Introduction: The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems. In generally, global waters are classified into two types of water (the first case and the second case). In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However Case-2 waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter and Total suspended matter. Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities. Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves. Chlorophyll-a considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale. The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-a concentration. Materials & Methods: In this study, Landsat 8, Sentinel 2 satellite time series data and field data collected from the study area were used. First, the satellite images used in ENVI 5. 3. 1 software were converted to Surface Reflectance and Top of Atmosphere Reflectance. Then, MATLAB 2018a software was used for image processing and coding. to estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results two statistical parameters R2 and RMSE were used. Results & Discussion: Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere Reflectance Sentinel 2 and Landsat 8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat 8 and Sentinel 2 satellites systems. The OC2 algorithm had amounts R2 (0. 91 and 0. 64) and RMSE (0. 13 and 0. 33) in Landsat 8 and Sentinel 2 Surface Reflectance data, respectively, while Landsat 8 and Sentinel 2 Top of Atmosphere Reflectance data had amounts R2 (0. 12 and 0. 53) and RMSE (0. 45 and 0. 51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat 8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-a concentrations using the surface reflectance data (Sentinel 2) during the seasons estimated the chlorophyll concentration to be uniformly and downward. The reason for this poor performance in the Sentinel 2 is the lack of sufficient field data for calibration. Conclusion: In this study, we tried to evaluate and compare the reflectance algorithms (Landsat 8 and Sentinel 2) in the OC2 algorithm. Preliminary results indicate that the type of satellite data used (surface reflectance and Top Atmosphere reflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data. In general, the results showed that 10 field data is enough to calibrate with Landsat 8 data, but for Sentinel 2 data, more than 10 numbers field data must be calibrated to obtain a good result.

In this paper fully centralized, hierarchical decentralized, and fully decentralized Multisensor structures of Kalman filter are introduced and compared with together and with one sensor structure of Kalman filter. Decentralized structures are obtained through parallelism in centralized Kalman filter and give high processing speed. Fully decentralized structure requires no central processor and each processor estimates system's states. Therefore, the system is very resilient to loss of one or more of its processors and its survivability and robustness are increased. Decentralized algorithms are modified so that t-he computation complexity is reduced with keeping the same accuracy. Simulation results 'for time-variant system illustrate clearly that despite of reducing the computation complexity, the accuracy of the estimation is remained the same. Also, Multisensory structures have high accuracy of estimation in comparison to one sensor structure.

Over the last two centuries, natural hazards and widespread human activities have led to significant changes in water, soil, and vegetation covers. Although, multi-temporal remote sensing (RS) images provide continuous monitoring of changes in land surface, one of the most important challenges is applying multi-sensor images to detect land cover changes in unsupervised flow. This study aims to provide a method for changes detection in water, soil, and vegetation covers within multi-sensor RS images. In this regard, new biophysical parameters have been defined for the Sentinel2B sensor, as well as a new unsupervised method for binary and multiple changes detection has been developed. Landsat8 OLI and Sentinel2B images of the southwestern shore of the Urmia Lake were used to evaluate this method. In the proposed method,first, the generalizability of the Tasseled Cap (TC) transformation was investigated and a new TC transformation for the Sentinel2B sensor was estimated. After TC, the images were transferred from multispectral feature space to biophysical feature space, and a binary changes map was generated using the proposed multivariate iterative trimming method. Then, via FCM clustering, the changed samples were separated into a certain number of clusters determined by the WSJI criterion which is one of the innovations of the proposed method. Overall accuracy, missed error, and false alarm of the proposed approach are %92. 06, %9. 62, and %6. 27, respectively. The proposed method in this paper can be used as an unsupervised, accurate, and reliable technique for changes detection in water, soil, and vegetation covers.

These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. Of course, there are other important applications for this information. There are a variety of methods and tools for measurement, gathering, and analysis of the physical behaviors and activities’ information. One of the most successful tools for this aim are ubiquitous intelligent electronic devices, specifically smartphones, and smart watches. There are many sensors in these devices, some of which can be used to understand the activities of daily living. As an output result, these sensors produce many raw data. Thus, it is needed to process these information and recognize the individual behavior of the output of this processing. In this paper, the basic components of the analysis phase for this process have been proposed. Simulations validate the benefits and superiority of this method.

Introduction: Long-term clinical evaluation of dental implants and their surrounding structures is of utmost importance to acquire knowledge about reasons for implant success and failure.However, accurate and reproducible results are difficult to obtain. The aim of the present study was to examine bone height around endosseous implants on digital conventional radiographs (DCR) and direct digital subtraction images (DSI) prior to loading.Materials and methods: In this experimental study, bone height around 10 implants in 6 patients was assessed by 2 observers. Standardized digital radiographs were obtained just a week and 3 months postoperatively and subtracted by means of EMAGO software. Then the observers evaluated bone height on DCRs and DSIs. MINITAB software and paired t-test were used for statistical analysis (a=0.05).Results: Comparative evaluation of bone height indicated significantly higher values on DCR than on DSI (p value=0.002). The observers also had statistically significant variability in this assessment (p value=0.00003).Conclusion: DSI demonstrated lower values of linear measurement of bone height around endosseous implants compared with DCR. Interobserver variability should be considered when comparing values from follow-up studies.

Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.

Purpose: Something that happens in the future of a scientific field depends on today's view of young people about the future. The perception of young people from the future and their conception of the future is very important. The purpose of this study was to imaging the future of sports science from the perspective of educated youth. Method: The method of the present research is qualitative and exploratory research and in terms of purpose is applied research. The statistical population of the study included educated youth who are familiar with sports science and the snowball sampling method was used for interviews and theoretical saturation were obtained with 23 interviews. To analyze the data, the content analysis method has been used to obtain the information of this research. Findings: The results show the future of sports science among educated youth consists of seven main concepts that include: hope for a green education system, excessive interest in working in the sports industry, basic reengineering, relative disappointment from the ability of authorities, social position through academic education, desire for empowerment and lack of skills, injustice, and utilitarianism. Conclusion: Finally, by observing the mental images of the youth, it can be stated that sports science will move towards the dystopia if the current situation continues, and it can move towards the utopia with fundamental changes. In this direction, the stakeholders of sports sciences need to be together and disagreements need to be managed as well as arbitrariness of sports science professionals should be reduced.

Purpose: Recent Progress in multimooal imaging makes it possible to acquire several images from different physical properties of individual subject. Data fusion, image registration and especially image warping of these different images and sequential images have wide range of applications in medical diagnosis and treatment. Many registration techniques use interpolating function as a basis to map one image to another. Because of space variant and nonlinearity properties of imaging system, local warping algorithm improves performance of mapping.Materials and Methods: In this research we have developed a new method for local warping based on voronoi images.This method employs weighted average of partially warping function of distinct sets of landmark points.The method was tested on a large database including real as well as simulated magretic resonance images and its" results were compared with a standard benchmark sotware (AIR). Results and Discussions: Results indicated that our method has an excellent ability to compensate several geometrical distortions compare with the global warping methos.

fusion following the studies by FIFA (Federation Internationale de Football Associations). Research using images other than T1WI has not been reported. The aim of this study is to evaluate the grade of epiphyseal fusion by T2* weighted images (T2*WI) and to investigate new findings on T2*WI as compared with T1WI.Methods: A total of 87 subjects, all junior football players between the ages of 12 and 17 years old, were examined. T1 and T2* WI were obtained using a 1.2T Open type MR system. The T1WI and T2*WI were rated twice randomly by four radiologists using the FIFA grading system.Results: The intra-rater reliability for grading was higher in T1WI (The Intraclass Correlation Coefficient (ICC)=0.949-0.985) than in T2*WI (ICC=0.917-0.943). The inter-rater reliability for grading was also higher in T1WI (ICC=0.923) than in T2*WI (ICC=0.867).Conclusions: This research showed that T1WI is a better sequence than T2*WI to evaluate bone fusion following FIFA protocol. It was speculated that the reason for this is that T1WI has higher tissue contrast resolution and enables clearer images of the epiphyseal fusion than T2*WI and the grading system by T1WI was not suitable for T2*WI.