Frequency Energy Spectrum Research Articles

Fault diagnoses of a nonlinear cracked rotor-bearing system based on vibration energy space and incremental learning approach

Health services of rotor-bearing systems are directly related to safe and stable works of rotating machineries in the industrial production. To address the issues of a low impact on vibration characteristics caused by faults in the higher rotational speed region, narrow and weak excitation signals, high dimensionality related to fault induction mechanisms, and difficulty in diagnosing using artificial intelligence technology due to the small number of fault samples in rotor systems, a novel fault detection method of nonlinear rotor systems through the incremental two-dimensional principal component analysis (I2DPCA) of the machine learning on the vibration energy space is proposed. Unlike mainly analyzing dynamic characteristics of rotor-bearing systems in vibration space, the method applies signals of the vibration energy space such as energy tracks, energy-time histories and energy-frequency spectra to have more advantages in vibration amplitudes caused by cracks in the higher rotational speed region and to overcome narrow and weak signals in the early fault stage. Then, the I2DPCA is applied to extract online low dimensional fault features and to process small vibration features of the rotor system. Simulation and experiment results show that performances of between-class margins and within-class clusters of different health conditions on the vibration energy space are more perfect than that on the vibration space. Based on the I2DPCA algorithm and the energy space method, the recognition rate of crack faults within the high rotational speed region is up to 99.6%, and it has good convergence and online learning ability in the case of small samples. The achieved conclusions of the method could provide a novel reference direction for fault diagnoses of rotor systems.

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the finite-difference time-domain (FDTD) method was used to investigate the GPR response of void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image based on the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy from the CWT time–frequency result was extracted and combined to reconstruct the new B-scan image, where void areas have energy concentration phenomenon. Based on this, width and depth of void detection algorithm was proposed to recognize the void area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time–frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy in void width and depth detection, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.

Geostrophic Eddies Spread Near-Inertial Wave Energy to High Frequencies

Abstract The generation of broadband wave energy frequency spectra from narrowband wave forcing in geophysical flows remains a conundrum. In contrast to the long-standing view that nonlinear wave–wave interactions drive the spreading of wave energy in frequency space, recent work suggests that Doppler-shifting by geostrophic flows may be the primary agent. We investigate this possibility by ray tracing a large number of inertia–gravity wave packets through three-dimensional, geostrophically turbulent flows generated either by a quasigeostrophic (QG) simulation or by synthetic random processes. We find that, in all cases investigated, a broadband quasi-stationary inertia–gravity wave frequency spectrum forms, irrespective of the initial frequencies and wave vectors of the packets. The frequency spectrum is well represented by a power law. A possible theoretical explanation relies on the analogy between the kinematic stretching of passive tracer gradients and the refraction of wave vectors. Consistent with this hypothesis, the spectrum of eigenvalues of the background flow velocity gradients predicts a frequency spectrum that is nearly identical to that found by integration of the ray tracing equations.

MP27-13 PERIPHERAL NEUROMODULATION OF VAGINAL BLOOD FLOW: A POTENTIAL TREATMENT FOR FEMALE SEXUAL DYSFUNCTION

You have accessJournal of UrologyCME1 Apr 2023MP27-13 PERIPHERAL NEUROMODULATION OF VAGINAL BLOOD FLOW: A POTENTIAL TREATMENT FOR FEMALE SEXUAL DYSFUNCTION Priyanka Gupta, Elizabeth Bottorff, Patricia Maymi-Castrodad, Mackenzie Moore, Giulia Lane, Gianna Rodriguez, and Tim Bruns Priyanka GuptaPriyanka Gupta More articles by this author , Elizabeth BottorffElizabeth Bottorff More articles by this author , Patricia Maymi-CastrodadPatricia Maymi-Castrodad More articles by this author , Mackenzie MooreMackenzie Moore More articles by this author , Giulia LaneGiulia Lane More articles by this author , Gianna RodriguezGianna Rodriguez More articles by this author , and Tim BrunsTim Bruns More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003255.13AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Female sexual dysfunction (FSD) is a potentially life altering condition that impacts an estimated 40% of women. Unfortunately, the few treatment options available have limited success due to their high incidence of adverse events and inability to improve subjective arousal. One potential treatment option, peripheral neuromodulation, has been successful in improving female sexual function index scores. However, the underlying mechanisms of how neuromodulation improves FSD are still largely unknown. We hypothesize that improvements in FSD symptoms are due to improvements in genital hemodynamics. METHODS: We developed a clinical study to investigate changes in vaginal blood flow (VBF) in response to dorsal genital nerve (DGN) and tibial nerve stimulation in three cohorts of women: healthy controls, women with FSD, and women with spinal cord injury (SCI). Incorporating women with SCI allows us to investigate which neural pathways contribute to genital sexual arousal. Each participant attends two study visits: one for each nerve target, randomly ordered. At each visit, a vaginal photoplethysmography (VPP) sensor is used to measure blood flow during a 5-minute baseline, 20 minutes of nerve stimulation, and a 5-minute washout period. The VPP signals are filtered in the neurogenic energy frequency spectrum (0.76 – 0.20 Hz) for analysis. RESULTS: Seven out of eight participants reported genital sensations due to nerve stimulation, three of whom have SCI. One participant with a complete SCI had increases in their VPP neurogenic energy during dorsal genital nerve stimulation (Figure 1). An animal study is being planned to investigate the VBF response observed in complete SCI. CONCLUSIONS: This work provides further support for neuromodulation as a treatment for neurogenic and non-neurogenic FSD. Source of Funding: Craig H. Neilsen Foundation and International Society for the Study of Women’s Sexual Health © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e367 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Priyanka Gupta More articles by this author Elizabeth Bottorff More articles by this author Patricia Maymi-Castrodad More articles by this author Mackenzie Moore More articles by this author Giulia Lane More articles by this author Gianna Rodriguez More articles by this author Tim Bruns More articles by this author Expand All Advertisement PDF downloadLoading ...

Relativistic frequency shifts in a rotating waveguide

A photon source is located in a rotating waveguide. An absorber with a sharp absorbing frequency absorbs some of the emitted photons. This decreases the number of photons which are detected by a detector. The frequency (energy) spectrum measured in the absorber depends on the setup (the positions of the source and the detector), and this spectrum determines the amount of absorption. The shifts are calculated for different configurations. Among other things, it is seen that even if the source and the absorber are at rest (while the waveguide is rotating), there is a nonzero frequency shift.

Wind waves in the North Atlantic from ship navigational radar: SeaVision development and its validation with the Spotter wave buoy and WaveWatch III

Abstract. Wind waves play an important role in the climate system, modulating the energy exchange between the ocean and the atmosphere and effecting ocean mixing. However, existing ship-based observational networks of wind waves are still sparse, limiting therefore the possibilities of validating satellite missions and model simulations. In this paper we present data collected on three research cruises in the North Atlantic and Arctic in 2020 and 2021 and the SeaVision system for measuring wind wave characteristics over the open ocean with a standard marine navigation X-band radar. Simultaneously with the SeaVision wind wave characteristic measurements, we also collected data from the Spotter wave buoy at the same locations, and we ran the WaveWatch III model in a very high-resolution configuration over the observational domain. SeaVision measurements were validated against co-located Spotter wave buoy data and intercompared with the output of WaveWatch III simulations. Observations of the wind waves with the navigation X-band radar were found to be in good agreement with buoy data and model simulations with the best match for the wave propagation directions. Supporting datasets consist of significant wave heights, wave directions, wave periods and wave energy frequency spectra derived from both SeaVision and the Spotter buoy. All supporting data are available through the PANGAEA repository – https://doi.org/10.1594/PANGAEA.939620 (Gavrikov et al., 2021). The dataset can be further used for validation of satellite missions and regional wave model experiments. Our study shows the potential of ship navigation X-band radars (when assembled with SeaVision or similar systems) for the development of a new near-global observational network providing a much larger number of wind wave observations compared to e.g. Voluntary Observing Ship (VOS) data and research vessel campaigns.

Estimating the filtering of turbulence properties by finite-sized particles using analytical energy spectra

Finite-sized neutrally buoyant particles suspended in a turbulent flow do not typically follow the fluid motion, whereas sufficiently small neutrally buoyant particles, known as tracers, do. Turbulence properties probed by the two types of particles, thus, differ primarily due to spatial filtering, whereby scales of motion in the energy spectrum smaller than the particle diameter D are suppressed, whereas those larger are retained. In this study, this filtering effect is quantified for flows with Reynolds numbers in the range Reλ≈32–2000 using a model of isotropic and homogeneous turbulence based on analytical wavenumber and Lagrangian frequency energy spectra. The coefficients scaling these spectra are estimated by comparing the dissipation rate, amplitude of the frequency spectrum, and acceleration variance for the fluid motion, as well as the acceleration and velocity variances of the particle motion, with laboratory experiments and numerical simulations. The model reproduces scalings for the acceleration variances of both the fluid and the particles at high Reynolds number. The model is then used to predict the ratios of the velocity variance, acceleration variance, and the dissipation rate obtained from the particles to those of the flow. These ratios depart from 1 as D increases (as expected), but the fluid velocity variance is much less severely underestimated by the particle motion than the acceleration variance and dissipation rate, for a given D and Reλ. These results allow delimiting more systematically the conditions under which finite-sized neutrally buoyant particles could be as useful to probe turbulent flows as tracer particles in laboratory experiments.

Faulty feeder detection method for SLG faults in distribution networks based on comprehensive fault characteristics across entire frequency spectrum

In the event of a single line-to-ground fault in a non-effectively grounded distribution network, reliable detection and isolation of faulty feeders aid in ensuring safe network operation. To improve the accuracy and reliability of faulty-feeder detection, this paper proposes a faulty-feeder-detection method based on the entire-frequency-domain fault characteristics. First, the zero-sequence current variation characteristics of feeders are analyzed using a wavelet packet algorithm; subsequently, the frequency-spectrum energy and zero-sequence current direction are employed to build a detection criterion. Second, the frequency-spectrum energy and direction criteria are combined to obtain a comprehensive detection criterion across the entire frequency spectrum. Third, the ensemble-learning algorithm is utilized to construct the proposed comprehensive criterion. An extreme-gradient-boosting algorithm is employed for efficient modeling along with large amounts of simulated data that are used as training dataset. To verify the reliability and generalization capability of the proposed method, PSCAD simulation, RTDS, and practical fault data are employed considering different topologies, parameters, and fault conditions of various distribution networks. The results obtained in this study reveal the proposed method to improve the accuracy of faulty-feeder detection significantly relative to conventional approaches, which demonstrates that the proposed method shows considerable reliability and generalization potential for practical utility.

Method of the Laboratory Wave Generation for Two Dimensional Hydraulic Model Experiment in the Coastal Engineering Fields: Case of Random Waves

The experiments in coastal engineering are very complex and a lot of components should be concerned. The experience has an important role in the successful execution. Hydraulic model experiments have been improved with the development of the wave generator and the advanced measuring apparatus. The hydraulic experiments have the advantage, that is, the stability of coastal structures and the hydraulic characteristics could be observed more intuitively rather than the numerical modelings. However, different experimental results can be drawn depending on the model scale, facilities, apparatus, and experimenters. In this study, two-dimensional hydraulic experiments were performed to suggest the guide of the test wave(random wave) generation, which is the most basic and important factor for the model test. The techniques for generating the random waves with frequency energy spectrum and the range for the incident wave height [(HS)M/(HS)T = 1~1.05] were suggested. The proposed guide for the test wave generation will contribute to enhancing the reliability of the experimental results in coastal engineering.

Multiscale Decomposition and Spectral Analysis of Sector ETF Price Dynamics

We present a multiscale analysis of the price dynamics of U.S. sector exchange-traded funds (ETFs). Our methodology features a multiscale noise-assisted approach, called the complementary ensemble empirical mode decomposition (CEEMD), that decomposes any financial time series into a number of intrinsic mode functions from high to low frequencies. By combining high-frequency modes or low-frequency modes, we show how to filter the financial time series and estimate conditional volatilities. The results show the different dynamics of the sector ETFs on multiple timescales. We then apply Hilbert spectral analysis to derive the instantaneous energy-frequency spectrum of each sector ETF. Using historical ETF prices, we illustrate and compare the properties of various timescales embedded in the original time series. Through the new metrics of the Hilbert power spectrum and frequency deviation, we are able to identify differences among sector ETF and with respect to SPY that were not obvious before.

Variability and Sources of the Internal Wave Continuum Examined from Global Moored Velocity Records

AbstractEnergy for ocean turbulence is thought to be transferred from its presumed sources (viz., the mesoscale eddy field, near-inertial internal waves, and internal tides) to the internal wave continuum, and through the continuum via resonant triad interactions to breaking scales. To test these ideas, the level and variability of the oceanic internal gravity wave continuum spectrum are examined by computing time-dependent rotary spectra from a global database of 2260 current meter records deployed on 1362 separate moorings. Time series of energy in the continuum and the three “source bands” (near-inertial, tidal, and mesoscale) are computed, and their variability and covariability examined. Seasonal modulation of the continuum by factors of up to 5 is seen in the upper ocean, implicating wind-driven near-inertial waves as an important source. The time series of the continuum is found to correlate more strongly with the near-inertial peak than with the semidiurnal or mesoscale. The use of moored internal-wave kinetic energy frequency spectra as an alternate input to the traditional shear or strain wavenumber spectra in the Gregg–Henyey–Polzin finescale parameterization is explored and compared to traditional strain-based estimates.

A novel pavement transverse cracks detection model using WT-CNN and STFT-CNN for smartphone data analysis

ABSTRACT This paper proposes a novel pavement transverse crack detection model based on time–frequency analysis and convolutional neural networks. The accelerometer and smartphone installed in the vehicle collect the vibration response between the wheel and the road, such as pavement transverse cracks, manholes, and normal pavement. Since the original vibration signal can only contain a one-dimensional domain (time–acceleration). Time–frequency analysis, including Short-Time Fourier Transform and Wavelet Transform, can transfer the one-dimensional vibration signal into a two-dimensional time–frequency-energy spectrum matrix. The energy spectrum matrix obtained from STFT and WT can effectively obtain different signal features in terms of time and frequency features. If STFT and WT are further combined with CNN models, STFT-CNN and WT-CNN, respectively, pavement transverse cracks can be detected more accurately. In this study, the reliability of the developed pavement transverse cracks detection model was evaluated based on the data collected by conducting a road driving test. Analysis results of the developed model show that the accuracies of WT-CNN and STFT-CNN are 97.2% and 91.4%, respectively. The F1 scores to analyse the practicability and the adaptability of the crack detection model of WT-CNN and STFT-CNN are 96.35% and 89.56%, respectively.

Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices

In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

Quantifying the role of Darrieus–Landau instability in turbulent premixed flame speed determination at various burner sizes

To probe the impact of Darrieus–Landau (DL) instability on turbulent premixed flame propagation at various burner sizes, methane–air premixed flames from five Bunsen-type burners with different nozzle diameters (4 mm, 6 mm, 8 mm, 10 mm, and 12 mm) were investigated at Reynolds numbers ranging from 1000 to 8500. The flame curvatures used to identify DL instability were determined using Mie scatter images captured by a particle image velocimetry system. The flame speed was further derived by applying an asymmetric hypothesis to the images. The energy-frequency spectrum of the inflow disturbance was determined using a hot-wire anemometry system, and specific wavelet transform analysis was performed to investigate the dependence of DL instability on the proportion of effective disturbances (Ped) and quantify the role of DL instability in determining the turbulent flame speed. The results showed that the burner diameter had an obvious effect on the presence of DL instability and its role in flame propagation. The ability of DL instability to enhance the flame curvature skewness and the turbulent flame speed was closely related to Ped. Ped increased when the burner diameter increased from 6 mm to 12 mm, thus enhancing the DL instability. Changing the burner diameter also affected the interplay between DL instability and turbulence. The above interactions and their effects on the flame speed during the change of inflow disturbances could be formulated by Ped. Finally, a Ped-based correlation was proposed to describe the dependence of the turbulent flame speed on the burner size.

Adaptive Complementary Ensemble EMD and Energy-Frequency Spectra of Cryptocurrency Prices

We study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.

ASSESSING UNCERTAINTY IN THE MODELING OF RUNUP AND SWASH MORPHODYNAMICS USING XBEACH

Phase-resolving numerical models are frequently used tools to investigate short and long wave transformation, nonlinear wave interactions, and wave runup. Moreover, nearshore morphodynamics can be explored with the recent advancement of the models and computational resources. Sea surface elevation time series that force phase-resolving models at the offshore boundary are often unavailable. Therefore, time series are usually recreated from wave energy-frequency spectra through the superposition of harmonics. The wave phases of the harmonics are unknown and therefore assumed to be randomly distributed. This implies that an infinite number of time series with different sequencing of waves can be recreated from a single wave-energy spectrum and, for that reason, recreated time series are a source of uncertainty in model predictions. This intrinsic uncertainty has been found to cause variability in wave overtopping of structures (e.g., Pearson et al, 2002; Williams et al., 2014; Romano et al., 2015) and in setup and runup at beaches (McCabe et al., 2011; Torres-Freyermuth et al., 2019). Torres-Freyermuth et al. (2019) investigated the effect of intrinsic uncertainty on runup at planar beaches for different wave conditions and beach slopes and suggested that uncertainty is especially important under dissipative conditions. Yet unknown is the effect of intrinsic uncertainty on bed evolution. Here we assess the effect of intrinsic uncertainty on inner surf and swash zone evolution at three beaches with different beach morphology.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/w3zi3Yoo170

Mining Nanopore Big Data to Reveal the Single Molecule Heterogeneity

The recent development of nanopore techniques pushes the envelope in precision and complexity to maximize the information content. However, uncovering the interaction and kinetics of single molecules acquires statistical datasets by thousands of single molecule information. 1-2 The challenge still exists from effectively grouping the time-dependent ionic current trace into subpopulations. In this study, we applied the modified Hidden Markov Model to identify the ionic blockage merging inside current noise, which increase the temporal resolution and current resolution for nanopore techniques.3-4 Then, we traced the multi-level current variations inside a blockade event by using Markov Chain Model, which reveals the dynamic kinetics for the conversion of multi-intermediates.5 Since the ions migrate at different frequency inside nanopore, it acts as the smallest sensor to perceive and feedback the non-covalent interaction into the of ionic current frequency. We further employed Hilbert-Huang transform (HHT) to uncover these varied ionic frequencies which buried in the noise of the ionic current traces.6 The HHT-based frequency analysis arises a two-dimension signal from time domain (time and current) into a three-dimension spectrum of energy-frequency-time distribution. The frequency-energy spectrum represents the fingerprint spectra for the characterization of non-covalent interaction due to the interaction between residues inside nanopore and the analyte. The very beginning frequency analysis let us mine more information from the data, which could help to unravel different hierarchical pathways and enables to discovery the subpopulations and hidden kinetic during the dynamic motion of single molecules.REFERENCES1. Yi-Lun Ying, and Yi-Tao Long; Nanopore-Based Single-Biomolecule Interfaces: From Information to Knowledge. J. Am Chem. Soc. 2019, 10.1021/jacs.8b11970.2. R.-J. Yu, Y.-L. Ying, R. Gao, Y.-T. Long, Confined Nanopipette Sensing: From Single Molecules, Single Nanoparticles to Single Cells, Angew. Chem. Inter. Ed., 2019, 10.1002/anie.201803229.3. J. Zhang, X. Liu, Y.-L. Ying, Z. Gu, F.-N. Meng, Y.-T. Long, High-bandwidth nanopore data analysis by using a modified hidden Markov model, Nanoscale, 2018, 3458-3465.4. J. Zhang, X. Liu, Z. Hu, Y.-L. Ying, Y.-T. Long, Intelligent identification of multi-level nanopore signatures for accurate detection of cancer biomarkers, Chem. Commun., 2017, 10176-10179.5. Y.-L. Ying, S. Liu, X. Shi, W. Li, Y. Wan, Y.-T. Long, The Hidden Transition Paths During the Unfolding of Individual Peptides with a Confined Nanopore. 2018, ChemRxiv. https://doi.org/10.26434/chemrxiv.6394925.v16. S. Liu, M. Li, M. Li, Y. Wang, Y.-L. Ying, Y.-J. Wan, Y.-T. Long, Measuring a Frequency Spectrum for the Single-Molecule Interactions with A Confined Nanopore, Faraday Discuss., 2018, 210, 97-99. Figure 1

Energy-Frequency Spectrum for Financial Time Series via Complementary Ensemble EMD

We discuss the method of complementary ensemble empirical mode decomposition (CEEMD) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allows us to reconstruct the time series based on different timescales. Using Hilbert spectral analysis, we compute the associated instantaneous energy-frequency spectrum to illustrate and interpret the properties of various timescales embedded in the original time series.

Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning

We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.

Role of Color Doppler and Three-dimensional Ultrasound in Infertility

Color Doppler is emerging as a valuable diagnostic imaging modality in the field of medicine, primarily infertility. Reproductive disorders, including infertility and spontaneous abortions/miscarriages, have emerged as major public health problem(s) worldwide. Color Doppler energy imaging is a high throughput technology based on the total integral of energy frequency spectrum. Color Doppler is a high-throughput, sophisticated imaging technique for the assessment of uterine anomalies, intrauterine pathology, tubal patency, polycystic ovaries, ovarian follicular monitoring, endometrial receptivity, failed or an ectopic pregnancy, male infertility and uterine, endometrial and ovarian vascularity. Assessments of the utero-ovarian pulsatility indices (PIs), resistance indices (RIs), and endometrial color signals are important determinants of in vitro fertilization (IVF) cycles and pregnancy rates. With many clinical/scientific research experience in the field of reproductive medicine, I strongly believe that an overall public health model needs to be designed in managing infertility patients; therefore, major issues such as cost effectiveness and technical artifacts should address so as to achieve an accurate clinical diagnosis, successful IVF outcome/pregnancy, and overall patient satisfaction in a clinical rehearse setting. Simple, safe efficient and affordable diagnostic modalities should be incorporated at infertility clinics coupled with well-designed patient counseling sessions and community-based public health awareness campaigns conducted so as to reduce the morbidity and mortality rates associated with reproductive disorders. Three dimension (3-D) ultrasound is a new imaging modality which is being introduced into clinical practice. It has been proved that 3-D ultrasound is a very highly reproducible technique. With 3-D ultrasound, a volume of a region of interest can be acquired and stored. This volume can be further analyzed in several ways, such as navigation, multiplanar display and surface rendering or volume calculation. Power Doppler ultrasound, in combination with 3-D ultrasound, allows for a whole assessment of relevant vessels and quantitative assessment of vessel density and perfusion within a specified area. Faridpur Med. Coll. J. Jul 2018;13(2): 97-100