One of the problems in detecting radar Targets is the variation of Target cross section due to aspect angle change; which is named as "Target Fluctuation". This primarily depends on Target geometry and transmitted wavelength and may cause significant loss of detection. A method to deal with this challenge is improving the radar Range Resolution using stepped frequency signals. In this paper, after introducing this kind of radar signal and explaining how to access high Resolution, some techniques have been investigated for improving fluctuating Targets detection in the stepped frequency radars. To do so, three distinct methods have been firstly presented for modeling Range distributed Targets. Then two detectors have been suggested for detecting these kinds of Targets. Finally, it is shown that the distribution of Target as the result of high Range Resolution can fully compensate the Target fluctuation loss.

In this paper, in order to improve Target Range Resolution, Target detection problem in FM-based passive bistatic radar (PBR) is modeled as an M-ary hypothesis testing problem. To do so, we exploit some adjacent channels being transmitted from a same FM transmitter and solve the detection problem based on generalized likelihood ratio test (GLRT) framework. Due to the masking effect of strong Targets as well as the time-variant nature of FM radio signal, we implement the proposed detector in a multistage manner. Indeed, in our proposed detector, Targets are detected sequentially and the previously detected Targets are treated as interferences to be removed yielding the detection of the weakest ones. Extensive simulation results are provided to demonstrate the capability of the proposed detection algorithm. Our simulation results show that the proposed multi-channel detector not only improves the Target Range Resolution but also results in detection performance improvement offered by combination gain equal to the number of active channels.

For naval radars, the concept of “the sea state” is very important. In this article, the sea waveequation and its shape of movement have been determined. Then, the waveforms of the sea waves have been reconstructed using the Feko software. After that, the RCS resulting from the impact of radar waves (with high Range Resolution radar) to sea level has been calculated in order to acquire returned signals from sea waves in antenna input. Subsequent to determining returned signals in the time domain and receiving these signals by the radar receiver, its first to forth moments are calculated. On the following, the feature space is formed and by using it sea waves are classified in four different modes. And finally, the precision of this suggested method has been measured by adding noise to returned signals from the sea level.

Object detection is one of the fundamental issues in image interpretation process, especially from remote-sensing imagery. One of the most effective and efficient methods in this field is the use of deep learning algorithm for feature extraction and interpretation. An object is a collection of unique patterns that differ with own adjacent properties. This difference usually occurs in one or more features simultaneously, which can be indicated by the difference in shape, color, and gray values. In this regard, the use of deep learning as an efficient branch of machine learning can be useful in generating high-level concepts through learning in different layers. In this research, a database based on the environmental and geographical conditions from some Iranian airports was created. Additionally, an optimal learner model was developed with a convolutional neural network. For this purpose, in the raw data processing section, besides using the transfer learning method, some vectors were extracted to classify the objects and delivered to an SVM model. The output values were compared with the values obtained from the test image for each object, and they were analyzed in a repeatable process for structural matching. Precision of 98. 21% and F1-Measure of 99. 1% was achieved, for identification of the Target objects.

In this paper, we propose a new approach for selection of subsets of active sensors with some constraints on energy consumption and estimation error for tracking of a Target. The proposed approach exploits the decentralized estimation by using the extended information filter for Target tracking. Furthermore, a cost function is defined using spatial correlation for sensor selection. Consequently, the Spatial Split algorithm is proposed based on spatial correlation coefficients for sensor selection. At last, for high speed Targets, we propose a modification on spatial split algorithm by changing the sensing Range with respect to the Target speed. Simulation results show that the tracking accuracy is analogous to those of optimal estimation methods. It is also found that energy consumption decreases due to activating only necessary sensors. 

High Resolution Range profile (HRRP) is being known as one of the most powerful tools for radar Target recognition. The main problem with Range profile for radar Target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP (or feature vectors extracted from them) were assumed to be independent identically distributed (iid) in small frames of aspect angles in most of the previous works. In this work, an alternative method based on dynamic system is proposed to overcome the limitations of the recent methods such as the independency assumption. Two different feature extraction schemes using spectral features and PCA coefficients are utilized. When the first scheme is employed, modeling is done in short-term, whereas long-term modeling is done while employing the second scheme. Simulation results show superiority of our model over common models using either approach to feature extraction.

The high sensitivity and precision of the distribution optical fiber newfound sensors led to special attention to them and use them in the modern industry, and in particular applications such as monitoring of borders security and leakage of energy pipes, monitoring the safety of large urban structures, Railway lines and so on. The spatial Resolution and sensing length are two basic characteristic parameters for researchers. It is obvious that in the military and industrial affairs, whatever the spatial Resolution is better will be observed more safety and less damages. The highest obtained spatial Resolution between the Brillouin scattering sensors is corresponds to Brillouin dynamic grating sensors which is less than 1 cm. In this sensors, grating was generated by two pump waves and was read by probe wave that senses the intruder. This spatial Resolution is achieved using phase correlation technique. In this technique pump waves are modulated by a phase modulator PRBS that generated a periodic and random phase shift equal to 0 or π . Of course, the heavy signal processing and complicated mathematical equations are disadvantages of this technique. In this article, we simulate phase correlation technique using a straightforward matrix relations and calculated first, spatial Resolution and then sensing length. finally, with this innovative technique is obtained 9 mm spatial Resolution over a 295 m of sensing length and 14 mm spatial Resolution over a 460 m of sensing length that is full matching with the results of phase correlation tecnique without having its complexities.

Today, vision metrology is one of the known precise and automatic 3D measurements methods in industry. In vision metrology systems, coded Targets are utilized to automatically derive the initial value of unknowns. Therefore, automatic coded Target recognition is the precondition for automation of vision metrology systems. Nowadays, it is also used in other applications such as automatic registeration of multi station laser scanner data, automatic calibration of motile mapping systems, and automatic generation of panoramic images by linear array sensors.In this research project, different methods of coded Target design are reviewed first. Then a special design of coded Target called, spatial orthogonal coding, is presented. The proposed code design is highly stable to code recognition, geometric (scale, rotation and projectivity) and radiometric (focus and contrast) distortions, and has small size, low complexity and high number of possible coded Targets (256). After code designing, we propose a deterministic method for automatic coded Target recognition in two steps: recognition of Target location in the image and code extraction. Target location is recognized based on white Target frame and Target code is determined by sampling from coding cells after local projective distortion correction.The final section of the research is accuracy and robustness evaluation of the proposed coded Target design and recognition method.The experiments on more than 180 images of 12 different retro-reflective coded Targets showed that the proposed method is generally valid and has high robustness to geometric and radiometric distortions. The method was also checked by hidden areas and it was seen that it is highly sensitive to fully detectable Target frame. Therefore, we suggest to study on a new coded Target design and recognition method that is able to handle the free-frame coded Targets.

Purpose: PIK3CA gene mutations have clinical importance and their presence is associated with therapy response. They are also considered as a molecule for Targeted therapy. As regards to their importance, genetic variation within a population as well as among different populations, this study was conducted to detect common mutations of exons 9 and 20 and other probable mutations in PIK3CA gene as well as their frequencies in Iranian bladder cancer patients. Materials and methods: Paired tumor and adjacent normal tissues samples were obtained from 50 bladder cancer patients. Mutations of PIK3CA gene were detected using High Resolution Melting (HRM) analysis which is a highly sensitive, repeatable, rapid, and cost-effective technique. To determine the precision of the HRM analysis, Sanger sequencing analysis was used. Results: The result showed that mutations were present in 10% (5/50) of the subjects. The majority of these cases (4/5) had the mutation(s) in exon 9, spanning over five different mutations, among which three of them were actually novel mutations. Further analysis showed that 2 cases had simultaneous mutations for exon 9. In addition to novel mutations, the PIK3CA mutation rate observed in Iranian bladder patients was not as frequent as previous reports and COSMIC. Conclusion: HRM can be used as a rapid and sensitive method for mutation screening. Dysregulation of PIK3CA gene in bladder cancer reveals its potentials as a mechanistic link for cancer development, which in turn suggests its special use in interventional studies for Targeted therapy.