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The present study reports measurements of radon in groundwater conducted in China between January and December 2013. During the measurement period, a great Ms = 6.6 earthquake occurred in Gansu Province, China. The paper analyses data derived from three nearby stations via monofractal and multifractal detrended fluctuations analysis and explores whether pre-earthquake fractal and long-memory trends exist in the time series. Several critical epochs with characteristic pre-seismic long-lasting fractal patterns are identified in segmented parts of the series. In the whole data series, the fluctuation function, scaling exponent and generalised Hurst exponent depend on the fractal scales, which indicates multifractality. The multifractal spectrum of the raw, shuffled and truncated series showed a significant increase in the degree of fluctuation that can be explained as a pre-earthquake indicator attributable to the Gansu earthquake due to its very large magnitude.

This paper investigates the existence of chaos in concentration dynamics of particulate matter with an aerodynamic diameter less than or equal to $$10\,\upmu \hbox {m}$$ ( $$\hbox {PM}_{10}$$ ) in the greater Athens area (GAA), Greece. It reports findings on three 16-year $$\hbox {PM}_{10}$$ time series recorded by three different air pollution monitoring stations located in GAA and examines if critical fractal epochs with long memory exist. Detrended Fluctuation Analysis (DFA) and Rescaled Range (R/S) Analysis were used via sliding windows of approximately 1-month duration. In all $$\hbox {PM}_{10}$$ time series, several segments were found with critical fractal behaviour and hidden long-memory patterns. All these segments exhibited Hurst exponents above 0.75 and DFA exponents above 1.75. Twelve $$\hbox {PM}_{10}$$ segments with fractality and long memory were commonly identified by both techniques. In one case, long memory was identified concurrently across all three air pollution monitoring stations and in another case, across two stations. The importance of the agreement between two different and independent chaos-analysis techniques is discussed in association with the proper selection of threshold values. This is the second time to address chaos in $$\hbox {PM}_{10}$$ data series in GAA, and the first time to combine two widely accepted techniques, DFA and R/S analysis.

This paper reports one-month 3 kHz EM disturbances recorded at Kardamas, Ilia, Greece after a strong M L = 6 . 6 earthquake occurred on 2018/10/25 near Zakynthos and Ilia. During this period 17 earthquakes occurred with magnitudes M L = 4 . 5 and M L = 5 . 5 and depths between 3 km and 17 km, all near Zakynthos and Ilia. A two-stage, fully computational methodology was applied to the outcomes of five different time-evolving chaos analysis techniques (DFA, fractal dimension analysis through Higuchi, Katz and Sevcik methods and power-law analysis). Via literature-based thresholds, the out-of-threshold results of all chaos analysis methods were located and from these, the common time instances of 13 selected combinations per five, four, three and two methods. Numerous persistent segments were located with DFA exponents between 1 . 6 &le, &alpha, &le, 2 . 0 , fractal dimensions between 1 . 4 &le, D &le, 2 . 0 and power-law exponents between 2 . 2 &le, &beta, 3 . 0 . Out of the 17 earthquakes, six earthquakes were jointly matched by 13 selected combinations of five, four, three and two chaos analysis methods, four earthquakes by all combinations of four, three and two, while the remaining seven earthquakes were matched by at least one combination of three methods. All meta-analysis matches are within typical forecast periods.

The recent seismic activity in the eastern Aegean Sea, foregrounded by two major (ML = 6.1 and ML = 6.2) earthquakes occurring near Lesvos and Kos islands, respectively, is investigated in this work. Electromagnetic radiation measurements across different frequency bands and antenna orientations from a monitoring station in Agia Paraskevi, Lesvos Island, are analysed in order to reveal earthquake precursory signatures hidden in the electromagnetic data sequence. A straightforward, data-driven approach is employed in which day-to-day variations of the fractal characteristics of the measurements are adaptively monitored via a fractal spectral exponent similarity measure. The evolution of the fractal day-to-day variation in a 99-day period, which includes the two major earthquakes, shows a sustained, sudden increase lasting 1 to 4 days before every earthquake cluster of ML = 4.0 and above. Most importantly, this day-to-day variation subsides shortly after and remains relatively low during the absence of earthquakes, thus alleviating the emergence of false alarms.

This work examines if chaos and long memory exist in PM10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months&rsquo, duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz&rsquo, s algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM10 time series were combined in this manner.

This paper reports radioluminescence properties of the CdSe/ZnS quantum dots. Three quantum dot samples were prepared with concentrations 14.2 × 10 −5 mg/mL, 21.3 × 10 −5 mg/mL and 28.5 × 10 −5 mg/mL, respectively. The ultraviolet induced emission spectra of CdSe/ZnS dots exhibited a peak at 550 nm ranging between 450 nm and 650 nm. Discrepancies observed between 250 nm and 450 nm were attributed to the solvent and cuvette. The absolute efficiency calculated from random fractional-Gaussian luminescence segments varied. Long-memory fractional-Brownian segments were also found. The quantum dot solution with concentration of 21.3 × 10 −5 mg/mL exhibited the maximum absolute efficiency value at 90 kVp. The CdSe/ZnS dots have demonstrated potential for detection of X-rays in the medical imaging energy range.

A multichannel, multi-frequency approach on the analysis of critical electromagnetic (EM) signatures prior to earthquakes is presented. The algorithm employed is based on single-channel techniques for the identification of longmemory trends in fractal processes and attempts to take advantage of the increased information content that is provided by multichannel EM recordings. The EM measurements consist of four channels which correspond to four distinct EM radiation frequencies. Two of these frequencies lie in the kHz range and the other two in the MHz range. Our analysis of a three-month EM activity period shows that there exists some degree of similarity between EM channels that are close in frequency, in terms of an information theoretic measure. More importantly, the multichannel-based detection results seem to be in close agreement with the main earthquake occurrences of the three-month period. The combined output of the multiple channels is used to train a Support Vectors Machine (SVM) classifier in order to identify precursory EM signal segments of forthcoming seismic events and a high accuracy rate is reported.

This paper reports characteristic pre-seismic disturbances of electromagnetic radiation of the MHz range and of radon in soil. All the disturbances were collected in an organised manner from a telemetric network operating in Greece in a period of five distinct years during which a total of thirty seven earthquakes occurred. This study includes thirty-three pre-seismic MHz electromagnetic disturbances with duration of a few days up to one month and four lengthy radon precursors, all recorded prior to earthquakes that occurred in years 2007, 2008, 2009, 2014, 2015 with M L ≥ 5.0. The paper is a systematic investigation of the outputs of the fractal analysis from the thirty seven earthquakes. The results indicated that the majority of the investigated signals (both electromagnetic radiation and radon) exhibited characteristic epochs with fractal organisation. Continuous epochs were detected in several one-month, electromagnetic radiation, signals. As fractal epochs were considered those with successive (r2 ≥ 0.95) power-law behaviour and b - exponent in the fBm class (1 ≤ b ≤ 3). As enhanced precursory fractal epochs were considered the successive fBm ones with many successive (r2 ≥ 0.95) segments above 1.5 with extra attention given to the ones above 2.0. These epochs indicated well-established long-memory dynamics well away from fGn randomness. Several successive ( r2 ≥ 0.95) fractal electromagnetic and radon segments showed anti-persistency (1.5 ≤ b < 2.0). Nevertheless, numerous persistent ( 2.0 < b ≤ 3.0 ) parts were detected. Switching between persistency and anti-persistency was identified. This switching was considered of enhanced precursory value of the electromagnetic and radon signals. The findings indicated selforganised critical state characteristics of the last stages of the investigated earthquakes. It was concluded that the fractal analysis can be employed as a first screening method for the identification of long-memory patterns hidden in pre-seismic time-series. The method is reliable in identifying pre-earthquake patterns.

This paper reports characteristic pre seismic electromagnetic disturbances in the MHz range detected prior to seven significant earthquakes of M L ≥ 5.0 which occurred in Greece between 2009 and 2015. The whole data set was investigated through Rescaled Range (R/S) analysis and wavelet based spectral fractal analysis. The long-memory trends hidden in the analysed signals were investigated via the Hurst exponent (H). The R/S method indicated that in the majority of the segments of the investigated signals, the H - exponents were in the range 0.7-0.9. Several exponents were above 0.9. This is associated with persistency. In several segments, the Hurst exponent exhibited small variance. Noteworthy fluctuations were also found. In many cases the H - time fluctuations were independent of the time evolution of the associated electromagnetic signals. The spectral fractal method, being more suited for this purpose, highlighted the characteristic signals’ epochs, governed by long-lasting fractal organisation. The calculated Hurst exponents in the characteristic long-lasting fractal epochs were associated with medium anti-persistent behaviour or with continuous switching between anti-persistency and persistency. The H - exponents of the random fractal organised segments were mainly persistent. From the data presented in this paper, it is deduced that the R/S method provides additional information on the estimation of the Hurst exponent when combined with the spectral fractal method. Both techniques should be employed in sequential steps to enhance the precursory value of the results.

This paper attempts to survey and catalog published short-term pre-earthquake precursors based on radon gas emissions. A series of papers were searched to collect relevant data, such as the epicentral distance, the extent, time and duration of the radon disturbance and to analyze the precursory value of each observable. In general, enhanced radon emissions have been observed prior to earthquakes and this has been recorded all over the world. The abnormal radon exhalation from the interior of earth has been associated with earthquakes and is considered an important field of research. The proposed physical models attempt to relate the observed radon disturbances with deformations occurring in the earth's crust prior to forthcoming earthquakes. While the models provide some physical explanations, there are many parameters that require further investigation.

This paper is a survey of pre-earthquake short-term electromagnetic precursors. Extensive studies were carried out the last decades in analysis of electromagnetic emissions from 10-3 Hz up to MHz. These signals were analyzed through visual, statistical and chaotic techniques. The paper presents cumulative elements from scientific investigations performed during the last 40 years in this area. Physical models have also been developed for the interpretation of production and propagation of electromagnetic radiation during the fracture process. The Ultra Low Frequency (ULF) fracture-re-lated emissions attempted to be explained via magneto-hydrodynamic, piezomagnetic and electroki-netic effects whereas for the kHz-MHz observations the model of asperities was proposed.

In this paper, a novel algorithm is introduced for the analysis of long-memory patterns hidden in electromagnetic (EM) readings prior to earthquakes. The algorithm builds upon previous work on long-memory detection in EM measurements by fusing Support Vector Machine (SVM) classifiers with well-deployed power law fit tests and Rescaled-Range (R/S) time-series variability methods. To apply the algorithm, fractal power law in the wavelet domain is assessed so as to identify fractional Brownian motion (fBm) segments of continuously monitored pre-earthquake EM activity. The selected segments are then further processed through R/S Analysis in order to further refine the detection of prominent fBm behaviour. The combined output of the two methods is used to train a SVM classifier which is subsequently employed to verify similar fBm states in existing EM data and to allow for rapid fBm detection in large data sequences of unprocessed or newly incoming EM readings. The SVM classifier is added in a modular fashion, on top of pre-earthquake monitoring algorithms, and can be trained with a small fraction of a huge available dataset of EM readings. Three earthquake events in Greece, corresponding to different time occurrences and geographic locations, were investigated. For each of the three earthquakes, data collected by a nearby EM measurement station one month prior to the peak event were analysed by the proposed method. The results yielded an overall accuracy rate of at least 90% for the detection of specific, prominent fBm segments despite the fact that the fBm profile in the three investigated earthquake sequences was very different.

A novel scheme for diagnosing non-stationary electrocardiogram (ECG) records using a combination of stochastic time-series detection and wavelet-based classification methods is presented. The ECG diagnosis algorithm stems from a two-stage procedure, which initially detects and subsequently classifies ECG segments bearing cardiac abnormalities. In the first stage, recursive stochastic time-series representations (as applied to fault diagnosis of mechanical systems) are used for detecting any potentially abnormal heartbeat incidents in the ECG signal. During the second stage, the detected incidents are fed into a wavelet classifier, which assigns the corresponding heartbeats to different classes, i.e. Supraventricular, Ventricular, Fusion and Normal. The resulting classification task is thus focused on abnormal ECG segments, as the segments related to healthy heart status are discarded by the detection step. The scheme's performance is evaluated on several ECG recordings from the MIT-BIH Arrhythmia database. Despite minimal preprocessing of the ECG recordings and the simplicity of the ECG features extraction scheme with respect to other well-established schemes, the algorithmic performance is comparable.

Many state-of-the-art audio codecs operating in a transform domain provide scalability as a core function by allowing to selectively subtract bits -- usually according to a nonperceptual criterion from the full bit rate data stream. This work presents a different, or even reverse, scalability approach in which a scalable codec can selectively add perceptually significant bits to a low bit rate data stream. The scalable enhancement algorithm presented here operates in the Modified Discrete Cosine Transform domain, which is popular among perceptual audio transform encoders, but its extension on other domains is straightforward. By exploiting the information of an existing low bit rate base layer, the algorithm adds perceptually significant data to the data stream according to a psycho acoustic model, and improves the audio quality at a fraction of the bit rate that would normally be required for the encoding or transmission of the whole audio piece of the same quality. Applications of this can be found in packet retransmission schemes of compressed audio networks and in remote audio enhancement.

Multichannel audio is an emerging technology with continuously increasing applications. Audio reproduction through multiple channels has the advantage of recreating the acoustic scene with unprecedented fidelity and of immersing the listener in an acoustic environment that is virtually indistinguishable from reality. However, one of the greatest challenges of this scheme is its high transmission requirements especially since accurate rendering through as many possible channels is the main purpose. This paper follows previous techniques on spectral conversion and a recently introduced concept called audio resynthesis. In audio resynthesis, a reference channel is transmitted and then used to recreate the remaining channels at the receiver. An alternative approach to audio resynthesis is presented based on the generalized Gaussian mixture model. This model incorporates most of the standard mixtures (Laplace, Gaussian etc) but this flexibility comes with high structural complexity due to the increased number of model parameters. A scheme is presented here that bypasses this issue and avoids the use of the expectation-maximization (EM) algorithm. A smoothing technique is also introduced which optimizes the performance during the spectral conversion stage and significantly improves the resynthesis results.

This paper outlines the ongoing research at the Structure, Pointing and Control Engineering (SPACE) Laboratory to achieve a decentralized control of a segmented reflector testbed. The main effort is concentrated on achieving the system's top-level requirements including figure maintenance of the primary mirror to within 1 micron RMS distortion with respect to a nominal shape of the primary mirror, and precision pointing with accuracy of 2 arc seconds using the decentralized control technique. Having the time realization of the system at hand permits the implementation of such strict control requirements. Therefore, this entails system identification methods to achieve such a realization. In addition, by capitalizing on the characteristics of decentralized control, a fault-tolerant pipelined parallel processing design is developed. This approach features both improved load balancing for any number of processors and tasks and allows recovery from one or more processor failures.

Most audio compression formats are based on the idea of low bit rate transparent encoding. As these types of audio signals are starting to migrate from portable players with inexpensive headphones to higher quality home audio systems, it is becoming evident that higher bit rates may be required to maintain transparency. We propose a novel method that enhances low bit rate encoded audio segments by applying multiband audio resynthesis methods in a postprocessing stage. Our algorithm employs the highly flexible Generalized Gaussian mixture model which offers a more accurate representation of audio features than the Gaussian mixture model. A novel residual conversion technique is applied which proves to significantly improve the enhancement performance without excessive overhead. In addition, both cepstral and residual errors are dramatically decreased by a feature-alignment scheme that employs a sorting transformation. Some improvements regarding the quantization step are also described that enable us to further reduce the algorithm overhead. Signal enhancement examples are presented and the results show that the overhead size incurred by the algorithm is a fraction of the uncompressed signal size. Our results show that the resulting audio quality is comparable to that of a standard perceptual codec operating at approximately the same bit rate.