Seismic waves are nonstationary signals because their Frequency contents are changed when they propagate through layers of different elastic properties. Hydrocarbon reservoirs and fault zone are two major sources of attenuating high Frequency components of a seismic wave. Dropping of higher Frequency components of a seismic wave while passing through a hydrocarbon reservoir appears as low Frequency shadow zone on a seismic section.In this study, we used Instantaneous spectral attributes of real seismic data in time-Frequency domain in order to trace the existence of hydrocarbon reservoir and fault zone in the study area. For this purpose, we used short time Fourier transform and continuous wavelet transform. In order to reduce the run time, the Instantaneous Frequency was calculated directly from scalograms.

Instantaneous Frequency Measurement (IFM) devices are the essential parts of any ESM, ELINT, and RWR receiver. Analog IFMs have been used for several decades. However, these devices are bulky, complex and expensive. Nowadays, there is a great interest in developing a wide band, high dynamic range, and accurate Digital IFMs. One Digital IFM that has suitably reached all these requirements is mono-bit zero-crossing IFM, made by some different producers at present. In this paper, the performance of mono-bit digital Instantaneous Frequency Measurement (IFM) device is analyzed. This analysis includes quantization error, thermal noise, clock jitter, comparator bias and also "Pulse-on-Pulse" occurrence. The error limits due to all these factors are computed and analyzed, and a unified approach to the system design is presented.

Islanding is one of the important challenges in power networks in the presence of distributed generation which is considered an undesirable incident due to the possibility of damage to operators, network equipment and consumers. Therefore, it is necessary to quickly detect islanding and make a decision regarding the connection status of local distributed generation units in the network. In this paper, a passive islanding detection method is proposed using adaptive threshold-based Instantaneous Frequency droop characteristic. The proposed threshold limit is dynamically changed depending on under studied conditions in order to discriminate the island from load changes, capacitor bank switching, motor start-up and short circuit types. Two medium voltage networks of Cigre and 34-bus IEEE are used to evaluate the proposed method. The simulations are performed in Digsilent software and implementation of the proposed method carried out in MATLAB software. The simulation results indicate that the island is correctly detected with appropriate accuracy in a short period of time from other disturbances in the presence of wind, solar and diesel types of distributed generations.

Power transmission lines are vital components of today's power systems. These power lines transmit the electricity produced in power plants in high volume and with very low losses to distant areas so that it can be reached to consumer through distribution networks. In fact, these lines are the intermediary between major energy producers and distribution networks. Accordingly, these transmission lines are of a great importance and must be protected appropriately with a suitable protection system. Distance relays are widely used to protect these lines due to their convenient coordination characteristics and simplicity. High impedance fault (HIF) can be a critical challenge for distance relays due to their low current amplitude and similarity to conventional events in power systems such as capacitive bank switching. For this reason, in this paper a new approach is presented based on the Instantaneous Frequency variations obtained from the current RMS in order to detect the high impedance fault. This method detects high impedance faults via calculating a Detection index (DI) and considering a threshold value. The proposed method has been tested using DIgSILENT and MATLAB software in an IEEE standard 39-bus network. The presented results evidently demonstrate that the proposed method is suitable for detecting HIF and Low impedance fault (LIF). In addition, this method has a proper performance during capacitor bank switching and can well distinguish between HIF and capacitive bank switching. Moreover, the presented method is resistant to noise and also is capable to detect the faulty phase.

Instantaneous spectral analysis is a continuous time-Frequency analysis technique that provides a Frequency spectrum for each time sample of a seismic trace. Spectral analysis is a powerful tool for analysis of seismic data. Fourier transform determines the Frequency contents of a signal. But for analysis of non-stationary signals, 1D transform to Frequency domain is not sufficient. In early years, transforming of seismic traces into time and Frequency domains was done with windowed Fourier transform, called a short time Fourier transform (STFT). In this method, the resolution of the results in the time-Frequency domain is controlled by the width of the selected window. Continuous wavelet transform (CWT) is a remedy to solve this problem by using the scalable wavelets. CWT uses dilation and transition of a wavelet to produce a time-scale map. By converting the scale to Frequency one can get the time-Frequency map which is comparable to the time-Frequency map obtained from STFT. Converting a scalogram into a time-Frequency spectrum using the center Frequency of a scale gives an erroneous attenuation in the spectrum. The time-Frequency continuous wavelet transform (TFCWT) overcomes this problem and gives a more robust technique of time-Frequency localization. Since TFCWT is fundamentally derived from the continuous-wavelet transform, wavelet dilation and compression effectively provides the optimal window length, depending upon the Frequency content of the signal. Thus, it eliminates the subjective choice of a window length and provides an optimal time-Frequency spectrum without any erroneous attenuation effect for a nonstationary signal. Instantaneous spectral analysis utilizing TFCWT provides high-Frequency resolution at low frequencies and high time resolution at high frequencies. Mapping of a seismic trace into the time-Frequency domain produces a two dimensional data set by adding a Frequency axis. In a similar way a 2D seismic section will generate a 3D data cube in which the third axis is the Frequency up to the Nyquist Frequency. Sections of single Frequency extracted from the cube are called single Frequency seismic section (SFS). Comparison of different SFSs can be utilized to detect low-Frequency shadows caused by the presence of the hydrocarbon reservoirs. This method can potentially be utilized as a tool for direct hydrocarbon detection. In this study, we applied TFCWT on 2D seismic sections and extracted a single Frequency seismic attribute and Instantaneous spectral attributes. The purpose of this study is to detect low-Frequency anomalies in the real data is obtained from seismic. Hydrocarbon reservoirs in the path of seismic propagation, including the factors that high-Frequency seismic signal components absorb and phenomenon called low-Frequency shadows creates in the seismic section. This phenomenon directly indicates the presence of hydrocarbons in the region in seismic. For this purpose, single- Frequency seismic sections and Instantaneous spectral attributes using time-Frequency continuous wavelet transform spectrum are calculated and the results of them used to detect hydrocarbon reservoirs. In the next stage, for increasing computational speed, Instantaneous spectral attributes directly determined by time-scale spectrum and results implemented on the same data set.

Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance Frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

Current-based methods for bearing fault diagnosis primarily rely on analyzing the current signal, leading to challenges in detecting fault frequencies due to their low magnitude amid the noise in the current spectrum. This issue intensifies for weak bearing faults in their early stages. The presence of noise components increases the risk of false alarms, as fault characteristics are often obscured in the raw current spectral analysis. To address this, effective bearing fault diagnosis necessitates the reduction of noise components. This paper presents a novel noise cancellation method that enhances the estimation of bearing fault signals in induction motors by utilizing the deviation of Instantaneous Frequency in synchronized motor voltage and current signals. The proposed method efficiently diagnoses bearing fault characteristic frequencies during spectral analysis. Simulation and experimental results substantiate the effectiveness of this approach in detecting outer/inner raceway and ball-bearing faults.

A control algorithm based on the Instantaneous optimal control method is presented for on-line control of structures subjected to earthquake excitations. This algorithm employs the digital state-space equation to discretize the continuous dynamical equation of motion, and named discrete Instantaneous optimal control method. Based on the Lyapunov stability method, a procedure to obtain a discrete stable weighting matrix is developed. To demonstrate the precision and the efficiency of the proposed control algorithm an 8-story shear-type building frame equipped with one active mass damper/driver (AMD) mechanism is used. Behavior of different weighting matrices is also examined.

Soil erosion and sediment yield from watershed are among key limitations to achieve sustainable land use and to maintain the water quality. Development of the sediment graph (SG) is essentially required for accurate estimation of sediment yield from the watershed. However, development of SGs for watersheds is a tedious and time consuming task. Development of the sediment graph models based on easily accessible physical characteristics of a watershed and precipitation data is therefore a viable and convenient tool for designing the efficient soil and water conservation measures. In this regard, driving synthetic SGs using Instantaneous unit sediment graph (IUSG) is an applied approach in watersheds where detailed discharge and sediment data are not available. However, the performance of this approach in watershed scale with different governing conditions has rarely been evaluated. The present study formulated IUSG for Kojour watershed comprising an area of some 500 Km2. The Watershed located at east of Nowshahr City in Mazandaran Province, northern Iran. The suspended sediment samples were taken from Kojour River during eight storm events in 2008 and corresponding SGs were then developed. The results showed that the IUSG model is unable to predict observed SGs components of peak, time to peak, total sediment yield, and the base time having respective estimation errors of 764, 111,750 and 101%.