r. ali abbaspour

This article addresses a new approach to 3D path planning of UCAVs. To solve this NP-hard problem, imperialist competitive algorithm (ICA) was extended for path planning problem. This research is related to finding optimal trajectories before UCAV missions. Developed planner provides 3D optimal paths for UCAV flight with real DTM of Tehran environment. In UCAV mission, final computed paths should be smooth that made the path planning problems constrained. This planner can offer flyable 3D paths based on mission requirements. It’s a comprehensive study for efficiency evaluation of EA planners, and then novel approach will be proposed and compared to ICA, GA, ABC and PSO algorithms. Then path planning task of UCAV is performed. Simulations show advantage of proposed methodology.

With the increasing use of spatial data in daily life, the production of this data from diverse information sources with different precision and scales has grown widely. Generating new data requires a great deal of time and money. Therefore, one solution is to reduce costs is to update the old data at different scales using new data (produced on a similar scale). One approach to updating data is to use the updated large-scale dataset as reference data to update small-scale datasets; in this way, the modified features are identified in the two datasets and then updated by modifying the changes to fit the small-scale dataset. In terms of the type of the updated feature, map updating issues in the vector dataset are divided into three categories: pointwise, linear, and polygon. One of the most important features of the class of polygonal features in urban environments are the buildings that are vital in urban maps and their updating process in urban applications has a high priority. In this research, an attempt has been made to study the issue of updating polygonal features from different perspectives by a careful and comprehensive examination. These perspectives include spatial clustering methods, pattern extraction, and updating methods. 
Spatial clustering methods are classified into five

natural principles, partition-based, graph-based, Region Merging, and density-based approaches. Each clustering method have been used in different studies according to Gestalt criteria (proximity, similarity, and continuity). In pattern extraction, different types of patterns have been studied in various studies while the linear pattern extraction as a sample has been comprehensively examined in this study.  In the updating methods, three updating approaches including Propagating, Local, and Constraint-based are examined. In the Propagation updating approach, only large-scale data are updated, then these updates can be propagated into small-scale data. This update is especially used for the MRDB spatial database by propagating updates at various scales. Local updating consists of three steps: 1) Change detection between the recently updated large-scale dataset and the old small-scale dataset. 2) Integrating the discovered changes into the small-scale dataset (by quantifying and formulating these changes). 3) Ensuring that the consistency is maintained. In Constraint-based updating approach which demonstrates the necessities of this research, first, by grouping the buildings, useful information such as the area of ​​the buildings, the average and the standard deviation of the separation distance between the buildings is obtained. Then, with the appropriate operators, Constraint-based generalization is performed to update the maps.  There are various criteria for evaluating the updating methods, while the Precision and Recall criteria have been used to evaluate the effectiveness of these problems. Precision criterion examines the ratio of the number of correctly matched features to the total number of features that that have been matched correctly or incorrectly. Recall criterion evaluates the ratio of correctly matched features to the total features that have been matched correctly or not matched. In other words, in the Precision criterion, the number of features that are incorrectly matched and in the Recall criterion, the number of features that are not matched, are effective in comparing the two criteria. The categorization mentioned in each perspective and the advantages and disadvantages of different methods are also presented in this research.

The expansion of location-based services (LBS) and their applications has led to a growing interest in localization, which can be done on the smartphone platform. Various positioning techniques can be used for indoor or outdoor positioning. Indoor positioning systems have been one of the most challenging technologies in location-based services over the past decade. Considering the increase of people activities inside buildings such as offices, hospitals, and large stores, determining the position and guidance of people inside these buildings is one of the most urgent and important issues to be discussed and challenged in the area of Location-based Services (LBS). There are various ways to determine the position inside a building. The method(s) used to determine the position in an indoor environment depends on several factors such as cost, accuracy, independence of, or dependence on the infrastructure, security, and system scalability. This study focuses on the infrastructure requirements necessary to determine the position of individuals thorough a comprehensive study of previous studies. Moreover, focusing on the Pedestrian Dead Reckoning positioning method using smartphones as an infrastructure-free method, several effective aspects of the accuracy and positioning process are examined. The effective measures examined include the use of a variety of noise filtering, combined filters (Particle filter, Kalman filter), the criterion of the of sensor data classification algorithm, the criterion of the initial point determination, the use of landmarks as checkpoints and plot maps for setting the estimated position, the detection criteria and estimation of the length of the step, and the user direction estimation criteria. The particle filter has good accuracy in small-scale areas, but in large-scale areas, it is out of date and has problems due to the limited source of the smartphone. In studies, Kalman filter has been used to integrate the information of different sensors, some of which have reached the desired accuracy according to the state model and the measurement model. Given that the generalized Kalman filter has a simple formula for nonlinear estimation, the linearization of the positioning problem causes an error in the Jacobi Matrix model and reduces the accuracy of the estimate, which negatively affects the cost of calculations and system timeliness. Step length varies from person to person. In fact, there should be a variable associated with pedestrians in the step estimation model. Also, a person's walking rate during a walk is not constant. Accordingly, assuming a constant value of step length for users causes an error during positioning and a large drift at the end of the path. Determining the heading is one of the most challenging parts of the PDR system because the heading error leads to a quick increase in the positioning error. It is difficult to determine the reliable heading in the environments with high magnetic disturbances. Another problem is that the heading of the smartphone may vary with the heading of the pedestrian movement. Therefore, two main tasks must be performed before implementing indoor positioning. One of them is to determine the heading of the smartphone. Another is to infer the offset heading between the smartphone and the pedestrian movement. Therefore, determining the state of the smartphone is necessary for specifying the heading of the pedestrian movement.  Finally, the advantages and disadvantages of each of the infrastructure-based and infrastructure-free methods are compared and evaluated. Also, the research uses algorithms such as Naive Baye, MLP, SVM, DT and KNN to classify the type of user movement and phone holding mode.

The rapid growth of mobile phone technology and its combination with various technologies like GPS has added location context to social networks and has led to the formation of location-based social networks. In social networking sites, recommender systems are used to recommend points of interest (POIs) to users. Traditional recommender systems, such as film and book recommendations, have a long history. However, due to the existence of the location component and the physical connection of users with the outside world in social networking sites, several special features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of location-based social network data, spatial factor plays an important role in improving recommendations. Because people's desire to visit places is greatly influenced by the distance between the person and the place. Also, the distribution of POIs in the region changes the pattern of user visits. In the first part of this study, we discuss challenges which social networking sites may face by comparing location-based recommender systems with traditional recommender systems. In the following, we mention some important contexts and factors in POI recommendation. Spatial factor, social relations, different types of contents, different categories, sequential pattern, and time factor are contexts which are commonly used in POI recommendation. Next, we mention different types of location-based recommender systems: the fused model and the joint model. In the fused model we model user’s preferences and other additional contexts individually and after that, we combine their results with collaborative filtering. In a joint model, all contexts are learned Simultaneously.  In the next part, we discuss methods for extracting spatial context in location-based recommender systems. There are three major ways of modeling spatial data: independent, dependent, and restrictive models.  In independent modeling, we model spatial factor independently without considering the user’s preferences and other contexts. Here we discuss four basic independent models in detail: power law, Gaussian distribution, Kernel Density Estimation, and distance-based models. The power law is a relationship between two quantities in which a relative change in one quantity causes a change in another quantity, and this change is independent of the initial values ​​of the two quantities. This rule is used for modeling spatial data in recommender systems. Changes in many natural quantities around a constant value follow the Gaussian distribution, and this has led to its use to model spatial factors. Kernel density estimation is a non-parametric method for estimating the probability density function of a random variable. To recommend personalized items this method can be very useful because we could model spatial data of every user individually. distance-based methods model spatial factor by considering the distance between users and items or items with each other.  At dependent modeling spatial context is learned with other contexts Simultaneously. For this, we determine four popular methods matrix factorization, probability-based models, artificial intelligence, and combined models. These methods are general algorithms for recommending items in recommender systems and spatial factor is just one of their components. Restricted models filter recommendations by considering spatial constraints.  At the end of the article, we summarize the various features of the proposed methods and mention their advantages and disadvantages.rapid growth of mobile manufacturing technologies and its combination with various technologies have led to the addition of location dimension to social networks and the formation of location-based social networks. Recommender systems are used on location-based social networks to recommend points of interest to users. Traditional recommender systems such as movies and book recommendations have a long history. However, due to the locational component and physical connection of users with the outside world in location-based social networks, several specific features such as spatial, temporal, and social factors are considered to improve recommendations. Among the specific features of the location-based social network’s data, location factor plays an important role in improving recommendations. Because people's desire to visit places is largely influenced by the distance between the person and the place. The distribution of attractive places in the area also changes the pattern of user visits. In this study, we first discuss the challenges that location-based social networks face by comparing them with traditional recommender systems. Next, the factors that influence location recommendation in location-based recommender systems are discussed in detail. Finally, a variety of location modeling methods, which is one of the most important factors in recommending attractive locations to users using location-based social network data, are discussed.

The purpose of this paper is to evaluate and improve the accuracy of indoor positioning using smartphone sensors based on Pedestrian Dead Reckoning (PDR) method. In some specific situations, such as fires or power outages that disable infrastructure-based positioning techniques, using PDR method based on smartphone sensors that perform positioning continuously is a good solution.This paper focuses on determination of the user’s movement type to evaluate effective components of indoor positioning method. First, movement samples are evaluated with the feature-vectors of data from sensors and three classification algorithms (Decision Trees (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN)). From the perspective of feature-vectors, the proposed features significantly improve the performance of three classification algorithms compared to previous research features. From the perspective of classification algorithm also Support Vector Machine had best performance with %99.3 accuracy, while spending the most time.  In the second phase, step detection is performed for norm acceleration values based on the definition of the upper and lower threshold and the time threshold. The directional component is also obtained by combining accelerometers, magnetometer and gyroscope sensors. Localization tests were performed while the user holding the phone in front of him with two states (normal walking, running) in three paths of different geometry (squares, circles and rectangles). The final accuracy obtained from normal walking test for three paths of square, circular and rectangular shapes was %4.8, %3.6, and %2, respectively. The final accuracy of the running mode was also obtained for three paths of square, circular and rectangular shapes equal to %8.4, %5.7, and %4, respectively.