A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal.

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

Modified Nested Barker Codes for Ultra-Wideband Signal-Code Constructions.

Currently, various applications of ultra-wideband signal-code constructions are among the most vibrant technologies, being implemented in very different fields. The purpose of this research consists of analyzing Barker codes and searching for the optimal nested representations of them. We also aim to synthesize signal-code constructions based on the tenets of nesting of alternative modified Barker codes, which employ an asymmetric alphabet. The scientific merit of the paper is as follows: on the basis of new analytic expressions, modified nested codes and signal-code constructions were obtained, applicable for the establishment of the unambiguous association of the component values of the nested codes with any lobes of the normalized autocorrelation function. With these analytical expressions, we are, hence, able to determine the values of the binary asymmetrical components of the nested codes related to the side lobes of the normalized autocorrelation function. In this way, we clearly obtain better (low) levels for these lobes than by using the autocorrelation function, as established by the equivalent conventional Barker codes, including the nested constructions. Practical application of these modulated ultra-wideband signals ensures improved correlational features, high-fidelity probabilistic detection, and more precise positional detection of physical bodies depending on the range coordinate.

A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal.

The signal measured by the maglev gyro sensor is sensitive to the influence of the instantaneous disturbance torque caused by the instantaneous strong wind or the ground vibration, which reduced the north-seeking accuracy of the instrument. To address this issue, we proposed a novel method combining the heuristic segmentation algorithm (HSA) and the two-sample Kolmogorov-Smirnov (KS) test (named HSA-KS method) to process the gyro signals and improve the north-seeking accuracy of the gyro. There were two key steps in the HSA-KS method: (i) all the potential change points were automatically and accurately detected by HSA, and (ii) the jumps in the signal caused by the instantaneous disturbance torque were quickly located and eliminated by the two-sample KS test. The effectiveness of our method was verified through a field experiment on a high-precision global positioning system (GPS) baseline at the 5th sub-tunnel of the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi Province, China. Our results from the autocorrelograms indicated that the jumps in the gyro signals can be automatically and accurately eliminated by the HSA-KS method. After processing, the absolute difference between the gyro and high-precision GPS north azimuths was enhanced by 53.5%, which was superior to the optimized wavelet transform and the optimized Hilbert-Huang transform.

Partial Autocorrelation Diagnostics for Count Time Series.

In a time series context, the study of the partial autocorrelation function (PACF) is helpful for model identification. Especially in the case of autoregressive (AR) models, it is widely used for order selection. During the last decades, the use of AR-type count processes, i.e., which also fulfil the Yule-Walker equations and thus provide the same PACF characterization as AR models, increased a lot. This motivates the use of the PACF test also for such count processes. By computing the sample PACF based on the raw data or the Pearson residuals, respectively, findings are usually evaluated based on well-known asymptotic results. However, the conditions for these asymptotics are generally not fulfilled for AR-type count processes, which deteriorates the performance of the PACF test in such cases. Thus, we present different implementations of the PACF test for AR-type count processes, which rely on several bootstrap schemes for count times series. We compare them in simulations with the asymptotic results, and we illustrate them with an application to a real-world data example.

Improved Adaptive Multipoint Optimal Minimum Entropy Deconvolution and Application on Bearing Fault Detection in Random Impulsive Noise Environments.

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise.

Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar.

In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and lower linearity requirements (e.g., compared to orthogonal-frequency division multiplexing radars), as well as its high velocity unambiguity and multiple-input multiple-output operation capability, all of which are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed.

Molecular Dynamics Study of Structural and Transport Properties of Silver Iodide Using Effective Charges.

The superionic conductor, solid state, and body-centered cubic structure, silver iodide at room temperature, has been studied via molecular dynamics simulations. The calculated results using pairwise Coulomb-Buckingham potential, zero pressure on the sample, a semi-rigid model system of 1000 Ag and 1000 I ions, (NVE) as a statistical ensemble, and an effective charge of Z=0.63 for the pairs Ag-Ag and I-I, were found to be consistent with experimental data and one study using Z=0.60, different potential, and simulation software. For the pair Ag-I, there is a discrepancy due to the high silver ion diffusion. The calculated value of the diffusion constant of the silver ion is greater than iodide ion. The dynamic transport properties (mean square displacement, velocity autocorrelation function) results indicated typical behavior reported by other authors, using different potentials in their DM simulations for iodine and silver ions.

Modeling Long-Range Dynamic Correlations of Words in Written Texts with Hawkes Processes.

It has been clarified that words in written texts are classified into two groups called Type-I and Type-II words. The Type-I words are words that exhibit long-range dynamic correlations in written texts while the Type-II words do not show any type of dynamic correlations. Although the stochastic process of yielding Type-II words has been clarified to be a superposition of Poisson point processes with various intensities, there is no definitive model for Type-I words. In this study, we introduce a Hawkes process, which is known as a kind of self-exciting point process, as a candidate for the stochastic process that governs yielding Type-I words; i.e., the purpose of this study is to establish that the Hawkes process is useful to model occurrence patterns of Type-I words in real written texts. The relation between the Hawkes process and an existing model for Type-I words, in which hierarchical structures of written texts are considered to play a central role in yielding dynamic correlations, will also be discussed.

Noise Radar Technology: Waveforms Design and Field Trials.

Performance of continuous emission noise radar systems are affected by the sidelobes of the output of the matched filter, with significant effects on detection and dynamic range. Hence, the sidelobe level has to be controlled by a careful design of the transmitted waveform and of the transmit/receive parts of the radar. In this context, the average transmitted power has to be optimized by choosing waveforms with a peak-to-average power ratio as close to the unity as possible. However, after coherent demodulation and acquisition of the received signal and of the reference signal at the transmitting antenna port, the goodness (low sidelobes) of the output from the matched filter can be considerably reduced by the deleterious effects due to the radar hardware, including the analog-to-digital converter (ADC). This paper aims to solve the above problems from both the theoretical and the practical viewpoint and recommends the use of tailored waveforms for mitigating the dynamic range issues. The new findings are corroborated by the results from two noise radar demonstrators operating in Germany (rural environment) and in Turkey (coast and sea environment) and the related lessons learnt.

Identifying periodicity in nurse call occurrence: Analysing nurse call logs to obtain information for data-based nursing management.

AIM: To verify our hypothesis that 'there is periodicity in nurse call occurrence'. BACKGROUND: It is difficult to plan nursing management because nursing tasks can vary widely, seemingly at random. One of the most useful pieces of information for decision-making is periodicity. If periodicity is present, it should be possible to predict the occurrence of tasks and make preventive strategies. In this study, we focused on the nurse call, which plays an important role in nursing practice. METHOD: We used nurse call logs that accumulated automatically when patients pushed the button. Data were obtained from 1 January 2014 to 30 September 2017 (1,369 days) in a university hospital. The total number was 5,982,935. Periodicity was verified by the autocorrelation function. RESULTS: The value of the autocorrelation function increased regularly, which demonstrates there was periodicity in nurse call occurrence. CONCLUSION: Our hypothesis was accepted. The presence of periodicity indicates that nurse call occurrence is not a random event but has a pattern. IMPLICATIONS FOR NURSING MANAGEMENT: If we can identify patterns such as the time that nurse calls frequently occur, managers can implement two strategies: one, assigning more nurses and two, moving tasks other than nurse calls to another time.

Time-Delay Identification Using Multiscale Ordinal Quantifiers.

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.

A Simple Derivation of Diffusion Fluorescence Correlation Spectroscopy Equations.

Since its introduction in the 1970s, Fluorescence Correlation Spectroscopy (FCS) has become a standard biophysical and physical chemistry tool to investigate not only a diffusion but also a broad range of biochemical processes including binding kinetics and anomalous diffusion. Since the derivation of FCS equations for many biochemical processes shares many common steps with the diffusion FCS equation, it is important to understand the mathematical theory behind the ​diffusion FCS equation. However, because the derivation of FCS equations requires advanced Fourier Transform and inverse Fourier Transform theory, which most biologists and biochemists are not familiar with, it is often treated as a black box in practice. In this study, we provide a simple and straightforward step-by-step derivation of FCS equations for free ​diffusion based on calculus-level mathematics, so that FCS equations and its applications can be accessible to a broad audience. Additionally, we compare our derivation with the conventional Fourier Transform and inverse Fourier Transform theory based approach.

Complex Diffusion in Bacteria.

Diffusion within bacteria is often thought of as a "simple" random process by which molecules collide and interact with each other. New research however shows that this is far from the truth. Here we shed light on the complexity and importance of diffusion in bacteria, illustrating the similarities and differences of diffusive behaviors of molecules within different compartments of bacterial cells. We first describe common methodologies used to probe diffusion and the associated models and analyses. We then discuss distinct diffusive behaviors of molecules within different bacterial cellular compartments, highlighting the influence of metabolism, size, crowding, charge, binding, and more. We also explicitly discuss where further research and a united understanding of what dictates diffusive behaviors across the different compartments of the cell are required, pointing out new research avenues to pursue.

A novel spectral analysis method for distinguishing heavy metal stress of maize due to copper and lead: RDA and EMD-PSD.

Monitoring heavy metal stress in crops via hyperspectral remote sensing technology is an effective way. A new approach, namely the ratio difference of autocorrelation function first derivative (RDA), is proposed to extract characteristic regions of maize leaves spectra for the initially identification on contaminated category of copper (Cu) and lead (Pb). Simultaneously, empirical mode decomposition (EMD) and power spectral density (PSD) are integrated to construct EMD-PSD to visually discrimination on Cu and Pb stress from frequency domain perspective. In our work, pot experiment contaminated by Cu and Pb were designed and carried out, and corresponding chemical data, chlorophyll and spectra of maize leaves were collected. Based on acquired spectra, RDA is used to obtain indicators and characteristic intervals of spectra, and then EMD-PSD is applied to obtain intrinsic mode functions (IMFs) from spectra and PSD maps. Through experimental analysis, the following conclusions are drawn: 1) the red edge and red shoulder region of spectra can be used as candidate on indicator to find the characteristic regions of spectra, and integrated intersection spectral intervals are considerable to distinguish Cu and Pb; 2) PSD maps extracted by EMD-PSD significantly can discriminate stress of Cu and Pb with three-dimensional visualization. This study takes the combination of spectral domain and frequency domain as the exploration point, the stress of Cu and Pb was distinguished by mutual verification based on spectra (group I and group II and 2014 experiment). In summary, the proposed method can identify the weak differences of spectra and distinguish the stress of Cu and Pb qualitatively, which provides a new perspective for the identification of heavy metal stress categories.

Proliferation and Cluster Analysis of Neurons and Glial Cell Organization on Nanocolumnar TiN Sub-Strates.

Biomaterials employed for neural stimulation, as well as brain/machine interfaces, offer great perspectives to combat neurodegenerative diseases, while application of lab-on-a-chip devices such as multielectrode arrays is a promising alternative to assess neural function in vitro. For bioelectronic monitoring, nanostructured microelectrodes are required, which exhibit an increased surface area where the detection sensitivity is not reduced by the self-impedance of the electrode. In our study, we investigated the interaction of neurons (SH-SY5Y) and glial cells (U-87 MG) with nanocolumnar titanium nitride (TiN) electrode materials in comparison to TiN with larger surface grains, gold, and indium tin oxide (ITO) substrates. Glial cells showed an enhanced proliferation on TiN materials; however, these cells spread evenly distributed over all the substrate surfaces. By contrast, neurons proliferated fastest on nanocolumnar TiN and formed large cell agglomerations. We implemented a radial autocorrelation function of cellular positions combined with various clustering algorithms. These combined analyses allowed us to quantify the largest cluster on nanocolumnar TiN; however, on ITO and gold, neurons spread more homogeneously across the substrates. As SH-SY5Y cells tend to grow in clusters under physiologic conditions, our study proves nanocolumnar TiN as a potential bioactive material candidate for the application of microelectrodes in contact with neurons. To this end, the employed K-means clustering algorithm together with radial autocorrelation analysis is a valuable tool to quantify cell-surface interaction and cell organization to evaluate biomaterials' performance in vitro.

Nanoscale Relative Emission Efficiency Mapping Using Cathodoluminescence g(2) Imaging.

Cathodoluminescence (CL) imaging spectroscopy provides two-dimensional optical excitation images of photonic nanostructures with a deep-subwavelength spatial resolution. So far, CL imaging was unable to provide a direct measurement of the excitation and emission probabilities of photonic nanostructures in a spatially resolved manner. Here, we demonstrate that by mapping the cathodoluminescence autocorrelation function g(2) together with the CL spectral distribution the excitation and emission rates can be disentangled at every excitation position. We use InGaN/GaN quantum wells in GaN nanowires with diameters in the range 200-500 nm as a model system to test our new g(2) mapping methodology and find characteristic differences in excitation and emission rates both between wires and within wires. Strong differences in the average CL intensity between the wires are the result of differences in the emission efficiencies. At the highest spatial resolution, intensity variations observed within wires are the result of excitation rates that vary with the nanoscale geometry of the structures. The fact that strong spatial variations observed in the CL intensity are not only uniquely linked to variations in emission efficiency but also linked to excitation efficiency has profound implications for the interpretation of the CL data for nanostructured geometries in general.

A systematic benchmarking of computational vibrational spectroscopy with DFTB3: Normal mode analysis and fast Fourier transform dipole autocorrelation function.

Computational vibrational spectroscopy serves as an important tool in the interpretation of experimental infrared (IR) spectra. In this article, we present a systematic benchmarking study of DFTB3 with two different computational vibrational spectroscopic methods, based on either normal mode analysis (NMA) or fast Fourier transform dipole autocorrelation function (FT-DAC). The results were compared with experimental data and theoretical calculations with B3LYP/cc-pVTZ. The empirical scaling factors for DFTB3/NMA, DFTB3-freq/NMA, and DFTB3/FT-DAC methods are 0.9993, 1.0059, and 0.9982, respectively. We also demonstrate the significance of anharmonicity and conformational sampling in vibrational spectroscopic calculations on flexible molecules. As expected, DFTB3/FT-DAC predicted the anharmonic vibrational peaks more accurately than DFTB3/NMA and NMA spectra are highly dependent on the initial structures. The potential limitations of DFTB3 for vibrational spectroscopic calculations and the challenges in assigning the FT-DAC spectral peaks were noted. DFTB3/FT-DAC is expected to serve as a promising technique in computational spectroscopy in complex biomolecular systems. © 2018 Wiley Periodicals, Inc.

Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC.

The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this study, we propose a novel model called MACC-PSSM by integrating Moran autocorrelation and cross correlation with PSSM. Then a 3600-dimensional feature vector is constructed to predict apoptosis protein subcellular localization. Finally, 210 features are selected using principal component analysis (PCA) on the ZW225 dataset, and support vector machine is adopted as classifier. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark datasets: ZW225 and CL317. Our model achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies for datasets ZW225 and CL317, which reach 84.9% and 90.5%, respectively. Comparison of our results with other methods demonstrates that MACC-PSSM model can be used as a potential candidate for the accurate prediction of apoptosis protein subcellular localization.

[Rhythm analysis of body surface potential mapping recordings from atrial fibrillation patients based on autocorrelation function].

The study of atrial fibrillation (AF) has been known as a hot topic of clinical concern. Body surface potential mapping (BSPM), a noninvasive electrical mapping technology, has been widely used in the study of AF. This study adopted 10 AF patients' preoperative and postoperative BSPM data (each patient's data contained 128 channels), and applied the autocorrelation function method to obtain the activation interval of the BSPM signals. The activation interval results were compared with that of manual counting method and the applicability of the autocorrelation function method was verified. Furthermore, we compared the autocorrelation function method with the commonly used fast Fourier transform (FFT) method. It was found that the autocorrelation function method was more accurate. Finally, to find a simple rule to predict the recurrence of atrial fibrillation, the autocorrelation function method was used to analyze the preoperative BSPM signals of 10 patients with persistent AF. Consequently, we found that if the patient's proportion of channels with dominant frequency larger than 2.5 Hz in the anterior left region is greater than the other three regions (the anterior right region, the posterior left region, and the posterior right region), he or she might have a higher possibility of AF recurrence. This study verified the rationality of the autocorrelation function method for rhythm analysis and concluded a simple rule of AF recurrence prediction based on this method.

New insights about time-varying diffusivity and its estimation from diffusion MRI.

PURPOSE: Characterizing the relation between the applied gradient sequences and the measured diffusion MRI signal is important for estimating the time-dependent diffusivity, which provides important information about the microscopic tissue structure. THEORY AND METHODS: In this article, we extend the classical theory of Stepisnik for measuring time-dependent diffusivity under the Gaussian phase approximation. In particular, we derive three novel expressions which represent the diffusion MRI signal in terms of the mean-squared displacement, the instantaneous diffusivity, and the velocity autocorrelation function. We present the explicit signal expressions for the case of single diffusion encoding and oscillating gradient spin-echo sequences. Additionally, we also propose three different models to represent time-varying diffusivity and test them using Monte-Carlo simulations and in vivo human brain data. RESULTS: The time-varying diffusivities are able to distinguish the synthetic structures in the Monte-Carlo simulations. There is also strong statistical evidence about time-varying diffusivity from the in vivo human data set. CONCLUSION: The proposed theory provides new insights into our understanding of the time-varying diffusivity using different gradient sequences. The proposed models for representing time-varying diffusivity can be utilized to study time-varying diffusivity using in vivo human brain diffusion MRI data. Magn Reson Med 78:763-774, 2017. © 2016 International Society for Magnetic Resonance in Medicine.