Effect of Filtered Back-Projection Filters to Low-Contrast Object Imaging in Ultra-High-Resolution (UHR) Cone-Beam Computed Tomography (CBCT)

In this study, the effect of filter schemes on several low-contrast materials was compared using standard and ultra-high-resolution (UHR) cone-beam computed tomography (CBCT) imaging. The performance of the UHR-CBCT was quantified by measuring the modulation transfer function (MTF) and the noise power spectrum (NPS). The MTF was measured at the radial location around the cylindrical phantom, whereas the NPS was measured in the eight different homogeneous regions of interest. Six different filter schemes were designed and implemented in the CT sinogram from each imaging configuration. The experimental results indicated that the filter with smaller smoothing window preserved the MTF up to the highest spatial frequency, but larger NPS. In addition, the UHR imaging protocol provided 1.77 times better spatial resolution than the standard acquisition by comparing the specific spatial frequency (f50) under the same conditions. The f50s with the flat-top window in UHR mode was 1.86, 0.94, 2.52, 2.05, and 1.86 lp/mm for Polyethylene (Material 1, M1), Polystyrene (M2), Nylon (M3), Acrylic (M4), and Polycarbonate (M5), respectively. The smoothing window in the UHR protocol showed a clearer performance in the MTF according to the low-contrast objects, showing agreement with the relative contrast of materials in order of M3, M4, M1, M5, and M2. In conclusion, although the UHR-CBCT showed the disadvantages of acquisition time and radiation dose, it could provide greater spatial resolution with smaller noise property compared to standard imaging; moreover, the optimal window function should be considered in advance for the best UHR performance.

A Recursive Frequency Smoothing Method of Cyclic Spectrum Density Estimation

Cyclic spectrum density estimation plays a significant role in cyclostationary signals analysis. Generally, the methods of cyclic spectrum density estimation are mainly focus in time-domain, such as FFT Accumulation Method (FAM) and Strip Spectral Correlation Algorithm (SSCA). In this paper, based on the principle of frequency smoothing methods, an improved method in frequency-domain was proposed, which could reduce the computational cost greatly by using recursion. The recursive method was based on the fact that the range difference of smoothing window was small when window slid. In other words, we could get a new point of cyclic spectrum density just by computing the complex conjugate products of newly coming into window. In addition, the simulation results were given at last, which showed the good performance of our proposed method.

Improvement of Differential Method for Estimating the Axial Intensity Derivative in the Transport-of-Intensity Equation

Phase retrieval is an important problem in many areas of physics while transport-of-intensity Equation (TIE) is a traditional method for recovering phases by intensity detection. Derivative of axial intensity is an important parameter in TIE and usually obtained by finite difference (FD) method. However, nonlinearity error is unavoidable in these FD methods. We suggest a method for eliminating this error by setting an axial smoothing filter, and the width of smoothing window varies with the parameters of the observation point. Simulation and experimental results of reconstructed wave-front phase with this filter are shown in this paper for demonstrating the performance of this technique.

Strong random noise attenuation by shearlet transform and time-frequency peak filtering

Directional wavelet transforms combined with coefficient thresholding are very competitive in denoising seismic signals. However, these techniques struggle when the coefficients of signal and noise have comparable magnitudes. To better address this problem, we have developed an improvement to this method by applying time-frequency peak filtering (TFPF) to the directional wavelet coefficients. TFPF consists of computing the instantaneous frequency of a frequency-modulated analytic signal. The use of a longer or shorter smoothing window helps to emphasize either signal or remove random noise. In our method, we use the shearlet transform as a directional wavelet transform and estimate signal dips based on the cumulative energy in each decomposition direction. TFPF is then applied to the fine-scale wavelet coefficients to enhance signal and remove high-frequency noise. Coefficient thresholding is applied to all other scales. Experimental results demonstrate that our algorithm can effectively eliminate strong random noise and preserve events of interest.

Identification of fresh and expired ground roasted robusta coffee using UV-visible spectroscopy and chemometrics

The freshness of ground roasted coffee escapes extremely fast. For this reason, the evaluation of conservation state of ground roasted coffee must be taken into account for acceptability of coffee. Unfortunately, it is difficult to discriminate the fresh and expired ground roasted coffee physically by our naked eyes. Thus, it is desired to develop an analytical method to evaluate the fresh and expired ground roasted coffee using reliable methods. The objective of this research was to evaluate the potential of UV-visible spectroscopy and chemometrics method for classification of fresh and expired ground roasted robusta coffee. A number of 200 samples of robusta fresh coffee and 200 samples of robusta expired coffee was used. The spectral data were pre-treated using standard normal variate (SNV), moving average smoothing (window: 9) and Savitzky-Golay 2nd derivative (order: 2; window: 11). The analysis data was done statistically using multivariate chemometric techniques, including principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) in the spectral range of 230-400 nm. PCA with PC1 = 94% and PC2 = 4% showed clear clustering of samples (p ≤ 0.05). UV-visible spectroscopy with SIMCA analysis allowed to classify between fresh and expired ground roasted robusta coffee with a correct classification rate of 100%.

Applying Kolmogorov-Zurbenko Adaptive R-Software

The Kolmogorov-Zurbenko Adaptive, kza package provides algorithms to deal with abrupt changes or breaks in the presence of heavy background noise. In a practical way, one-dimensional and high-dimensional simulated samples are generated to demonstrate signal recoveries and their accuracy evaluation by mean of squared error, mean difference and specificity index. Simulation investigation showed that smoothing window size need consider whenever applying kza package, and that kza could tolerate background noise about 10-folds heavier in higher-dimensional data compared to 1-dimensional data.

The Pluviophone: Measuring Rainfall by Its Sound

Rainfall, one of the most important resources for all life and for the environment, is also the most difficult meteorological parameter to measure, mainly due to its nonstationnarity in space and time. Several powerful instruments exist today to measure rainfall, but they often suffer from some strong disadvantages, ranging from high costs and variable space and time coverage to low accuracy. In this study, we explain how a measure the sound of the falling rain could provide a reliable metric of the rainfall. We demonstrate its reliability in a specific case study, for a long rainy tropical event of the Indian monsoon and in noisy conditions. The final determination coefficient computed in cross validation reaches R2 = 0.9, without any treatment of the signals other than a simple smoothing window. If confirmed through more intensive research, our findings could help in the design of a highly useful acoustic rain gauge, of great value to developing countries that experience intense but poorly studied rainy seasons.

Optimal Hatch Filter with an Adaptive Smoothing Window Width

The Hatch filter is a code-smoothing technique using integrated carrier phase observations. It is an easy technique that non-experts can use to reduce receiver noise on the pseudorange. This paper suggests a new algorithm for the optimal Hatch filter whose smoothing window width varies adaptively depending on the regional, diurnal and seasonal ionospheric variation and satellite elevation angle. We consider both quiet and storm conditions of the ionosphere. Using the well-known quiet ionospheric model, a conservative boundary value for ionospheric storm and the receiver noise statistics function of the satellite elevation angle, this algorithm can mathematically solve the optimal averaging constant for each satellite in every epoch. From a 24 hr data process result and real-time experiment, we found that the position accuracy of the optimal Hatch filter is better and more robust than that of the traditional Hatch filter. The optimal Hatch filter algorithm and its results are expected to provide a new solution for a single-frequency DGPS receiver and a thorough understanding of the relationship between the position error and the averaging constant. Furthermore, a DGPS user who applies this algorithm to a low-cost single-frequency receiver can obtain a more accurate and robust position result than via the classical Hatch filter.

Time-Frequency Feature Extraction of a Cracked Shaft Using an Adaptive Kernel

Adaptive time-frequency representations have many advantages compared with conventional methods. In this paper, a new method is proposed to adapt Smoothed Pseudo Wigner- Ville distribution to match signal’s time-frequency content. It is based on maximizing a local timefrequency concentration measure for different time and frequency smoothing window lengths. Subsequently, the optimized values are used for constructing an adaptive kernel over time. The proposed transform is then applied to vibration signals of healthy and cracked shafts which are acquired through run-up, and the crack signature is obtained. Results show that enhanced improvement in resolution is obtained while the computational cost is not very high.