias during summer when the Tskin range is smallest.

Detrended cross-correlation patterns between North Atlantic oscillation and precipitation

In this study, the long-range relationships between North Atlantic Oscillation (NAO) and precipitation data obtained from Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP) from 1979 to 2016 are investigated using Detrended Fluctuation Moving Average Cross-Correlation Analysis (DMCA). In the atmosphere, teleconnections through strong convective processes sporadically affect various climatic regimes in Europe, Mediterranean basin, North Africa, Middle East, and Caucasus. The NAO is one of the teleconnection processes and results in heavy rainfall in the Mediterranean basin during its negative phase while it gives rise to rain in Europe during its positive phase. The DMCA technique shows that the NAO fluctuation series exhibit different long-range cross-correlation coefficients, ρDMCA(s) with “s” being the moving average time window length, between the precipitation values and NAO. Large ρDMCA(s) coefficients with time window(s) larger than 12 months were obtained particularly over the Mediterranean basin, near the North Pole including northern Europe. Furthermore, the ρDMCA(s) coefficients were grouped into clusters using K-mean method to distinguish the similar patterns. The 1st cluster refers to the negative phase of the NAO indicating warm-rainy conditions and dry spells, which is especially evident in the Mediterranean basin. The 2nd cluster represents the long-range cross-correlation with respect to the positive phase of NAO and precipitation values, particularly for the Western and Northern Europe. Conversely, the 3rd cluster is evaluated as power law of long-range cross-correlations between the precipitation and NAO with respect to the different time scale processes.

Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion

BackgroundGene co-expression studies can provide important insights into molecular and cellular signaling pathways. The GeneNetwork database is a unique resource for co-expression analysis using data from a variety of tissues across genetically distinct inbred mice. However, extraction of biologically meaningful co-expressed gene sets is challenging due to variability in microarray platforms, probe quality, normalization methods, and confounding biological factors. In this study, we tested whether literature derived functional cohesion could be used as an objective metric in lieu of ‘ground truth’ to evaluate the quality of probes and microarray datasets.ResultsWe examined Sirtuin-3 (Sirt3) co-expressed gene sets extracted from either liver or brain tissues of BXD recombinant inbred mice in the GeneNetwork database. Depending on the microarray platform, there were as many as 26 probes that targeted different regions of Sirt3 primary transcript. Co-expressed gene sets (ranging from 100–1000 genes) associated with each Sirt3 probe were evaluated using the previously developed literature-derived cohesion p-value (LPv) and benchmarked against ‘gold standards’ derived from proteomic studies or Gene Ontology classifications. We found that the maximal F-measure was obtained at an average window size of 535 genes. Using set size of 500 genes, the Pearson correlations between LPv and F-measure as well as between LPv and mitochondrial gene enrichment p-values were 0.90 and 0.93, respectively. Importantly, we found that the LPv approach can distinguish high quality Sirt3 probes. Analysis of the most functionally cohesive Sirt3 co-expressed gene set revealed core metabolic pathways that were shared between hippocampus and liver as well as distinct pathways which were unique to each tissue. These results are consistent with other studies that suggest Sirt3 is a key metabolic regulator and has distinct functions in energy-producing vs. energy-demanding tissues.ConclusionsOur results provide proof-of-concept that literature cohesion analysis is useful for evaluating the quality of probes and microarray datasets, particularly when experimentally derived gold standards are unavailable. Our approach would enable researchers to rapidly identify biologically meaningful co-expressed gene sets and facilitate discovery from high throughput genomic data.

Wireless physical layer key generation with improved bit disagreement for the internet of things using moving window averaging

Achieving information security in the Internet of things (IoT) by means of physical layer key generation is an open research area. In the key generation, the secret key is generated using the physical (PHY) layer characteristics of the communication channel as a source of randomness. IoT networks are characterized as noisy & heterogeneous networks with lower transmission power. In this paper, we propose a wireless secret key generation approach by pre-processing the received signal strength indicator (RSSI) of beacons exchanged between Alice and Bob using moving window averaging (MWA). Our proposed approach has significant improvement in performance, at lower SNR range. The size of the moving window is selected on the basis of improvement in the standard deviation of the RSSI pattern. It is also suggested to quantize the pre-processed RSS samples through Lloyd–Max based quantizer to minimize quantization error, which further improves bit disagreement (BDR). It is clear from results that the proposed approach has considerable improvement in bit disagreement rate (BDR) at low SNR by applying the selected window size. The performance of the proposed approach is also evaluated by performing randomness testing of the generated keys using National Institute of Standards and Technology (NIST) test set for randomness.

On the Design of Low-Cost IoT Sensor Node for e-Health Environments

The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low power-consumption, and increased data accuracy based on open-source platforms. The method utilizes a Sensor Controller (SC) within the IoT SN, which is capable of performing medical checks supporting a broad coverage of medical uses. A communication protocol has been developed for data and command exchange among SC, local gateways, and physicians’ or patients’ mobile devices (tablets, smart phones). The SC supports moving average window (MAW) and principle component analysis (PCA) filtering algorithms to capture data from the attached low-cost body sensors of different sampling profiles. Significant extensions in SN’s portability is achieved through energy consumption minimization based on the idle time gaps between sensors’ activations. SN’s components are either deactivated or set to low activity operation during these idle intervals. A medical case study is presented and the evaluated results show that the proposed SN can be incorporated into e-health platforms since it achieves comparable accuracy to its certified and high-cost commercial counterparts.

Ambient ozone and fine particulate matter exposures and autism spectrum disorder in metropolitan Cincinnati, Ohio

BackgroundEpidemiological studies report fairly consistent associations between various air pollution metrics and autism spectrum disorder (ASD), with some elevated risks reported for different prenatal and postnatal periods. ObjectivesTo examine associations between ASD and ambient fine particulate matter (PM2.5) and ozone concentrations during the prenatal period through the second year of life in a case-control study. MethodsASD cases (n = 428) diagnosed at Cincinnati Children's Hospital Medical Center were frequency matched (15:1) to 6420 controls from Ohio birth records. We assigned daily PM2.5 and ozone estimates for 2005–2012 from US EPA's Fused Air Quality Surface Using Downscaling model to each participant for each day based on the mother's census tract of residence at birth. We calculated adjusted odds ratios (aORs) using logistic regression across continuous and categorical exposure window averages (trimesters, first and second postnatal years, and cumulative measure), adjusting for maternal- and birth-related confounders, both air pollutants, and multiple temporal exposure windows. ResultsWe detected elevated aORs for PM2.5 during the 2nd trimester, 1st year of life, and a cumulative period from pregnancy through the 2nd year (aOR ranges across categories: 1.41–1.44, 1.54–1.84, and 1.41–1.52 respectively), and for ozone in the 2nd year of life (aOR range across categories: 1.29–1.42). Per each change in IQR, we observed elevated aORs for ozone in the 3rd trimester, 1st and 2nd years of life, and the cumulative period (aOR range: 1.19–1.27) and for PM2.5 in the 2nd trimester, 1st year of life, and the cumulative period (aOR range: 1.11–1.17). DiscussionWe saw limited evidence of linear exposure-response relationships for ASD with increasing air pollution, but the elevated aORs detected for PM2.5 in upper exposure categories and per IQR unit increases were similar in magnitude to those reported in previous studies, especially for postnatal exposures.

An Improved Method to Determine Coda‐Q, Earthquake Magnitude, and Site Amplification: Theory and Application to Southern California

AbstractSeismic coda waves can be used to constrain attenuation, estimate earthquake magnitudes, and determine site amplification factors. We have developed a new multistation and multievent method to determine these three important seismic parameters simultaneously. We analyze 642 representative local (≤100 km) and shallow (≤20 km) earthquakes with catalog magnitudes between 1.8 and 5.4 in southern California at multiple frequency bands centered at 1.5, 3, 6, and 12 Hz. We find that the length of the moving average time window can affect the measurement of coda attenuation QC, but our tests indicate that the optimal window length is about 15 times the dominant data period. We use linear regression to fit each coda section and use only those portions that agree with the model decay rate with a correlation coefficient larger than 0.9. For a frequency‐dependent coda‐QC model (QC = Q0fn) at 1‐Hz reference frequency, our results yield estimates for Q0 and n of 107–288 and 0.42–1.14, respectively. Our coda magnitude estimates are linearly correlated with catalog magnitudes, and our observed lateral variations in coda‐QC and our site amplification factors are in general agreement with previous results, although there are notable differences at some locations. This approach provides a unified, accurate, and stable method to measure coda‐QC, earthquake magnitude, and site amplification using coda waves of locally recorded earthquakes.

Findings of a Statewide Environmental Lead Inspection Program Targeting Homes of Children With Blood Lead Levels as Low as 5 μg/dL.

There are limited data on the nature of environmental lead hazards identified during residential inspections for child blood lead levels (BLLs) of less than 10 μg/dL. We compare inspection findings for child BLLs of 5 to 9 μg/dL versus 10 μg/dL or more. We reviewed inspection reports in Maine from September 2016 to March 2018. We used continuity-adjusted or Fisher's exact test for categorical variables and Wilcoxon rank-sum tests for continuous variables to compare differences in child, family, household, and lead hazard characteristics between BLL categories (5-9 μg/dL vs ≥10 μg/dL). We used Spearman correlation coefficients to assess relationships between home surface lead dust measurements and BLLs. Of 351 residential inspections, 272 (77%) were for children with BLLs of 5 to 9 μg/dL. Children with BLLs of 5 to 9 μg/dL as compared with children with BLLs of 10 μg/dL or more were less likely to chew window sills and door frames (8% vs 21%; P = .01), but otherwise were similar with respect to other established risk factors for lead poisoning. Children with BLLs of 5 to 9 μg/dL tended to have fewer paint hazards inside their homes (64% vs 78%; P = .03), and they were more likely to have dust-only hazards (8% vs 3%) or no identified lead paint hazards (23% vs 15%), though these differences were not statistically significant. For children with BLLs of 5 to 9 μg/dL, BLL was weakly correlated with average window sill dust level (Spearman r = 0.16; P = .01) and average floor dust level (r = 0.13; P = .03), but these correlations were not observed for children with BLLs of 10 μg/dL and higher. We have found that inspections of homes of children with BLLs of 5 to 9 μg/dL are nearly as likely to identify lead hazards that require abatement as inspections of homes of children with BLLs of 10 μg/dL.

Average Window Smoothing for an Indonesian Language Online Speaker Identification System

Average Window Smoothing for an Indonesian Language Online Speaker Identification System

Sea Ice Albedo from MISR and MODIS: Production, Validation, and Trend Analysis

The Multi-angle Imaging SpectroRadiometer (MISR) sensor onboard the Terra satellite provides high accuracy albedo products. MISR deploys nine cameras each at different view angles, which allow a near-simultaneous angular sampling of the surface anisotropy. This is particularly important to measure the near-instantaneous albedo of dynamic surface features such as clouds or sea ice. However, MISR’s cloud mask over snow or sea ice is not yet sufficiently robust because MISR’s spectral bands are only located in the visible and the near infrared. To overcome this obstacle, we performed data fusion using a specially processed MISR sea ice albedo product (that was generated at Langley Research Center using Rayleigh correction) combining this with a cloud mask of a sea ice mask product, MOD29, which is derived from the MODerate Resolution Imaging Spectroradiometer (MODIS), which is also, like MISR, onboard the Terra satellite. The accuracy of the MOD29 cloud mask has been assessed as >90% due to the fact that MODIS has a much larger number of spectral bands and covers a much wider range of the solar spectrum. Four daily sea ice products have been created, each with a different averaging time window (24 h, 7 days, 15 days, 31 days). For each time window, the number of samples, mean and standard deviation of MISR cloud-free sea ice albedo is calculated. These products are publicly available on a predefined polar stereographic grid at three spatial resolutions (1 km, 5 km, 25 km). The time span of the generated sea ice albedo covers the months between March and September of each year from 2000 to 2016 inclusive. In addition to data production, an evaluation of the accuracy of sea ice albedo was performed through a comparison with a dataset generated from a tower based albedometer from NOAA/ESRL/GMD/GRAD. This comparison confirms the high accuracy and stability of MISR’s sea ice albedo since its launch in February 2000. We also performed an evaluation of the day-of-year trend of sea ice albedo between 2000 and 2016, which confirm the reduction of sea ice shortwave albedo with an order of 0.4–1%, depending on the day of year and the length of observed time window.

An asymptotic approximation for TCP CUBIC

In this paper, we derive an expression for computing the average window size of a single TCP CUBIC connection under random losses. For this we consider a throughput expression for TCP CUBIC computed earlier under deterministic periodic packet losses. We validate this expression theoretically. We then use insights from the deterministic loss-based model to scale appropriately a sequence of Markov chains with random losses indexed by the probability of loss p. We show that this sequence converges to a limiting Markov chain as p tends to 0. The stationary distribution of the limiting Markov chain is then used to derive the average window size for small packet error rates. We then use a simple approximation to extend our current results with negligible queuing to a setup with multiple connections and non-negligible queuing. We validate our model and approximations via simulations.

Does Financial Integration Reduce Output Volatility? New Evidence from Cross‐Country Data

We examine the effect of financial integration, measured based on both volume and equity, on output volatility using five‐year non‐overlapping annual average data windows for sixty countries over the 1971–2015 period. We construct aggregate‐ as well as sub‐panels based on income and region. Financial integration reduces output volatility in aggregate and income panel, but not in all regions. Foreign direct investment (FDI) and foreign portfolio investment (FPI) reduces output volatility in developed countries, but only FDI reduces output volatility in developing countries. Financial regulatory architecture should aim at attracting FDI and macroeconomic and structural reforms to reap the benefits of FPI flows.

Spatial and Temporal Averaging Windows and Their Impact on Forecasting: Exactly Solvable Examples

Abstract In making weather and climate predictions, the goal is often not to predict the instantaneous, local value of temperature, wind speed, or rainfall; instead, the goal is often to predict these quantities after averaging in time and/or space-for example, over one day or one week. What is the impact of spatial and/or temporal averaging on forecasting skill?Here this question is investigated using simple stochastic models that can be solved exactly analytically. While the models are idealized, their exact solutions allow clear results that are not affected by errors from numerical simulations or from random sampling. As a model of time series of oscillatory weather fluctuations, the complex Ornstein-Uhlenbeck process is used. To furthermore investigate spatial averaging, the stochastic heat equation is used as an idealized spatiotemporal model for moisture and rainfall. Space averaging and time averaging are shown to have distinctly different impacts on prediction skill. Spatial averaging leads to improved forecast skill, in line with some forms of basic intuition. Time averaging, on the other hand, is more subtle: it may either increase or decrease forecast skill. The subtle effects of time averaging are seen to arise from the relative definitions of the time averaging window and the lead time. These results should help in understanding and comparing forecasts with different temporal and spatial averaging windows.

A Time Picking Method for Microseismic Data Based on LLE and Improved PSO Clustering Algorithm

Time picking is of great concern in the processing of microseismic data. However, the traditional method based on time/frequency domain cannot pick the first arrival time accurately in low signal-to-noise ratio. Besides, the traditional time picking methods which based on clustering are sensitive to selecting the initial clustering centers and easy to converge to local optimal value. To solve the above problems, we propose a time picking method for microseismic data based on locally linear embedding (LLE) and improved particle swarm optimization (PSO) clustering algorithm. First, the LLE algorithm can obtain the inherent characteristics and the rules hidden in high-dimensional data by calculating Euclidean distances and reconstruction weights between microseismic data points. The input is represented in a low-dimensional form. Then, the improved PSO clustering algorithm is used to select the optimal clustering centers from low-dimensional data through global search method. After that, the low-dimensional data can be classified into noise cluster and signal cluster by the K-means algorithm. Finally, the initial time of the signal cluster can be considered as the first arrival time of microseismic data. The experimental results show that accuracy of the proposed method is higher than that of the improved PSO clustering algorithm, Akaike information criterion method, and short- and long-time window ratio method (short-time window averaging/long-time window averaging).

口吃者加工汉语歧义短语的神经过程

口吃者加工汉语歧义短语的神经过程

Financialized Commodities and Stock Indices Volatilities

We investigate the relationship between commodity returns and macroeconomic fluctuations. Using realized volatility at different series and different moving average windows in the eight financialized commodities and three stock indices (NASDAQ, DJIA and SP500), we find that the fluctuation of NASDAQ, DJIA and SP500 returns tend to be statistically significant in explaining the variation of volatilities of commodity returns.This finding is strong for all eight financialized commodities and at different returns series. However, the directions on how the index volatility affect the commodity volatility are different. Higher realized volatility of NASDAQ return causes lower realized volatility of financialized commodity returns while higher realized volatility of SP500 return causes higher realized volatility of financialized commodity return.DJIA has different directions in each series. Realizing that a lot of companies comprising NASDAQ and SP500 indices while only few companies comprising DJIA index, we conclude that our finding is consistent with the theory of well-diversified portfolio.

IN-SERVICE CONDITION ASSESSMENT OF LONG-SPAN BRIDGES BASED ON TRAFFIC LOAD EFFECTS USING MONITORING DATA

Long span bridges are widely equipped with structural health monitoring (SHM) systems. Using SHM, a wide range of real-time structural responses under ambient loading could be recorded, which provides the basis for bridge load rating and structural performance assessment. During these structural responses, displacement is an important index to represent the bridge in-service condition. In the paper, monitored girder displacements of a cable-stayed bridge are examined to assess the bridge in-service condition. The finite element model of the studied bridge is first updated based on monitored natural frequencies. Realistic influence lines of the bridge could be obtained based on the updated model, which is the baseline for assessment. Then, moving average method and window smoothing method are employed to extract the displacement induced only by traffic loading from that by wind, temperature, and environmental noise. Finally, using the extracted traffic load effects of girder displacements, the characteristic values under various remaining lives are extrapolated through constructing the limit distribution using block maxima based generalized extreme value (GEV) model fitting. Results show the predicted displacements would exceed the design level of the Chinese code after the bridge serves for another 10 years, indicating potential risk. At that time, the management of traffic loads and maintenance of bridge components should be enhanced to ensure the bridge in-service condition.

Exploring Auditory-Inspired Acoustic Features for Room Acoustic Parameter Estimation From Monaural Speech

Room acoustic parameters that characterize acoustic environments can help to improve signal enhancement algorithms such as for dereverberation, or automatic speech recognition by adapting models to the current parameter set. The reverberation time (RT) and the early-to-late reverberation ratio (ELR) are two key parameters. In this paper, we propose a blind ROom Parameter Estimator (ROPE) based on an artificial neural network that learns the mapping to discrete ranges of the RT and the ELR from single-microphone speech signals. Auditory-inspired acoustic features are used as neural network input, which are generated by a temporal modulation filter bank applied to the speech time-frequency representation. ROPE performance is analyzed in various reverberant environments in both clean and noisy conditions for both fullband and subband RT and ELR estimations. The importance of specific temporal modulation frequencies is analyzed by evaluating the contribution of individual filters to the ROPE performance. Experimental results show that ROPE is robust against different variations caused by room impulse responses (measured versus simulated), mismatched noise levels, and speech variability reflected through different corpora. Compared to state-of-the-art algorithms that were tested in the acoustic characterisation of environments (ACE) challenge, the ROPE model is the only one that is among the best for all individual tasks (RT and ELR estimation from fullband and subband signals). Improved fullband estimations are even obtained by ROPE when integrating speech-related frequency subbands. Furthermore, the model requires the least computational resources with a real time factor that is at least two times faster than competing algorithms. Results are achieved with an average observation window of 3 s, which is important for real-time applications.

The Effects of Donald Trump’s Tweets on US Financial and Foreign Exchange Markets

Twitter is the US president’s, Donald Trump, preferred media for communicating his thoughts to his followers. This project looks at the effect of the daily flow of Donald Trump’s tweets on the US financial and foreign exchange markets, represented by the Dow Jones Industrial Average (DJI) index and some exchange rates, over the period of Donald Trump’s ongoing presidency. Using text mining techniques, some correlation is found between various moving average window lengths of tweet content and the DJI index. Some short term and lasting effects are also detected on US-Canada and US composite exchange rates.