This work proposes a novel method to find the PHASE ERROR and oscillation amplitude in multiPHASE LC oscillators. A mathematical approach is used to find the relationship between every stage's output PHASE and its coupling factor. To much more general analysis, every stage assumed to have a different coupling factor. The mismatches in LC tanks are considered as the main source of PHASE ERRORs and accordingly the PHASE deviation of each stage is calculated in a distinct equation. Then, the amplitude of oscillation for each stage is individually derived using PHASE ERROR results. Not only considering different coupling factors increases the generality of this work but also the results are in better conformance with simulations compared to previous works. The theoretical results are evaluated and confirmed through extensive simulations using ADS software.

Each channel of the PHASEd array antenna intrinsically has the PHASE ERRORs. This is because of the position and orientation ERROR of antenna elements due to assembling tolerance or mechanical distortion, and the electric length ERROR of each RF channel. These ERRORs can decrease the antenna performance, for instance the gain reduction, sidelobe level enhancement, and inaccurate beam direction. In order to improve the performance of the antenna in the presence of PHASE ERRORs, a PHASE correction method using Genetic Algorithm (GA) is proposed. By using the proposed method, the antenna overall radiation pattern is recovered close to ideal radiation pattern without ERROR. By applying the simulation data to a large array of 32  40 elements at the frequency of S-band the effectiveness of the proposed method is verified.

In the past years, a number of algorithms have been introduced for synthesis aperture radar (SAR) imaging. However, they all suffer from the same problem: The data size to process is considerably large. In recent years, compressive sensing and sparse representation of the signal in SAR have gained a significant research interest. This method offers the advantage of reducing the sampling rate but also suffers from speed processing limitation and it needs a huge amount of memory to reconstruct the image. On the other hand, inaccuracy in SAR model induces PHASE ERROR to the results and makes the reconstructed image blurry. Existing sparse methods in the presence of PHASE ERROR, have high computational costs and need a lot of processing time. In addition, these methods take up considerable space in the memory for saving the measurement matrix. In this paper, a fast method is proposed to reduce the computational cost of image reconstruction, based on the signal sparsity in the presence of PHASE ERROR. The proposed method consists of substituting accurate observations of sparsity methods with approximated observations of matched filter methods. In this method, the output of Range-Doppler matched filter is reconstructed with sparse representation, and ERROR PHASE is estimated simultaneously. This method leads to a nonconvex optimization problem and to solve that, we use the majorization minimization method. The PHASE ERROR and reconstructed image are estimated in an iterative procedure. The use of approximated observation, eliminates the need for carrying out big matrix multiplications, and Fast Fourier Transformation, as a low computational cost operation, can be employed instead. In addition to computation speed, this method does not need any memory space for saving measurement matrices. In our numerical simulations, we compared the speed of processing and the mean square ERROR (MSE) of reconstructed images for the proposed method with the state-of-the-art sparse method for different sizes of image and under-sampling rates. It is shown in simulations that the reconstructed image from our method has a slightly lower quality and higher MSE, because of the sidelobes effect of the matched filter output. However, in certain conditions, the speed of the proposed method is more than a hundred times faster than the compared method. The achieved processing speed with no need for the memory to store the measurement matrix at the expense of slightly lower image quality would be acceptable for most applications.

Today, the best coordinates of stations on the ground are obtained by using Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS). There are many ERROR sources affecting the GNSS observations that limit the required accuracies. But differential positioning methods, like double difference, are big helps to us to achieve an accuracy of millimeter. Differential operation of GNSS is based on placing a reference station with a GNSS receiver at a known location. One of such ERRORs is the coordinate ERROR of reference station and its propagation on unknown stations. In fact the coordinates of a reference station should be known in a reference system coordinate, such as WGS84 used in GPS, which we usually assume is exactly known. In practice, the position of the reference station in the reference system coordinate may not be exactly known due to different reasons. Therefore, in this study, the effect of the reference station position ERRORs on various ranges from ~4 km to ~90 km, in static mode and using double difference carrier PHASE, is investigated. The results show this effect could be of the order of a few ppms depending on ERROR magnitude of reference position and the range of baseline.

Background & objective: Each laboratory should determine the type of ERRORs and turnaround time (TAT), especially in the preanalytical PHASE to report quality and timeliness of the test results. The current study aimed at investigating the common causes of preanalytical ERRORs in biochemistry and hematology laboratories and evaluating the preanalytical TAT for outpatient samples. Methods: Data of rejected samples in the laboratory information system from September 2014 to September 2015 were retrospectively reviewed. Also, the preanalytical TAT of the outpatient samples was evaluated over the period of three months from June to August 2015. Preanalytical TAT was calculated from order entry to barcode scanning in the autoanalyzer. Results: With respect to the ratios of blood sample transfers, 1% of samples (2305 out of 225, 563) in the hematology laboratory and 0. 6% (1467 out of 255, 943) in the biochemistry laboratory were rejected. The most common cause of rejection in the hematology and biochemistry laboratories was insufficient volume (48. 8%) and hemolyzed sample (74. 1%), respectively. The average preanalytical TAT for the outpatient samples was 62. 3 minutes. The preanalytical TAT accounted for 10. 8% (order entry-sample collection), 49% (sample collection-sample receipt), and 40. 2% (sample receipt-barcode scanning in the autoanalyzer), respectively. Conclusion: Of all the samples received in the biochemistry and hematology laboratories, the overall percentage of rejections were 0. 6% and 1%, respectively. The main target to improve preanalytical TAT was determined as the transportation (sample collection-sample receipt) step.

Introduction: Use of different methods for GnRH administration in ART cycles is a controversy.Microdose regimens in poor- responder patients are very useful and this has been determined by some studies, but it is not clear how this regimen affects patients with first (IVF/ICSI) cycle. Materials & Methods: In a prospective, randomized, clinical trial study at The Research and Clinical Center for Infertility and Madar Hospital, Yazd, we compared 140 patients who were candidates for IVF/ICSI for the first time. 70 patients received Microdose flare (A Group) and 70 patients were administered GnRH in long luteal PHASE (B group). Number of oocytes, follicles, embryos and the pregnancy outcome were compared in both groups. In microdose group, after administration of 21 contraceptive pills in the last cycle from day 3 , GnRH was given to patients ( 0.05 cc twice a day) and H MG from day 5 (3 vials a day). In long protocol, the patients received 0.5 cc Buserelin daily from day 21 subcutaneously and with the beginning of bleeding , the dosage was decreased to half. HMG was given three times a day. As the follow up of some patients was difficult and their pregnancy outcome was unknown, 14 patients of A group and 3 patients of B group were left out from the study. The data was analyzed by chi-square and T test. Results: Number of oocytes, embryos and outcome of pregnancy were not different the in two groups. However, the number of mature follicles in long method was higher but not significant statistically. Conclusion: The Microdose flare regimen thus offers no further advantage for patients with first cycle of IVF/ICSI.    

One of the factors affecting the positioning ERROR in Loran-C receivers is the accuracy of measuring the time of receiving the signals required to calculate the time difference. Generally, the third zero crossing of the Loran-C signal is used as the basis for determining the receiving time of the pulse, the accuracy of which is highly dependent on the signal-to-noise ratio. The linear digital average algorithm helps to increase the signal-to-noise ratio by averaging the pulses of the consecutive pulse coding intervals received by the Loran-C receiver. The linear digital average in a high-speed receiver, despite the reduction of noise power, distorts the average pulse due to the delay of the participant's pulses. In other words, in fast receivers, in addition to the signal-to-noise ratio and the envelope-to-cycle difference, its distortion is also an important factor in accuracy of the third zero crossing detection. This paper seeks to analyze the effects of the motion vector of the receiver on linear digital average efficiency and to determine the threshold of the receiver’ s velocity to measure the third zero crossing of the average pulse with the desired accuracy. By decomposition of the average pulse into two desirable and undesirable parts, it is getting possible to define the average pulse distortion with known characteristics such as envelope-to-cycle difference and the amplitude attenuation coefficient, and the distortion volume is obtained as a function of velocity vector. The proposed analytical framework is simulated to show the impact of the velocity vector.