Signal Processing Scheme Research Articles

A Novel Method for Improving Quality of Oblique Incidence Sounding Ionograms Based on Eigenspace-Based Beamforming Technology and Capon High-Resolution Range Profile

Ground-based oblique incidence sounding (OIS) is an important means to investigate the ionosphere. As the OIS ionogram is a visual representation of the OIS parameters, such as group distance and maximum usable frequency (MUF), it is of great significance for improving the quality and the range resolution. This will facilitate the automatic interpretation and inversion of OIS ionograms to obtain the fine structure and spatial–temporal evolutions of the ionosphere. In this paper, a novel OIS signal processing scheme is proposed based on the Eigenspace-based (ESB) beamforming technology and Capon high-resolution range profile (HRRP). First, by applying the ESB beamformer to a compact L-shaped antenna array, the energy of the OIS signals received by multiple antennas can be added, while the interference and noise will be suppressed. Subsequently, the Capon HRRP algorithm is used to improve the range resolution. This is achieved due to the slow variation in the characteristics of the ionosphere resulting in good short-term coherence of the narrowband signals. The experimental results show that the two-stage signal-processing method significantly improves the imaging quality of OIS ionograms. In particular, the structure inside the ionosphere and its temporal and spatial evolution can be observed more precisely after the range resolution of the OIS ionogram is improved; therefore, it has great application potential.

GNSS-SDR pseudorange quality and single point positioning performance assessment

In recent years, we have witnessed a growing demand for GNSS receiver customization in terms of modification of signal acquisition, tracking, and processing strategies. Such demands may be addressed by software-defined receivers (SDRs) which refers to an ensemble of hardware and software technologies and allows re-configurable radio communication architectures. The crux of the SDRs is the replacement of the hardware components through software modules. In this paper, we assess the quality of GNSS observables acquired by SDR against the selected u-blox low-cost receiver. In the following, we investigate the performance level of single point positioning that may be reached with an ultra-low-cost SDR and compare it to that of the low-cost GNSS receiver. The signal quality assessment revealed a comparable performance in terms of carrier-to-noise density ratio and a significant out-performance of the u-blox over SDR in terms of code pseudorange noise. The experimentation in the positioning domain proved that software-defined receivers may offer a position solution with three-dimensional standard deviation error at the level of 5.2 m in a single point positioning mode that is noticeably poorer accuracy as compared to the low-cost receiver. Such results demonstrate that there is still room for SDR positioning accuracy improvement.

A fuzzy logic-based approach for fault diagnosis and condition monitoring of industry 4.0 manufacturing processes

Since the introduction of the industry 4.0 paradigm, manufacturing companies are investing in the development of algorithmic diagnostic solutions for their industrial equipment, relying on measured data and process models. However, process and fault models are not usually available for complex productions plants and production data are usually unlabeled. Thus, to classify machine status, unsupervised approaches such as anomaly detection and signal processing strategies have to be employed. Due to the unsupervised nature of the problem, it is meaningful to apply several diagnostic algorithms to cover most of the process anomalous behaviors. Additionally, in some contexts, the experience of process operators in grasping the correct functioning of machines as well as their ability in understanding early signs of deterioration is relevant for the diagnosis of incoming failures. However, seldom these information can be included in failure diagnosis algorithms. In this paper, we propose a diagnostic scheme for condition monitoring of mechanical components. The proposed scheme combines anomaly detection algorithms, envelope analysis of vibration data, and eventually additional qualitative information on machine functioning. The combination of all the fault indicators is obtained leveraging on a fuzzy inference system. The proposed scheme is experimentally validated on a steel making plant with real process data, making use of heuristic information such monitoring reports of machine health status.

Nanometer precision height gauge for small holes with high aspect ratio

The demand for measuring the feature size of small holes is increasing. In this study, a nanometer precision height gauge for small holes with a high aspect ratio was developed. The height gauge comprises a Michelson interferometer, a moving and weight balance system, a tactile probing system, and a positioning control system. The errors due to the angular deflection of the moving stage were analyzed, and a corner-cube-based interferometer was designed. The structural design of the moving and weight balance systems was explored. A tactile micro/nano-probing system was developed and used. The signal processing, positioning control and measurement strategy, and software were developed. The system was calibrated and verified by testing the reference ring gauge. The experimental results show that the height gauge can achieve a resolution of 5 nm, a measurement uncertainty of 90 nm (k = 2), and a measuring range of 30 mm. The prototype can be used to measure the depth of small holes with a high aspect ratio.

On the Use of Reciprocal Filter Against WiFi Packets for Passive Radar

This article derives a novel unified signal processing scheme for WiFi-based passive radar in order to limit its complexity and enhance its suitability for short range civilian applications. To this purpose, the exploitation of a reciprocal filtering (RpF) strategy is investigated as an alternative to conventional matched filtering at the range compression stage. Along with the well-known advantage of a remarkable sidelobes control capability for the resulting range-Doppler response, the use of a RpF is shown to provide additional benefits for the specific sensor subject of this article. Specifically, it allows to streamline the disturbance cancellation stage and to implement a unified signal processing architecture which is capable to handle the different modulation schemes typically adopted in WiFi transmissions. Appropriate adjustments are also proposed to the theoretical RpF in order to cope with the inherent loss in terms of signal-to-noise power ratio. The effectiveness of the proposed signal processing scheme encompassing the RpF strategy is proved against both simulated and experimental datasets.

Empirical wavelet decomposition and BFindex for early detection of bearing defects

In this paper, a signal processing scheme is developed for early detection of bearing defects .This scheme combines Empirical Wavelet Decomposition (EWD) technique and Frequency Weighted Energy Operator (FWEO). EWD employs an adaptive process that is able to exploit the frequency information contained in the signal and decompose it into a finite number of Empirical Modes (EMs). However, this technique still faces the problem of how to optimally divide the Fourier spectrum during the application process to extract empirical modes with physical meanings from the vibration signal. To overcome this problem we propose a new segmentation method based on the research of the frequency resonance band in the power spectrum which is excited by defect impacts. In the other hand, identifying the mode that carries the fault information among all the obtained modes is the key to successful application of EWD technique. In this paper we propose a new criterion named Bearing Fault index (BFindex) for automatically selecting the correct mode. Then, this mode is demodulated using FWEO to separate the defect frequency from the natural frequency excited by the impacts. The proposed scheme is evaluated using real bearing vibration signals with inner race and outer race defects. The experimental results confirmed its ability in detecting fault characteristic frequencies, which validates its effectiveness in the diagnosis of bearing defects.

Angle of arrival estimation in a multi-antenna software defined radio system: impact of hardware and radio environment

Contemporary wireless communication transceivers can utilize their multiple antennas to improve positioning abilities. Angle of Arrival (AoA) estimation utilizes shifts in phase between the signal of interest arriving at different receiving antennas. Software Defined Radio (SDR) USRP B210 and GNU Radio implementing the Root Multiple Signal Classification (Root-MUSIC) algorithm are used in this paper. However, this setup requires the consideration of errors caused by hardware and radio environment. The impact of these effects is shown using an analytical model, assessed, mostly by measurements, and relevant solutions are proposed. The hardware distortions are caused mostly by synchronization errors. The intricacies of the most problematic phase synchronization are investigated with the wired setup showing, e.g., changes with frequency or gain. Both synchronizations emitting calibration tone from the same radio front end, resulting in cross-talk, or a separate one are tested. Recommendations enhancing the performance of the system and alleviating hardware imperfections are provided in this paper. The accuracy of AoA estimation is degraded by multipath propagation or radio interference. A signal processing scheme including filtering has been proposed. A set of over-the-air experiments assessed the performance of the system. The presented and publicly available software package, scalable to a higher number of RX antennas, enables straightforward implementation by other researchers.

Model-based hearing-enhancement strategies for cochlear synaptopathy pathologies

It is well known that ageing and noise exposure are important causes of sensorineural hearing loss, and can result in damage of the outer hair cells or other structures of the inner ear, including synaptic damage to the auditory nerve (AN), i.e., cochlear synaptopathy (CS). Despite the suspected high prevalence of CS among people with self-reported hearing difficulties but seemingly normal hearing, conventional hearing-aid algorithms do not compensate for the functional deficits associated with CS. Here, we present and evaluate a number of auditory signal-processing strategies designed to maximally restore AN coding for listeners with CS pathologies. We evaluated our algorithms in subjects with and without suspected age-related CS to assess whether physiological and behavioural markers associated with CS can be improved. Our data show that after applying our algorithms, envelope-following responses and perceptual amplitude-modulation sensitivity were consistently enhanced in both young and older listeners. Speech-in-noise intelligibility showed small improvements after processing but mostly for young normal-hearing participants, with median improvements of up to 8.3%. Since our hearing-enhancement strategies were designed to optimally drive the AN fibres, they were able to improve temporal-envelope processing for listeners both with and without suspected CS. Our proposed algorithms can be rapidly executed and can thus extend the application range of current hearing aids and hearables, while leaving sound amplification unaffected.

A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder.

A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD.

A Sensorless Control Strategy for Permanent Magnet Synchronous Motor at Low Switching Frequency

The high-frequency (HF) square-wave voltage injection method can be used in permanent magnet synchronous motor (PMSM) drive systems. However, when the switching frequency is too low, the injection frequency will also decrease, which will reduce the update frequency of the HF response current, making it difficult to extract the position quadrature signal and affecting the accuracy of position estimation and control performance. This paper proposes a method for extracting position quadrature signals based on sampling rate transformation, and a signal processing strategy based on Cascade Integrator Comb (CIC) interpolation filtering, which can solve the problem of waveform distortion caused by the low sampling rate of the extracted position quadrature signal. This strategy can increase the sampling rate of the position quadrature signal to the pulse width modulation (PWM) update frequency by interpolating in the sampling current, thereby reducing the harmonic content of the position quadrature signal and improving the position estimation. precision. In addition, the PWM update frequency and estimated rotational speed information are used to compensate for the delay caused by position estimation and inverter update, which effectively improves the accuracy of position estimation. Finally, the effectiveness of the proposed control strategy is verified by simulation.

On-Chip RF Signal Shaping by Coherent Control of Optically Driven Acoustic Wave Interference

Photonic–phononic systems that mix photons and acoustic waves have been actively investigated for the development of next-generation integrated photonics for signal processing. Here, we propose a new method of arbitrary RF signal shaping using active control of optically driven acoustic-wave interference, offering new photonic–phononic–microwave signal processing schemes with MHz linewidths at GHz carrier frequencies. The demonstrated system consists of two suspended optical waveguides and an asymmetric Mach–Zehnder interferometer. The two waveguides generate acoustic waves at a 3.03 GHz carrier frequency with a 3.3 MHz bandwidth, and one of the paths of the asymmetric interferometer experiences phase modulation induced by the acoustic waves. Then, the interferometer converts the phase information to RF signals of complicated shapes using a linear combination of multiple optical envelopes. This RF signal-shaping method will open new applications of optomechanical systems for microwave photonics.

Tactile rendering of textures by an Electro-Active Polymer piezoelectric device: mimicking Friction-Induced Vibrations

In order to mimic Friction-Induced Vibrations (FIV) and render the perception of textures, a tactile device, named PIEZOTACT, has been developed by a thin, lightweight and flexible Electro-Active Polymer (EAP) piezoelectric actuator. The presented signal processing strategy, exploiting the Transfer Function of the electro-mechanical device, allowed for mimicking and accurately reproducing the FIV stimuli, measured when touching real textures. The correct FIV reproduction, by the developed device, has been verified by comparing the spectra of the measured signals when touching real surfaces and the ones reproduced on the fingertip by the PIEZOTACT device. A pilot discrimination campaign, performed on a single subject, highlighted the capabilities of the device, being the subject able to discriminate the simulated textures with a low number of mistakes. Moreover, the errors in discrimination were consistent with the ones occurred when discriminating the corresponding real surfaces. The spectra of the induced vibrations (tactile stimuli) allowed as well for explaining the difficulties to discriminate certain textures.

Semi-Automated Inversion-Specific Data Selection for Volcano Tomography

Active seismic experiments allow reconstructing the subsurface structure of volcanoes with unprecedented resolution and are vital to improve the interpretation of volcanic processes. They require a quality assessment for thousands of seismic waveforms recorded at hundreds of stations in the shortest amount of time. However, the processing necessary to obtain reliable images from such massive datasets demands signal processing and selection strategies specific to the inversions attempted. Here, we present a semi-automated workflow for data selection and inversion of amplitude-dependent information using the original TOMODEC2005 dataset, recorded at Deception Island (Antarctica). The workflow is built to tomographic techniques using amplitude information, and can be generalised to passive seismic imaging. It first selects data depending on standard attributes, like the presence of zeroes across all seismic waveforms. Then, waveform selections depend on inversion-specific attributes, like the delay of the maximum amplitude of the waveform or the quality of coda-wave decays. The automatic workflow and final visual selections produce a dataset reconstructing anomalies at a node spacing of 2 km, imaging a high-attenuation anomaly in the centre of the Deception Island bay, consistent with previously-published maps. Attenuation models are then obtained at a node spacing of 1 km, highlighting bodies of highest attenuation scattered across the island and a NW-SE trend in the high-attenuation anomaly in the central bay. These results show the effect of the local extension regime on volcanic structures, providing details on the eruptive history and evolution of the shallow magmatic and hydrothermal systems. The selection workflow can be easily generalised to other amplitude-dependent tomographic techniques when applied to active seismic surveys. Image improvements from the original dataset are minor when selecting data using standard attributes, like signal-to-noise ratios. Tomographic maps become drastically more stable and consistent between different frequencies and resolutions when data selection targets attributes specific to the inversion.

Deep neural network models of speech-in-noise perception for hearing technologies and research

Widespread adoption of artificial intelligence has yet to occur in the hearing field. Hearing technologies, such as cochlear implants (CIs), provide limited benefits of noise reduction, even with current state-of-the-art signal processing strategies. Recent developments in machine learning have produced deep neural network (DNN) models achieving remarkable performance in speech enhancement and source separation tasks. However, there are currently no commercially available CI audio processors that utilize DNN models for noise suppression. Furthermore, the current research community lacks a computational tool to match the complexity of natural auditory processing. To address these gaps, we implemented two DNN models: a recurrent neural network (RNN)—a lightweight template model for speech enhancement, and the SepFormer—the current top-performing speech-separation model in the literature. The DNN models resulted in significant improvements in terms of objective evaluation metrics, as well as intelligibility scores obtained with CI users at different signal-to-noise ratios. Given their flexibility and good performance on complex tasks, these models can also be used to generate hypotheses about speech-in-noise perception and serve as richer substitutes for models commonly used in research. This work serves as a proof-of-concept and a guide for the next steps towards integrating DNN technology into hearing technologies and research.

Comparative Analysis of Derivative Parameters of Chemoresistive Sensor Signals for Gas Concentration Estimation

Signals from resistive gas sensors based on zirconium dioxide and silicon–carbon films have been extensively investigated to estimate gas concentration. In this study, the change in the normalized resistance of the sensor’s response under NO2 exposure is shown and the analysis of the first and second derivatives of the response curves were carried out. A signal-processing scheme, reducing the effect of noise and signal drift, is proposed. The extreme of the second derivative of the sensor response, the initial reaction rate, and the slope of the curve of the approximating line in the coordinates of the Elovich equation are proposed as calibration dependencies. The calibration curves built from the values of the maximum second derivative turned out to be the most stable, with the lowest relative error in estimating gas concentration compared to the traditional fixed-time point method.

Placement Matters

WiFi-based contactless sensing has found numerous applications in the fields of smart home and health care owning to its low-cost, non-intrusive and privacy-preserving characteristics. While promising in many aspects, the limited sensing range and interference issues still exist, hindering the adoption of WiFi sensing in real world. In this paper, inspired by the SNR (signal-to-noise ratio) metric in communication theory, we propose a new metric named SSNR (sensing-signal-to-noise-ratio) to quantify the sensing capability of WiFi systems. We theoretically model the effect of transmitter-receiver distance on sensing coverage. We show that in LoS scenario, the sensing coverage area increases first from a small oval to a maximal one and then decreases. When the transmitter-receiver distance further increases, the coverage area is separated into two ovals located around the two transceivers respectively. We demonstrate that, instead of applying complex signal processing scheme or advanced hardware, by just properly placing the transmitter and receiver, the two well-known issues in WiFi sensing (i.e., small range and severe interference) can be greatly mitigated. Specifically, by properly placing the transmitter and receiver, the coverage of human walking sensing can be expanded by around 200%. By increasing the transmitter-receiver distance, a target's fine-grained respiration can still be accurately sensed with one interferer sitting just 0.5 m away.

Input torque measurements for wind turbine gearboxes using fiber-optic strain sensors

Abstract. Accurate knowledge of the input torque in wind turbine gearboxes is key to improving their reliability. Traditionally, rotor torque is measured using strain gauges bonded to the shaft. Transferring the resulting signal from the rotating shaft to a stationary data acquisition system while powering the sensing devices is complex and costly. The magnitude of the torques involved in wind turbine gearboxes and the high stiffness of the input shaft pose additional difficulties. This paper presents a new alternative method to measure the input torque in wind turbine gearboxes based on deformation measurements of the static first-stage ring gear. We have measured deformation using fiber-optic strain sensors based on fiber Bragg gratings because of their advantages compared to conventional electrical strain gauges. The present study was conducted on a Siemens Gamesa Renewable Energy gearbox with a rated power of 6 MW, in which a total of 54 fiber-optic strain sensors were installed on the outer surface of the first-stage ring gear. The gear mesh forces between the planets and the ring gear cause measurable deformations on the outer surface of the stationary ring gear. The measured strains exhibit a dynamic behavior. The strain values change depending on the relative position of the strain sensors to the planet gears, the instantaneous variations of the input torque, and the way load is shared between planets. A satisfactory correlation has been found between the strain signals measured on the static ring gear and torque. Two signal processing strategies are presented in this paper. The first procedure is based on the peak-to-peak strain values computed for the gear mesh events, and therefore torque can only be estimated when a gear mesh event is detected. The second signal processing procedure combines the strain signals from different sensors using a Coleman coordinate transformation and tracks the magnitude of the fifth harmonic component. With this second procedure, it is possible to estimate torque whenever strain data of all sensors are available, leading to an improved frequency resolution up to the sampling frequency used to acquire strain data. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines and enable novel data-driven control strategies, as well as a more accurate assessment of the consumed fatigue life of the gearboxes throughout their operation.

Induction Machine Sensorless Control Based on Saliency Extraction That Uses One Single SVPWM Active State

Some induction motors present saliencies that periodically modify the air-gap flux following a sinusoidal shape. Consequently, these modulations are reflected in the transient leakage inductance. Therefore, transient leakage inductance can be expressed as a constant term or offset, which comes from the symmetrical machine, plus all motor saliency sinusoids, modulated by the mentioned saliencies. Since saliencies are linked to the rotor position or flux, calculation of transient leakage inductance enables encoderless vector control. A way to calculate transient leakage inductance is to excite the machine with voltage steps and estimate the resulting phase current slopes since they are inversely proportional to phase transient inductance. For creating a vector, only the three current slopes resulting from a single active state are sufficient. The vector sum of the three current slopes leads to the so-called saliency-offset vector, which is a vector composed of an offset, which is created due to the contribution of the transient inductance offset, plus several saliency phasors, superposed to the offset. To access saliencies, it is critical to accurately identify the offset of the saliency-offset vector, especially in induction motors since offset is much bigger than saliencies. This article presents an advanced signal processing scheme that identifies the offset in the saliency-offset vector, assuming offset amplitude differsdepending on the voltage step applied. Using a feedforward scheme, the offset is eliminated, leading to a saliency vector where rotor slotting can be extracted from. A novel excitation is proposed to reduce additional current distortions coming from the one-active excitation.

CCi-MOBILE: A Portable Real Time Speech Processing Platform for Cochlear Implant and Hearing Research.

Experimental hardware-research interfaces form a crucial role during developmental stages of any medical, signal-monitoring system as it allows researchers to test and optimize output results before perfecting the design for the actual FDA approved medical device and large-scale production. These testing platforms, intake raw signals through which performance of novel algorithms can be analyzed and modified to generate the desired data points for an optimized output, allowing the advancement of the medical device. With cochlear implants (CIs) and hearing aids (HAs) becoming a more common solution for varying degrees of hearing impairment, having modern signal processing strategies tested for such speech sensitive systems is a necessity. But the rigid design requirements of commercial CI and HA processors make it difficult to explore novel algorithms for research investigations and conducting longitudinal studies. This study presents the design, development, clinical evaluation, and applications of CCi-MOBILE, a computationally powerful signal processing testing platform built for researchers in the hearing-impaired field. The custom-made, portable research platform allows researchers to design and perform complex speech processing algorithm assessment offline and in real-time. It can be operated through user-friendly, open-source software and is compatible with implants manufactured by Cochlear Corporation. The FPGA design and hardware processing pipeline for CI stimulation is discussed followed by results from an acute study with implant users' speech intelligibility in quiet and noisy conditions. The results show a consistent level of performance compared with CI users' clinical processor, thus confirming the viability of the platform in chronic CI based studies.

Range and Doppler reconstruction for sparse frequency agile linear frequency modulation‐orthogonal frequency division multiplexing radar

Sparse frequency agile linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) radar can improve anti-interference ability as well as reduce the sampling rate of the radar system. However, the frequency agility makes it difficult to realize coherent processing by using the fast Fourier transform. To overcome this problem, a signal processing scheme for the sparse frequency agile LFM-OFDM radar is proposed to obtain the high range and Doppler resolution in this study. The subcarriers are synthesised to an LFM signal in the time domain at first. Then, within the framework of the compressed sensing theory, an improved orthogonal matching pursuit algorithm is proposed to obtain the high-resolution range-velocity profile while reducing the computation complexity. Finally, several numerical simulations are provided to verify the effectiveness of the proposed method for moving targets.