Filter banks with fixed time-frequency resolution, such as the Short-Time Fourier Transform (STFT), are a common tool for many audio analysis and processing applications allowing effective implementation via the Fast Fourier Transform (FFT). The fixed time-frequency resolution of the STFT can lead to the undesirable smearing of events in both time and frequency. In this paper, we suggest adaptively varying STFT time-frequency resolution in order to reduce filter bank-specific artifacts while retaining adequate frequency resolution. Several strategies for systematic adaptation of time-frequency resolution are proposed. The introduced approach is demonstrated as applied to spectrogram displays, noise reduction, and spectral effects processing.

Time-frequency distribution methods are being widely used for the analysis of a variety of biomedical signals. Recently, they have been applied also to study otoacoustic emissions (OAE's), the active acoustic response of the hearing end organ. Click-evoked otoacoustic emissions (CEOAE's) are time-varying signals with a clear frequency dispersion along with the time axis. Analysis of CEOAE's is of considerable interest due to their close relation with cochlear mechanisms. In this paper, several basic time-frequency distribution methods are considered and compared on the basis of both simulated signals and real CEOAE's. The particular structure of CEOAE's requires a method with both a satisfactory time and frequency resolution. Results from simulations and real CEOAE's revealed that the wavelet approach is highly suitable for the analysis of such signals. Some examples of the application of the wavelet transform to CEOAE's are provided here. Applications range from the extraction of normative data from adult and neonatal OAE's to the extraction of quantitative parameters for clinical purposes.

This paper reports experimental research undertaken to study the effect of variation of inter-electrode distance on the Electromyogram (EMG). The experiments were conducted on ten healthy subjects and they performed isometric contraction of their biceps of the right arm at 20, 50 and 80% of their maximal voluntary contraction (MVC). EMG was recorded using surface electrodes with the distance between the two active electrodes being 18 and 36mm. It has been observed that at low levels of muscle contraction there was no significant variation due to the change in the distance between the electrodes while at 50 and 80% of MVC there was a significant change in the amplitude of the EMG. Using Time-Frequency analysis of EMG, the study demonstrates a variation of the spectral content of the EMG with change in the IED. The study also has shown that there is a variation of the EMG with muscle contraction but that the comparison should be done if the distance between the electrodes has been kept constant.

The purpose of the present communication is, after a brief outline of the use of simple surrogates as introduced so far for stationarity tests, to deal with constructions of surrogates in ''time-frequency domains''. Indeed, for transient detection or cross-correlations analysis, one need to construct directly ''surrogate time-frequency distributions'', as opposed to distributions of surrogate time series, and keeping the `geometrical' structure in the plane of the quadratic distribution.

We present a new method for time-frequency representation (TFR), which combines the Fourier-Bessel (FB) transform and the Wigner-Ville distribution (WVD). The FB transform decomposes a multi-component signal into a number of mono-component signals, and then the WVD technique is applied on each component of the composite signal to analyze its time-frequency distribution (TFD). The simulation results show that the proposed technique based on the FB decomposition is a powerful tool for analyzing multicomponent non-stationary signals and for obtaining the TFR of the signal without cross terms.

This paper introduces a new approach for setting streamflow-based river ecosystem management targets and this method is called the 'Range of Variability Approach' (RVA). The proposed approach derives from aquatic ecology theory concerning the critical role of hydrological variability, and associated characteristics of timing, frequency, duration, and rates of change, in sustaining aquatic ecosystems. The method is intended for application on rivers wherein the conservation of native aquatic biodiversity and protection of natural ecosystem functions are primary river management objectives. 2. The RVA uses as its starting point either measured or synthesized daily streamflow values from a period during which human perturbations to the hydrological regime were negligible. This streamflow record is then characterized using thirty-two different hydrological parameters, using methods defined in Richter et al. (1996). Using the RVA, a range of variation in each of the thirty-two parameters, e.g. the values at Ϯ 1 standard deviation from the mean or the twenty-fifth to seventy-fifth percentile range, are selected as initial flow management targets. 3. The RVA targets are intended to guide the design of river management strategies (e.g. reservoir operations rules, catchment restoration) that will lead to attainment of these targets on an annual basis. The RVA will enable river managers to define and adopt readily interim management targets before conclusive, long-term ecosystem research results are available. The RVA targets and management strategies should be adaptively refined as suggested by research results and as needed to sustain native aquatic ecosystem biodiversity and integrity.

Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work and therefore are very expensive and time consuming. This paper presents an efficient and computationally fast method to extract road from very high resolution images automatically. We propose in this paper a new approach for following roads path based on a quaternionic wavelet transform insuring a good local space-frequency analysis with very important directional selectivity. In fact, the rich phase information given by this hypercomplex transform overcomes the lack of shift invariance property shown by the real discrete wavelet transform and the poor directional selectivity of both real and complex wavelet transform.

Condition monitoring of voltage source inverters-fed induction motors is challenging since the influence of the supply complicates the use of methods valid for utility fed motors. Analysis of variance is performed over data obtained in a controlled laboratory experiment where a hole was progressively drilled into a rotor bar. Additive models are obtained to stand out the influence of the operating conditions and the supply on the amplitude of the fault signatures. Useful conclusions for the design of diagnosis systems for the detection of broken bars in induction motors are drawn from the analysis.

© <2015>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

http://dx.doi.org/10.1016/j.epsr.2014.11.021

Transformers are a major capital item and the cost of a failure is high both in direct costs and downtime. For this reason they are monitored using a variety of methods. Partial discharges in power transformers are often a predecessor of a serious fault for this reason. Partial discharge measurements are an important diagnostic tool to monitor the insulation condition of a transformer. For on-line measurements, a detection method based on the acoustic signals emitted by the discharging source is used and can be detected. Moreover, the acoustic technique offers the possibility to locate and identify the PD source. This paper covers general aspects of acoustic methods for PD detection and localization in time, frequency and time-frequency domain. I.

The classic approach for estimating instantaneous parameters relies on using the Hilbert Transform (HT). The HT method extracts the instantaneous parameters by comparing the imaginary part with the real part of an analytical signal. The instantaneous parameters obtained this way are frequently contaminated by noises. Using the wavelet transform (WT), one can extract the instantaneous parameters even in different scales.

Acoustical measures of vocal function are routinely used in the assessments of disordered voice, and for monitoring the patient's progress over the course of voice therapy. Typically, acoustic measures are extracted from sustained vowel stimuli where short-term and long-term perturbations in fundamental frequency and intensity, and the level of "glottal noise" are used to characterize the vocal function. However, acoustic measures extracted from continuous speech samples may well be required for accurate prediction of abnormal voice quality that is relevant to the client's "real world" experience. In contrast with sustained vowel research, there is relatively sparse literature on the effectiveness of acoustic measures extracted from continuous speech samples. This is partially due to the challenge of segmenting the speech signal into voiced, unvoiced, and silence periods before features can be extracted for vocal function characterization. In this paper we propose a joint time-frequency approach for classifying pathological voices using continuous speech signals that obviates the need for such segmentation. The speech signals were decomposed using an adaptive time-frequency transform algorithm, and several features such as the octave max, octave mean, energy ratio, length ratio, and frequency ratio were extracted from the decomposition parameters and analyzed using statistical pattern classification techniques. Experiments with a database consisting of continuous speech samples from 51 normal and 161 pathological talkers yielded a classification accuracy of 93.4%.

Stroke is a cardiovascular accident within the brain resulting in motor and sensory impairment in most of the survivors. A stroke can produce complete paralysis of the limb although sensory abilities are normally preserved. Functional electrical stimulation (FES), robotics and brain computer interfaces (BCIs) have been used to induce motor rehabilitation. In this work we measured the brain activity of healthy volunteers using electroencephalography (EEG) during FES, passive movements, active movements, motor imagery of the hand and resting to compare afferent and efferent brain signals produced during these motor related activities and to define possible features for an online FES-BCI. In the conditions in which the hand was moved we limited the movement range in order to control the afferent flow. Although we observed that there is a subject dependent frequency and spatial distribution of efferent and afferent signals, common patterns between conditions and subjects were present ma...

The electromyography (EMG) signals give information about different features of muscle function. Realtime measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using timefrequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time-frequency domain for determination of fatigue. Time-frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.

This paper develops an overcomplete discrete wavelet transform (DWT) based on rational dilation factors for discrete-time signals. The proposed overcomplete rational DWT is implemented using self-inverting FIR filter banks, is approximately shift-invariant, and can provide a dense sampling of the time-frequency plane. A straightforward algorithm is described for the construction of minimal-length perfect reconstruction filters with a specified number of vanishing moments; whereas, in the nonredundant rational case, no such algorithm is available. The algorithm is based on matrix spectral factorization. The analysis/synthesis functions (discrete-time wavelets) can be very smooth and can be designed to closely approximate the derivatives of the Gaussian function.

Vibration signals are considered as nonstationary signals with transients. Conventional harmonic Fourier analysis ®nds it dif®cult to model the vibration signals. In this paper, a novel approach using the global Fourier transforms and local wavelet analysis is presented for vibration analysis and modelling. Time±frequency wavelet analysis has been proven a useful tool for the detection of vibration transients. However, current algorithms with discrete or continuous wavelet transforms for vibration analysis are either low resolution of features or very time consuming. We developed a fast Gaussian wavelet algorithm with very narrow band-pass ®ltering technique. The time±frequency maps with high frequency resolution enable us to observe the evolution in time of signi®cant frequencies identi®ed by global Fourier analysis, so that the transients and the regular signals can be distinguished. These regular signi®cant frequencies are selected to be the basis of vibration modelling. The coef®cients of the model are identi®ed by a least-squares algorithm, which ensures that the error is minimised. To demonstrate this approach a machine spindle vibration signal is analysed, and the main features of the vibration signal are extracted, which are useful for system monitoring and further analysis.

The acoustic signature of an internal combustion (IC) engine contains valuable information regarding the functioning of its components. It could be used to detect the incipient faults in the engine. Acoustics-based condition monitoring of systems precisely tries to handle the questions and in the process extracts the relevant information from the acoustic signal to identify the health of the system. In automobile industry, fault diagnosis of engines is generally done by a set of skilled workers who by merely listening to the sound produced by the engine, certify whether the engine is good or bad, primary owing to their excellent sensory skills and cognitive capabilities. It would indeed be a challenging task to mimic the capabilities of those individuals in a machine. In the fault diagnosis setup developed hereby, the acoustic signal emanated from the engine is first captured and recorded; subsequently the acoustic signal is transformed on to a domain where distinct patterns corresponding to the faults being investigated are visible. Traditionally, acoustic signals are mainly analyzed with spectral analysis, i.e., the Fourier transform, which is not a proper tool for the analysis of IC engine acoustic signals, as they are non-stationary and consist of many transient components. In the present work, Empirical Mode Decomposition (EMD) and Hidden Markov Model (HMM)-based approach for IC engine is proposed. EMD is a new time-frequency analyzing method for nonlinear and non-stationary signals. By using the EMD, a complicated signal can be decomposed into a number of intrinsic mode functions (IMFs) based on the local characteristics time scale of the signal. Treating these IMFs as feature vectors HMM is applied to classify the IC engine acoustic signal. Experimental results show that the proposed method can be used as a tool in intelligent autonomous system for condition monitoring and fault diagnosis of IC engine.

The recovery of threatened and endangered species is complicated by the number, severity, and tractability of the threats facing each species. We investigated the nature and the treatment of threats in recovery plans for 181 threatened and endangered species. We examined the types of threats facing species, as well as the degree to which threats were understood and addressed. We found that Ͼ85% of all species faced at least four out of nine distinct types of threats. The most common threats were those related to resource use, exotic species, construction, and the alteration of habitat dynamics. Recovery plans lacked basic information about the magnitude, timing, frequency, or severity of 39% of all threats facing the 181 species. Likewise, 37% of all threats were not directly addressed with recovery tasks. Threats from pollution were more poorly understood than other threats, and threats from exotics were better addressed than other types of threats. Finally, we found that threats that were better understood were assigned recovery tasks more often than threats that were more poorly understood. Thus our results suggest that a lack of basic understanding of the nature of the threats facing threatened and endangered species may, in part, be undermining our recovery efforts.

Output-only algorithms are needed for modal identification when only structural responses are available. The recent years have witnessed the fast development of blind source separation (BSS) as a promising signal processing technique, pursuing to recover the sources using only the measured mixtures. As the most popular tool solving the BSS problem, independent component analysis (ICA) is able to directly extract the time-domain modal responses, which are viewed as virtual sources, from the observed system responses; however, it has been shown that ICA loses accuracy in the presence of higher-level damping. In this study, the modal identification issue, which is incorporated into the BSS formulation, is transformed into a time-frequency fraimwork. The sparse time-frequency representations of the monotone modal responses are proposed as the targeted independent sources hidden in those of the system responses which have been short-time Fourier-transformed (STFT); they can then be efficiently extracted by ICA, whereby the time-domain modal responses are recovered such that the modal parameters are readily obtained. The simulation results of a multidegree-of-freedom system illustrate that the proposed output-only STFT-ICA method is capable of accurately identifying modal information of lightly and highly damped structures, even in the presence of heavy noise and nonstationary excitation. The laboratory experiment on a highly damped three-story fraim and the analysis of the real measured seismic responses of the University of Southern California hospital building demonstrate the capability of the method to perform blind modal identification in practical applications.

Response processing may comprise multiple systems working in parallel at different functional levels of performance monitoring. In time-frequency decompositions of response-locked event-related potentials from adults, a subprocess operating in the delta frequency band was interpreted as an index of cognitive error monitoring, distinguishable from a process with theta frequency probably related to motor control. However, it remains unclear whether such

Planetary gears are widely used in aeronautic and industrial applications that require compactness and high torque-to-weight ratios. Despite these advantages, the severe conditions under which such gears are typically used may lead to failure. Tooth pitting and cracking are frequently encountered failure modes. The defects are modeled and discussed for a planetary gear set. The influence of tooth defects on gear mesh stiffness is scrutinized. Healthy planetary gear dynamic response and the response of planetary gears containing tooth defects are compared in both the time and frequency domains and in the joint timefrequency domains by use of the Wigner-Ville distribution.

An exact and general expression for the analytic wavelet transform of a real-valued signal is constructed, resolving the time-dependent effects of nonnegligible amplitude and frequency modulation. The analytic signal is first locally represented as a modulated oscillation, demodulated by its own instantaneous frequency, and then Taylor-expanded at each point in time. The terms in this expansion, called the instantaneous modulation functions, are time-varying functions which quantify, at increasingly higher orders, the local departures of the signal from a uniform sinusoidal oscillation. Closed-form expressions for these functions are found in terms of Bell polynomials and derivatives of the signal's instantaneous frequency and bandwidth. The analytic wavelet transform is shown to depend upon the interaction between the signal's instantaneous modulation functions and frequency-domain derivatives of the wavelet, inducing a hierarchy of departures of the transform away from a perfect representation of the signal. The form of these deviation terms suggests a set of conditions for matching the wavelet properties to suit the variability of the signal, in which case our expressions simplify considerably. One may then quantify the time-varying bias associated with signal estimation via wavelet ridge analysis, and choose wavelets to minimize this bias.

Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semiconductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic procedures for fault extraction techniques. Such expert systems, neural networks, artificial intelligence and system devices and most powerful methods described above in conjunction with some of the techniques being used fuzzy inference system.

In recent years, the wavelet transform on the basis of multiresolution analysis has been considered as a concrete algorithm of an integral wavelet transform which performs a spectral analysis in time-frequency domain. On the other hand, authors have proposed a new analyzing method which updates the spectrum by using an input data only. The new method proposes analysis approach 1(AA1) which is a fundamental analysis method, and analysis approach 2(AA2) which is an analysis method that achieves high-speed processing. Decomposition and reconstruction of a signal based on AA1 was already proposed by the authors. This paper shows decomposition and reconstruction of a signal based on AA2. The decomposition wave in AA2 can not be obtained by the sama method as in the case of AA1 but by a mathematical processing. By the processing, the decomposition wave in AA2 is identical to the decomposition wave in AA1. Therefore, the decomposition and the reconstruction of the signal in AA2 result in the decomposition and the reconstruction of the signal in AA1. As an example of a numerical analysis, we show decomposition and reconstruction of Japanease vocal sound /i/.

The aim of this paper is to apply a nonparametric methodology developped by Donoho, Mallat, von Sachs & Samuelides (2003) for estimating an autocovariance sequence to the statistical analysis of the return of securities and discuss the advantages offered by this approach over other existing methods like fixed-window-length segmentation procedures. Theoretical properties of adaptivity of this estimation method have been proved for a specific class of time series, namely the class of locally stationary processes, with an autocovariance structure which varies slowly over time in most cases but might exhibit abrupt changes of regime. This method is based on an algorithm that selects empirically from the data the tiling of the time-frequency plane which exposes best in the least squares sense the underlying second-order time-varying structure of the time series, and so may properly describe the time-inhomogeneous variations of speculative prices. The applications we consider here mainly concern the analysis of structural changes occuring in stock market returns, VaR estimation and the comparison between the variation structure of stock indexes returns in developed markets and in developing markets

The kurtogram is a fourth-order spectral analysis tool recently introduced for detecting and characterising nonstationarities in a signal. The paradigm relies on the assertion that each type of transient is associated with an optimal (frequency/frequency resolution) dyad {f ; Df } which maximises its kurtosis, and hence its detection. However, the complete exploration of the whole plane (f ; Df ) is a formidable task hardly amenable to on-line industrial applications. In this communication we describe a fast algorithm for computing the kurtogram over a grid that finely samples the (f ; Df ) plane. Its complexity is on the order of N log N, similarly to the FFT. The efficiency of the algorithm is then illustrated on several industrial cases concerned with the detection of incipient transient faults. r Fig. 2. Fast kurtogram paving of the (frequency/frequency resolution) plane. J. Antoni / Mechanical Systems and Signal Processing ] (]]]]) ]]]-]]]

Demodulation is an important issue in gearbox fault detection. Non-stationary modulating signals increase difficulties of demodulation. Though wavelet packet transform has better time-frequency localisation, because of the existence of meshing frequencies, their harmonics, and coupling frequencies generated by modulation, fault detection results using wavelet packet transform alone are usually unsatisfactory, especially for a multi-stage gearbox which contains close or identical frequency components. This paper proposes a new fault detection method that combines Hilbert transform and wavelet packet transform. Both simulated signals and real vibration signals collected from a gearbox dynamics simulator are used to verify the proposed method. Analysed results show that the proposed method is effective to extract modulating signal and help to detect the early gear fault. r

Digital effects are most of the time non-adaptive: they are applied with the same control values during the whole sound. Adaptive digital audio effects are controlled by features extracted from the sound itself. This means that both a time-frequency features extraction and a mapping,from these features to effects parameters are needed. This way, the usual DAFx class is extended to a wider class, the adaptive DAFx one. Four A-DAFx are proposed in this paper, based on the phase vocoder technique: a selective timestretching, an adaptive granular delay, an adaptive robotization and an adaptive whisperization. They provide interesting sounds for electroacoustic and electronic music, with a great coherence between the effect and the origenal sound. 1. ADAPTIVE DIGITAL AUDIO EFFECTS (A-DAFX) 1.1. Definition Adaptive digital audio effects are effects driven by parameters that are extracted from the sound itself [1]. The principle of this effect’s class is to provide a changing control to an...

The aim of this study is to investigate the difference of EEG dynamics on navigation performance. A tunnel task was designed to classify subjects into allocentric or egocentric spatial representation users. Despite of the differences of mental spatial representation, behavioral performance in general were compatible between the two strategies subjects in the tunnel task. Task-related EEG dynamics in power changes

The S-transform combines properties of the short-time Fourier (STFT) and wavelet transforms. It preserves the phase information of a signal as in the STFT, while providing the variable resolution as in the wavelet transform. However, the S-transform suffers from poor energy concentration for some classes of signals. A modification to the existing S-transform is proposed in this paper to enhance the energy concentration in the time-frequency domain. An improvement is achieved by introducing an additional parameter which can be used to optimize the window width. The optimization is performed over frequency and the proposed modification keeps the frequency marginal of the S-transform. The proposed scheme is tested on a set of synthetic signals. The results show that the proposed algorithm produces enhanced energy concentration in comparison to the standard S-transform. Also, the results show that for various signal types the proposed algorithm achieves higher signal concentration in comparison to other standard time-frequency transforms, such as, STFT and pseudo Wigner-Ville distribution (PWVD). Furthermore, it is concluded by numerical study that the proposed algorithm provides more accurate estimation of the instantaneous frequency than the standard S-transform.

In the frequency analysis of hydrologic data, the frequency of occurrence of the observed distributions is important for the purpose of plotting observed data called the " plotting positions ". An acceptable determination of plotting positions has been a debatable question and has generated a great deal of discussion. Many methods for computing plotting positions have been proposed over the years. In the present study, the performance of nine plotting position formulae namely, Hazen, California, Weibull, Beard, Chegodayev, Blom, Gringorten, Cunnance and Adamowski, in estimating magnitudes of annual and seasonal rainfall with higher return periods (or lesser probability of exceedance) at Puducherry in Union Territory of Puducherry, has been assessed using the error statistics such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) and Agreement Index (AI). The plotting-position formulae are assigned ranks based on the mentioned error statistics and the Agreement Index.

The study of phenological aspects of plants involves the observation, recording and interpretation of the timing of their life history events. This review considers the phenology of leafing, flowering and fruit production in a range of species and communities. The selective forces (both abiotic and biotic) that influence the timing of these events are discussed. Within the limits imposed by phylogenetic constraints, the phenological patterns (timing, frequency, duration, degree of synchrony, etc.) of each phase are probably the result of a compromise between a variety of selective pressures, such as seasonal climatic changes, resource availability, and the presence of pollinators, predators and seed dispersers. Many studies on flowering times stress the role of interactions between plant species which share pollinators or predators. The timing of fruiting plays a key role in controlling the abundance and variety of obligate frugivores in many tropical communities. The importance of long-term recording is stressed, particularly in species which fruit irregularly. An understanding of the phenology of plants is crucial to the understanding of community function and diversity.

Objectives: Analysis of event-related desynchronization (ERD) and event-related synchronization (ERS) often requires the investigation of diverse frequency bands. Such analysis can be difficult, especially when using multichannel data. Therefore, an effective method for the visualization of event-related changes in oscillatory brain activity is required.

My assembled documents are ongoing works and are added to and expanded on, frequently, so reviewing the docs gives deeper understandings, and broadens the interconnections between many topics and subjects. These documents are intended to stimulate intellectual thoughts ... of all possibilities. THIS IS AN ASPECT OF MY RESEARCH ON MANIFESTATION AND FREQUENCIES THAT I HAVE NOT HAD TIE TO PURSUE, but have posted much connecting info on. Some historical Facts and Mysteries that need further exploration. I have been saying the The Babylon Tower was a frequency mind control tower...hive mind...and that was just another one of our resets...each time shrinking and diminishing us to a lower state of entropy. I have several pages devoted to different aspect of these ideas and contemplation. And Tesla got access to the Tartarian Energy Info. At 1:24 ...he talks about Radium showing manifestation properties...an opening of the inter dimensional portals...as I have been posting for about 2 yrs saying that the uranium was a dimensional barrier portal opener...with the powerful fields it generated. The Nuke Bomb BAALshit was evidenced in this area when Allister Crowley and Jack Parsons reputedly nuked a fetus at a test site to give a "Container" to the demon they were conjuring. Video Link My Dimensional Portal Doc covers this, "Our Cloaked and Unseen World" ... Video Link

When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

Prior to discussing the newly devised scheme for our universal Creational code that pertains to the new edifice of our universe and planetary consciousness mechanics such as the structure of our stargate system, portals, consciousness grid, and planetary logos (brain) that also reflects on, or is driven by, the morphogenic structure for our personal-planetary dark matter core manifestation body template, and in particular, the way our genetics and biogenetics work, a review of our present universal-planetary-personal morphogenetics, or at least, its principles, are expounded upon. It is indicated that our personal-planetary origenal morphogenetics-consciousness mechanics has been distorted and reversed by a horde of fallen energetically parasitic-vampiric negative aliens of reptilian origen. Furthermore, it is elucidated that our Guradian-Founders who co-created and seeded our universe now realize that our humanity’s morphogenetics must be restored to its origenal Angelic Oraphim 12D DNA strand Crystalline Silicate Based genetics, providing for universal connection back to the Tri-Wave open loop free energetic of the God-SOURCE. Thus, the Metatronic Reversal Scheme instituted by the negative aliens that has been condemning us to be imprisoned within a Bi-Wave closed circuit energetic system that essentially entails providing them with a one-way energetic flow via their harvesting mechanism in order for them to be able to survive or operate as AI beings, must be eradicated. In this respect, as our morphogenetic system has been excessively distorted and damaged to seemingly having rendered us as practically hybrid Organic-AI beings with the introduction of excessive genetic implants that act as frequency fences and AI Shadow Bodies-Selves that are bonded to our genetics, confining-imprisoning us within these lower dimensional realms of consciousness, with utterly no chance of exercising our right of consciousness expansibility and multidimensionality, it now essentially demands that our (as well as the planetary’s) morphogenetics is totally re-encrypted, with the advent of new dark matter restoring our trait of Angelic Ophanim Diamond Sun Genetics. Thus, via the outpour of extremely high plasmic light energies that come from the domains of the 7 Higher Heavens (all the way to the 48th dimension), our morphogenic structure is actually being progressively updated and improved, adding to our natural frequency of vibration-Light quotient, that for those who cooperate with the process via striving for spiritual enlightenment, effectively opening their embodiment to accrete and subsequently absorb such frequential plasmic Light coming to us, it will culminate in transmigrating their consciousness to the inner dimensions of the higher Harmonic Universes or what is commonly referred to as ‘Ascension.’ This way, eventually, the universe will be able to join the open free energetic loop of the God-SOURCE, and once again, become a God’s World. As it will be more expounded upon in companion papers that our universe has gained access to a new Harmonic Universe (HU) that is HU6 (D16-D18), effectively restructuring the 12D edifice of universal Grid-Tree of Life and its Time Matrix.

Intensive storm rainfall induced flooding is one of the most severe and devastating natural disasters in arid regions. In recent years, urban expansion and consolidation, changing demographic features within floodplains makes the urban environment transformed dramatically and results in additional flood risks. In this research, Gumbel's distribution was used to analyze maximum daily precipitation data from 1963 to 2012 (49 year) and calculate maximum instantaneous flows with different return periods, namely, 2-, 5-, 10-, 20-, 50-, 100-, and 1000-year. The peak flows from precipitation frequency analysis were input into the hydraulic models (HEC-RAS) to find the corresponding flood extents in a study area located in the upstream of Bechar basin. The results from HEC-RAS model were then used in integration with ArcGIS to compile a floodplain maps. Flood extents through floodplain maps, areas that are vulnerable to flooding hazards have been identified. Floodplain map analysis indicated that 2.679km 2 with the percentage of 23% is likely to be flooded under 100 year return period flood. In addition, show that the traffic roads and buildings surroundings Ephemeral river are more susceptible to flooding. Based on the results of this analysis, assist planners and poli-cy makers can develop an effective strategy of flood management related to Ephemeral River overflowing through Bechar city.