Search Results (23)

Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China’s Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of “supercooling and warm rain” caused by the “warm–cold” temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events. Full article

The estimation of the frequency of sinusoids has been the object of intense research for more than 40 years. Its importance in classical fields such as telecommunications, instrumentation, and medicine has been extended to numerous specific signal processing applications involving, for example, speech, audio, and music processing. In many cases, these applications run in real-time and, thus, require accurate, fast, and low-complexity algorithms. Taking the normalized Cramér–Rao lower bound as a reference, this paper evaluates the relative performance of nine non-iterative discrete Fourier transform-based individual sinusoid frequency estimators when the target sinusoid is affected by full-bandwidth quasi-harmonic interference, in addition to stationary noise. Three levels of the quasi-harmonic interference severity are considered: no harmonic interference, mild harmonic interference, and strong harmonic interference. Moreover, the harmonic interference is amplitude-modulated and frequency-modulated reflecting real-world conditions, e.g., in singing and musical chords. Results are presented for when the Signal-to-Noise Ratio varies between −10 dB and 70 dB, and they reveal that the relative performance of different frequency estimators depends on the SNR and on the selectivity and leakage of the window that is used, but also changes drastically as a function of the severity of the quasi-harmonic interference. In particular, when this interference is strong, the performance curves of the majority of the tested frequency estimators collapse to a few trends around and above 0.4% of the DFT bin width. Full article

Discrete cosine and sine transforms closely approximate the Karhunen–Loeve transform for first-order Markov stationary signals with high and low correlation coefficients, respectively. Discrete sinusoidal transforms can be used in data compression, digital filtering, spectral analysis and pattern recognition. Short-time transforms based on discrete sinusoidal transforms are suitable for the adaptive processing and time–frequency analysis of quasi-stationary data. The generalized sliding discrete transform is a type of short-time transform, that is, a fixed-length windowed transform that slides over a signal with an arbitrary integer step. In this paper, eight fast algorithms for calculating various sliding sinusoidal transforms based on a generalized solution of a second-order linear nonhomogeneous difference equation and pruned discrete sine transforms are proposed. The performances of the algorithms in terms of computational complexity and execution time were compared with those of recursive sliding and fast discrete sinusoidal algorithms. The low complexity of the proposed algorithms resulted in significant time savings. Full article

For the first time precise measurements of the groundwater level variations in the territory of the Mikhnevo geophysical observatory in an aseismic region (Moscow region, Russia) have been carried out since February 2008 at a sampling rate of 1 Hz. The groundwater level variations under quasi-stationary filtration are considered indicators of the dynamic deformation of a fluid-saturated reservoir represented by carbonate-terrigenous sediments. Both permanent (long-term) factors—atmospheric pressure, lunar-solar tides, and periodic (short-term) ones—seismic impacts from distant earthquakes, are used as probing signals for analyzing the filtration parameters of aquifers of different ages. Hydrogeological responses to the passage of seismic waves from earthquakes with magnitudes of 6.1–9.1 with epicentral distances of 1456–16,553 km was recorded in 2010–2023. Dependences of dynamic variations of the pore pressure in the upper weakly confined and lower confined aquifers on the ground velocity are approximated by different regression functions. Spectral analysis of hydrogeological responses made it possible to identify coseismic and postseismic effects from distant earthquakes. The postseismic effects in the form of an episodic increase in the pore pressure may be caused by a skin effect—clogging of microcracks nearby the wellbore by colloidal particles under intensive seismic impact. Full article

Wave-mean flow interaction is usually regarded as accounting for the origin of the Arctic Oscillation/Northern Hemisphere Annular Mode (AO/NAM). It is inferred that the combination of the local wave-mean flow interactions at the AO/NAM’s three regional centers of action on three important time scales contributes to the main behavior of the AO/NAM index. To discuss the variations of the AO/NAM indices on the three prominent time scales, we take the 2007/08 and 2009/10 winters as two comparative examples to analyze the local wave-mean flow interactions at the AO/NAM’s three centers. The following three facets are identified: (1) Synoptic-scale wave breakings in the North Atlantic can explain the variances of the AO/NAM index on a time scale of 10–20 days. In the 2007/08 winter, there were both cyclonic and anticyclonic synoptic wave breakings, while in the 2009/10 winter, cyclonic synoptic wave breaking was dominant, and the flow characteristics were strikingly similar to the blocking. (2) In the 2007/08 and 2009/10 winters, the signals of the AO/NAM indices on the time scale of 30–60 days are mainly from the interactions between the upward propagating quasi-stationary waves and the polar vortex in the stratosphere. (3) This work also demonstrates that the AO/NAM is linked to the El Niño–Southern Oscillation (ENSO) by the Pacific–North American pattern (PNA) on the winter mean time scale. In the 2007/08 (2009/10) winter, La Niña (El Niño) forced the Pacific jet to shift poleward (equatorward), in favor of weakening (enhancing) the polar waveguide; thus, the polar vortex became stronger (weaker), corresponding to the positive (negative) winter mean AO/NAM index. Full article

The article presents a solution to the problem of limited accuracy of dynamic measurements performed with GNSS receivers. The proposed measurement method is a response to the needs related to the assessment of the measurement uncertainty of the position of the track axis of the rail transport line. However, the problem of reducing the measurement uncertainty is universal for many different situations where high accuracy of positioning of objects is required, especially in motion. The article proposes a new method to determine object’s location using geometric constraints of a number of GNSS receivers arranged in symmetric configuration. The proposed method has been verified by comparing signals recorded by up to five GNSS receivers during stationary and dynamic measurements. The dynamic measurement was made on a tram track within the framework of a cycle of studies upon effective and efficient methods to catalogue and diagnose tracks. A detailed analysis of the results obtained with the quasi-multiple measurement method confirms remarkable reduction in their uncertainty. Their synthesis shows the usability of this method in dynamic conditions. The proposed method is expected to find application in measurements requiring high accuracy, and in case of deterioration of the signal quality from satellites by one or more of GNSS receivers due to the appearance of natural obstacles. Full article

This paper proposes the use of quasi-stationary aircraft and reconfigurable intelligent surfaces (RIS) to improve the system performance in satellite–terrestrial laser communication downlink. Single-input multiple-output (SIMO) technology is applied to the relay node of a quasi-stationary aircraft. The closed expression of the bit error rate (BER) of an RIS-assisted satellite quasi-stationary aircraft–terrestrial laser communication system (RIS-SHTLC) is derived under the M-distributed atmospheric turbulence model while considering the influence of atmospheric turbulence and pointing errors caused by RIS jitter. The effects of coherent binary frequency shift keying (CBFSK), coherent binary phase-shift keying (CBPSK), non-coherent binary frequency shift keying (NBFSK), and differential binary phase-shift keying (DBPSK) on the performance of an RIS-SHTLC system are simulated and analyzed under weak turbulence. The results show that the RIS-SHTLC system with CBPSK modulation has the best communication performance. Simultaneously, the relationships between the average signal-to-noise ratio (SNR) and BER of the RIS-SHTLC system under different RIS elements are simulated and analyzed, and compared with the traditional SHTLC system. In addition, the influence of the zenith angle, receiving aperture and divergence angle on the performance of the system is studied. Finally, Monte Carlo simulations are used to validate the analytical results. Full article

Motivated by observational evidence of the electromagnetic signal from the X-ray binary system known as quasi-periodic oscillations in the light curves of astrophysical black holes or neutron stars, we examined the general relativity and alternative theory of gravity in the strong gravity regime. The orbital and epicyclic motion of test particles in general axially symmetric spacetime was investigated. We provide a general description to derive the exact analytical expressions for the fundamental frequencies, namely, Keplerian epicyclic (radial and vertical) frequencies of test particles in an arbitrary axisymmetric and stationary spacetime. The detailed derivation of the expressions for the orbital and epicyclic frequencies of test particles orbiting around the Kerr–Newman-NUT black hole is also shown. Full article

The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri–Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results. Full article

Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies. Full article

Short-time (sliding) transform based on discrete Hartley transform (DHT) is often used to estimate the power spectrum of a quasi-stationary process such as speech, audio, radar, communication, and biomedical signals. Sliding transform calculates the transform coefficients of the signal in a fixed-size moving window. In order to speed up the spectral analysis of signals with slowly changing spectra, the window can slide along the signal with a step of more than one. A fast algorithm for computing the discrete Hartley transform in windows that are equidistant from each other is proposed. The algorithm is based on a second-order recursive relation between subsequent equidistant local transform spectra. The performance of the proposed algorithm with respect to computational complexity is compared with the performance of known fast Hartley transform and sliding algorithms. Full article

El Niño is an important quasi-cyclical climate phenomenon that can have a significant impact on ecosystems and societies. Due to the chaotic nature of the atmosphere and ocean systems, traditional methods (such as statistical methods) are difficult to provide accurate El Niño index predictions. The latest research shows that Ensemble Empirical Mode Decomposition (EEMD) is suitable for analyzing non-linear and non-stationary signal sequences, Convolutional Neural Network (CNN) is good at local feature extraction, and Recurrent Neural Network (RNN) can capture the overall information of the sequence. As a special RNN, Long Short-Term Memory (LSTM) has significant advantages in processing and predicting long, complex time series. In this paper, to predict the El Niño index more accurately, we propose a new hybrid neural network model, EEMD-CNN-LSTM, which combines EEMD, CNN, and LSTM. In this hybrid model, the original El Niño index sequence is first decomposed into several Intrinsic Mode Functions (IMFs) using the EEMD method. Next, we filter the IMFs by setting a threshold, and we use the filtered IMFs to reconstruct the new El Niño data. The reconstructed time series then serves as input data for CNN and LSTM. The above data preprocessing method, which first decomposes the time series and then reconstructs the time series, uses the idea of symmetry. With this symmetric operation, we extract valid information about the time series and then make predictions based on the reconstructed time series. To evaluate the performance of the EEMD-CNN-LSTM model, the proposed model is compared with four methods including the traditional statistical model, machine learning model, and other deep neural network models. The experimental results show that the prediction results of EEMD-CNN-LSTM are not only more accurate but also more stable and reliable than the general neural network model. Full article

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set. Full article

Recurrence-plot (RP) analysis is a graphical tool to visualize and analyze the recurrence of nonlinear dynamic systems. By combining the advantages of the RP and a convolutional neural network (CNN), a fault-classification scheme for planetary gear sets is proposed in this paper. In the proposed approach, a vibration is first picked up from the planetary-gear test rig and converted into an angular-domain quasistationary signal through computed order tracking to eliminate the frequency blur caused by speed fluctuations. Then, the signal in the angular domain is divided into several segments, and each segment is processed by the RP to constitute the training sample. Moreover, a two-dimensional CNN model was developed to adaptively extract faulty features. Experiments on a planetary-gear test rig with four conditions under three operating speeds were carried out. The results of measured vibration demonstrated the validity of CNN and recurrence plot analysis for the fault classification of planetary-gear sets. Full article

The need to detect outliers or otherwise unusual data, which can be formalized as the estimation a particular quantile of a distribution, is an important problem that frequently arises in a variety of applications of pattern recognition, computer vision and signal processing. For example, our work was most proximally motivated by the practical limitations and requirements of many semi-automatic surveillance analytics systems that detect abnormalities in closed-circuit television (CCTV) footage using statistical models of low-level motion features. In this paper, we specifically address the problem of estimating the running quantile of a data stream with non-stationary stochasticity when the absolute (rather than asymptotic) memory for storing observations is severely limited. We make several major contributions: (i) we derive an important theoretical result that shows that the change in the quantile of a stream is constrained regardless of the stochastic properties of data; (ii) we describe a set of high-level design goals for an effective estimation algorithm that emerge as a consequence of our theoretical findings; (iii) we introduce a novel algorithm that implements the aforementioned design goals by retaining a sample of data values in a manner adaptive to changes in the distribution of data and progressively narrowing down its focus in the periods of quasi-stationary stochasticity; and (iv) we present a comprehensive evaluation of the proposed algorithm and compare it with the existing methods in the literature on both synthetic datasets and three large “real-world” streams acquired in the course of operation of an existing commercial surveillance system. Our results and their detailed analysis convincingly and comprehensively demonstrate that the proposed method is highly successful and vastly outperforms the existing alternatives, especially when the target quantile is high-valued and the available buffer capacity severely limited. Full article