amirmohammad beigzadeh

The operation principle of calorimeters used for dosimetry of ionizing radiations is based on measuring the induced temperature difference in the adsorbent due to thermal energy deposition of the ionization radiation. In recent years, one of these methods has been the holographic optical calorimetry by laser beams. One of the problems that affect the response accuracy of the calorimeters is the heat transfer phenomenon in the adsorbent material. This phenomenon affects the measurement accuracy of the absorbed dose. In this research, a model was developed for the post-processing of camera images of the interferometric calorimeter system with a water phantom under vertical and horizontal irradiation with electron and proton beams.

X - ray - based backscattering imaging has attracted increasing attention worldwide and has found a variety of applications in the industry , including non -destructive testing, security inspection , and quality control . In the present study, a backscattering imaging device with a pencil X- ray beam was designed and manufactured . The scanner includes an industrial X - ray generator, two plastic detectors, a chopper wheel, a data collection system, a data processing software, and MATLAB - based image forming . The rotary chopper wheel creates pencil X - rays that sweep the entire sample . The performance of the system was evaluated through a series of tests using oil and paint cans ( full and semi - filled ) . The findings confirm that backscattering imaging is very effective for low atomic and low -density objects such as adhesives, paints, and oils and provides high - quality images . Also , combining the data of both detectors and applying a low - pass filter to the images will significantly improve the quality of the backscatter images . The inherent advantage of backscattering imaging devices is the scanning of materials with low atomic numbers ( adhesives , paints, fluids derived from petroleum, plastics, polymers , explosives, and even aluminum ) , which the results of this study attest to . The findings of this study showed that a wide range of industrial applications can be drawn for this device . The next activity of this paper will be to use the spatial division method to process the signals and compare it with the time division algorithm ( the present study ) . Also , imaging of other light materials is also on the authors' agenda .

The presence of bubbles is evident in most two-phase gas-liquid flows. The measurement of void fraction in various industries, such as nuclear, chemical and petrochemical, provides valuable insight into the distribution of phases in a multiphase system. This understanding can lead to the improvements in the design, operation and maintenance of industrial processes in sectors like oil and gas, petrochemicals, chemical reactors, heat exchangers, and geothermal power plants. In many cases, bubbles are evenly distributed in the liquid, creating uncertainty in measurements. To address this issue, this research utilized a wide gamma beam in conjunction with a flat panel detector to minimize systematic errors caused by fluctuations in bubble placement along the path of the gamma ray passing through the flow tube. Next, in the Monte Carlo simulation environment, the numerical density of the bubble is calculated in a fixed volume. Using the calibration chart, the fraction of the bubble is determined in terms of the numerical density for various bubble radius values. Ultimately, after validating the code by comparing the results of the spectrum produced from the code with the spectrum of a reference sample from an X-ray tube, the graph displaying numerical density changes in terms of bubble fraction at different radii demonstrates the method’s ability to distinguish varying amounts of bubble fraction for different radii.

Detection and identification of radioactive elements is a vital aspect in various fields, including nuclear safety, environmental monitoring, and radiation health physics. Traditional methods of radioactivity detection often rely on time-consuming manual techniques or specialized equipment that may not be readily available. However, advances in machine vision technology offer a promising alternative that uses the power of artificial intelligence and image processing to increase the accuracy and efficiency of identifying radioactive elements. This paper examines the application of machine vision techniques in identifying the type of out-of-control radioactive elements and focuses on the integration of imaging algorithms, deep learning frameworks, and data analysis of detectors that can automate the identification process and minimize human error. The equations of the tracking method follow the KLT method. The modeled system simultaneously receives and processes moving images and detects the movement path of objects, and the radiation count is recorded in the detector at the same time. From the recorded spectra in the detectors, the presence of a radioactive source or sources and their type is determined. In the following, by having the spectra recorded at different times and by identifying the identification number of the objects present in the area with the maximum spectrum and sharing, it is determined which objects are contaminated with radioactive material and what type of radioactive source each of them carries.

This study has investigated the performance of the car scanner cargo system in determining the linear attenuation coefficient of aluminum metal from the images obtained from a modeled car scanner imaging system. This system is based on a scintillation array detector and a radiation generator based on gamma emitter isotope. This research includes modeling the interaction of gamma radiation with the commonly used metal aluminum and analyzing the properties of its linear attenuation coefficient based on the data obtained from the scintillation detector system. For this purpose, radiographic images of aluminum sheets with different thicknesses were first extracted using modeling. Mcnpx monte Carlo code was used to model the system. The obtained images were processed using the algorithm written with MATLAB. Linear attenuation coefficient histograms were obtained from the processed images. Eight conventional functions were fitted on histogram graphs of linear attenuation coefficient. The parameters of the fitted graphs and the fitting accuracy of each were obtained and compared with each other. The average value of the calculated linear attenuation coefficient was compared with the experimental and modeling results of other researches, which showed a good agreement with each other.

Detecting and monitoring radioactive contamination is very important. It ensures public safety and environmental protection. However, exploring out-of-control radioactive sources in crowded places is hard. This is true, for example, among passengers or cars. This study proposes a new approach. It is based on data fusion and machine vision methods. The approach detects radiological contamination among similar moving objects. At first, we use the motion algorithm to define 5 moving objects. They are of the same shape and size and in a two-dimensional plane. Their motion equations were inspired by the small wheeled robot. These objects move with the same speed in the plane. Next, with another algorithm based on the KLT method, we extracted related features and tracked the same objects from the image data. The algorithm combines the beam detection system's data and machine vision. It finds one or more infected targets. It successfully detects the infected moving object. This research shows a promising approach to improve monitoring of radiation environments. It suggests integrating surveillance camera images and radiation detection systems for public and large areas.

Nuclear technology is rapidly expanding worldwide. However, radioactive materials pose a big risk to human societies and the environment. This is due to threats from terrorism, misuse, or unauthorized movement. Thus, we need to improve radioactive source detection and tracking systems. This will boost security and stop terrorist actions. This paper introduces a novel approach for beam mapping and detection by employing machine vision algorithms and modeling nuclear detection systems that incorporate scintillating crystal detectors and photomultiplier tubes. The primary objective is to enhance the efficiency and accuracy in identifying and locating out-of-control radioactive sources within complex and dynamic environments through the utilization of modern machine vision techniques. The tracking method employed in this approach is based on the Kanade-Lucas-Tomasi (KLT) method. The developed system simultaneously acquires and processes moving images for detecting the trajectory characteristics of objects, while recording radiation data using the detector. By effectively combining spatial and radiation data with high precision, the out-of-control radioactive source is successfully identified amidst other moving objects.

This paper presents a novel artificial neural network-based method for the classification of lung CT images to enable early diagnosis of lung cancer. For this purpose, the entire lung is segmented from the CT images, and statistical parameters such as mean, standard deviation, skewness, kurtosis, fifth-order central moment, and sixth-order central moment are computed from the segmented images. A feedforward backpropagation neural network is employed for the classification process. The results show that among the existing training functions for backpropagation neural networks, the best classification accuracy of 91.1% is achieved using the Traingdx training function. Additionally, two novel training functions are introduced in this paper, one of which achieved an accuracy of 93.3%, 100% detection rate, 91.4% sensitivity, and a mean squared error of 0.998, while the other achieved an accuracy of 93.3% and a mean squared error of 0.0942. Overall, the early diagnosis of lung cancer using artificial neural networks is a promising approach to increase the survival rate of patients.

Pigging is one of the conventional processes in the oil and gas industries, which is done in order to remove sediments from pipelines and also perform some non-destructive tests. On the other hand, due to various branches in the main pipeline, there is a possibility of stopping the pig. The right solution is to use mesh caps that not only prevent the pig from getting stuck, but also guarantee the passage of flow through all the main and secondary lines. According to the opinions of technical inspectors, there is no comprehensive information about the presence/absence of these caps in a large number of branches. The industrial radiography lacks the ability to detect the presence and absence of the cap in a fully filled pipe due to the average energy of the emitted beam and its geometrical structure. It is focused on the conceptual design of the nuclear system for investigating the possibility of pigging in the location of pipeline branches in the Monte Carlo environment. Ultimately, the results show the fact that in the situation where the source and the detector are perpendicular to the axis of the main and secondary branches and facing each other, the maximum counting sensitivity and discrimination can be achieved. Moreover, the relative difference of two states with and without the presence of metal grid for two states of filling the pipe with air and oil is equal to 53.8% and 57.1%, respectively.

One of the fundamental issues in physics is measuring the refractive index of materials. Knowing the magnitude of the refractive index of a material plays a decisive role in predicting its behavior and the amount of light passing through it. There are different methods for measuring the refractive index. In this work, to measure the refractive index of thin samples, a system based on the optical deflectometry method has been designed and built. The theoretical basis of the work, the effective parameters and the measurement error of the system in measuring the refractive index of thin samples with a thickness of about a few millimeters have been investigated. After optimizing the system, the magnitude of the refractive index of YAG and quartz crystal samples was measured, which are widely used in various applications. Using three lasers of helium-cadmium (blue color, wavelength 442 nm), argon ion (green color, wavelength 514.5 nm) and helium-neon (red color, wavelength 632.8), dispersion of refractive index of these two substances were measured. The measured values for the refractive index in these three wavelengths were 1.44, 1.45, and 1.37 for the quartz sample and 1.81, 1.83, and 1.73 for the YAG crystal sample.

A fire extinguisher is a critical piece of safety equipment used to suppress or extinguish small fires in emergency situations to prevent large and deadly fires. This study examines the possibility of using a measurement system based on a transmitted gamma ray and a linear pixel needle detector of a vehicle cargo scanner based on gamma ray technology to inspect fire extinguishers.. The aim is to determine the effectiveness and accuracy of this approach in detecting any defects or irregularities in fire extinguishers, which would enhance the safety and reliability of these crucial emergency devices. The study involves testing the system on four different models of fire extinguishers and analyzing the results to assess its viability as an inspection method. The results were analyzed, with output in the form of images and intensity profiles recorded in one direction from the images. The findings of this research could have a significant impact on the field of fire safety. They could help to contribute to the development of more advanced and efficient inspection techniques for fire extinguishers, in conjunction with other non-destructive inspection methods. In this study, the capabilities, limitations and potential applications of a transmission gamma based imaging system for fire extinguisher inspection were investigated.The imaging unit utilized a gamma source of cesium-137 and a linear detector array. The study discusses the potential applications of this technology in the field of fire safety and its limitations. The research highlights the importance of developing more advanced and efficient inspection techniques for fire extinguishers, as well as the need for ongoing research in this field. Ultimately, this study provides valuable insights into the potential use of vehicle cargo scanner systems for the inspection of fire extinguishers, and its findings have important implications for the field of fire safety. The results provide valuable insights into the feasibility of using gamma-ray technology-based automotive cargo scanner systems and pixel linear detectors as a reliable method for fire extinguisher inspection, ultimately enhancing public safety and emergency preparedness.

This study investigates the response of interferometric calorimeters to ionizing radiations through modeling. These calorimeters are promising devices for research in the field of energy physics of ionizing radiations due to their high precision and low sensitivity to energy loss of ionizing particles and special features. However, their response to ionizing radiations is not well understood. We develop a simulation framework to modeling the energy deposition and signal production in the main part of the calorimeter. The simulation includes effects such as heat transfer, changes in interference patterns and absorbing energy in calorimetric system.  We use the simulation to study the behavior of interferometric calorimeters in various radiation scenarios, including changing the direction of radiation, passage of time and change in the geometry of the absorbing material. The results show that the response of interferometric calorimeters to ionizing radiation is complex and depends on time and absorbing environment. These findings will help in the optimization of interferometric calorimeters for experiments in the field of nuclear physics.

Exploration of radioactive materials has been mainly performed by either airborne- or ground-based monitoring at the earlier steps. As the airborne monitoring using a helicopter (or airplanes) suffers from low spatial resolution, a radiation worker then surveys potential anomalies with a hand-held monitor to extract the radiological map. Due to possible rough terrain, ground-based monitoring faces some difficulties and is expensive or time-consuming. Drones fill a gap between airborne- and ground-based monitoring in the exploration of nuclear materials and facilities and complement the exploration process. Furthermore, the utilization of drones leads to a reduction in the time spent by a radiation worker in a radiological environment. In this study, a radiation detector-equipped unmanned aerial vehicle (UAV) has been utilized to explore nuclear materials. The UAV consisted of a hexacopter, a scintillation detector, a photomultiplier tube, a positioning system, a mini-computer, and a data-link system for radiation data. The results show the effect of the height of flight on the exploration is more considerable than the speed and thus low altitudes are preferred. It can be concluded that the developed radiological-monitored UAV can be utilized for the exploration of natural radioactive materials.

Today, the use of advanced environmental pollutants based on nuclear technology in processes associated with the production of gaseous, liquid and solid pollutants is highly recommended. Developing a strategy and planning for the development of the application of nuclear technology in the environment is necessary because this technology is advanced, complex and in the growth stage and requires high investment, highly specialized forces and cooperation and coordination of a series of projects.

 In order to develop a roadmap for the application of nuclear technology in the environment, first, by reviewing research in the field of applications of nuclear technology in the environment, potentials and key technologies in this field were identified. Then, by performing process steps through reviewing scientific articles and texts, the market was identified and then the types of products were determined.

The most important markets for nuclear technology-based treatment systems are power plants, chemical and petrochemical industries, and waste industries. Gaseous decontamination facilities, sludge and wastewater treatment and industrial waste facilities are classified in the product category. According to the roadmap, the acquisition of accelerator technology and gamma-rayirradiator systems is possible through technology transfer and equipment purchase. Since all stages of research and development of solid-state purification systems can be done with the existing facilities in the country and also the simplicity of the process, during the first three years, the focus on solid state purification systems was determined and in parallel by gaining the necessary experience, it is possible to transfer technology to liquid and gas purification systems.

Purification systems based on advanced nuclear technology are cost-effective, more efficient and environmentally friendly. In this regard, the use of roadmap as one of the most important management tools for planning the development of this technology is essential.

The nuclear industry has undergone a significant technological revolution in recent years, with neural networks playing an increasingly important role. These advanced machine learning algorithms are now widely used to analyze nuclear data obtained from various sources, such as calculations, modeling, simulation, and nuclear sensors including gamma ray-based gauges, experimental tests, and non-destructive tests like radiography. Non-destructive measurement methods based on gamma rays passing through materials or processes have gained popularity due to their effectiveness and safety in the nuclear industry. These methods typically use radioactive radioisotope sources like cesium-137 and cobalt-60, along with scintillation detection units, to capture data related to fluid thickness, density, and flow. By applying neural networks, these measurements can be made even more accurate and reliable, ensuring optimal safety and efficiency in the nuclear sector. To investigate this further, a study was conducted to design the geometry of the transmission mode for radioisotope thickness measurement through simulation using the MCNPX Monte Carlo code. The model included three commonly used metals in industries: copper, iron, and aluminum, to determine their thickness. To create the dataset, the thickness of the samples was varied from 0.5 to 50 mm, with a step size of 0.5 mm, and the results of Monte Carlo calculations were recorded as pulse height in the detector. Artificial neural networks were trained based on radial basis functions (RBF) and multilayer perceptron (MLP) techniques using all the results obtained from different energies. After training, the networks were tested to predict the thickness of different metals. The results obtained from the two networks were compared for the output of the model. The study showed that the response of the MLP network was more satisfactory than that of the RBF network in this application. Overall, the study highlights the potential of neural networks in enhancing the accuracy and reliability of non-destructive measurements in the nuclear industry, particularly in predicting the thickness of different materials.

In this study, the quality measurement of a plaster structure armed with steel rods and an internal air cavity was carried out. The scanner system is equipped with a cesium-137 gamma radioactive source with 661.7 keV photopeak energy and a linear array CdWO4 scintillating crystal of detector with 8 x 8 mm2 pixel size. The analysis of the internal structure of the sample was performed in two simulation and experimental phases. MCNPX Monte Carlo code was used to perform the simulation phase.  MLEM and Back Projection methods were used to reconstruct the images. The simulation results were compared with the experimental outputs, their performance in the 3D display inside this phantom was investigated. The results showed that the MLEM reconstruction method is more efficient than the back projection reconstruction method both in the simulation phase and in the experimental phase in providing internal images. The results showed that with modeling, it is possible to evaluate the tomographical pictures of the manufactured samples, and before setting the experimental, it can be used for different materials and geometries.

X-rays have wide applications in medicine, industry, scientific research, security issues, etc. The use of X and gamma rays to inspect cargo, cars, as well as persons has about 50 years old and today they are widely used around the world to prevent the carrying of weapons and drugs. In general, nowadays transmission systems have been more developed than back-scattering imaging. On the other hand, x-ray backscatter imaging system have special and unique applications due to their inherent advantages. Also, because the entire imaging system is on one side of the object, mobile imaging is more possible for this type of modality. In this research, an x-ray backscatter imaging system was modeled using the Monte Carlo method by means of MCNPX code. First, a 160 kV X-ray tube was modeled then the tube output was examined with the spectrum of the commercial tube. In the next step, the geometry of the device, the detection system and the scanned object were designed and modeled. The results showed that it is possible to detect bubbles with a diameter of 5, 10 and 20 mm for an object at a distance of 5 cm.

This radiological monitoring system consisting of a 2-inch NaI(Tl) crystal coupled to a PM tube and mounted on a multi-rotor UAV has been developed to fast detect radioactive sources. To assess its performance, a set of experiments for the detection of low-level Co-60 and Cs-137 point sources in a soccer field was performed. Then, the radiological map was made and then fused with the geographical one using image processing techniques. The results show that at 50 cm, all 5-point sources were successfully identified in their actual locations. By decreasing the flight height, the ability of the drone-based radiological monitor in recognizing radioactive sources significantly increases. The results confirmed that the drone system with the scintillation detector enabled the accurately identifying radiological anomalies by surveying a contaminated area.

Today, computed tomography has a wide of applications in medical and industrial imaging systems. In CT the inside image of the objects is obtained by scanning it at different angles and reconstructing the image. Industrial CT is used in many industrial areas for internal inspection of parts. Some of its key applications in industry are fault detection, non-destructive inspection, failure analysis, metrology, assembly analysis and reverse engineering. In this study, the CT feasibility study of a standard phantom specimen was performed with a cargo scanner system. The phantom contains 5.0 mm, 10.0 mm, 20.0 mm, diameter rods made of iron, aluminium and polyethylene and the total number of phantom rods was 9. The Imaging system geometry consists of a linear array detector and a gamma ray source. Two mathematical methods, MLEM and BackProjection, were used for reconstructing the projection that obtained from the sample. First the proposed CT system was simulated using the MCNPX Monte Carlo code. The CT images of phantom were compared in terms of the reconstruction methods and the experimental and simulated geometry.

The operation principle of calorimeters used for dosimetry of ionizing radiations is based on measuring the induced temperature difference in the adsorbent due to thermal energy deposition of the ionization radiation. In recent years, one of these methods has been the holographic optical calorimetry by laser beams. One of the problems that affect the response accuracy of the calorimeters is the heat transfer phenomenon in the adsorbent material. This phenomenon affects the measurement accuracy of the absorbed dose. In this work, using numerical coding in the FORTRAN environment, the dose profile change due to heat transfer effects in the PMMA tissue-equivalent material inside a holographic interferometry calorimeter has been investigated and the results have been compared with the finite element method results.

Digital Holographic interferometry is a powerful and widely used optical technique for accurate measurement of variations in physical quantities such as density, refractive index, and etc. In this study, an experimental digital holographic interferometry setup was designed and used to measure the amount of energy changes induced by absorption of radiation from a non-ionizing infrared laser beam in a water cell. An effective theoretical method is used to measure the absorption dose, which is based on tracing of the interference pattern displacement due to changes in the energy content of the adsorbent material. It is shown that the results of the interferometric method are in good agreement with the results obtained from the measurement with a precise temperature sensor. Experimental results show the capability of this optical method for non-contact and non-intrusive monitoring of the changes in the amount of absorbed energy by non-ionizing laser radiation in material.

In radiation calorimetry by using laser beams and interferometry setups, variations induced by dose absorption in phantom can be precisely measured. Dose absorption and the induced temperature change result in refractive index variation of the material. In order to be able to measure the low amount of absorbed dose in the phantom, temperature dependence of refractive index of the material must be precisely known. Water and Plexiglas phantoms can be used as tissue-equivalent materials and a multitude of reports have been published in recent years regarding their use for radiation calorimetry.  In this study, temperature dependence of refractive index of water and Plexiglas (poly- methyl methacrylate) is measured using a laser interferometry setup. The results show that the refractive index of Plexiglas has more variations with temperature compared to water. This shows that use of Plexiglas as the absorbing material for optical calorimetry, due to its nearly zero heat defect, increases the accuracy of absorbed dose measurements.

Holographic interferometry is one of the accurate methods for measuring physical quantities such as stress, pressure, vibration, and temperature and refractive index changes especially in transparent solid materials. In this study, a digital Mach-Zehnder holographic interferometry setup was used to measure the induced temperature changes by infrared laser irradiation in a polyethylmethacrylate cell. The obtained results from the interferometric method were in good agreement with the results of the measurement by a temperature sensor.

Double-exposure digital holographic interferometry is a powerful and widespread technique for fine measurement of the induced changes in special physical properties, like density, refractive index and etc. In this paper, application of this technique for measuring the absorbed dose from an electron radiation source in poly (methyl methacrylate) material is studied through modeling. The method of applying this technique for interpretation of the resulted fringe pattern and obtaining the pattern of isodose regions and the percent depth dose curve, that are ubiquitously used for treatment planning and monitoring of the absorbed dose, is investigated. Our results prove the capability of this optical technique for online monitoring of the absorbed dose from a radiation source through interpreting the formed fringe pattern.