Variable Sampling Rate Research Articles

Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health

Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.

A Hybrid Technique for Analysis of Low-Frequency Oscillation in Power System

Estimating the low-frequency oscillation in an interconnected power system is the most important requirement to keep the power system in a stable operating condition. This research work deals with a hybrid robust and accurate approach using a combination of Estimation of signal parameters via rotational invariant techniques (ESPRIT) and Prony algorithm to extract the low-frequency oscillatory modes present in the power system. The observation inspires the hybrid method that the true modes of the signal are present in any signal processing technique (for example, Prony algorithm) along with other fictitious modes regardless of the order of the power system. Moreover, this research obtained true modes by calculating Euclidean distance and applying the threshold value concept. The proposed technique is tested with different noise conditions and varying sampling rates of Phasor Measurement Unit (PMU) to check the proposed hybrid technique’s robustness compared to Prony and the multiple ESPRIT method. Finally, the proposed method is applied to the real signal obtained from the Western Electricity Coordinating Council (WECC) network, and it estimates accurate and precise parameters compared to other methods. The accuracy for estimation of frequency and attenuation factor is calculated for the three-mode synthetic signal at a noise level of 10dB by the hybrid algorithm, multiple ESPRIT, and Prony algorithm, which shows that hybrid algorithm has minimum percentage error. Thus, the proposed hybrid algorithm accurately estimates the parameters of low-frequency oscillation as compared to other existing methods without involving any fictitious modes.

Visual Analytics of Multivariate Intensive Care Time Series Data

AbstractWe present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.

Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition

Recurrent neural networks (RNNs) have been widely used to solve sequence problems due to their capability of modeling temporal dependency. Despite the rich varieties of RNN models proposed in the literature, the problem of different sampling rates or performing speeds in sequence tasks has not been explicitly considered in the network and the corresponding training and testing processes. This paper addresses the problem of different sampling rates or performing speeds in the skeleton-based action recognition with RNNs. Specifically, the recently proposed independently recurrent neural network (IndRNN) is used as the RNN network due to its well-behaved and easily regulated gradient backpropagation through time. Samples are extracted with variable sampling rates and thus of different lengths, then processed by IndRNN with different time steps. In order to accommodate the differences in terms of gradients introduced by the backpropagation through time under variable time steps, a learning rate adjustment method is further proposed in the paper. Different learning rate adjustment factors are obtained for different layers by analyzing the gradient behavior under IndRNN. Experiments on skeleton-based action recognition are conducted to verify its effectiveness, and the results show that the proposed variable rate IndRNN network can significantly improve the performance over the RNN models under the conventional training strategies.

Effects of using different algorithms and fiducial points for the detection of interbeat intervals, and different sampling rates on the assessment of pulse rate variability from photoplethysmography

ObjectivePulse Rate Variability (PRV) has been widely used as a surrogate of Heart Rate Variability (HRV). However, there are several technical aspects that may affect the extraction of PRV information from pulse wave signals such as the photoplethysmogram (PPG). The aim of this study was to evaluate the effects of changing the algorithm and fiducial points used for determining inter-beat intervals (IBIs), as well as the PPG sampling rate, from simulated PPG signals with known PRV content. MethodsPPG signals were simulated using a proposed model, in which PRV information can be modelled. Two independent experiments were performed. First, 5 IBIs detection algorithms and 8 fiducial points were used for assessing PRV information from the simulated PPG signals, and time-domain and Poincaré plot indices were extracted and compared to the expected values according to the simulated PRV. The best combination of algorithms and fiducial points were determined for each index, using factorial designs. Then, using one of the best combinations, PPG signals were simulated with varying sampling rates. PRV indices were extracted and compared to the expected values using Student t-tests or Mann-Whitney U-tests. ResultsFrom the first experiment, it was observed that AVNN and SD2 indices behaved similarly, and there was no significant influence of the fiducial points used. For other indices, there were several combinations that behaved similarly well, mostly based on the detection of the valleys of the PPG signal. There were differences according to the quality of the PPG signal. From the second experiment, it was observed that, for all indices but SDNN, the higher the sampling rate the better. AVNN and SD2 showed no statistical differences even at the lowest evaluated sampling rate (32 Hz), while RMSSD, pNN50, S, SD1 and SD1/SD2 showed good performance at sampling rates as low as 128 Hz. ConclusionThe best combination of IBIs detection algorithms and fiducial points differs according to the application, but those based on the detection of the valleys of the PPG signal tend to show a better performance. The sampling rate of PPG signals for PRV analysis could be lowered to around 128 Hz, although it could be further lowered according to the application. SignificanceThe standardisation of PRV analysis could increase the reliability of this signal and allow for the comparison of results obtained from different studies. The obtained results allow for a first approach to establish guidelines for two important aspects in PRV analysis from PPG signals, i.e. the way the IBIs are segmented from PPG signals, and the sampling rate that should be used for these analyses. Moreover, a model for simulating PPG signals with PRV information has been proposed, which allows for the establishing of these guidelines while controlling for other variables, such as the quality of the PPG signal.

A customizable, low-power, wireless, embedded sensing platform for resistive nanoscale sensors

Customizable, portable, battery-operated, wireless platforms for interfacing high-sensitivity nanoscale sensors are a means to improve spatiotemporal measurement coverage of physical parameters. Such a platform can enable the expansion of IoT for environmental and lifestyle applications. Here we report a platform capable of acquiring currents ranging from 1.5 nA to 7.2 µA full-scale with 20-bit resolution and variable sampling rates of up to 3.125 kSPS. In addition, it features a bipolar voltage programmable in the range of −10 V to +5 V with a 3.65 mV resolution. A Finite State Machine steers the system by executing a set of embedded functions. The FSM allows for dynamic, customized adjustments of the nanosensor bias, including elevated bias schemes for self-heating, measurement range, bandwidth, sampling rate, and measurement time intervals. Furthermore, it enables data logging on external memory (SD card) and data transmission over a Bluetooth low energy connection. The average power consumption of the platform is 64.5 mW for a measurement protocol of three samples per second, including a BLE advertisement of a 0 dBm transmission power. A state-of-the-art (SoA) application of the platform performance using a CNT nanosensor, exposed to NO2 gas concentrations from 200 ppb down to 1 ppb, has been demonstrated. Although sensor signals are measured for NO2 concentrations of 1 ppb, the 3σ limit of detection (LOD) of 23 ppb is determined (1σ: 7 ppb) in slope detection mode, including the sensor signal variations in repeated measurements. The platform’s wide current range and high versatility make it suitable for signal acquisition from resistive nanosensors such as silicon nanowires, carbon nanotubes, graphene, and other 2D materials. Along with its overall low power consumption, the proposed platform is highly suitable for various sensing applications within the context of IoT.

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

The high‐speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast‐growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high‐speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR‐NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR‐LSTM algorithm for real‐time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR‐LSTM), we simulate the prediction accuracy of the VSR‐LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR‐LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.

Panalysis of the Efficiency of the VRS Algorithm in the Transmission of Weather Data

In the first part of this paper a Variable rate sampling, algorithm for prediction has been described. Variable rate sampling systems were used for reducing power demand, by reducing the number of sent samples. In the second part is performed an experiment where is tested algorithm for prediction of data for transmission and transmitted data. For the purpose of this analyze transmission and predictions are performed for temperatures, air pressure, wind speed, and visibility. Prediction on the side of the transmission is performed using three different extrapolations (mam, mab, and ng). At the receiving side reconstruction of the received signal is performed applying Linear, Cubic, Makima, and Spline extrapolations, built into Matlab. A simple reconstruction is also performed, using the last known value for prediction the next value (this prediction is in the paper named Step extrapolation). Objective quality measures SR (correlation coefficient of reducing the number of samples between a number of measured and transmitted samples), and MAE (mean absolute error between the measured and predicted values of temperatures, pressures and visibility values) were calculated. The results are presented in the table and graphically.

Transmitting strategy with high degrees of freedom for pulsed‐coherent FDA radar

Multidimensional and flexible radar resource transmission in time, frequency and space domains is strongly demanded for advanced digital array radar to meet the challenging observation requirements. In this study, a high degrees of freedom (DOF) transmitting strategy is proposed for pulsed-coherent frequency diverse array (FDA) radar. The flexible beam scanning property of coherent FDA radar is well developed by combining with spatial transmit array weighting and temporal piecewise waveform design. The proposed method fully exploits the transmitting DOFs of coherent FDA radar, which not only enables the transmit beampattern shaping but also promises the ability of spatial waveform diversity and transmitting energy allocation. Since the spatial transmitting signal bandwidth is preset, a received signal processing method with a variable sampling rate is proposed to avoid over sampling in all receiving channels. Simulation results validate the effectiveness of the proposed method and further demonstrate its potential applications.

DANA

Motion sensors embedded in wearable and mobile devices allow for dynamic selection of sensor streams and sampling rates, enabling several applications, such as power management and data-sharing control. While deep neural networks (DNNs) achieve competitive accuracy in sensor data classification, DNN architectures generally process incoming data from a fixed set of sensors with a fixed sampling rate, and changes in the dimensions of their inputs cause considerable accuracy loss, unnecessary computations, or failure in operation. To address these limitations, we introduce a dimension-adaptive pooling (DAP) layer that makes DNNs flexible and more robust to changes in sensor availability and in sampling rate. DAP operates on convolutional filter maps of variable dimensions and produces an input of fixed dimensions suitable for feedforward and recurrent layers. Further, we propose a dimension-adaptive training (DAT) procedure for enabling DNNs that use DAP to better generalize over the set of feasible data dimensions at inference time. DAT comprises the random selection of dimensions during the forward passes and optimization with accumulated gradients of several backward passes. Combining DAP and DAT, we show how to transform existing non-adaptive DNNs into a Dimension-Adaptive Neural Architecture (DANA), while keeping the same number of parameters. Compared to existing approaches, our solution provides better average classification accuracy over the range of possible data dimensions at inference time and does not require up-sampling or imputation, thus reducing unnecessary computations. Experimental results on seven datasets (four benchmark real-world datasets for human activity recognition and three synthetic datasets) show that DANA prevents significant losses in classification accuracy of the state-of-the-art DNNs and, compared to baselines, it better captures correlated patterns in sensor data under dynamic sensor availability and varying sampling rates.

Novel Algorithm for Mutual Alignment of Railway Track Geometry Measurements

Track geometry measurements are regularly collected to monitor the condition of a railway network. To detect deterioration patterns and enable predictive maintenance, sequential measurement runs must be mutually aligned which has been proven a serious challenge. This paper presents a novel algorithm for mutual alignment of track geometry signal data. It resolves several previously intractable alignment problems: highly segmented data with variable sample rate, spatially correlated and uncorrelated measurement errors, convergence to true locations, and consistency over time. The algorithm adjusts spatial measurement errors by splitting signals in continuous segments. Re-sampled, error-corrected signals are mutually aligned using cross correlation, and this process is repeated until the mutual alignment meets a pre-defined precision threshold. Missing measurement values are handled by imputing an interpolated offset from nearby segments, ensuring that the signals remain continuous. By using weighted average offsets over all aligned signals, the law of large numbers guarantees convergence and consistency. The practical feasibility of the algorithm is demonstrated on empirical track geometry measurement data from the British railway network, owned and operated by Network Rail.

Cross-modal context-gated convolution for multi-modal sentiment analysis

When inferring sentiments, using verbal clues only is problematic because of the ambiguity. Adding related vocal and visual contexts as complements for verbal clues can be helpful. To infer sentiments from multi-modal temporal sequences, we need to identify both sentiment-related clues and their cross-modal interactions. However, sentiment-related behaviors of different modalities may not occur at the same time. These behaviors and their interactions are also sparse in time, making it hard to infer the correct sentiments. Besides, unaligned sequences from sensors also have varying sampling rates, which amplify the misalignment and sparsity mentioned above. While most previous multi-modal sentiment analysis works only focus on word-aligned sequences, we propose cross-modal context-gated convolution for unaligned sequences. Cross-modal context-gated convolution captures the local cross-modal interactions, dealing with the misalignment while reducing the effect of unrelated information. Cross-modal context-gated convolution introduces the concept of cross-modal context gate, enabling itself to catch useful cross-modal interactions more effectively. Cross-modal context-gated convolution also brings more possibilities to the layer design for multi-modal sequential modeling. Experiments on multi-modal sentiment analysis datasets under both word-aligned and unaligned conditions show the validity of our approach.

A Map Inference Approach Using Signal Processing from Crowd-sourced GPS Data

The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. We explore map inference using a three-stage approach, which incorporates a novel Multi-source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first use of estimation theory for map inference. In particular, our approach addresses the unique challenges of vehicular GPS data. This data is plentiful but suffers from noise in location and variable coverage of regions. This makes it difficult to differentiate between noise and sparsely covered regions when increasing coverage and reducing noise. Due to the asynchronous, variable sampling rate, and often under-sampled nature of the data, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. We evaluated our method for map inference by comparing to Open Street Map maps as ground truth. We use the F-Measure, Precision, and Recall metrics to evaluate our method on Tsinghua University’s Beijing Taxi Dataset and Shanghai Jiao Tong University’s SUVnet Dataset. On these datasets, we obtained a mean<?brk?> F-Measure, Precision, and Recall of 0.7212, 0.9165, and 0.6021, respectively, outperforming a well-known method based on Kernel Density Estimation in terms of these evaluation metrics.

Decentralized sampled-data fuzzy controller design for a VTOL UAV

This study focuses on a sampled-data fuzzy decentralized tracking control problem for a quadrotor unmanned aerial vehicle (UAV) under the variable sampling rate condition. To this end, the overall dynamics of the quadrotor is expressed as a decentralized Takagi–Sugeno (T–S) fuzzy model interconnected with each other. Although the proposed decentralized control technique divides the overall UAV control system into attitude and position subsystems, the stability of the entire control system is guaranteed. Besides, in this paper, the model uncertainty, interconnection, and reference trajectory are considered as disturbances acting on the tracking error. To attenuate these disturbances, a novel sampled-data tracking control design technique is derived based on a linear reference model to be tracked and the time-dependent Lyapunov–Krasovskii functional (LKF). By doing so, both the stability of the tracking error dynamics and the minimization of tracking performance are guaranteed. Also, the proposed tracking control design method is derived as a linear matrix inequality (LMI)-based optimal problem. Finally, a simulation example is provided to demonstrate the effectiveness and feasibility of the proposed design methodology.

ASIC Implementation of Low PAPR Multidevice Variable-Rate Architecture for IEEE 802.11ah

The advancement of next-generation systems has led to a rapid increase in day-to-day applications by connecting billions of devices. Mobile networks require a considerable cost to support these billions of devices, and conventional IEEE 802.11 is meant for the usage of small-scale applications. To support long-range applications, IEEE task group-ah has proposed a new standard named IEEE 802.11ah. However, it has challenges like efficient device grouping based on variable sampling rates and power consumption. This standard employs orthogonal frequency-division multiplexing as a multicarrier scheme that suffers from high peak-to-average power ratio (PAPR). Hence, to support multiple devices of variable rates in the physical layer along with low PAPR and acceptable bit error rate, a new framework and an optimized architecture of carrier interferometry-group orthogonal–orthogonal frequency-division multiple access architecture is proposed. The proposed architecture reduces the 16-point inverse fast Fourier transform area by 57.1%, device padding area by 50% along with three extra adders functional area compared with the existing architecture. Furthermore, the proposed architecture is prototyped on Virtex5 field-programmable gate array for on-chip validation, and the hardware parameters are measured in real time. The area-timing complexity of the proposed architecture is estimated and further achieves the lowest area- and power-delay product than the existing architecture. Application-specific integrated circuit implementation is carried out using commercially available 32 - nm technology library, where the postlayout results reported less area utilization and low power consumption.

Differential confocal measurement for surface topography with microstructures based on spiral scanning and wavelet filter.

This paper proposes a novel spiral-scanning laser differential confocal measurement method (SSLDCM) for fast and precise measurement of surface topography with microstructures. Spiral plane scanning is used to eliminate frequent acceleration and deceleration problems in traditional raster-scanning differential confocal measurement systems and helps to keep the measuring process efficient and stable. To solve the problem of uneven sampling distribution during spiral scanning, a variable sampling rate method is adopted to distribute the sampling points at equal intervals, which would help to reduce the time of the 3D imaging process. A denoising method based on an adaptive wavelet threshold is proposed to filter the existing noise during the measuring process. An experimental measurement platform based on SSLDCM is constructed, and the axial response curve is tested and analyzed. The linear region range of the experimental platform reaches 13 µm, and the slope is about 164.15 mV/µm. In addition, the measurement results of a silicon wafer specimen by SSLDCM show good consistency with a commercial high-precision microscope, and the largest deviation is less than 2.71%. The SSLDCM has great potential to be used in various noncontact surface measurement applications with high efficiency and accuracy.

Discovering locations and habits from human mobility data

Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals’ visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual’s habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.

Disturbance Observer-Based Discrete Sliding-Mode Control for a Marine Diesel Engine with Variable Sampling Control Technique

This paper proposes a disturbance observer-based discrete sliding-mode control scheme with the variable sampling rate control for the marine diesel engine speed control in the presence of system uncertainties and disturbances. Initially, a sliding-mode controller based on the fast power reaching law is employed, which has a good dynamic quality of the arrival stage and can suppress chattering. To satisfy the practical requirements in the digital controller and the crank angle-based fuel injection in engine speed control, the proposed method is discretized under the variable sampling rate condition. A disturbance observer based on the second-order sliding-mode control is designed to compensate the system uncertainties and disturbances, by doing such the requirement of the parameters of the sliding-mode controller to be reduced significantly. In addition, a cylinder-by-cylinder mean value engine model (MVEM) is built by restructuring the combustion torque model, based on which numerical simulations are carried out by comparing the proposed method with PID and the extended state observer (ESO)-based sliding mode controllers. The common operation situations of the marine diesel engines are taken into account, including starting process, acceleration and deceleration, load variation, and varied propulsion system parameters. The results demonstrate that the proposed disturbance observer-based sliding-mode controller has prominent control performance and strong robustness.

Sampling Rate Decay in Hindsight Experience Replay for Robot Control.

Training agents via deep reinforcement learning with sparse rewards for robotic control tasks in vast state space are a big challenge, due to the rareness of successful experience. To solve this problem, recent breakthrough methods, the hindsight experience replay (HER) and aggressive rewards to counter bias in HER (ARCHER), use unsuccessful experiences and consider them as successful experiences achieving different goals, for example, hindsight experiences. According to these methods, hindsight experience is used at a fixed sampling rate during training. However, this usage of hindsight experience introduces bias, due to a distinct optimal policy, and does not allow the hindsight experience to take variable importance at different stages of training. In this article, we investigate the impact of a variable sampling rate, representing the variable rate of hindsight experience, on training performance and propose a sampling rate decay strategy that decreases the number of hindsight experiences as training proceeds. The proposed method is validated with three robotic control tasks included in the OpenAI Gym suite. The experimental results demonstrate that the proposed method achieves improved training performance and increased convergence speed over the HER and ARCHER with two of the three tasks and comparable training performance and convergence speed with the other one.

An Architecture for Real-Time Arbitrary and Variable Sampling Rate Conversion With Application to the Processing of Harmonic Signals

The paper presents a new solution for sampling rate conversion and processing of harmonic signals with known but possibly varying fundamental frequency. This problem is commonly found in particle accelerators, for tracking the beam signals whose revolution frequency varies during the acceleration ramp. It is also common among many other fields such as speech and music processing, removal of mechanical noises, filtering of biomedical recordings, active crack imaging, etc. The key element in the proposed solution is a new architecture for a Farrow-based resampler, in which the resampling ratio can take any value and can be modified continuously to follow the signal fundamental frequency. The combination of two complementary resamplers creates a processing region where signal synchronous processing is performed. The resampler architecture is optimized for modern FPGA features. It decouples the processing and sampling clocks, and uses a single processing (hardware) clock whose frequency remains fixed. The functional model was migrated to Xilinx System Generator and the overall performance is evaluated with an application that filters a periodic signal whose frequency follows a known linear ramp in the presence of additive white noise.