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This article addresses the saturated stabilization control problem for nonholonomic vehicles with a novel c-nonholonomic trajectory approach on $\text{SE}(2)$ , with applications to automatic parking. First, by defining the c-nonholonomic configuration, a c-nonholonomic trajectory is obtained, which provides a novel approach to design stabilization controller to reach an arbitrary configuration. Second, a global discontinuous time-invariant feedback controller with input saturation is proposed, which does not involve time signal information, and its convergence is illustrated by a Lyapunov function approach. Thereafter, the motion trajectory of the proposed controller is analyzed, and the application scenario in automatic parking with the approximate desired trajectory is demonstrated. Finally, the performance of the proposed controller is validated by both numerical simulations and experiments.

In this letter, a deep-learning-assisted signal detector is developed for communication systems with unknown channel models. By embedding domain knowledge into a self-attention model, a novel detection unit is devised that enables both reliable estimation and fast training. Furthermore, a sliding-window structure is in combined use with the detection unit to realize real-time signal recovery. We evaluate the performance of the proposed detector using a chemical communication experimental platform, and show the superiority of our design in terms of detection accuracy as well as implementation complexity.

Made available in DSpace on 2022-05-01T06:31:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 The delay in signal transmission for oscillator networks influences in the synchronization, getting oscillatory behavior. The synchronization occurs by the phase-locked loops (PLL), synchronizing the local oscillator with a reference phase and frequency. The oscillators are implemented in data and telecommunications communication systems, as elements (nodes) for distribution of time signals (clock). In the work is implementated the time signal distribution network, with three nodes, in order to establish a common time base, with adjustment of disturbance in clock. The phase detection process generates a periodic oscillation called double-frequency jitter (DFJ). The DFJ is inevitable, appearing even with the PLL operating in the blocking range. Therefore, this work aims to study the effect of the double frequency oscillation on a network with signal transmission delay, its influence on the synchronous states of network, and the performance of oscillators to recover the common time base of clock, when disturbed. Institute of Sciente and Technology São Paulo State University-UNESP, Av. Três de Março 511 Fedeal University of ABC-UFABC, R. Arcturus 3 School of Engineering São Paulo State University-UNESP, Av. Eng. Luís Edmundo Carrijo Coube, 14-01 Institute of Sciente and Technology São Paulo State University-UNESP, Av. Três de Março 511 School of Engineering São Paulo State University-UNESP, Av. Eng. Luís Edmundo Carrijo Coube, 14-01

Summary The Z-transform of a complex time signal (or the analytic signal of a real signal) is equal to the Z-transform of a prediction error divided by the Z-transform of the prediction error operator. This inverse is decomposed into a sum of partial fractions, which are used to obtain impulse response operators formed by non-causal filters that complex-conjugate symmetric coefficients. The time components are obtained by convolving the filters with the original signal, and the peak frequencies, corresponding to the poles of the prediction error operator, are used for mapping the time components into frequency components. For non-stationary signals, this decomposition is done in sliding time windows, and the signal component values, in the middle of each window, are attributed to the peak value of its frequency response that corresponds to the pole of this partial fraction component. The result is an exact, but non-unique, time–frequency representation of the input signal. A sparse signal decomposition can be obtained by summing along the frequency axis in patches with similar characteristics in the time–frequency domain. The peak amplitude frequency of each new time component is obtained by computing a scalar prediction error operator in sliding time windows, resulting in a sparse time–frequency representation. In both cases, the result is a time–frequency matrix where an estimate of the frequency content of the input signal can be obtained by summation over the time variable. The performance of the new method is demonstrated with excellent results on a synthetic time signal, the LIGO gravitational wave signal and seismic field data.

Abnormal activity in the human brain is a symptom of epilepsy. Electroencephalogram (EEG) is a standard tool that has been widely used to detect seizures. A number of automated seizure detection systems based on EEG signal classification have been employed in present days, which includes a mixture of approaches but most of them rely on time signal features, time intervals or time frequency domains. Therefore, in this research, deep learning-based automated mechanism is introduced to improve the seizure detection accuracy from EEG signal using the Asymmetrical Back Propagation Neural Network (ABPN) method. The ABPN system includes four levels of repetitive training with weight adjustment, feed forward initialization, error and update weight and bias back-propagation. The proposed ABPN-based seizure detection system is validated using Physionet EEG dataset with matlab simulation, and the effectiveness of proposed seizure system is confirmed through simulation results. As compared with Deep Convolutional Neural Network (CNN) and Support Vector Machine–Particle Swarm Optimization (SVM-PSO)-based seizure detection system, the proposed ABPN system gives the best performance against various parameters. The sensitivity, specificity and accuracy are 96.32%, 95.12% and 98.36%, respectively.

A study is performed of ionospheric disturbances caused by an interplanetary shock wave on January 24, 2012, and its subsequent substorm. Results are presented from an analysis of how changes in the vertical electron density profile affect the amplitude and phase characteristics of signals with frequencies of the Beta time signal service, recorded at the Earth’s surface.

In this paper, gear pump failure because of bush bearing looseness is investigated. Vibrations analysis is one of the main manners of the diagnosis of mechanical failure. After bearing looseness, gears clearance is changed because of change between gears’ clearances. Time signal, spectrum velocity, spectrum accelerations, envelope velocity, and envelope accelerations are methods of vibrations analysis and signal processing to diagnose a failure in rotary machines. In this study, bush bearing failure and adhesive wear in gear using signal processing including machine spectrum, envelope spectrum acceleration, and acceleration spectrum are detected. Gear failure because of bush looseness has many symptoms that these symptoms are pronounced in this research. The best time for failure detection is the beginning of failure so the beginning of bush and worn gear’s teeth failure was found in this paper.

The monitoring and the management of smart grid require an advanced communication infrastructure. The time synchronization is among the most important services such an infrastructure should offer. Several solutions are available to disseminate the time information in a distributed system; for instance, most of the times a global positioning system (GPS) receiver is used to recover accurate time information. However, GPS receivers need a clear view of the sky. Other solutions, like the use of a network-based synchronization mechanism, require a dedicated communication infrastructure, which is not always feasible for economical reason. Thus, the use of the power grid itself to disseminate a time signal is extremely interesting. The aim of this article is to investigate the possibility to distribute a time signal over a medium voltage (MV) grid using a chirp-based modulation. The timing data are coded using IRIG-B time code. Such an approach does not require the installation of a dedicated infrastructure, and it is noninvasive, because the signal is transmitted on a different band with the respect to power line communication (PLC). The effectiveness of the approach has been experimentally evaluated on a test MV line available in a laboratory. Although the tested chirp-based symbol has a limited frequency bandwidth (80 kHz), the jitter in the identification of the start of frame is on the order of 7.5 $\mu \text{s}$ , fulfilling the requirements of the most smart grid applications.

We present a real-time intermediate frequency noise radar signal generator that provides pseudo-random signals with uniform, normal, exponential, and Rayleigh probability distributions. In addition, normally distributed samples with uniform power spectral distribution with arbitrary bandwidths can be generated. The noise signal generator is implemented and tested on a Xilinx Ultrascale+ RFSoC FPGA based development board with the aim of testing the effect of distributions on target detection performance of the noise radar. The generator can also be used as a baseband signal source and an environment for noise radar waveform design.

Sudden cardiac death (SCD) is an unexpected death of a person with or without knowing cardiac causes are often occurring in less than an hour after the incidence of symptoms. In the case of physicians' knowledge of this incident, they can make appropriate decisions for the patients at-risk and reduce the number of such deaths significantly. The purpose of this paper is to examine different methods for predicting sudden cardiac death using electrocardiogram (ECG) signal from 1998 to recent years that can contribute to researchers to become familiar with the wide range of research conducted in this field.In this paper, studies using various methods to predict sudden cardiac death that has applied the data from the Physionet and MIT-BIH databases with a sampling frequency of 256 samples per second are reviewed. Both types of data have normal and abnormal sampling labels and the data recording time varies from a few seconds to minutes. In the field of SCD prediction, various studies have addressed the processing of the electrocardiogram (ECG) signal as well as the heart rate variability (HRV) signal in different domains, including time, time-frequency, and nonlinear domain. In time-domain processing the statistical characteristics of time signal such as the mean and standard deviation of heart rate, the mean and standard deviation of RR intervals, and Root Mean Square of the Successive Differences (RSSD) are used. Also, in the frequency domain, the power spectral density (PSD) of the signal energy is used in a very-low-frequency band, low-frequency band and, high-frequency band. Similarly, in the nonlinear domain, features such as Poincare plot, detrended fluctuation analysis (DFA), common entropy, wavelet transform coefficients (WTC), and features of the recursive graph including Lmax, Lmean, correlation dimension (CD), etc. are used. In all of the proposed algorithms so far, researchers have been trying to inform the sudden death alarm in a larger interval than the time of death by separating the signals into different time periods and extracting various features.To evaluate the results of the proposed methods, each of the researchers analyzed the a-few-minute intervals before the SCD. Different classification methods are available to identify the efficiency of the proposed algorithm, such as support vector machine (SVM), multilayer perceptron neural network (MLP), radial base function neural network (RBF), k-nearest neighbor (KNN) and mixture expert (ME). The use of features introduced in different domains and different classifiers has led to the observation of different horizons of prediction in various studies. The results of these predictions are often free from the interpretations of clinical symptoms, and their maximum presented time with acceptable validity eventually reaches 4 minutes before the event, which is not an acceptable time for people who have attacked outside the hospital. Accordingly, the most prominent of these evaluations is the mixture expert methodology in which the best feature extraction methods are used in a new method for selecting the optimal feature space locally. This method makes it possible to select different features every minute before the event by choosing the optimal features for each one-minute interval of the signal as an episode which increases the prediction time from 4 minutes before the death to 12 minutes and allows the interpretation of clinical symptoms in terms of multiplication of the presence of the features per minute.Given the non-linear nature of the HRV signal and the similarity of the ECG signal in many time intervals, the use of the HRV signal has become more popular among scholars. The analysis of various studies shows that by approaching the time of death, linear features (time and frequency) can be predictive of death according to the sensible behavior and variations in patients’ signal. Instead of moving away from the death interval, the use of chaotic and non-linear features is more effective. Therefore, a more precise selection of features in this area can be useful for increasing the horizon of prediction of death.

Optimal traffic control and signal planning can significantly reduce traffic congestion and potential delays at intersections. However, a major challenge to optimize traffic signal timing is to accurately predict traffic before commencing the next cycle. An optimal strategy cannot be achieved with a poor prediction of future traffic. In this study, using a deep learning approach, we develop a data-driven real-time queue length prediction technique. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync adaptive traffic control system data to train a long short-term memory neural network model capturing time-dependent patterns of a queue of a signal. To avoid overfitting and select the best combination of hyperparameters, we use sequential model-based optimization technique. Our experiment results show that the proposed model performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well.

The noise sources of the tip-jet driven rotor are mainly classified as rotor blade noise and jet noise. The rotor blade noise includes thickness noise, loading noise, and nonlinear quadrupole noise, while the jet noise includes nozzle momentum noise and jet radiation noise. Each noise component has different noise source mechanisms related to flow characteristics and the rotating motion. Especially, the nozzle momentum noise has two noise source mechanisms based on the rotating steady aerodynamic load and the nozzle mass flow rate. The details of the time signal and spectrum are discussed to study the unique spectral characteristics. The tip-jet driven rotor noise has both tonal and broadband spectral characteristics. The periodicity of each instance of jet radiation noise is discussed, while the total jet radiation noise also has a tonal noise component. The noise source mechanisms and spectral characteristics are important considerations for an acoustically efficient tip-jet driven rotor.

To use the Automatic identification system (AIS) as a land-based positioning system for coastal vessels is a leadingedge research field. The timestamp detection method used in the AIS is quite different from the one used in general positioning system. We researches the transmission model of AIS signal over sea surface. The influence of different sea surface conditions on the propagation losses of AIS signal was analyzed. Based on the relationships between different meteorological conditions and the propagation losses, the relationship between the propagation losses and the performance of timestamp detection, an adaptive method is proposed to be used in the AIS real-time signal detection. The proposed method can adaptively determine whether to conduct the noise resistance procedure or not according to the sea surface conditions. The experimental results indicate that the timestamps can be detected precisely while the processing time is reduced about 67%. The proposed method is valid under different conditions and achieves a good performance in the field test.

Spectral broadening in pulsed-wave Doppler caused by the transit-time effect deteriorates the frequency resolution and may cause overestimation of maximum velocities in high-velocity blood flow regions and for large beam-to-flow angles. Data-adaptive spectral estimators have been shown to provide improved frequency resolution, especially for small ensemble lengths, but offer little or no improvement when the transit-time effect dominates. In this work, a method is presented that combines a data-adaptive spectral estimation method, the power spectral Capon, and 2-D tracking Doppler to enable improved frequency resolution for both high and low velocities. For each velocity, a time signal is extracted by tracking scatterers over time and space to decrease the transit-time effect, and power spectral Capon is used for spectral estimation. The method is evaluated using simulations, flow phantom recordings, and recordings from healthy and stenotic carotid arteries. Simulation results showed that the spectral width was decreased by 60% compared to 2-D tracking Doppler for velocities around 2.3 m/s using 12 time samples. The reduction was estimated to be 66% using the flow phantom results for 0.85-m/s mean velocity. A 5-dB SNR gain was observed from the in vivo results compared with Welch’s method. Computer simulations confirm that in the presence of velocity gradients or out-of-plane motion, the proposed method can be used to reduce spectral broadening by requiring shorter observation windows.

In this paper, we propose a fiber-optic time synchronization technique based on the bidirectional wavelength division multiplexing (WDM) transmission over a fiber link. The effect of fiber dispersion on the uncertainty of the system is effectively suppressed by properly allocating the forward and backward wavelength maps. Thus, only an initial easy-to-implement calibration in a back-to-back configuration instead of multiple complicated link calibrations is required. To compensate the propagation delay fluctuations, a large-dynamic-range time pre-compensator with high precision is implemented at the local site, enabling a time signal at the remote site is accurately synchronized with the clock at the local site. Our experiments demonstrate that stabilities in terms of time deviation (TDEV) of less than 29.8 ps at 1s and 5.2 ps at 104s and the clock difference of less than 28 ps can be achieved for the fiber link up to 200 km.

In the WR network, a dedicated time and frequency system is set up to deliver and distribute the time signal from WR switches or WR nodes locally. Accordingly, the design of a flexible, transferrable, and extensible time and frequency system is required for complementing existing WR equipment and WR networks. Here we propose the concept of “universal module”. Based on the universal modules, a variety of devices and TF system can be developed. In particular, three applications are demonstrated in this paper. Finally, the first measurement results of the integrated devices will be demonstrated in this paper.

A simple technique for the orthogonal time multiplexing and demultiplexing of quadrature amplitude modulated (QAM) signals is demonstrated. To demultiplex a time signal from a parallel time-wavelength multiplexed superchannel, a Mach-Zehnder Modulator is sufficient for I and Q without the need of any optical filter.

The article substantiates the expediency of introducing a new class of vibration test signals — virtual standards of defective rotor units — into use. Possible ways of implementing such signals are described. The first method is to process the results of active experiments. The vibration signals recorded as a result of the experiment will be virtual standards. This method is very expensive. The second method is a synthesis of the time vibration signal. The task of the test signal formation involves modeling a complex signal from individual components. An analytical description of the time signal is given. A new method for obtaining vibration test signals by reconstructing a time signal from three types of spectra is considered. The results of mathematical modeling and experiments, as well as recommendations on the practical use of virtual test signals are presented.

In Chap. 4, we represented continuous time periodic signals as a sum of everlasting exponentials by Fourier series. Similarly, in Chap. 5, the discrete time periodic signals was represented by discrete time Fourier series using a parallel development of continuous time system. The Fourier series representations of CT and DT signals in these chapters are however applicable only if the signal is periodic. If the signal is non-periodic, then applying a limiting process the aperiodic continuous time signal was expressed as a continuous sum of everlasting exponential or sinusoids and this method was termed as Fourier transform of continuous time signal which was discussed in Chap. 6. On similar line, the discrete time periodic signal represented as a sum of everlasting exponential by Fourier series in Chap. 5, by applying limiting process to aperiodic signal x[n], it can be expressed as a sum of everlasting exponentials. The spectrum \(X[\Omega ]\) so obtained is called Discrete Time Fourier Transform (DTFT).

In this paper, we demonstrate a wavelength division multiplexing (WDM) based system for simultaneously delivering ultrastable optical frequency reference, 10 GHz microwave frequency reference, and one pulse per second (1 PPS) time signal via a 50 km fiber network. For each signal, a unique noise cancellation technique is used to maintain the precision of them. After being compensated, the transfer frequency instability in terms of overlapping Allan deviation (OADEV) for the optical frequency achieves 2E-17/s and scales down to 2E-20/10000 s, which for the 10 GHz microwave reference approaches 4E-15/s and decreases to 1.4E-17/10000 s, and the time uncertainty of the 1 PPS time signal along the system is 2.08 ps. In this scheme, specific channels of WDM are respectively occupied for different signals to avoid the possible crosstalk interference effect between the transmitted reference signals. To estimate the performance of the above scheme, independent of these signals is also demonstrated in this 50 km link, the results are similar to that in the case of simultaneous delivery. This work shows that the WDM-based system is a promising method for building a nationwide time and frequency fiber transfer system with a communication optical network., 11 pages,4 figures,1 table

Wireless power transmission (WPT) for electrical vehicle (EV) charging is a relatively new application of wireless energy transfer and its potential electromagnetic (EM) interference impact on other systems has not been investigated in depth. The proposed frequencies for WPT-EV are also used by radiocommunication systems or services. WPT systems transfer high power and can cause out-of-band and spurious EM emissions. In the analysis, both current and proposed emission limits for WPT systems are considered.For a number of radiocommunication services, separation distances from WPT-systems to protect the radio services in the VLF to MF-bands are derived. The interference risk is analyzed for the radio services standard frequency and time signal (SFTS), non-directional beacon (NDB) and differential transmissions for global navigation satellite systems (DGNSS). The overall conclusion is that radiated emission from WPT may cause non-negligible interference impact on radio-based services at co-location distances ranging from a few tens of metres up to the order of tens of kilometres.

During solution of engineering problems of machinery dynamics a need of revealing the harmonic components often arises in the narrow timing gate. This requires the use of wavelet-transformation oscillation methods and introduction of intelligent systems to hardware and software used in the experiment. The wavelet is considered as a short in time signal functional window, which has its internal structure in the form of a fading wavelike burst, and it is characterized by a scale of display of certain events in the field of the signal frequency spectrum, as well as and by time axis shifts. Complex-functioned continuous functions of real arguments (Daubechies wavelets, Gaussian wavelets, MHat-wavelets), complex-valued functions of real arguments (Morlet and Paul wavelets), as well as real discrete functions (HААRT- and FHat-wavelets) are used as wavelet functions. The wavelet analysis method of vibration signals is disclosed at acoustic diagnostics of machines and mechanisms. Digital implementation of discrete indications of wavelets with the subsequent visualization of results in the form of scalotons is the mathematical basis of the algorithm for procession of vibration signals. It has been suggested that engineering analysis and reconstruction of signals should be implemented by means of directed and reverse continuous wavelet conversions, which are discrete by arguments. The structural and functional scheme of the multichannel system of the intelligent wavelet analysis of vibration signals in machines has been considered. The intelligent system for study of vibration signals makes it possible to form the totality of photographic parameters, when scalotons are calculated by wavelet functions. An example of experimental implementation of the wavelet conversion method of vibration signals parameters is shown. Results of scalotons calculation are shown, when MHat-wavelet and DOG-wavelet are used.

Time signal violation material is a basic material that must be mastered by a basketball referee. This research includes the type of research and development. The results of this research and development is a learning tool basketball refereeing time signal violation tool packed in a dvd video. Results of small group testing of the product obtained a feasibility percentage of 83.33%. While the results of large group trials obtained a percentage of eligibility of 88.97%.

A voice enhancement algorithm based on the Empirical Mode Decomposition (EMD) and the improved spectral subtraction is proposed for the low-SNR (Signal Noise Ratio) shortwave time signal. This method is proposed to solve the problem that the shortwave time signal cannot be used for timing in complex noisy environments. The core idea of this method is to use the Hilbert-Huang Transform (HHT) algorithm to make the empirical mode decomposition on the noisy shortwave signal, and to select the intrinsic mode functions containing the shortwave signal information for the signal reconstruction by through the maximum correlation. Then, to make the spectral subtraction on the reconstructed signal to achieve the purpose of noise reduction. The experimental result shows that this method has a better noise reduction than the traditional methods.

In Press, Corrected Proof, Available online 12 April 2019 The evaluation of economic data and the monitoring of the economy is often concerned with an assessment of the mid- and long-term dynamics of time series (trend and/or cycle). Frequently, one is interested in the most recent estimate of a target signal, a so-called real-time estimate. Unfortunately, real-time signal extraction is a difficult estimation problem that involves linear combinations of possibly infinitely many multi-step ahead forecasts of a series. Here, we address the performances of real-time designs by proposing a generic direct filter approach. We decompose the ordinary mean squared error into accuracy, timeliness and smoothness error components, and we propose a new tradeoff between these competing terms, the so-called ATS-trilemma. This formalism enables us to derive a general class of optimization criteria that allow the user to address specific research priorities, in terms of the accuracy, timeliness and smoothness properties of the corresponding concurrent filter. We illustrate the new methods through simulations, and present an application to Indian industrial production data.

This study explored the phenomenon of the detonation formation delay time in an RDE with an annular combustion chamber with an array of holes fueled with hydrogen. It was determined that the delay time increased when the combustor prefilling time increased. Due to the explosion, the interaction of the shock wave and the pressure wave in the combustor leads to an increase in pressure near the inlet wall surface, thereby impeding the gas feeding. The wavelet transformation was used to analyze the pressure time signal associated with the detonation wave, which was found to be better than the fast Fourier transform typically used. Moreover, the phenomenon of single–double–single wave transformation was observed, of which the underlying mechanism can be explained by an interactive-adjusting process. Also, the experiment captured the reinitiation phenomenon of detonations after a long period of time (around 300 ms) of extinction.

The objective of this research is to develop a new approach to estimate earthquake arrival azimuth using seismological records of the "El Rosal" station, near to the city of Bogota - Colombia, by applying support vector machines (SVMs). The algorithm was trained with time series descriptors of 863 events recorded from January 1998 to October 2008, considering only events with magnitude ≥ 2 ML. The earthquake signals were filtered in order to remove diverse kind of low and high-frequency noise not related to typical seismic activity in the area. During training stages of SVMs, several combinations of kernel exponent and complexity factor were applied to time series of 5, 10 and 15 seconds along with earthquake magnitudes of 2.0, 2.5, 3.0 and 3.5 ML. The best classification of SVMs was obtained using time windows of 5 seconds and earthquake magnitudes greater than 3.0 ML with kernel exponent of 10 and complexity factor of 2, showing an accuracy of 45.4 degrees. This research is an improvement of previous works related to earthquake arrival azimuth determination from single station data employing machine learning techniques. RESUMEN El propósito de esta investigación es desarrollar un nuevo enfoque para estimar el azimut de llegada de terremotos utilizando registros sismológicos de la estación El Rosal, cercana a la ciudad de Bogotá - Colombia, mediante la aplicación de máquinas de vectores de soporte (MVS). El algoritmo fue entrenado con descriptores de series de tiempo de 863 eventos adquiridos desde Enero 1998 hasta Octubre de 2008, considerando solamente eventos con magnitudes ≥ 2 ML. Las señales de los terremotos fueron filtradas para remover diversos tipos de ruidos de alta y baja frecuencia no relacionados con la actividad sísmica típica en el área. Durante las etapas de entrenamiento de la MVS fueron aplicadas varias combinaciones del exponente kernel y factor de complejidad, a series de tiempo de 5, 10 y 15 segundos junto con terremotos de magnitudes mayores a 2.0, 2.5, 3.0 y 3.5 ML. La mejor clasificación de la MVS fue obtenida utilizando ventanas de tiempo de 5 segundos y terremotos de magnitud mayor a 3.0 ML con exponente kernel de 10 y factor de complejidad de 2, mostrando una precisión de 45.4 grados. Esta investigación es una mejora a trabajos previos relacionados con determinación del azimut de llegada de un terremoto a partir de datos de una única estación sismológica empleando técnicas de aprendizaje de máquinas.

The generation of cavitation by ultrasound is used in a large number of processes in different scientific and industrial applications. Chemical reactions are made possible or accelerated by locally occurring high temperatures and pressures generated by the collapse of cavitation bubbles. Mixing or separating substances, emulsification, ultrasonic cleaning, degassing, and microbiological treatment of fluids are some more applications for ultrasonic generated cavitation. In most of these applications, an optical examination of the events within the cavitating medium is not possible. Non-transparent media or closed containers prevent an optical process monitoring. In addition, the use of sensors is often impossible for cost reasons, limited construction space or disturbance of the process. In order to still enable process monitoring, the authors follow a novel approach: the analysis of the electrical signals of the ultrasound transducer used for cavitation generation. The current signal of the ultrasound transducer is inspected for frequency components, known as acoustic cavitation indicators. For this, the time signal is recorded and transferred to the frequency domain for further processing and evaluation. In previous studies, acoustical sensors like hydrophones or microphones were used as reference for the self-sensing technique. In order to link cavitation events inside the fluid container to cavitation indicators in the current signal, a photo study of the cavitation events inside a transparent water container is conducted. In contrast to previous self-sensing attempts, the ultrasound transducer’s transfer characteristic is also considered. The evaluation of the acquired data shows that a frequency component which is 3/2 times the driving frequency (∼30 kHz) can be used to determine the onset of transient cavitation. Once the transient cavitation threshold has been exceeded, broad band noise levels show a good correlation with cavitation intensity.

As a consequence of the rapid growth of vehicles in cities, urban traffic congestion has become more and more serious nowadays. As an effective control approach, Model Predictive Control (MPC) has been deeply studied for transportation management. However, the relatively complex on-line computing hinders MPC from being further applied on transportation system, especially for large scale transportation networks. Based on the refined store-and-forward model, we propose a signal split control approach with Explicit Model Predictive Control (EMPC) in this paper, by which the complexity of online optimization can be significantly reduced. With multi-parametric Quadratic Program (mp-QP), our approach can generate explicit signal control law, and then shifts the former repeated online signal split optimization offline. As a result, the real time signal control becomes an easier task by searching the lookup table corresponding to the transportation state. The simulation experiments demonstrate that our EMPC based approach outperforms the traditional fix-time signal control. Meanwhile, compared with traditional MPC based approach, our approach can decrease the total computation complexity, and then increase the applicability for real transportation system.

A frequency-encoded real-time and long-distance displacement measurement method based on two cross-referencing optoelectronic oscillators (OEOs) is proposed and experimentally demonstrated. In the system, one OEO is used for reference, and the other is used for measurement. The initial distance is evaluated by the oscillation frequency and free spectral range of the OEO for reference. The small displacement is calculated by using the frequency of the intermediate frequency (IF) signal generated by mixing the two OEOs. The two OEOs with the same characters are placed in the same environment. This protects the displacement measurement from the influence of the variation of ambient temperature. A high measurement sensitivity, which improves the precision of the displacement measurement, is also implemented due to the accumulative magnification effect at high-order resonant frequency modes. In addition, the low frequency of the IF signal, which is far smaller than the oscillation frequency of the two OEOs, allows a cost-effective and real-time measurement by a frequency counter. The proposed displacement measurement method is demonstrated experimentally. When the sampling time is 1 ms, displacement measurement errors less than ±3.71 and ±11.14 μm are realized at emulated distances of around 800m and 6 km, respectively. The real-time velocity and acceleration measurements are also implemented owing to the introduction of a time signal in the measurement process.

In order to evaluate the performance of standard time remote reproduction, this paper implements a low-cost standard time frequency reproduction device, which can provide a time signal with a deviation of less than 10ns (3σ) from the standard time under the conditions of zero baseline and long baseline. This article using the standard time reproduction system that has been built so far, the standard time remote reproduction experiment was carried out at the National Time Service Center (NTSC) of the Chinese Academy of Sciences and the National Astronomical Observatory Tianjin Monitoring Station. The experiments show that under the condition of zero baseline, the synchronization accuracy (std) between the reproduced signal and the standard time is 1.36ns, and the maximum deviation from the standard time for 24 hours is 3.46ns. Under long baseline conditions, the synchronization accuracy (std) between the reproduced signal and the standard time is 1.65ns, and the maximum deviation from the standard time for 24 hours is 4.58ns.

Response reconstruction is used to obtain accurate replication of vehicle structural responses of field recorded measurements in a laboratory environment, a crucial step in the process of Accelerated Destructive Testing (ADA). Response Reconstruction is cast as an inverse problem whereby an input signal is inferred to generate the desired outputs of a system. By casting the problem as an inverse problem we veer away from the familiarity of symmetry in physical systems since multiple inputs may generate the same output. We differ in our approach from standard force reconstruction problems in that the optimisation goal is the recreated output of the system. This alleviates the need for highly accurate inputs. We focus on offline non-causal linear regression methods to obtain input signals. A new windowing method called AntiDiagonal Averaging (ADA) is proposed to improve the regression techniques’ performance. ADA introduces overlaps within the predicted time signal windows and averages them. The newly proposed method is tested on a numerical quarter car model and shown to accurately reproduce the system’s outputs, which outperform related Finite Impulse Response (FIR) methods. In the nonlinear configuration of the numerical quarter car, ADA achieved a recreated output Mean Fit Function Error (MFFE) score of 0.40% compared to the next best performing FIR method, which generated a score of 4.89%. Similar performance was shown for the linear case.

With the increasing demand for reliable power supply of distribution network in economic society, how to realize sensitive and reliable perception of holographic information of distribution network and accurate fault diagnosis of urban distribution network has become the key to realize intelligent distribution network. Aiming at the problem of sensitive sensing and accurate positioning of distribution network, this paper proposes a comprehensive fault diagnosis method for distribution network based on "time, signal, algorithm and sample". The fault features of time, signal, algorithm and sample are used for accurate analysis. The multi-level fault analysis method of switch, feeder and substation is used to build a multi-dimensional fault feature analysis library, and a comprehensive fault diagnosis model of distribution network is established, which integrates data fusion, feature extraction and intelligent decision-making. Using waveform correlation, empirical mode decomposition technology and other fault feature extraction methods, as well as the introduction of depth of historical sample analysis method, a multi-objective normalized distribution network comprehensive fault diagnosis method is established, which provides an effective means for accurate fault identification of smart distribution network and improves power supply reliability.

NPL is leading a programme called the National Timing Centre (NTC) to develop a robust, geographically distributed time scale infrastructure across the UK. The aims of the programme are to improve security and resilience in national time and frequency distribution, whilst supporting UK innovation and skills. Recent government studies have highlighted the dependence of services such as telecommunications, broadcasting, energy and finance on Global Navigation Satellite Systems (GNSS) for time and frequency reference signals. As GNSS signals are vulnerable to disruption, the availability of accurate time and frequency signals from a robust national timing infrastructure will, in future, complement GNSS and mitigate the impact of any disruption to GNSS signals to these critical services. At present, the UK’s national time scale UTC(NPL) is maintained by NPL in Teddington, London. Time dissemination services are provided over the whole country via the millisecond-level accuracy MSF radio time signal and an NTP internet time service. The NPLTime® service provides higher accuracy to a number of users in the London area and in several European locations. The new National Timing Centre programme will significantly improve the resilience and capability of this UK time scale infrastructure. In future, a distributed UTC(NPL) time scale infrastructure will operate from several locations across the UK, including a new NPL facility in Teddington, interconnected by two or more time and frequency transfer methods. The time scale design is based on signal measurement, frequency steering and distribution from hydrogen masers with significant built-in hardware redundancy, which will be replicated at each site. At NPL, development of the new time scale facility is underway with the arrival of new hydrogen masers and construction of an additional caesium fountain. Work is also in progress to develop a suite of software to automate, monitor and control the time scale. To enable geographically widespread availability of signals from this new time scale, several distribution nodes will provide points of access across the UK. At these nodes, a range of distribution technologies including fibre, communication satellites, terrestrial broadcasts and GNSS will deliver resilient, UTC-traceable time and frequency signals to critical national infrastructure, businesses and academia. As a result, the NTC programme will not only support essential services, it will enable technological growth and innovation in industry through the development of new time and frequency products and applications. Additionally, the NTC programme will enhance the UK’s research and development capabilities in time and frequency generation and dissemination. The NTC programme will also respond to the specialist skills shortage in the field of time and frequency. An assessment of the current and future workforce requirements in this field has been carried out to create a Training & Education Blueprint. Through collaboration with industry and academia, new training opportunities will be created, suitable for learners at a range of levels.

Today, technology had made life easy by providing an alternate smart solution to every problem in society; but when it comes to managing the increasing volume of traffic at a busy junction, the technology implementation is still lagging. Hence a technology-based solution is required to automatically control the traffic signal based on the density of the traffic on a specific lane. It is achieved by sensing the traffic in a particular lane by the ultrasonic sensor and according to the volume of traffic, the green time signal/clearing signal is increased or decreased with the respective red time signal. The proposed system also consists of an automatic SMS alert triggering system to traffic authority when the volume of traffic in a particular lane is increasing even after two consecutive clearing signals. The smart pole is installed with LCD to display the alert message and it contains emergency alert buttons which are used to alert police, traffic, ambulance, fire authorities if any problem occurred in a particular lane. Wireless speed limit notifier is installed in desired lanes which notifies the driver about the speed limit of a zone via wireless technology.

With the development of automated driving functions, more and more environmental sensors are combined for the vehicle perception. A problem that arises with the extensive use of radar sensing is called interference. It describes the confounding effects from the wave overlay of two or more radar sensors operating in the same frequency-band. At this point, methods for interference avoidance and mitigation come to apply. For a valid design and development of such methods, real sensor measurements were required in the past. This publication instead proposes a novel sensor modelling technique that represents the interference mechanisms within the radar sensor signals. It is based on a full radar time signal simulation coupled with a broad range of influencing factors. The concept is validated by comparing the simulated signal processing steps to the real sensor measurement behavior. As a result, mitigation methods for the sensor fault behavior can be fully assessed within a simulation environment. The opportunity for applying new scenario data and a variable set of radar sensors underlines the importance of this approach in the development of future radar systems.

Distributed acoustic sensing (DAS) systems based on fiber brag grating (FBG) have been widely used for distributed temperature and strain sensing over the past years, and function well in perimeter security monitoring and structural health monitoring. However, with relevant algorithms functioning with low accuracy, the DAS system presently has trouble in signal recognition, which puts forward a higher requirement on a high-precision identification method. In this paper, we propose an improved recognition method based on relative fundamental signal processing methods and convolutional neural network (CNN) to construct a mathematical model of disturbance FBG signal recognition. Firstly, we apply short-time energy (STE) to extract original disturbance signals. Secondly, we adopt short-time Fourier transform (STFT) to divide a longer time signal into short segments. Finally, we employ a CNN model, which has already been trained to recognize disturbance signals. Experimental results conducted in the real environments show that our proposed algorithm can obtain accuracy over 96.5%.

Acoustic cavitation has been utilized in many industrial applications to enhance the process intensity. To obtain the most energy-efficient sonochemical activity, the excitation signal specifications are of great importance. This investigation focuses on the effect of different wave characteristics on sonochemical activity including erosion rate and measured sound pressure levels below the surface and beside the high power sonotrode. Signal characteristics as frequency bandwidth, sweep rate, and direction are considered aspects of time signal shape transformation. Altogether eight groups of factors were evaluated in a two-level and replicated design. Numerical simulation has been conducted to achieve the optimized geometrical design and to prevent parasitic modes in sonotrode’s configuration. Results show that negative direction with 100 ms sweep rate and 800 Hz frequency bandwidth generates both the highest sound pressure level and erosion rate. The findings from this study are aimed to be implemented in an energy-efficient flow through sonochemical reactor design. ISBN för värdpublikation: 978-1-6654-0355-9; 978-1-6654-4777-5;Funders: European innovative part-nership (EIP); IDB Innovative Drinks Balsgård AB

Although point clouds are characterized as a type of unstructured data, timestamp attribute can structure point clouds into scanlines and shape them into a time signal. The present work studies the transformation of the street point cloud into a time signal based on the Z component for the semantic segmentation using Long Short-Term Memory (LSTM) networks. The experiment was conducted on the point cloud of a real case study. Several training sessions were performed changing the Level of Detail of the classification (coarse level with 3 classes and fine level with 11 classes), two levels of network depth and the use of weighting for the improvement of classes with low number of points. The results showed high accuracy, reaching at best 97.3% in the classification with 3 classes (ground, buildings, and objects) and 95.7% with 11 classes. The distribution of the success rates was not the same for all classes. The classes with the highest number of points obtained better results than the others. The application of weighting improved the classes with few points at the expense of the classes with more points. Increasing the number of hidden layers was shown as a preferable alternative to weighting. Given the high success rates and a behaviour of the LSTM consistent with other Neural Networks in point cloud processing, it is concluded that the LSTM is a feasible alternative for the semantic segmentation of point clouds transformed into time signals.

We report the extension of our frequency dissemination system to the time transfer capability, and a synchronized time signal dissemination system over 120 km fiber is proposed and experimentally demonstrated. The optical fiber propagation delay variation-induced phase fluctuation of RF is identically transferred to an IF by the mean of the DHPT scheme. By using the phase-locked loop (PLL) to compensate the phase fluctuation, a stable frequency signal is obtained, at the same time, a high-precision time delay fluctuation measurement is also achieved by monitoring the phase of IF VCO at the local end. The resolution of fiber transfer delay measurement achieves about 60 fs. After the active compensation of propagation delay variation, the time variation of 120 km fiber link is about ±100 ps during 3600 s.

We present an automated system repair framework for cyber-physical systems. The proposed framework consists of three main steps: (1) system simulation and fault detection to generate a labeled dataset, (2) identification of the repairable temporal properties leading to the faulty behavior and (3) repairing the system to avoid the occurrence of the cause identified in the second step. We express the cause as a past time signal temporal logic (ptSTL) formula and present an efficient monotonicity-based method to synthesize a ptSTL formula from a labeled dataset. Then, in the third step, we modify the faulty system by removing all behaviors that satisfy the ptSTL formula representing the cause of the fault. We apply the framework to two rich modeling formalisms: discrete-time dynamical systems and timed automata. For both of them, we define repairable formulae, the corresponding repair procedures, and illustrate them over case studies.