مسعود خزاعی فدافن

Detecting the charge particles at Giga hertz rate is one of the applications of UFSD (Ultra-Fast Silicon Detectors). The UFSD test in front of the proton beam to count the beam particles and use it for a more precise Dose Delivery System for the treatment of the cancerous tumor by charge particles can become an effective step for the development of cancer treatment. In fact, this assessment is a prerequisite and guarantees the use of these detectors in the dose delivery system. In this regard, the best method for time measurement, which was CFD, was chosen. MATLAB software was used to measure and analyze the UFSD output signals. The results of the 50-μm-thick UFSD test and the various geometries that we performed in several different experiments at the CNAO Cancer Treatment Center in Italy, as well as the data obtained at the laboratory of CERN accelerator in Germany, were analyzed by the CFD method. The results of many different runs of programs in MATLAB for many registered signals show: 1- These sensors are reliable to count the proton particles in Giga hertz rate. 2-The CFD devices could be used to record the UFSD output signals. Therefore, they can be used in the correction of the dose delivery system as a counter for the proton number of the proton beam.

Raisins are one of the most important agricultural products obtained from drying grapes. At present, grading raisins and determining the percentage of different types of raisins in a sample, as well as identifying or not having a tail in it is done manually, and therefore requires a lot of time. In this study, the aim is to provide effective and powerful algorithms using image processing techniques in the field of machine vision for grading raisins as well as detecting and determining the percentage of tailed and tailless raisins. In order to analyze the proposed algorithm, the performance of the algorithm is evaluated by preparing photos of different samples of raisins and implementing the proposed algorithms on these samples in MATLAB software and comparing the results obtained with manual methods. To evaluate the performance of the proposed methods, the criteria of total accuracy, sensitivity and accuracy of positive output were calculated that the findings obtained from the evaluation of the proposed method for rating raisins total accuracy 98/65%, sensitivity 98/47% and positive output accuracy 97/83% as well as the findings of the proposed method for Determination of raisin tailing percentage showed total accuracy of 98%, sensitivity of 92/32% and positive output accuracy of 98/69%, indicating the optimal and reliable performance of these methods with low cost (low software calculations) compared to traditional methods.

In this study, an effective and efficient algorithm for detection a saliency map is presented based on the modeling of the rapid response of the human visual system to changes in the intensity, texture and color. Some cases such as inspiration from performance of human visual system, requiring no training, reduce number of image colors, reduce color channels and Proper use of the least texture information in this algorithm have increased its efficiency. In the proposed method in the first step , Due to sensitivity of the human visual system to higher contrast signals, only higher contrast channel has been used to extract the color saliency map, Then the intensity saliency map as well as the texture saliency map are extracted using the intensity component in lab color space using Simple cell computational model of the visual cortex and finally, with the perfect combination of the saliency maps of the color, the intensity, and the texture, object saliency map is obtained. The proposed method and existing methods have been tested on MSRA10K and ECSSD databases. The results of the implementations show that the proposed hybrid algorithm for the detection saliency map using the dominant color and texture features, On the ECSSD database, the mean absolute error, F-measure score and the area under the ROC curve are 0.173, 0.789 and 0.891, respectively, and on the MSRA10K database are 0.178, 0.790 and 0.919, respectively, compared to other models, it indicates better performance of the proposed method than other methods.