Novel OSNR Measurement Techniques for Coherent-Detection Systems

The stratigraphic and tectonic setting in the northwest part of Himalayan belt is complex and thrusted due to the collision of Indian plate and the Eurasian plate. During the past, the Himalaya is divided into four parts; these are Outer Himalaya, Lesser Himalaya, Greater or Higher Himalaya and Tethys Himalaya. The appearance of basement rocks played a significant role in the Himalayan periphery for stratigraphic, structural and tectonic movement. The deformation pattern of the crustal rocks causing the rise of basement rocks which constitutes an integral part of crustal configuration during the evolutionary stages of the Himalaya. In this study, an attempt has been made to estimate the basement depth configuration using spectral analysis and Euler deconvolution technique of gravity data in the northwest Himalaya region. The elevation increases continuously from 500 m to 5100 m in SW to NE direction, however, Bouguer gravity anomaly decreases continuously from -130 mGal to -390 mGal in SW to NE direction due to the isostaic adjustment. Gravity anomaly is very low near Harsil, Badrinath and Joshimath area and observed higher elevation due to the deep rooted basement. However, there are extrusion of crystalline basement in and around the Badrinath area due to the resettlement of geologic process which are overlaid to the top surface of the sedimentary layers. Euler deconvolution technique has been applied to detect the direct basement depth and results show a good correlation with the average depth of the spectral analysis and other works carried by different authors. Three gravity profiles are selected in appropriate places orienting SW-NE direction with a profile length of 160 km, 150 km and 140 km respectively in the study area for calculating the average depth of the basement rock. The average basement depth calculated is around 11.27 km using the spectral analysis technique and results are well correlated with the results of various workers. Euler deconvolution studies along the three selected profiles have been interpreted. It has been observed that there are more number of cluster points falling between depth ranges of 10 to 15 km, dipping in SW to NE direction. Euler’s study shows deep rooted connection near Main Frontal Thrust (MFT), Main Boundary Thrust (MBT), Main Central Thrust (MCT), Bearing Thrust (BT) and Vaikrita Thrust (VT) locations as per profile study. Based on these studies three geological models have been prepared along the profiles showing different tectonic resettlement and depth of crystalline basement. Crystalline rocks exposed at the surface may be due to uplift along the shear in the MCT zone by kinetic flow basically, Munsiayari Thrust (MT) and VT in the of NW-Himalaya region.

Terrain methods on spectral analysis for paleoclimate interpretations: A novel visualization technique using python

Gravity survey and interpretations play a very vital role more especially in petroleum prospecting. Spectral analysis of gravity anomaly has been successful in the estimation of sedimentary basement. Spectral analysis technique can be used in designing filter for the residual and region separation of complete Bouguer anomaly. The residual gravity anomaly which is of prime importance for applied geophysicists interested in the subsurface features is considered most useful for the interpretation of sedimentary basin. In this chapter, interpretation of the complete Bouguer gravity anomaly, the importance of the separation of the Bouguer gravity anomaly into its residual and regional component is presented. The residual component is considered for the application of the spectral analysis approach.

Research into network anomaly detection has become crucial as a result of a significant increase in the number of computer attacks. Many approaches in network anomaly detection have been reported in the literature, but data or solutions typically are not freely available. Recently, a labeled network traffic flow dataset, Kyoto2006+, has been created and is publicly available. Most existing approaches using Kyoto2006+ for network anomaly detection apply various clustering techniques. This paper leverages existing well known statistical analysis and spectral analysis techniques for network anomaly detection. The first popular approach is a statistical analysis technique called Principal Component Analysis (PCA). PCA describes data in a new dimension to unlock otherwise hidden characteristics. The other well known spectral analysis technique is Haar Wavelet filtering analysis. It measures the amount and magnitude of abrupt changes in data. Both approaches have strengths and limitations. In response, this paper proposes a Hybrid PCA–Haar Wavelet Analysis. The hybrid approach first applies PCA to describe the data and then Haar Wavelet filtering for analysis. Based on prototyping and measurement, an investigation of the Hybrid PCA–Haar Wavelet Analysis technique is performed using the Kyoto2006+ dataset. The authors consider a number of parameters and present experimental results to demonstrate the effectiveness of the hybrid approach as compared to the two algorithms individually.

The full spectral analysis (FSA) technique is investigated using both experimental and model‐generated data. The relationship between the formulae presented by different authors is discussed, and the similarities and differences inherent in using these formulae are evaluated. The FSA is applied both to sample spectra calculated using individual time series records and to spectra produced by the incoherent average of the sample spectra. The results reveal that the velocities obtained using sample spectra can be significantly biased in the situation where large turbulent motions exist for two particular implementations of the technique. These biases are shown to decrease as the number of sample spectra used in the incoherent average are increased, thereby producing FSA velocity estimates in excellent agreement with those produced using the full correlation analysis (FCA) technique.

Reaction of N-bromosuccinimide with lysozyme.

The multiwindow approach is a meaningful framework for nonparametric spectral estimation. It also encompasses several conventional methods as WOSA and frequency-averaged periodogram. Recently, some authors claimed that the Slepian windows of Thomson's method and other related optimal sets of windows show a better performance in terms of resolution, variance and leakage. In this paper, that claim is discussed by means of some simulation examples and by applying the various methods to speech recognition. In conclusion, frequency averaging of the periodogram is a computationally simple method that has a great flexibility for band specification and comparatively shows good performance. In fact, it is the spectral analysis technique most extensively employed for speech recognition.

A computer program for advance heart rate Variability analyzed is presented using Matlab6.5 The program calculates all the commonly used time-frequency domain measures of HRV using parametric spectrum estimates. HRV power spectral analysis shows two main components which have been interpreted as different physiological rhythms. Spectral analysis technique usually based on either FFT or Autoregressive modeling requires the stationary of data and for this reason should be a applied on a short time window. This paper represent An effective algorithm for PSD estimate using AR model based parametric method, which gives better result then classical nonparametric method when data length of the available signal is relatively short i.e. shirt term record of ECG signal.(⇐ 5 min.)Parametric AR spectral estimates is commonly used to analysis the HRV signal association with the power spectrum bands related to simpatho-vagal activities. This study aims to indicate and to evaluate the performance of amulet, recursive, time varying AR identification of HRV power spectral density.

Control systems are used in a number of different industrial sectors and critical infrastructures, including manufacturing, distribution, and transportation. Failure of these control systems because of malware or other faults would be catastrophic for critical infrastructure and would result in significant losses. Adverse operating conditions, such as those found in networked control systems, exacerbate the likelihood of vulnerability to malware attacks. The prospect of severe adversarial assaults such as buffer overflow, conventional faults, and especially stealthy Ladder Logic Bombs is a serious cause of concern in Industrial Control Systems (ICS) with novel attacks such as Struxnet. Decoupled at-distance Electromagnetic spectrum-based monitoring of faults and malware is more robust and can likely be integrated into Industrial Control System frameworks. Our failure analysis and Machine Learning (ML) modeling approach is fundamentally different from traditional methods in that we propose a Hybrid Quantum Artificial Intelligence-based Electromagnetic (EM) Spectral domain-based analysis approach that is completely decoupled from the controller processor and can predict PLC fault/malware conditions, including stealth Ladder Logic Bombs and buffer overflow attacks. Further, robust Supervisory Control Systems (SCSs) that interface with programmable logic controllers (PLCs) are essential components of Industrial Control Systems (ICSs) that handle critical infrastructure. Modern industrial control systems, on the other hand, are made up of densely linked Internet of Things (IoT) nodes. Because of their networked nature, supervisory control systems are also susceptible to widespread cyberattacks. As a result, in the case of a coordinated attack on IoT control systems, the dependability of Supervisory control decision-making procedures is jeopardized and need to be decoupled from the attacks. This work also develops a Fuzzy inference Engine-based supervisory monitoring technology based on the characteristics of the Hybrid Quantum AI-based analysis of the EM spectrum to reduce industrial plant downtime and false alarms by localizing adversarial attacks/faults, risk severity levels, and impacted nodes, as well as having dependability and malware immunity in networked Industrial control systems. The Hybrid QAI technique improved prediction accuracy by up to 88 percent when identifying different control system viruses and defects in 6-stage pipelined processors. The Fuzzy inference engine in the Mamdani Inference System architecture has been found to reduce the risk of false alarms and downtime.

The present study aims to evaluate the subsurface structural framework of the area north of Ras Gharib in order to determine the locations, limits and depths of the main sedimentary basins in this area. These targets were carried out through integrated analysis and interpretation of the aero-magnetic map aided by geological information from number of the drilled wells in the area. This map was treated using different techniques of processing and interpretation including; reduction of the aeromagnetic data to the north magnetic pole, analysis of tectonic trends, different methods of regional-residual separation, estimation of the basement depths using spectral analysis technique, two and half-dimensional modeling and Euler deconvolution. The results obtained from various analytical and interpretation techniques are integrated and used to the construction of basement relief and basement tectonic maps of the studied area.The results illustrate that the studied area is affected greatly by three major fault trends in addition to three minor ones. The major trends are; the Suez (NW-SE), Tethyan (EW) and Najd (WNW-ESE) trends. While, the minor sets of faulting are the ENE-WSW, Aqaba (NNE-SSW) and East African (N-S) trends. Both the major and minor faults play the important role in the complication of the structural setting of the area. The depths to the basement rocks range from 0 km in the southwestern parts to more than 5.6 km in the northern parts. Moreover, the area has three large sedimentary basins in the eastern and northern parts of the area.The integrated results of the different separation methods in addition to the constructed basement relief and basement tectonic maps reflect clearly that, the studied area is composed of alternative uplifted and downfaulted blocks running in the NW- SE direction (Suez trend) and intersected by another set of transeform faults mainly in the NNE-SSW direction (Aqaba trend), the NE-SW and E-W directions are also present. The magnetic modeling reveals the NE general dip regime of the area and the acidic composition of the basement blocks

A large but conflicting literature exists that suggests that the scattering properties of the Earth in seismogenic areas may be temporally variable and that this variability could be correlated with the nucleation times of moderate to large earthquakes. To search for temporal variations in coda Q in the vicinity of an expected moderate earthquake nucleation region along the San Jacinto fault zone, pairs and clusters of earthquakes recorded by the Anza Seismic Network between 1982 and 1992 are examined. To minimize differences arising from path and source variability, only the most similar examples of microearthquake wave fields are intercompared. To assess coda Q differences between similar microearthquake signals, we search for systematic temporal changes in moving-window spectral amplitude ratios. For a single-scattering coda model with an envelope decay function of the form t−me−πft/Qc, the relationship between the natural log amplitude spectral ratio and ΔQ−1 = Q−1c2 − Q−1c1 is linear; i.e., ln r(f, t) = πftΔQ−1(f) + W(f), where W(f) is a time-independent relative source term. To obtain robust uncertainty estimates of ΔQ−1(f) and W(f), we utilize multi-taper spectral analysis coupled with a nonparametric Monte Carlo confidence interval estimation procedure. For the most similar events in the Anza region (coda cross-correlation values of approximately 0.7 for 16 sec of coda), we find that coda Q as a function of time is stable at a typical 1σ resolution of only between −25% and +50% of the reference coda Q value for the area, even in the best-constrained frequency bands. Comparison of similar microearthquake coda signals with both short- and long-temporal separations indicates that all of this variability can be reasonably attributed to random fluctuations in the coda that are driven by source variability and is not indicative of any systematic temporal variability in coda Q. To obtain estimates of relative coda Q that are more precise than this level, the spectral ratio analysis technique requires significantly more similar earthquakes than are produced by seismogenic processes in the Anza region. Such earthquakes have been observed to occur in other regions of the San Andreas Fault system and are analyzed in Antolik et al. (1996).

Application of the spectral analysis technique Ensemble Empirical Mode Decomposition (EEMD) to hydroclimatic time series produces an orthogonal set of decreasing frequency Intrinsic Mode Functions (IMFs) and a residual (or trend). As the EEMD algorithm is locally adaptive it is suitable for decomposing time series that appear non-stationary in terms of mean and variance: characteristics that are not uncommon in hydroclimatic series. EEMD utilises ensemble averaging to overcome the mode mixing problem observed in traditional Empirical Mode Decomposition (EMD). Here EEMD is briefly outlined and issues relating to the calculation of the Hilbert spectrum from EEMD output are discussed. EEMD is then applied to three climatic indices relevant to Australian hydroclimatic variability, namely the Southern Oscillation Index, Indian Ocean Dipole mode index and the Tasman Sea Index. Analysis of these indices reveals EEMD is capable of identifying physical process signals. For example, the known spectral structure of the SOI is identified by the EEMD algorithm. It was observed during analysis of the Indian Ocean Dipole mode index that EMD post processing may introduce end effects previously minimised by the EEMD ensemble average. These effects may inflate existing features, or potentially introduce low frequency artefacts, though this danger can be mitigated through careful examination of the complete Hilbert spectrum. EEMD and Hilbert spectrum analysis of hydroclimatic data provides insight into long-term trends, dominant frequencies and changes in energy with time for time series that are usually problematic to analyse with traditional spectral analysis techniques. The spectral fingerprints of the climatic indices assessed here are used in a companion paper that looks for those fingerprints within Australian precipitation and temperature data (Srikanthan et al., 2011).

Several possible approaches for airborne vector gravimetry are compared using the spectral analysis technique. The airborne gravimetry equations are reduced to a time-invariant form using a special averaging method. Then the Fourier transform is applied to the equations. The accuracy of each approach is determined as the power spectral density of the gravity Wiener estimate error. Numerical results for the accuracy of each approach are presented given a priori stochastic models for the gravity disturbance vector and measurement errors.

Optical spectrum prediction based on first-principles calculations is important for the development of optical materials. In particular, Time Dependent Density Functional Theory (TDDFT) in real-time is one of the most widely used calculation methods. In real-time TDDFT, the dynamic dipole moment is used to obtain the polarizability by Fourier transform (FT). The optical spectrum can be obtained from this polarizability. However, if the time length is not sufficient, the spectrum resolution depends on the length and becomes ambiguous. To solve this problem, we introduced Dynamic Mode Decomposition (DMD). This spectral analysis technique uses both Singular Value Decomposition and Proper Orthogonal Decomposition to obtain frequencies and intensities of spectrum. The signal frequency and the intensity are directly obtained. This method was applied to TDDFT time series data for ethylene and small molecules of benzene, naphthalene, anthracene, and tetracene. Compared to conventional FT, clear spectra were obtained from a short time series data.

Health was brilliantly defined in our traditional Chinese medicine masterpiece Huangdi Neijing more than two thousand years ago. For example, it deems the utility of body and soul, harmony between man and nature, equilibrium of Yin and Yang as the achievement of Yin Yang balance and good spirit. Meanwhile, Huangdi Neijing also creatively raised the preventive treatment of disease theory, a very good example to show the importance of health attached by TCM. In today's modern world, the combination between TCM and modern information technology like computer science, sensor technology, spectral analysis technique, etc., provides the possibility of conducting health assessment by the objectification of inspection, smelling and listening, interrogation and palpation, which are the well-known four diagnostic methods used by TCM doctor. As a result, the TCM theory-based health management system could be realized with a focus on tongue manifestation, pulse condition collecting and analysis, where the information is collected and statisticized by mode identification technology and analyzed by various algorithms such as vector machine, as a series of modernized diagnostic instruments such as tongue manifestation meter, pulse condition detector, intelligent channel detector, abdominal examination device etc. have been put into use. This paper gives a brief introduction on the structure of TCM health management system as well as the idea of TCM health management.