A new method for noise cancellation of reflected RADAR signal is proposed in this paper. With customary signal processing techniques such as Fourier transform or short time Fourier transform, some signal parameters are extracted. Using Chirplet transform which represent signal in time, frequency, shear, rotation and scale with non-stationary assumption, some signal parameters are estimated. Our presented algorithm is implemented on real RADAR signatures and the result illustrates very good performance in noise cancellation of reflected RADAR signal.

Kernel method is one of the most common nonparametric density estimation and recently B-spline is used for estimation of a probability density function. These two methods in some how depend on selecting a smoothing parameter that has an important effect on precision of the estimators. In this paper, we consider kernel and B-spline methods of density estimation and smoothing parameter selection for these two methods. Then, the accuracy of the obtained estimators is compared by their mean square errors. Also, the effect of the number and dispersion of data on precision of estimators are studied. The results show that for a symmetric probability density, if the dispersion of data increases, the precision of both estimators decreases, while, for an asymmetric probability density function, the precision of the estimators increases for dispersion data.

Sandwich plates containing magnetic fluids in the core are considered nowadays as a smart structure. Due to the ability of rapid change in the viscosity of the fluid in the core, plate can be used to control the vibration of the structure and to deplete the energy. In the present study, free vibration behavior of a sandwich plate, which is made of composite glass-epoxy fiber face sheets and contains magnetic fluid in the core, is investigated. Based on the Hamilton's principle and considering independent bending degrees of freedom for upper and lower faces, free vibration governing equations of the plate are derived using the spline finite strip method. To validate the proposed method, the results are compared with those of other similar studies using different methods. Then, the effects of different parameters, e. g., diverse boundary conditions, which are rarely considered elsewhere, are evaluated based on the proposed formulation. Moreover, the frequency impacts as well as the modal loss factor, which are the two main parameters in the vibration analysis of these structures, are considered and the effect of different factors such as magnetic field intensity and fluid thickness in the core are investigated. The results indicate that the spline finite strip method has good accuracy in analyzing sandwich plates containing a flexible core. Besides, the results show that increasing the intensity of the magnetic field causes an increase in the frequency and loss factor corresponding to each mode. Moreover, increasing the thickness of the fluid leads to increase in the loss factor and decrease in the frequency. The results indicate that despite the change in the strain energy of the structure, due to changes in the fiber orientation of the composite layers, the frequency of each mode does not change significantly. It was also found that the clamped plate had the highest frequency and the simply supported boundary condition had the lowest frequency.

Background: Clinical reasoning plays an important role in the accurate diagnosis and treatment of diseases. Script Concordance test ((SCT)) is one of the tools that assess clinical reasoning skill. This study was conducted to determine the reliability and concurrent and predictive validity of (SCT) in assessing final lessons and gynecology exams of undergraduate midwifery students. Methods: At first, 20 clinical scenarios followed by 3 questions were designed by 2 experienced midwives. Then, after examining the content validity, 15 scenarios were selected. The test was used for 55 midwifery students. The correlation of (SCT) results with grade point average (GPA) was measured. To evaluate the concurrent validity of (SCT), the correlation between (SCT) scores and the final exam of the gynecology course was measured. To measure predictive validity, the correlation of (SCT) scores with comprehensive exams of midwifery was calculated. Data were analyzed using SPSS software. Descriptive statistics, Pearson correlation, and coefficient Cronbach's alpha were used for analysis. The test’ s item difficulty level (IDL) and item discriminative index (IDI) were determined using Whitney and Sabers’ method. Results: The internal reliability of the test (calculated using Cronbach’ s alpha coefficient) was 0. 74. All questions were positively correlated with the total score. The highest correlation coefficient was related to GPA and comprehensive test with the score of 0. 91. The correlation coefficient between (SCT) and the final test (concurrent validity) was 0. 654, and the correlation coefficient between (SCT) and comprehensive test (predictive validity) was 0. 721. The range of item discriminative index and item difficulty level in this exam was 0. 39-0. 59 and 0. 32-0. 66, respectively. Conclusion: (SCT) shows a relatively high internal validity and can predict the success rate of students in the comprehensive exams of midwifery. Also, it showed a high concurrent validity in the final test of gynecology course. This test could be a good alternative for formative and summative tests of clinical courses.

A new automatic target recognition algorithm to recognize and distinguish three classes of targets: personnel, wheeled vehicles and animals, is proposed using a low-resolution ground surveillance pulse Doppler radar. The Chirplet transformation, a time frequency signal processing technique, is implemented in this paper. The parameterized RADAR signal is then analyzed by the Zernike Moments (ZM) in order to feature extraction and they trained to the Nearest Neighbor classifier. The current work provides a new approach for multi-resolution analysis and classification of non stationary signals with the objective of revealing important features in noisy and cluttered environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class. Results show that the classifier is invariant to target aspect angle and speed.