Time-domain Windowing Research Articles

A Multi-functional Electronic Glove for Multidimensional Environmental Perception and Gesture Recognition

In recent years, wearable electronic gloves with rich sensing functions have garnered widespread attention. Researchers are exploring various aspects of wearable devices including structural design, material innovation, and algorithm optimization. An analysis of demand suggests that wearable electronic gloves should possess sensory functions like hand, which includes roughness of contacted objects, assessing temperature of touched objects, determining grasp force, and interpreting gesture language. This paper presents a multi-sensing wearable flexible electronic glove equipped with graphene foam material sensors that can perceive external pressure, roughness, temperature, and express gesture language. The experimental results show that this glove exhibits excellent approximately linear-range sensing characteristics for force perception (1.475 kPa-1 at 0-0.4 kPa), outstanding temperature sensing capability (temperature sensitivity is -1.22% within the range of 30-80℃), and recognition judgment of 12 different gestures. Additionally, an optimized KNN (K-nearest neighbor) algorithm based on time-domain window is proposed. Compared with traditional KNN algorithm, KNN based on time-domain window improves the accuracy of gesture intent recognition from 86.15% to 91.93%. Wearable electronic gloves represent promising developments for fields such as hand rehabilitation training for patients, intention expression for individuals suffering from language barriers, and wearable multifunctional sensing applications.

A Retrospective Study: Are the Multi-Dips in the THz Spectrum during Laser Filamentation Caused by THz–Plasma Interactions?

During the process of terahertz (THz) wave generation via femtosecond laser filamentation in air, as well as through the mixing of THz waves with externally injected plasma filaments, THz waves engage in interactions with the plasma. A characteristic feature of this interaction is the modulation of the THz radiation spectrum by the plasma, which includes the generation of THz spectral dips. This information is essential for understanding the underlying mechanisms of THz–plasma interactions or for inferring plasma parameters. However, a current debate exists on the number of THz spectral dips observed after the interaction, with different opinions of single versus multiple dips, thus leaving the interaction mechanisms still ambiguous. In this work, we retrospectively analyzed the experimental appearance of multiple dips in the THz spectrum and found that the current observations of such dips are predominantly a result of the water vapor absorption with a low spectral resolution. Additionally, we observed that altering the acquisition width of the temporal THz signal also influenced the dips’ number. Hence, in future research, simultaneous attention should be paid to the following two aspects of THz–plasma interactions: (1) It is necessary to ensure a sufficiently wide time-domain window to accurately represent the spectral dip characteristics. (2) The spectral dips should be carefully distinguished from the water absorption lines before being further studied. On the other hand, for the case of a single dip in the THz spectrum, we also put forward a new viewpoint of the resonance between surface plasmon waves and THz waves, which should also be taken into consideration in future studies.

Compact Symmetric Objects. II. Confirmation of a Distinct Population of High-luminosity Jetted Active Galaxies

Compact symmetric objects (CSOs) are compact (<1 kpc), jetted active galactic nuclei (AGN), whose jet axes are not aligned close to the line of sight, and whose observed emission is not predominantly relativistically boosted toward us. Two classes of CSOs have previously been identified: approximately one-fifth are edge dimmed and the rest are edge brightened. We designate these as CSO 1s and 2s, respectively. This paper focuses almost exclusively on CSO 2s. Using complete samples of CSO 2s we present three independent lines of evidence, based on their relative numbers, redshift distributions, and size distributions, which show conclusively that the vast majority (>99%) of CSO 2s do not evolve into larger-scale radio sources. These CSO 2s belong to a distinct population of jetted AGN, which should be characterized as “short-lived,” as opposed to “young,” compared to the classes of larger jetted AGN. We show that there is a sharp upper cutoff in the CSO 2 size distribution at ≈500 pc. The distinct differences between most CSO 2s and other jetted AGN provides a crucial new time domain window on the formation and evolution of relativistic jets in AGN and the supermassive black holes that drive them.

Characterized environmental influences on autocollimator measurement uncertainty using an extended Allan variance

Autocollimators play an important role in the high-precision testing of the slope errors of X-ray and large flat mirrors. However, the time-varying errors caused by environmental changes exhibit different characteristics over time, such as low-frequency drift errors and high-frequency random errors. Time-varying errors can seriously affect the accuracy of the autocollimator (AC) and the read repeatability. They have become important factors limiting the continued improvement of nano-optic-measuring machines and scanning pentaprism system accuracy. Considering the difficulty in quantitatively analyzing the influence of time-varying errors on the measurement uncertainties of the AC, this paper proposes the extended Allan variance to characterize environmental influences on the AC measurement uncertainty based on a point-wise scanning measurement mode. Taking advantage of the extended Allan variance's characteristics, which allow the identification of various errors and provide stability information about the AC measurement, the characteristic curves of the AC measurement uncertainty in a specific environment were obtained. Based on the characteristic curve parameters, the ultimate accuracy of AC in the current environment was obtained by optimizing the measurement strategy. Experimental results show that the measurement uncertainty of the AC can reach 2.3 nrad RMS in the measurement time-domain window.

Wrap-Around Based Frequency-Varying Viterbi Detection for Windowed OFDM Systems in Doubly Selective Fading Channels

In doubly selective fading channels, the conventional orthogonal frequency division multiplexing (OFDM) system suffers high inter-carrier interferences (ICIs). To deal with the ICIs, combining the time-domain windowing and Viterbi detection, we propose a wrap-around based frequency-varying Viterbi detection scheme for windowed OFDM systems. In a doubly selective fading channel, we theoretically derive the input-output relationship of windowed OFDM systems. It is observed that by selecting a proper window function, the equivalent channel matrix is sparse. Therefore, the severe ICIs can be significantly mitigated by our proposed wrap-around based frequency-varying Viterbi detection scheme. It is shown by simulation results that our proposed scheme achieves the performance very close to that of the maximum likelihood detector, even when the normalized Doppler frequency is as high as 0.667. Furthermore, the computational complexity of our proposed detection method is linear to the number of subcarriers, given the maximum iteration times and the constraint length.

A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries

Alzheimer’s disease (AD) is a progressive and fatal disease, due to the nonavailability of any permanent cure. Some treatments are under experimentation that can slow down and possibly pause the progression of the disease only if the disease is diagnosed earlier. The onset of AD can only be detected at the mild cognitive impairment (MCI) stage in which slight memory loss is observed but daily routine functions are intact. A small fraction of the patient progresses from MCI to AD. In this research, we have designed a cascaded deep neural network model to identify those MCI subjects who will progress to AD in the following year. The analysis and experimentation have been performed using twenty longitudinal neuropsychological measures (NMs) provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI). After normalization and ranking of longitudinal data, the deep neural network regression model is trained and tuned to forecast the next in-sequence biomarker value using two previous follow-up readings for each marker. Then, the three time-domain window samples are fed into another deep neural network classifier model for the classification of MCI progressor (MCIp) and MCI stables (MCIs). Our model presented regression forecasting MAE of 0.13 and classification accuracy of 86.9% with AUC of 92.1% (Sensitivity: 67.7%, specificity: 92.3%) over 5-fold cross-validation. We conclude that time-domain measures of NM alone can deliver comparable MCI to AD conversion prediction performance without leveraging more expensive and invasive counterparts such as MR imaging, PET scans, and CSF measures. Middle and low-income countries will benefit from such cheap and effective solutions greatly.

Methods for accurate acoustic characterization with ultra-low noise and minimal effect from reflection wave

It is common to measure the response of devices and structures to sound due to an imposed sound source. Unfortunately, acoustic reflections from walls and/or instruments often contaminate the results. In this presentation, methods of acoustic characterization are described to minimize the influence of acoustic reflections. It is shown that this process results in clean and smooth data. A simple time-domain window is implemented for diminishing the contribution from reflection waves. Moreover, a single frequency curve fitting approach is employed for better parameter identification and noise reduction, compared to traditional fast Fourier Transform analysis. Results obtained from a theoretical acoustic model with a reflection source are compared with measured results. Different cases of data acquisition with time and frequency analysis are experimentally demonstrated and validated. All experimental measurements are performed in an anechoic chamber. Results show that the approach presented here significantly reduces noise and also the influences of reflection waves in experimental data acquisition outcomes.

Radar Pulse Compression with Optimized Weighting Window for SAR Receivers

This paper proposes a novel design of a software-defined matched filter (MF) for digital receivers of synthetic aperture radar (SAR). The block diagram of the proposed receiver is described in detail. The purpose of this filter is to produce a SAR pulse with higher compression ratio (CR) and lower side lobe level (SLL) than that produced by the conventional MF. The proposed design is based on the idea of time windowing of the SAR pulse to construct the transfer function of the receiver filter. The shape of the proposed time-domain window is optimized to achieve the filter design goals including the minimization of the SLL and the realization of the target value of the CR. The transmitted SAR pulse is, first, subjected to linear frequency modulation and then subjected to the optimized window. The width (time duration) of the proposed window is divided into equal time intervals. The proposed time-domain window is constructed as a sequential continuous piecewise linear segments. The instantaneous value of the time-domain window at the start of each time interval is optimized so as to achieve the optimization goals. The width of the time-domain window is shown to be proportional to the width of the compressed pulse after optimization. The number of the time intervals into which the time duration of the window is divided is shown to have a significant effect on the optimization results. The particle swarm optimization (PSO) technique is then applied to get the window shape that minimizes the SLL for a specific predetermined value of the pulse CR. It is shown that the iterations of the PSO are fastly convergent and that the applied algorithm is computationally efficient. Also, it is shown that the desired value of the pulse CR is achieved with accuracy of 100%. Moreover, the achieved SLLs are about − 65,mathrm{dB}, - 90,mathrm{dB}, - 114,mathrm{dB}, and - 133,mathrm{dB} for pulse CR of 5, 3, 2, and 1.5, respectively. Finally, for practical implementation of the introduced SAR pulse processing technique, the proposed optimized window is placed as a building block in a software-defined receiver of the SAR system.

Precise sinusoidal signal extraction from noisy waveform in vibration calibration

Precise extraction of sinusoidal vibration parameters is essential for the dynamic calibration of vibration sensors, such as accelerometers. However, several standard methods have not yet been optimized for large background noise. In this work, signal processing methods to extract small vibration signals from noisy data in the case of accelerometer calibration are discussed. The results show that spectral leakage degrades calibration accuracy. Three methods based on the use of a filter, window function, and numerical differentiation are investigated to reduce the contribution of the calibration system noise. We demonstrate the effectiveness of these methods with theoretical calculations, simulations, and experiments. The uncertainty of microvibration calibration in the National Metrology Institute of Japan is reduced by two orders of magnitudes using the proposed methods. We recommend to use a combination of numerical differentiation and either a time-domain window function or a bandpass filter for most accelerometer microvibration sensitivity calibrations. For suppressing the effects of line noise, adjusting the data set length is also effective.

Intensity-guided depth image estimation in long-range lidar

Long-range lidar systems usually record large but extremely sparse data cubes. It is a great challenge to estimate accurate depth images of the photon-starved regime with fewer memory requirements and lower computational complexity. An intensity-guided method is introduced to estimate the depth image by using temporal-spatial correlation of the reflected signals. Multi-scale superpixels and fast time-domain windows are established in the preprocessing step, leading to smaller data cubes with reduced empty and noisy pixels. To strike a balance between smoothness and preserving sharp edges, the fast-converging alternating direction method of multipliers (ADMM) is used in the modified cost function to estimate depth images. Experimental results show that the proposed method yields better depth estimates than other state-of-the-art methods, especially for long-rang targets of field experiments with a low signal return level of ∼1 photon per pixel.

Градієнтна оптимізація двопараметричної віконної функції Найквіста для зменшення позасмугогово випромінювання в OFDM системі

The relationship between Nyquist pulses in the time domain and Nyquist window functions used in orthogonal frequency division multiplexing (OFDM) technology has been investigated. One of the ways to approximate the transition region of Nyquist window functions using piecewise linear functions has been analyzed. This method allowed increasing the number of degrees of signal freedom through the introduction of two additional parameters. The spectral density of the selected two-parameter Nyquist window function has been determined. Optimization of the synthesized signal by the criterion of minimum energy in the first side lobes of its spectral density has been performed. To solve the optimization problem, the gradient descent method with minimization of the function at each stage of the iteration by the golden section method is chosen. In the MATLAB programming environment, program has been created that works according to the selected optimization algorithm. With the maximum allowable relative error ε =10–3, the number of iterations of the gradient search method is 2 iterations. It is established that at the optimal values of the signal parameters the amount of energy concentrated in the first three side lobes of its spectral density is 12.6 dB less than for the rectangular window function. The graphs for the synthesized window function with optimal parameters in time and frequency domain are given in this paper. The use of the two-parameter Nyquist window function with reduced level of spectral density of the side lobes allows reducing the level of out-of-band radiation of the OFDM signal and increasing the resistance of the system with many subcarriers to frequency offsets caused by the mutual movement of the transmitter and receiver. It should be noted that the chosen gradient search algorithm is universal and can be used to optimize the Nyquist window functions by different criteria and regardless of the number of signal parameters.

Flexural wave parameter measurements of viscoelastic panels using a laser doppler correlation vibrometry

Characterizing broadband dynamic properties of viscoelastic panel materials represents a challenging task, particularly when the frequency band of interest needs to be extended toward low frequency range. This work applies a laser Doppler vibrometer in measuring flexural wave properties of the viscoelastic panel materials. The measurement relies on a time-domain broadband correlation technique which experimentally measures impulse responses at two points radially away from a flexural wave exciter. Using reasonable sized panel samples, boundary conditions of the measurements present insignificant obstacles towards high frequency range, since a time-domain windowing facilitates extraction of direct wave components. While boundary reflections will still obscure measurement results towards low frequencies. This work examines a possible mitigation approach by rotating the sample panel, the coherence of direct wave pulses measured equidistant from the exciter and the lack thereof disturbing boundary reflections enables largely removing the undesirable reflections. In this way angular averaging significantly reduces boundary reflections. This paper discusses experimental investigations on the characterization method of flexural wave parameters including wave speed, flexural modules and loss factor.

A Novel Ultrasonic Doppler Fetal Heart Rate Detection System Using Windowed Digital Demodulation

Fetal heart rate (FHR) is an essential indicator of fetal well-being. The ultrasonic Doppler FHR detector is widely used to monitor the fetus’s health due to its advantages of non-invasion, high sensitivity, and good directivity. However, the existing commercial Doppler FHR detector primarily uses an analog multiplier for demodulation, which has limited functions and insufficient sensitivity. The analog demodulation is performed only on a single-sideband signal(in-phase or quadrature) (IQ), which is impossible to derive the fetal heart movement’s vector velocity. Moreover, repetitive recognition of the mitral valve’s positive and negative motion easily causes the measured FHR twice the actual one (FHR doubling). Although the traditional digital demodulation FHR detector can obtain in-phase and quadrature demodulation signals, it needs a high-level field-programmable gate array chip and therefore cannot meet low-power consumption requirements in battery-powered situations. Herein we proposed a novel digital demodulation FHR algorithm using time-domain windowing. Then, we applied the algorithm into a low-power complex programmable logic device to achieve low-cost acquisition of vector velocity and suppress FHR doubling and finally designed a hand-held, noninvasive digital Doppler FHR detection system for continuous perinatal detection. In this work, the comparison curve between the TREND curve generated by the simulator and the measured curve of our FHR detector was obtained. The comparative experiment between the commercial FM-3A FHR detector and our proposed FHR detector was performed, and the relative error and standard error of the obtained FHR data were used to evaluate the two FHR detectors’ detection accuracy. The results showed that our FHR detector showed excellent detection accuracy at an accuracy rate of 98%, which was much higher than the average accuracy of the FM-3A FHR detector and indicated our proposed digital FHR detector showed excellent detection performance. It means that the proposed digital windowed FHR demodulation algorithm is practical, the multi-FHR monitoring system is feasible, and our proposed FHR detector has high detection accuracy, effectively suppresses FHR doubling, and meets the requirements of Chinese national standards. It provides an alternative scheme towards the low-cost hand-held digital demodulation FHR detection application.

Bayesian Approach to Channel Interpolation in Massive MIMO Receiver

We propose a new interpolation algorithm to enhance channel estimation accuracy in Massive Multiple Input, Multiple Output (MIMO) receiver. The algorithm employs a Bayesian approach to channel tap search and de-noise by taking into account the prior statistics of channel tap distribution. We validate our method using realistic state-of-the-art 5G simulations in a large number of non-line-of-sight scenarios of QuaDRiGa 2.0 channel. We show our method to outperform standard alternatives such as polynomial interpolation and time-domain windowing.

Photonic crystal slab between orthogonal polarizers: details on the guided mode resonance wavelength

The transmission spectrum of a photonic crystal slab features sharp dips created by guided mode resonances. The same photonic crystal slab placed between orthogonal polarizers shows peaks at the resonances, but the peak wavelength differs from the guided mode resonance wavelength by a few nanometres. We investigate the working principle of the orthogonal polarizer setup and the origin of the wavelength difference for the case of a TE resonance. We show that the peak in the orthogonal polarizer setup is formed by light from the non-resonant TM polarization. The wavelength difference is caused by the phase shift between the resonant TE and the non-resonant TM polarization. We compare our explanation to a temporal coupled-mode approach and the use of a time-domain window function in FDTD.

Multiplierless Filtered-OFDM Transmitter for Narrowband IoT Devices

In cyclic-prefix orthogonal frequency-division multiplexing (CP-OFDM)-based radio access, the coexistence of different technologies without precise time-frequency synchronization is limited due to high out-of-band (OOB) emissions. Therefore, the spectrum enhancement techniques play a key role in relaxing the synchronization and power control requirements. This allows a higher degree of opportunistic spectrum use with minimized interference. In addition, all the transmitting devices have to fulfill specific transmitted signal quality requirements, including the maximum OOB radiated signal power. With the orthogonal frequency-division multiplexing (OFDM)-based radio access, some additional signal processing for improved spectrum containment is commonly needed to achieve these requirements. The filtering and time-domain windowing are two fundamentally different approaches for spectrum enhancement. The filtered OFDM (F-OFDM) provides better spectrum localization than the time-windowing schemes [such as windowed overlap-add (WOLA)], with the cost of higher complexity. This article introduces low-complexity solutions for spectrally enhanced narrowband OFDM transmitters based on the use of lookup tables (LUTs). The proposed LUT approach, requiring only memory units and a low number of additions, allows to avoid all computationally expensive operations in online transmitter processing, as it builds the transmitted signal by summing the stored partial waveforms optimized offline. In certain cases, completely multiplication- and summation-free designs are possible. The transmitters of narrowband Internet of Things (NB-IoT) devices are natural applications for the proposed LUT approach, as they require additional digital baseband signal processing to reach the emission requirements. It is shown that the proposed LUT schemes can provide significant savings in real-time computations of NB-IoT devices, while fulfilling the 3GPP requirements.

Equalization of OSDM over time-varying channels based on diagonal-block-banded matrix enhancement

Orthogonal signal-division multiplexing (OSDM) has recently emerged as a promising alternative to orthogonal frequency division multiplexing (OFDM) for high-rate wireless communications. Although providing more flexibility in system design, it suffers from a special interference structure, namely inter-vector interference (IVI), when channel time variations are present. In this paper, we first derive the general OSDM signal model over time-varying channels, and then show that a time-domain window can be used to enhance the diagonal-block-banded (DBB) approximation of the channel matrix in a transformed domain. Furthermore, based on the DBB matrix enhancement, a low-complexity OSDM equalization algorithm is designed. Simulation results indicate that the proposed equalizer has significant performance advantages over that using the direct DBB approximation.

The search for statistical anisotropy in the gravitational-wave background with pulsar timing arrays

Pulsar-timing arrays (PTAs) are seeking gravitational waves from supermassive-black-hole binaries, and there are prospects to complement these searches with stellar-astrometry measurements. Theorists still disagree, however, as to whether the local gravitational-wave background will be statistically isotropic, as arises if it is the summed contributions from many SMBH binaries, or whether it exhibits the type of statistical anisotropy that arises if the local background is dominated by a handful (or even one) bright source. Here we derive, using bipolar spherical harmonics, the optimal PTA estimators for statistical anisotropy in the GW background and simple estimates of the detectability of this anisotropy. We provide results on the smallest detectable amplitude of a dipole anisotropy (and several other low-order multipole moments) and also the smallest detectable amplitude of a "beam'' of gravitational waves. Results are presented as a function of the signal-to-noise with which the GW signal is detected and as a function of the number of pulsars (assuming uniform distribution on the sky and equal sensitivity per pulsar). We provide results first for measurements with a single time-domain window function and then show how the results are augmented with the inclusion of time-domain information. The approach here is intended to be conceptually straightforward and to complement the results of more detailed (but correspondingly less intuitive) modeling of the actual measurements.

Dim small moving target detection and tracking method based on spatial-temporal joint processing model

In order to solve the problem of weak target detection in complex moving background, a joint algorithm model of space-time domain is proposed in this paper. The algorithm first selects appropriate length of the time domain window and loop step size. In each time domain window: (1) In time domain processing stage, the algorithm fully considers the factors of background movement, and uses neighborhood similarity measure based on Pearson correlation coefficient to suppress most part of background area. (2) In space domain processing stage, a target detection method based on regional gray level (RGL) is proposed to suppress residual background and re-enhance the target. In each iteration, this joint detection model can obtain part of the trajectory of the target in real time. Therefore, after all iterations are completed (all images in the sequence have been processed), the target can be detected and the target trajectory can be efficiently extracted. The experimental results in this paper show that the proposed algorithm can effectively detect targets with maximum speed of 5 pixel/frame in the image sequence with average SCR ≤ 1 and background moving speed ≤0.8 pixel/frame.

UAV Sensor FDI in Duplex Attitude Estimation Architectures Using a Set-Based Approach

A fault detection and isolation algorithm for the attitude estimation of an unmanned aerial vehicle (UAV) using low-cost magnetometers, accelerometers, and gyroscopes, implemented in an inertial measurement unit (IMU) is proposed. Assuming the availability of double triaxial gyros, accelerometers, and magnetometers, the possibility that fault can occur both in input (gyros outputs) and in output (accelerometers and magnetometers outputs) to the kinematic nonlinear relations underlying attitude estimation must be taken into account. If an extended Kalman filter (EKF) can compensate for biases on gyroscopes, fault detection, for all sensors, is first obtained with a comparison between homogeneous sensor outputs. Then, the isolation of the faulted gyro is carried out through the analysis of the EKF bias estimates, whereas a set-based approach is used to isolate faulted accelerometers or magnetometers. For these sensors, isolation tests involve the solution of a linear programming problem on a moving time window in the discrete time domain. In order to show the practical applicability and robustness against measurement noise and different kind of faults, a set of simulations involving experimental data collected during flights of a tricopter UAV are discussed.