This paper presents a new method for blind two-channel speech sources separation without the need for prior knowledge about speech sources. In the proposed method, by weighting the mixture signal spectrum based on the location of the speech sources in terms of distance to the microphone, the speech sources are separated. Therefore, by forming an angular spectrum by generalized cross-correlation function, the speech sources in the mixture signal are localized. First, by creating an angular spectrogram by generalized cross-correlation function, the speech sources in the mixture signal are localized. Then according to the location of the sources, the amplitude of the mixture signal spectrum is weighted. By multiplying the weighted spectrum by the values obtained from the angular spectrograms, a binary mask is constructed for each source. By applying the binary mask to the amplitude of the mixture signal spectrum, the speech sources are separated. This method is evaluated on SiSEC database and the measurement tools and criteria contained in this database are used for evaluation. The results show that the proposed method is comparable in terms of the criteria available in the database to the competing ones, has lower computational complexity.

This paper studies the possibility of LOCALIZATIONs for frame structures in static and dynamic analysis. A finite element model is used in which the sections resisting force is calculated using an introduced differential hysteretic model. A microplane constitutive law for concrete and bi-linear elasto-plastic material model for reinforcements evaluates parameters of the differential hysteretic model in sections. The sensitivity to probable changes in ground motion characteristics is assessed by a nonstationary ground motion generation process to obtain excitations with approximately the same amplitude and frequency content evolution as those of a base ground motion. The procedure is applied in two case studies and possibility of LOCALIZATION in response is investigated. A measure for the probability of occurrence of this behavior in code-designed structures is obtained. This study indicates that LOCALIZATION and resulting early collapse of structure is possible. It is concluded that some modifications in design code provisions are required.

Introduction: Increasing the production efficiency is an important goal in precision farming. The use of precision farming requires a lot of labor work. Also, due to the risk of agricultural operations, it is not recommended to do it directly by humans. Therefore, it is necessary for agricultural operations to be carried out automatically. For this reason, the application of robotics in agricultural environments, especially in the greenhouse, is increasing. The first step in automatic farming is autonomous navigation. For autonomous navigation, a robot must be the ability to understand its environment and recognize its position. In other words, a robot must be able to create a map of an unknown environment, locate itself on this map and finally plane for the path. This problem is solvable by Simultaneous LOCALIZATION and Mapping (SLAM). The SLAM problem is a recursive estimation process. In the other words, when a robot moves in an unknown environment, mapping and LOCALIZATION errors increase incrementally. To reduce these two errors, a recursive estimation process is used to solve the SLAM problem. Materials and Methods: In this research, two webcams, made by Microsoft Corporation with the resolution of 960×544, are connected to the computer via USB2 in order to produce a stereo parallel camera. For this study, we used a greenhouse that was located the Arak, Iran. Before taking stereo images, a camera path was designed in the greenhouse. This path may be either straight or curved. The designed path was implemented in the greenhouse. The entire path traversed by a stereo camera was 32. 7 m and 150 stereo images were taken. Graph-SLAM algorithm was used for Simultaneous LOCALIZATION and Mapping in the greenhouse. Using the ROS framework, the SLAM algorithm was designed with nodes and network for connecting the nodes. Results and Discussion: For evaluation, the stereo camera locations, every step was measured manually and compared with the stereo camera locations that were estimated in the graph-SLAM algorithm. The position error was calculated through the Euclidean distance (DE) between the estimated points and the actual points. The results of this study showed that, the proposed algorithm has an average of error 0. 0679412, standard deviation of 0. 0456431 and root mean square error (RMSE) of 0. 0075569 for camera LOCALIZATION. In this research, only a stereo camera was used to prepare a map of the environment, but other researches have used multiple sensor combinations. Another advantage of this research related to others was created a 3D map (point cloud) of the environment and loop closer detection. In the 3D map, in addition to determining the exact location of the plant, the height of the plant can also be estimated. Plant height estimate is important in some agricultural operations such as spot spray, harvesting and pruning. Conclusions: Due to the risk of agricultural activities, the use of robotics is essential. Autonomous navigation is one of the branches of the robotics. For autonomous navigation, a map of environment and LOCALIZATION in this map is need. The purpose of our research was to provide simultaneous LOCALIZATION and mapping (SLAM) in agricultural environments. ROS is a strong framework for solving the SLAM problem. So that, this problem can be solved by combining different nodes in ROS. The method depended only on the information from the stereo camera because stereo camera provided exact distance information. We believe that this study will contribute to the field of autonomous robot applications in agriculture. In future studies, it is possible to use an actual robot in the greenhouse with various sensors for SLAM and path planning.