Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. LOCALIZATION is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of LOCALIZATION error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system.Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z=0.4597.

In this research, by using 10 natural criteria and AHP model, Tabriz district was studied from natural environment perspective to identify the optimal location for construction of Maskan- Mehr and compare the current Mehr housing location. The method of this study is descriptive-analytical and the data were collected by documentary and field study methods. According to the proper zones identified for LOCALIZATION of Mehr housing in Tabriz district and since all zones were not optimal and lacked capability of planning, so by using TOPSIS, SAW and MORIS, the optimal zones were prioritized from implementation perspective. Also, the analysis of sensitivity of the identified zones was investigated based on 10 LOCALIZATION criteria. The results show that there are seven proper zones 10 km of Tabriz that have the best conditions for LOCALIZATION of the Mehr housing according to studied criteria. Also, ranking models show that the first zone is near to Tabriz- Maragheh road. The results show that Mehr housing in Tabriz that selected based on the economic conditions and land ownership issue could be localized in a proper environmental conditions near to Tabriz to consider environmental and natural disasters in addition to economic support of the owners of these dwellings.

Due to increasing growth of air traffic, it is very important to have a reliable air traffic control system. Usually secondary surveillance radar systems are employed for this task. In this paper, a new approach is presented to identify interrogators location of this system. The proposed method works passively and uses just a single receiver. This goal has been reached using angle and time difference of arrival information. How to determine the desired angle and calculate the time difference of arrival from the received data is described. knowing this information, a closed-form solution in polar coordinate is presented and its sensitivity is measured. Then a method is proposed based on maximum likelihood estimation of the target location. Afterward angle and time difference of arrival equations are linearized and then weighted least squares estimation is achieved. The suggested approach is evaluated on real and practical information and the results have finally been reported.

Although Hydatid disease eradicated in many countries, it is still widespread in communities where agriculture is dominant. Cystic hydatidosis is significant public health problem in the regions with endemic echinococcosis. The hydatid cysts tend to form mostly in the liver or lung. Brain involvement is very rare. In the present report, we describe magnetic resonance imaging findings in an 18-yr-old male with cerebral echinococcosis, in Shahid Madani Hospital, Karaj, Iran in 2015. The patient, presented with headache, hemiparesis, impairment of speech, vomiting, and nausea. Computed tomography, magnetic resonance imaging, and surgical exploration proved a cyst in the superior portion of left temporal lobe. Pathological examination showed it to be a solitary primary cerebral hydatid cyst.

Matched Field Processing (MFP) is one of the most famous algorithms for source detection and underwater LOCALIZATION. Traditional MFP relies on a match between the received signal at the hydrophone array and a replica signal, which is constructed using Green’ s Function, then by scanning the space in range and depth to provide an estimation of source position in shallow water and deep water. Different environment models relying on Green’ s function exist for constructing the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-Mirror Pattern, and the Image model. Using the proposed estimation algorithm, here, an analytical Lloyd-Mirror model is developed based on the reflection from the target surface for a case where a target is located in the source signal propagation path. So, in this paper, a new underwater acoustic target LOCALIZATION algorithm using the generalized Lloyd-Mirror Pattern is presented. This idea is verified using an acoustic data from a 2019 underwater communication trial in Grand Passage, Nova Scotia, Canada.

Localizing text regions in images taken from natural scenes is one of the challenging problems due to variations in font, size, color and orientation of text. In this paper, we introduce a new concept so called Edge Color Signature for localizing text regions in an image. This method is able to localize both Farsi and English texts. In the proposed method first a pyramid using different scales of the input image is created. Then for each level of the pyramid an edge map is extracted. Afterward, several geometric features are employed to filter out the non-text edges from the extracted edges. At this stage we describe an edge using colors of its neighboring pixels. We use the mean-Shift algorithm to obtain the color modes surrounding each edge pixel. Subsequently, the connected edge pixels with similar color signatures are clustered using Single-Linkage clustering algorithm to construct meaningful groups. Finally, each of the clusters is labeled as text or non-text using an MLP based cascade classifier. The proposed method has been evaluated on well-known ICDAR 2013 and our Farsi dataset, the result is very promising.

In this paper, the problem of elliptic target LOCALIZATION in distributed multiple-input multiple-output (MIMO) radars is investigated. The goal of elliptic LOCALIZATION is to estimate the target position from a set of noisy bistatic delay measurements. Since the maximum likelihood (ML) problem associated with elliptic LOCALIZATION is nonconvex, iterative methods can be trapped in local minimums, leading to inaccurate location estimation. To solve this problem, a number of (almost closed-form) estimators are proposed, which can locate the target without convergence concern. The proposed methods are efficient, achieving Cramer-Rao lower bound (CRLB) up to relatively high noise levels. These methods are of superior LOCALIZATION accuracy in comparison with the state-of-the-art methods. Furtheremore, according to the closed-form and algebraic nature of the proposed methods, they have very low computational complexity, which is similar to other existing closed-form methods in the literature. It should be noted that the ideas presented in this paper can be considered as a baseline for future research studies in the area of LOCALIZATION in radar systems.