SIGNAL AND DATA PROCESSING
Year:2017 | Volume:13 | Issue:4 (SERIAL 30) |Papers Count:10

SAR imaging systems are as a complement to passive remote sensing but the process of image formation in this systems is so complex So that the final image in the system is formed after the three basic steps: raw data acquisition, forming the signal space and image space. In addition, various factors within the system and outside the system are involved in the information that recorded by SAR, such as radar, platform, processing algorithm, imaging region and channel that each of them have many sub-parameters and this adds the complexity of the behavior of SAR. So due to the complexity, providing the model that describes how the SAR imaging system is highly important. In this paper, at first, the performance of the SAR image formation in spotlight mode placed on analytical modeling and after that the model comes in a soft implement. The implement includes three basic steps of image formation. So that raw data acquisition is done in CST and the signal and image formation are done in MATLAB software. This implementation provides a lot of abilities. So you can simulate the effect of the affect parameters in SAR images and better interpretation of themes. Also, the validity of the proposed solutions in electronic warfare or passive defense for SAR imaging systems can be studied by it.

Different methods have been proposed to increase the image spatial resolution by mixed pixels decomposition. These methods and their problems will be examined in this study. Common methods are reviewed with more emphasis. And finally, a new method for increasing the spatial resolution will be proposed to resolve some of the weaknesses of existing methods. This method uses a proper lookup table, which is derived from input image. By definition a similarity metric function, we obtain a similar pixel for every input pixel. These similar pixels have equal subpixel structures hence an input pixel will be decomposed to a proper set of subpixels. In the high quality images, these subpixels are usually belong to pure classes. Simulation results on artificial and real data show a good subpixel decomposition performance of proposed method relative to those of other comparable methods.

About 1% of the people in the world suffer from epilepsy. The first stage of the treatment of epilepsy is the accurate diagnosis. One way to diagnose epilepsy is the precise analysis of EEG signals. Therefore various techniques have been proposed in the literature for the detection of epileptic seizures in EEG signals. In this article, we have distinguished and separated the epileptic EEG signals from the normal EEGs, by using a new intelligent and heuristic algorithm, called Inclined Planes system Optimization. We used wavelet transform for extracting features, because of nonlinear and non-stationary properties of the EEG signals. Then we diagnosed epilepsy, by using extracted features from wavelet transform and implement them on IPO algorithm. According to this study, we found out the IPO has got a high ability to diagnosis epilepsy in complicated and non-stationary EEG signals.

Files are the most important sources of information presentation in various formats such as texts, audio, video, images, web pages, etc. … ; (in-depth) analysis of files for the purpose of recognition and investigating their unique properties (or characteristics) is one of the most significant issues in the field of personal security safety, information security, file-type identification, codes structuration analysis etc… . Statistical analytic methodology of working on the binary files contents based on the n-gram model has been opted for in the present paper in order to full investigate all the different aspects of a file’ s range of characteristics. Moreover, to reduce down the calculations volume and the n-gram model peculiar to the needed amount of memory, use has been made of word clustering. Later on analysis has been conducted on both files’ contents in two states of “ blocking” and “ full” : it is to be noted that in the “ full” case such characteristics as Chi-square, Auto-correlation, Weighted term frequency-Inverse document frequency (TF-IDF), Fractal dimension etc … have been brought under comprehensive study; while in the “ blocking” case, other properties like the entropy rate, the distance, etc … have been delved into. The gained results indicate that the extracted characteristics in the first method could well easily reflect the unique properties belonging to jpg, mp3, swf and html files; and in the second method, are able to clearly well reflect doc, html and pdf files properties

In recent years, estimation of protein-coding regions in numerical deoxyribonucleic acid (DNA) sequences using signal processing tools has been a challenging issue in bioinformatics, owing to their 3-base periodicity. Many digital signal processing (DSP) tools have been applied in order to identification task and concentrated on assigning numerical values to the symbolic DNA sequence and then applying spectral analysis tools such as the discrete Fourier transform (DFT) to locate the periodicity components. Despite of many advantages of Fourier transform in detection of exonic regions, this approach has some restrictions, such as high computational complexity and disability in locating the small length coding regions. In this paper, we improve the performance of the conventional DFT in estimating the protein coding regions utilizing a Gaussian window with variable length. First, the DNA strands are converted to numerical signals via the 3-D Z-curve method. Z curve is a robust, independent, less redundant approach, and has clear biological interpretation which can be regarded as a useful visualization technique for DNA analysis of any length. In the second stage, non-coding regions besides the background noise components are completely suppressed using the Gaussian variable length window. Also, we use a narrow-band band-pass filter in order to extract the period-3 components with central frequency. Performance of the proposed algorithm is tested on F56F11. 4 from C. elegans chromosome III, and also two eukaryotic datasets, HMR195 and BG570, and is compared with other state-of-the-art methods based on the nucleotide evaluation metrics such as sensitivity, specificity, approximation correlation, and precision. Results revealed that, the area under the receiver operating characteristic (ROC) curve is improved from 4% to 40%, in HMR195 and BG570 datasets compared to other methods. Furthermore, the proposed algorithm reduces the number of incorrect nucleotides which are estimated as coding regions.

The procedure of mapping points from one image to corresponding points in another image is called Image Registration. It is a spatial transform. By the nature of the image transformation, image registration can be classified into rigid image registration and non-rigid image registration. One approaches of non-rigid image registration is solving the registration problem as an optimization problem. An example of these methods is the graph-cuts based registration. The basic technique is to construct a specialized graph for the energy function to be minimized such that the minimum cut on the graph also minimizes the energy. Given that the time is one of the critical factors in medical applications, it seems that improvement of this method in terms of run time will be helpful for its clinical and medical applications. In order to achieve this goal, in this research we proposed a method that significantly reduces the run time of registration process. The implementation results of our proposed method on the images with artificial deformations shows that the proposed algorithm is about three times faster than the main algorithm, while the average amount of SAD criterion will be increased from 0.7 to 1 .

In this article a pre-processing method is introduced which is applicable in speech recognized texts retrieval task. We have a text corpus that generated from a speech recognition system and a query as inputs, want to search queries in these documents and find relevant documents. The main problem is that the typical speech recognized texts suffer from some percentage of recognition error. This problem causes terms to have erroneously assign to irrelevant documents. The idea of our proposed method, is to detect error-prone terms and to find similar words for each term. A parameter is defined which calculate the probability for occurring error in the error-prone words. To recognize similar words for each specific term, based on a criterian which is called average detection rate (ADR) and levenshtein distance criterion, some candidates are chosen as the initial similar words set. Then, a conversion probability is defined based on the conversion rate (CR) and the noisy channel model (NCM) and the words with higher probability based on a threshold level are selected as the final similar words. In the retrieval process, these words are considered in the search step in addition to the base word. Implementation result shows a significant improvement up to 30% in F-measure in information retrieval method with consideration this pre-processing.

Mobile Adhoc Networks (MANET) are vulnerable to both active and passive attacks. In Wormhole attacks, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer. If source chooses this fake route, malicious nodes have the option of sniff, modify, selectively forward packets or drop them. Existing solution to defend wormhole attacks, such as SECTOR, Packet Leashes, DelPHI, directional antenna. These solutions require special hardware or strict synchronized clocks or cause message overhead, or generate false-positive alarms. A novel approach MAODV: Modified AODV is proposed to defend wormhole attacks, launched in AODV. The proposed approach is based on weight per hop. Each node in network has its own weight, given by administration due to trusty, power capability. Sum of weight will not be exceeded from 100. Whenever a source node want to send a traffic to destination, put its minimum weight in RREQ packet to constitute the route. The destination node is selected the route, that its weight is close to destination announcement weight. Since no special hardware and no encryption technique are used, it will have less overhead and delay compare to other technique. The effectiveness of the propose mechanism is evaluated using ns2 network simulator.

In this paper, the role of Part-of-Speech (POS) tagging for parsing in automatic processing of the Persian language will be studied. To this end, the impact of the quality of POS tagging as well as the impact of the quantity of information available in the POS tags on parsing are studied. To reach the goals, three parsing scenarios are proposed and compared. In this study, various evaluation metrics are used to show the impacts from different points of views. The experimental results show that the quality of the POS tagger and the quantity of the information available in the POS tags have a direct effect on parsing performance. The high quality of the POS tags causes error reduction in parsing and also it increases parsing performance. Moreover, lack of morpho-syntactic information in the POS tags has a high negative impact on parsing performance. This impact is more pronounced than the impact of POS tagger performance.

Artificial immune system (AIS) is one of the most meta-heuristic algorithms to solve complex problems. With a large number of data, creating a rapid decision and stable results are the most challenging tasks due to the rapid variation in real world. Clustering technique is a possible solution for overcoming these problems. The goal of clustering analysis is to group similar objects. AIS algorithm can be used in data clustering analysis. Although AIS is able to good display configure of the search space, but determination of clusters of data set directly using the AIS output will be very difficult and costly. Accordingly, in this paper a two-step algorithm is proposed based on AIS algorithm and hierarchical clustering technique. High execution speed and no need to specify the number of clusters are the benefits of the hierarchical clustering technique. But this technique is sensitive to outlier data. So, in the first stage of introduced algorithm using the proposed AIS algorithm, search space was investigated and the configuration space and therefore outlier data are determined. Then in second phase, using hierarchical clustering technique, clusters and their number are determined. Consequently, the first stage of proposed algorithm eliminates the disadvantages of the hierarchical clustering technique, and AIS problems will be resolved in the second stage of the proposed algorithm. In this paper, the proposed algorithm is evaluated and assessed through two metrics that were identified as (i) execution time (ii) Sum of Squared Error (SSE): the average total distance between the center of a cluster with cluster members used to measure the goodness of a clustering structure. Finally, the proposed algorithm has been implemented on a real sample data composed of the earthquake in Iran and has been compared with the similar algorithm titled Improved Ant System-based Clustering algorithm (IASC). IASC is based on Ant Colony System (ACS) as the meta-heuristics clustering algorithm. It is a fast algorithm and is suitable for dynamic environments. Table 1 shows the results of evaluation. The results showed that the proposed algorithm is able to cover the drawbacks in AIS and hierarchical clustering techniques and the other hand has high precision and acceptable run speed.