Signal Processing Model Research Articles

Non-Invasive Groundwater Velocity Measurements Using a Novel Electromagnetic Flowmeter

Tracing groundwater flow is of vital importance for managing water resources and understanding the natural water cycle. However, it is a challenge to make accurate measurements of groundwater flow due to its extremely low velocity. A new electromagnetic flowmeter has been developed to conduct non-invasive groundwater flow velocity measurements. In the setup, a customized trapezoidal current source was connected to a 1 m <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1 m square coil to generate a strong vertical magnetic field. Two electrodes were used to sense the potential difference caused by water flowing through the magnetic field. A simulation has been developed based on Faraday’s law to evaluate the flow signal under an idealized condition. Two different lab environments have been built for testing the device: an artificial mini-aquifer and a rolling gantry. The measurements show that the flow signal, in practice, generated by the slow-moving water was orders of magnitude smaller than the inductive and capacitive interferences in the system. A linear signal processing model has been developed, allowing the flow signal to be extracted from the measured results. The results show good agreement with simulation and a strong correlation between the flow speed and the processed flow signal, having an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> coefficient of 0.94.

Cramér-Rao Bounds for Holographic Positioning

Multiple antennas arrays combined with high carrier frequencies play a key role in wireless networks for communications but also for localization and sensing applications. To understand the fundamental limits of electromagnetically large antenna arrays for localization, this paper combines wave propagation theory with estimation theory, and computes the Cramér-Rao Bound (CRB) for the estimation of the source position on the basis of the three Cartesian components of the electric field, observed over a rectangular surface area. The problem is referred to as holographic positioning and it intrinsically depends on the radiation angular pattern of the transmitting source, which is typically ignored in standard signal processing models. We assume that the source is a Hertzian dipole, and address the holographic positioning problem in both cases, that is, with and without a priori knowledge of its orientation. To simplify the analysis and gain further insights, we also consider the case in which the dipole is located on the line perpendicular to the surface center. Numerical and asymptotic results are given to quantify the CRBs, and to quantify the effect of various system parameters on the ultimate estimation accuracy. It turns out that square surfaces with side comparable to the distance are needed to guarantee a centimeter-level accuracy in the mmWave bands. Moreover, we show that the CRBs with and without a priori knowledge of the source dipole orientation are numerically the same. The provided CRBs are also used to benchmark different maximum-likelihood estimators (MLEs) derived on the basis of a discrete representation of different models of the electric field. The analysis shows that, if the standard models are used (neglecting the radiation angular pattern), the MLE accuracy is far from the CRB. On the other hand, it approaches the CRB when the more detailed electromagnetic model is considered.

Experimental Study of a Transmission System Based on FBMC/OQAM

Introduction. This paper investigates a transmission system based on FBMC/OQAM multiplexing. This system is characterized by a high spectral efficiency, thereby attracting interest as an alternative transmission method in future wireless mobile communication standards. However, a disadvantage of the system is the high complexity of signal processing. There are numerous publications that study the FBMC/OQAM system from a theoretical perspective. This paper presents an experimental study of a transmission system based on FBMC/OQAM.Aim. Verification of a transmission system based on FBMC/OQAM multiplexing in a wireless channel.Materials and methods. Computer simulation modeling in Matlab and experimental research using Keysight and Rohde &amp; Schwarz certified measuring instruments.Results. A model of synthesis and signal processing was developed, and a frame structure was proposed. The processing included synchronization, since the study was carried out in a wireless double-dispersive channel. Time synchronization was provided by the method of time-domain correlation. A preamble consisting of two symbols was used for CFO compensation. Channel estimation in FBMC/OQAM was conducted by pilot symbols spread over the time-frequency domain, a method with an auxiliary pilot to compensate for intrinsic interference, as well as Zero Forcing and a linear interpolator. As a result, dependences of the bit error rate on the Eb/N0 in various channels were obtained. An error rate of 10−4 was achieved under the Eb/N0 equal to 13.4 dB, 15.3 dB and 20.9 dB in the first, second and third channel, respectively.Conclusion. A FBMC/OQAM-based transmission system with a linear equalizer can operate without a cyclic prefix in a multipath wireless channel, providing comparable noise immunity to OFDM-CP. Long frames should be used to obtain greater spectral efficiency, due to the presence of a transition zone at the beginning and end of the FBMC/OQAM frame.

The Role of Brain Signal Processing and Neuronal Modelling in Epilepsy – A Review

Epilepsy is a neurological disorder characterized by recurrent seizures due to spontaneous changes of chemical synaptic coupling within the central nervous system. Numerous studies have been done in order to increase the level of cognition in epilepsy. Electroencephalography (EEG) as a non-invasive technique with the ability of presenting potentials on the head surface due to neural activity is widely used in epilepsy studies. The signals have been analyzed by brain signal processing techniques which mainly are categorized in feature extraction, feature dimensionally reduction and classification. The limitations such as inapproachability to intracranial in vivo and few seizure occurrences during sampling led to investigate on a model of signals and neural activity. This paper reviews the fundamentals of epilepsy toward using brain signal processing and neuronal modeling in three major branches; detection, prediction and source localization. It resulted a rare number of investigations on seizure epilepsy prediction due to the lack of long-term epilepsy EEG recording ending to the seizure. Subsequently, this review paper suggests to consider brain signal processing techniques in sub-branches of epilepsy detection; status, type, markers and surface localization, whilst it plays a remarkable role targeting to the source localization by neuronal modeling.

Using Social Signals to Predict Shoplifting: A Transparent Approach to a Sensitive Activity Analysis Problem.

Retail shoplifting is one of the most prevalent forms of theft and has accounted for over one billion GBP in losses for UK retailers in 2018. An automated approach to detecting behaviours associated with shoplifting using surveillance footage could help reduce these losses. Until recently, most state-of-the-art vision-based approaches to this problem have relied heavily on the use of black box deep learning models. While these models have been shown to achieve very high accuracy, this lack of understanding on how decisions are made raises concerns about potential bias in the models. This limits the ability of retailers to implement these solutions, as several high-profile legal cases have recently ruled that evidence taken from these black box methods is inadmissible in court. There is an urgent need to develop models which can achieve high accuracy while providing the necessary transparency. One way to alleviate this problem is through the use of social signal processing to add a layer of understanding in the development of transparent models for this task. To this end, we present a social signal processing model for the problem of shoplifting prediction which has been trained and validated using a novel dataset of manually annotated shoplifting videos. The resulting model provides a high degree of understanding and achieves accuracy comparable with current state of the art black box methods.

Robust speech processing for voice interfaces and video content understanding at Facebook

As voice interfaces to devices and digital assistants have increased in popularity, so too, have the challenging environments in which they are expected to perform. In this talk, we’ll present an overview of the signal processing and speech recognition AI modeling techniques that we have developed at Facebook to enable robust voice interaction on Portal video calling devices and Oculus VR headsets. We will also describe progress in captioning and understanding the wide variety of video content shared on Facebook apps, where the acoustic conditions are diverse and challenging and the audio is typically captured on commodity mobile phones. While such systems have been historically developed to run on powerful servers in the cloud, there is increasing interest in speech models that can run locally on the client device. We will describe the challenges of on-device processing and our recent progress in creating efficient, low-footprint speech models. Finally, we will present the challenges and future directions we are exploring to enable rich voice interactions on the next generation of computing devices, including augmented reality glasses.

Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications

The novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.

Diagnosis of Broken Bars in Wind Turbine Squirrel Cage Induction Generator: Approach Based on Current Signal and Generative Adversarial Networks

To ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state.

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of disease diagnosis and treatment, a signal processing and machine learning model was constructed. Then, the methods of signal preprocessing, signal feature extraction, new neural network model structure, training mechanism, optimization algorithm, and efficiency are studied, and experimental verification is carried out for public data sets and clinical big data. Then, the principle of intensive cardiac monitoring, the mechanism of disease diagnosis, the types of arrhythmia, and the characteristics of the typical signal are studied, and the rhythm performance, individual variability, and neurophysiological basis of electrical signals in intensive cardiac monitoring are researched. Finally, the automatic signal recognition technology is studied. In order to improve the training speed and generalization ability, a multiclassification model based on Least Squares Twin Support Vector Machine (LS-TWIN-SVM) is proposed. The computational complexity of the classification model algorithm is compared, and intelligence is adopted. The optimization algorithm selects the parameters of the classifier and uses the EEG signal to simulate the model. Support Vector Machines and their improved algorithms have achieved the ultimum in shallow neural networks and have achieved good results in the classification and recognition of bioelectric signals. The LS-TWIN-SVM algorithm proposed in this paper has achieved good results in the classification and recognition of bioelectric signals. It can perform bioinformatics processing on intensive cardiac care EEG signals, systematically biometric information, diagnose diseases, the real-time detection, auxiliary diagnosis, and rehabilitation of patients.

An Efficient Signal Processing Model for Malicious Signal Identification and Energy Consumption Reduction for Improving Data Transmission Rate

One of the fields which needs the most security is Ad hoc Network (ANET). The term ANET guarantees that there is no central authority so as to administer the signals. Security is a basic issue while using ANET for establishing communication. A ANET is an assortment of remote signals that can progressively be set up at anyplace and whenever without utilizing any prior system framework. Because of its volatile nature, it has mobility issues to improve the arrangement of the system. One of the difficulties is to recognize the malicious signals in the system. Because of malicious signals, data loss or high energy consumption will occur which reduce the overall performance of the ANET. There are a few circumstances when at least one signal in the system become malevolent and will destroy the limit of the system. The point of this work is to recognize the malignant signals quickly to avoid loss of data. The conventional strategy for firewall and encryption isn't adequate to secure the system. In this way a malicious signal identification framework must be added to the ad hoc network. A signal needs to be secured when utilizing the resources and to provide secure communication. The ad hoc networks have several issues like, congestion, overload, data loss and energy consumption. In the proposed work a framework for Rapid Malicious Signal Detection with Energy Consumption Reduction (RMSDwECR) Method is proposed. The proposed method is compared with the traditional methods in terms of load in the network, data loss ratio, signal transmission rate, energy consumption levels, malicious signal identification time and throughput levels. The proposed method exhibits better performance than the traditional methods.

Sparse signal recovery based on adaptive algorithms for debris detector

Inductive debris sensors are generally used in online debris detection and perform well in monitoring the wear condition of rotating facilities. The detection accuracy is restricted by the superposition and noise of the impedance signal. From the perspective of machine learning, superposition is underfitting and noise is overfitting, and both are caused by inappropriate model complexity. Therefore, a series of machine learning approaches is proposed in this paper to devise a sparse signal processing model that can adapt to model complexity. We propose that the algorithm applied to debris detection can recover the impulse signals from the impedance signals and help determine the material and the size of the debris. Numerical simulations are carried out to prove that the superposition resolution is 0.6 half-wave width and has the ability to resist noise with a signal-to-noise ratio of more than 10 dB. With the test results, we demonstrate how the proposed method can solve superposition and resist noise.

Characterization of dynamic distortion in LED light output for optical wireless communications

Light-emitting diodes (LEDs) are widely used for data transmission in emerging optical wireless communications (OWC) systems. This paper analyzes the physical processes that limit the bandwidth and cause nonlinearities in the light output of modern, high-efficiency LEDs. The processes of carrier transport, as well as carrier storage, recombination, and leakage in the active region appear to affect the communications performance, but such purely physics-based models are not yet commonly considered in the algorithms to optimize OWC systems. Using a dynamic modeling of these phenomena, we compile a (invertable) signal processing model that describes the signal distortion and a parameter estimation procedure that is feasible in an operational communications link. We combine multiple approaches for steady-state and dynamic characterization to estimate such LED parameters. We verify that, for a high-efficiency blue GaN LED, the models become sufficiently accurate to allow digital compensation. We compare the simulation results using the model against optical measurements of harmonic distortion and against measurements of the LED response to a deep rectangular current modulation. We show how the topology of the model can be simplified, address the self-calibration techniques, and discuss the limits of the presented approach. The model is suitable for the creation of improved nonlinear equalizers to enhance the achievable bit rate in LED-based OWC systems and we believe it is significantly more realistic than LED models commonly used in communications systems.

A prediction framework with time-frequency localization feature for detecting the onset of seismic events.

Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.

A comprehensive computational model of animal biosonar signal processing.

Computational models of animal biosonar seek to identify critical aspects of echo processing responsible for the superior, real-time performance of echolocating bats and dolphins in target tracking and clutter rejection. The Spectrogram Correlation and Transformation (SCAT) model replicates aspects of biosonar imaging in both species by processing wideband biosonar sounds and echoes with auditory mechanisms identified from experiments with bats. The model acquires broadband biosonar broadcasts and echoes, represents them as time-frequency spectrograms using parallel bandpass filters, translates the filtered signals into ten parallel amplitude threshold levels, and then operates on the resulting time-of-occurrence values at each frequency to estimate overall echo range delay. It uses the structure of the echo spectrum by depicting it as a series of local frequency nulls arranged regularly along the frequency axis of the spectrograms after dechirping them relative to the broadcast. Computations take place entirely on the timing of threshold-crossing events for each echo relative to threshold-events for the broadcast. Threshold-crossing times take into account amplitude-latency trading, a physiological feature absent from conventional digital signal processing. Amplitude-latency trading transposes the profile of amplitudes across frequencies into a profile of time-registrations across frequencies. Target shape is extracted from the spacing of the object’s individual acoustic reflecting points, or glints, using the mutual interference pattern of peaks and nulls in the echo spectrum. These are merged with the overall range-delay estimate to produce a delay-based reconstruction of the object’s distance as well as its glints. Clutter echoes indiscriminately activate multiple parts in the null-detecting system, which then produces the equivalent glint-delay spacings in images, thus blurring the overall echo-delay estimates by adding spurious glint delays to the image. Blurring acts as an anticorrelation process that rejects clutter intrusion into perceptions.

Periodic error while processing data from a velocity interferometer system for any reflector

When processing experimental data with systems such as velocity interferometer system for any reflector (VISAR), periodic measurement uncertainty arises. The appearance of its uncertainty is investigated and computer modeling of signal processing with random noise is carried out. Analytical dependences of the error value on the signal-to-noise ratio and signal phase are obtained. Methods of reducing this error are considered.

Learning and correcting non-Gaussian model errors

All discretized numerical models contain modeling errors – this reality is amplified when reduced-order models are used. The ability to accurately approximate modeling errors informs statistics on model confidence and improves quantitative results from frameworks using numerical models in prediction, tomography, and signal processing. Further to this, the compensation of highly nonlinear and non-Gaussian modeling errors, arising in many ill-conditioned systems aiming to capture complex physics, is a historically difficult task. In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems. The viability of the approach is demonstrated using simulated and experimental data arising from differing physical direct and inverse problems.

Distributed optical fiber hydrophone based on Φ-OTDR and its field test.

In this letter, a distributed optical fiber hydrophone (DOFH) based on Φ-OTDR is demonstrated and tested in the field. The specially designed sensitized optical cable with sensitivity up to -146 dB rad/µPa/m is introduced, and an array signal processing model for DOFH is constructed to analyze the equivalence and specificity of the distributed array of acoustic sensors. In the field test, a 104-meter-long optical cable and a Φ-OTDR system based on heterodyne coherent detection (Het Φ-OTDR) is utilized, and underwater acoustic signal spatial spectrum estimation, beamforming and motion trajectory tracking with high accuracy can be realized. As far as we know, this is the first report on the field trial of DOFH based on Φ-OTDR. The DOFH has the potential to achieve an array range of tens of kilometers, with elements spaced up to the meter level and flexible configuration, which has a broad application prospect for marine acoustic detection.

Physical Modeling of the Ancient Greek Wind Musical Instrument Aulos: A Double-Reed Exciter Linked to an Acoustic Resonator

We present a simulation method for the auralization of the ancient Greek double-reed wind instrument Aulos. The implementation is based on Digital Signal Processing and physical modeling techniques for the instrument's two parts: the excitation mechanism and the acoustic resonator with toneholes. Single-reeded instruments are in-depth studied firstly because their excitation mechanism is the one used in a great amount of modern wind-reed instruments and secondly because the physics governing the phenomena is less complicated than the double-reeded instruments. We here provide a detailed model of a system comprised of a double-reed linked to an acoustic resonator with toneholes to sonify Aulos. We validate our results by comparing our method's synthesized signal with recordings from a replica of Aulos of Poseidonia built in our lab. The comparison showed that the fundamental frequencies and the first three odd harmonics of the signals differ 6, 5, 3, and 2 cents on average, respectively, which is below the Just Noticeable Difference threshold.

Is Cloud RAN a Feasible Option for Industrial Communication Network?

Cloud RAN (C-RAN) is a promising paradigm for the next generation radio access network infrastructure, which offers centralised and coordinated base-band signal processing in a cloud-based BBU pool. This requires extremely low latency responses to achieve real-time signal processing. In this paper, we analysed the challenges to introduce cloud native model for signal processing in C-RAN. We studied the difficulties of achieving real-time processing in a cloud infrastructure by addressing its latency-constraint. To evaluate the performance of such a system, we mainly investigated a massive MIMO pilot scheduling process in a C-RAN infrastructure under a factory automation scenario. We considered the stochastic delays incurred by the cloud execution environment as the main constraint that has has impact on the scheduling performance. We use simulations to provide insights on the feasibility of C-RAN deployment for industrial communication, which has stringent criteria to meet Industry 4.0 standards under this constraint. Our experiment results show that, concerning a pilot scheduling problem, the CRAN system is capable of meeting the industrial criteria when the fronthaul and the cloud execution environment has introduced latency in the order of milliseconds.

A Morlet Wavelet-Based Two-Point FIR Filter Method for Phasor Estimation

This article proposes a method of phasor estimation called the Morlet wavelet-based two-point finite-impulse response (FIR) filter (MW-FIR), showing advantages on algorithm complexity, customizability, and zero overshoot. The method focuses on amplitude and frequency estimation, while phase and rate of frequency of change (ROCOF) estimation can be performed using the standard signal processing model with an FIR filter. The mathematical principle behind the method is presented. The causes of error are discussed, and the parameter selection procedure is introduced to minimize error and adjust algorithm performance according to different application requirements. The proposed method has been discussed and compared with the three-point interpolated discrete Fourier transform (ipDFT) method and the FIR method using a flat-top (FT) windowing function, under the static and dynamic situations given by the IEC/IEEE 60255-188-1 and noise immunity evaluation. Finally, the major advantages of the proposed frequency estimation method compared with the conventional FIR-based method using the derivative of angular position are discussed. It can be concluded that this method becomes a substitute for ipDFT in most cases and shows several advantages over the FIR-based method with an FT sequence.