We present an efficient method for the reduction of model equations in the linearized diffuse optical tomography (DOT) problem. We first implement the maximum a posteriori (MAP) estimator and Tikhonov regularization, which are based on applying preconditioners to linear perturbation equations. For model reduction, the precondition is split into two parts: the principal components are considered as reduced size preconditioners applied to linear perturbation equations while the less important components are marginalized as noise. Simulation results illustrate that the new proposed method improves the image reconstruction performance and localizes the abnormal section well with a better computational efficiency.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation models which can predict the timing of occurrence each development stage by integrating effective factors. The model was constructed based on linear equation of plant temperature response. In order to model evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed model was calibrated and run for simulation. Comparison of simulated and observed data showed that the model can strongly predict wheat development stages.

Research in the field of video surveillance systems has been improving because of the increasing need for intelligent monitoring, control and management. Given the large amount of data on these intelligent transportation systems, extracting patterns and automatically labeling them is a challenging task. In this paper, a topic model was used to detect and extract traffic patterns at intersections so that visual patterns are transformed into visual words. The input video is first split into clips. Then, the flow characteristics of the clips, which are based on abundant local motion vector information, are computed using optical flow algorithms and converted to visual words. After that, with a non-probabilistic topic model, the traffic patterns are extracted to the designed system by a group sparse topical coding method. These patterns represent visible motion that can be used to describe a scene by answering a behavioral question such as: Where does a vehicle go? The results of the implementation of the proposed method on the QMUL video database show that the proposed method can correctly detect and display meaningful traffic patterns such as turn left, turn right and crossing a roundabout.

A physical model is develpoed for operation of a functional quantum well optoelectronic integrated device (QW-OEID). Based on the model, static and dynamic characteristics of the device is analyzed numerically considering the effects of parameters such as internal optical feedback and lateral diffusion of carriers for both amplification and switching modes of operations.

In this paper, a new method based on ray optics and Kirchhoffs method, using tangential plane approximation for calculating the scattered power from rough surface is proposed. Meanwhile applying the Kernel's approach together with the proposed model, the irregular plane parameters for different irregularities are calculated, and then the scattered power with respect to different elevation and azimuthal angles is estimated and compared with measured data, Kirchhoffs and stochastic scattering models. It is observed that there is good agreement between results obtained from the proposed model and measurements. Therefore, it can be concluded that using the proposed model to calculate the path loss in mobile communication leads to a better design and field estimation than Kirchhoffs or stochastic scattering models. Also in radar cross section estimation, the proposed model can assist us in calculating and estimating the maximum and minimum scattered power from rough surface with different degrees of roughness, which is important in radar cross section design and analysis. In addition, the proposed model in contrast to Kirchhoffs method can be applied to any surface with any distribution function and different degrees of roughness. Therefore, using the proposed model for calculating scattering power can lead to better results than Kirchhoffs or stochastic scattering models.

Today, one of the most widely used natural language representation methods is meaning representation for text processing in different systems. Meaning representation methods have had many applications in the field of natural language processing in recent years, including automatic text summarization systems and question and answer systems, etc. Many text expressions may be different in terms of grammatical structure, but they are the same in terms of meaning, so how to apply a single and uniform meaning to them is one of the challenges of text processing. The main goal of this paper is to design an Integrated Semantic Representation (ISR) model for natural language. The proposed model, while maintaining its simplicity in annotation and understanding, does not depend on specific natural language features or on syntactic and lexical structure. In this regard, several examples in both English and Persian language, which have different in terms of written and grammatical structure, are presented in this paper. Moreover, by using the proposed model, the same representation is provided for texts with the same meaning and different grammatical structure. The proposed model is designed in graph and list format is recommended to annotate its corpuses. One of the main features of this model is that it can represent semantic relations at both sentence-level and document-level and is able to represent complex and important linguistic phenomena such as aspect, tense, and quantification. The simplicity of the proposed model helps to avoid making language processing slow or complicated in various applications, and the preparation of structures based on this model for different natural languages will not be too complicated, so that it can be used both for natural languages with low resources and for those with various resources. Further, features of the proposed model are compared with one of the most important related works.