Computers and Software

ISSN 1828-6003 Vol. 7 N. 6 November 2012

International Review on

Computers and Software (IRECOS)

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Contents:

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Study on Performance Evaluation of Crossbar Switch Interconnection Based Probability Distribution by Jianfeng An, Xiaoya Fan, Xiangdong He

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Radio-Frequency Identification Adoption, from Innovation to the Challenging Reality, Jordan Readiness by Rizik M. H. Al-Sayyed

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Implementation of Distance and Speed Measurement Algorithms for the Development of an Automatic Traffic Regulation System by Belal Alshaqaqi, Meriem Boumehed, Abdelaziz Ouamri, Mokthar Keche

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Fusion of Biometric Modalities Using Multi-Normalization and Genetic Algorithm by S. M. Anzar, P. S. Sathidevi

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Invariant and Reduced Features for Fingerprint Characterization by Ala Balti, Mounir Sayadi, Farhat Fnaiech

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Bacterial Foraging Optimization Algorithm Based Routing Strategy for Wireless Sensor Networks by Zhi Chen, Shuai Li, Wenjing Yue, Luoquan Hu, Wanxin Sun

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Image Based DLP Security for Risk Professionals - A High Impact Strategy by N. Deepa, S. Priyadarsini, R. Sathiyaseelan, M. Varun Kumar

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Distributed Fuzzy Optimal Spectrum Sensing in Cognitive Radio by Dilip S. Aldar

Based on Cloud Computing Inventory and Distribution Management Platforms by Guang Dong, Luping Jiang, Chunmei Liu

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Research on Congestion Control Fairness Based on WSN by Dingding Zhou, Songling Chen, Shi Dong

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A Novel Online EEG-Based Epileptic Seizure Onset Detection Algorithm Based on GTDA Features and KNN Classifier by Ehsan Azizi, Hossein Abedikia, Javad Haddadnia, Khosro Rezaee, Mohammad Rasegh Ghezelbash

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Design and Implementation of Full Integration of a Large Network by Qiang Fan, Jianhua Zhou, Min Tan

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Real Time Implementation of Medical Images Segmentation Using Xilinx System Generator by Fayçal Hamdaoui, Anis Ladgham, Anis Sakly, Abdellatif Mtibaa

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Ismail Khalil

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Marios Angelides

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Lipo Wang

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International Review on Computers and Software (I.RE.CO.S.), Vol. 7, N. 6 ISSN 1828-6003 November 2012

Implementation of Distance and Speed Measurement Algorithms for the Development of an Automatic Traffic Regulation System Belal Alshaqaqi, Meriem Boumehed, Abdelaziz Ouamri, Mokthar Keche Abstract – Technology has increasingly been used to improve road safety. In current automotive

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research, companies mainly focus on two methodologies in collision detection using active sensors and passive sensors. This paper presents a vision-based vehicle distance and speed measuring system. The purpose is to increase the road safety if the driving speed is too high. Our system is used monocular (one camera) technique and binocular (two cameras) technique. With the help of moving object detection stage, the region of interest (ROI) in the acquired images is identified. The ROI is then examined by rule-based algorithms that compute the distance and the speed between the cars and our system. Finally, we show the real time implementation of the monocular approach in Texas Instruments TMS320 DM6437 EVM platform. Experiments have shown the estimated speeds within 10% of actual speeds for the both vision techniques. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

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Keywords: Moving Objects Detection, Distance Measurement, Speed Measurement, DSP, TMS320 DM6437 EVM

task in the proposed system, whose block diagram is shown in Fig. 1. A common approach to identifying the moving objects is background subtraction (BS) [2], [3], [4] where each video frame is compared to a reference or background frame.

Nomenclature Background Subtraction Region of Interest Zero mean Normalized Cross Correlation

Introduction

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BS ROI ZNCC

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This paper presents a vision-based vehicle speed measuring system [1]. To achieve this aim, our system uses monocular and binocular techniques. With the help of a moving object detection stage, the region of interest (ROI) in the acquired images is identified. This region is then processed by using rule-based algorithms that compute the speed of the driver’s car as well as the distance that separates him from this system. Finally, we show the real time implementation of the mono-vision approach on Texas Instruments TMS320 DM6437 EVM platform. This paper is organized as follows. Background subtraction approach to detect moving objects [2], [3] is detailed in section 2. Section 3 presents monocular and binocular techniques for measure the distance between vehicle of interest and the preceding vehicle. The algorithm for speed measurement is presented in Section 4. In Section 5, we describe the real time implementation of the monocular method is described. Experimental results are presented in Section 6. Finally, conclusions are drawn in Section 7.

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Moving Object Detection

Fig. 1. Block diagram of our proposed system

Arguably the simplest BS technique [1], frame differencing, uses the video frame at time t-1 as the background frame for the frame at time t. The output pixel values are given by: D ( x, y ) = I t ( x, y ) − I t-1 ( x, y )

(1)

The thresholding process consists in separating the pixels corresponding to the moving object from the

Moving objects detection in a video scene is a crucial Manuscript received and revised October 2012, accepted November 2012

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B. Alshaqaqi, M. Boumehed, A. Ouamri, M. Keche

background. The result of this operation is a binary map, where only those pixels are set to ‘1’, as described by the following equation: M ( x, y ) = 1 if D ( x, y,t ) > threshold M ( x, y ) = 0 if D ( x, y,t ) ≤ threshold

D (TC ,TR ) =

TR ×f TC

(4)

(2)

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The threshold is defined empirically according to the video processed.

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Fig. 3. The distance measurement algorithm by vision field method

Fig. 2. Moving object detection by Background Subtraction technique

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III. Distance Measurement

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In this section we use the result of the previous section to determine the distance that separates the vehicle of interest to this system, by using two vision-based techniques. Subsection 3.1 describes the monocular technique called vision field approach, and subsection 3.2 presents the binocular or stereovision approach III.1. Vision Field Approach

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The vision field method uses a single camera, it is based on the detection stage, then on the tracking stage to determine the preceding vehicle distance as illustrated at Fig. 3. The vision field gives the scene’s volume that can be observed by the camera [5]. The computation is based on the sensor dimensions (horizontal and vertical), the focal length of the lens and the viewing angles (see Fig. 4). Assuming that: - the sensor dimension is known, thus the size of the projected object TC is also known; - the camera lens is known, so the focal length f is also known; - the object is known, then its size TR is known. the distance D (TC, TR) between the camera and the object can be evaluated by a simple triangulation method as follows:

Fig. 4. The vision field computation

III.2. Stereovision Approach A problem that arises in stereo vision algorithms is the problem of matching [3], [6], and [7]. A three dimensional localization of an object is based on determining corresponding reference points of that object in both images. Afterwards the coordinates of the object can be determined by epipolar geometry [8]. In this section, we describe our stereoscopic algorithm to measure the moving object distance. This algorithm consists of several blocks as shown in Fig. 8. III.2.1. Restriction of the Region of Interest Typically, the stereovision process is executed for the entire image. However, we can restrict the computation area to a narrow band around the detected object. This reduces considerbly the computation time without affecting the results. III.2.2. Contours Extraction by Sobel Filter

T T tan ( q ) = C = R f D

(3)


Far a farther reduction of the calculation time, we chose to work only on the object edges which can be International Review on Computers and Software, Vol. 7, N. 6

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determined, by applying the Sobel filter in the restricted region of interest. III.2.3. Corners Detection by Harris Detector

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To track the detected object in the frame following the first one, we only track the lower left corner of the object, determined by Harris detector [9] in the first frame.

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Fig. 5. Restriction of the region of interest

Fig. 8. The distance measurement algorithm by stereovision method

III.2.5. Distance Measurement by Stereovision

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This subsection describes the disparity technique that we use to determine the distance D between the observed object and the stereovision system. In Figure 9 illustrates the point M positions in the right and the left cameras coordinates systems are given by (Xr, Yr, Zr) and (Xl, Yl, Zl), respectively. The coordinates of the projection of point M in the right and the left images planes are denoted by (xr, yr) and (xl, yl), respectively.

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Fig. 6. Contours extraction by Sobel filter

Fig. 7. Corners detection by Harris detector

III.2.4. Tracking a Corner by Correlation The Zero mean Normalized Cross Correlation (ZNCC) method [6] is employed to compare parts of current frame to find the corresponding section in the following frame. The camera poses are known, allowing the search window to be restricted to a reasonable size and allowing a greater field of view for the cameras by maximizing the width of the valid region for disparity analysis.


Fig. 9. The basic calculating idea of the distance D by stereovision

The relationships between these coordinates are summarized in the following:

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xr = f

Xr x ⇒ X r = Zr r Zr f

(5)

xl = f

Xl x ⇒ X l = Zl l Zl f

(6)

2. Compute the time difference ∆t between frame (i) and frame (i +1). 3. Estimate the speed of vehicles by: D frame( i ) − D frame( i +1) Dd = S= Dt t frame( i ) − t frame( i +1)

In general, the two optical axes of the left and the right cameras are parallel, then if the two cameras are separated by the baseline B: Z r = Zl = D ⇒ X r = X l − B

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(9)

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then:

B⋅ f ; d = xl − xr d

Real Time Implementation

In this section, we describe the implementation on the TMS320DM6437 EVM platform of the monocular method, for computing the distance and the speed of a moving object. A prototype of the system was built using the DM6437 EVM board. This board comprises a DM6437 chip running @ 600 MHz, a level-one program cache L1P of 32KB and data cache L1D of 80KB ,128KB of internal SRAM which can also be configured as second-level unified program/data cache, video input and output ports and many other peripherals [10].

(7)

by substituting the Eqs. (5) and (6) into (7), we can write the relationship between the distance D and the baseline B as follows: xl D X D −B= r (8) f f

D=

(10)

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Fig. 11. Video Port to Video Port Communication TMS320DM64x

Fig. 10. Tracking corner by correlation: (left column) images (i), matching (right column) images (i+1)

IV.

The Speed Estimation of a Moving Object

Speed is defined as the measurement of how far an object moves per unit of time. It is distance over time. Therefore, the speed estimation technique presented here utilizes the following algorithm: 1. Estimate the distance difference ∆d of the object between frame (i) and frame (i +1), using methods of distance measurement described previously. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved

The code was organized and written to allow efficient cache utilization. Hence, different code snippets that use the same memory areas were gathered together as long as the algorithm consistency remained intact. Video input/output raises many performance issues related to transferring the video data in real-time. In far a good code optimization, the port video of dm64x+ was used after running the distance and speed functions the algorithm needs some special functions such as the timer function to calculate the number of frames, and the write function to display the results in video output, as show in Fig. 11. In this project the system runs at twenty six (720x480) frames per second. This number of frame per second is for real-time implementation. Field experiments conducted using the system with the platform DM6437 EVM and a standard video camera in different real situations gave accurate results. The results of one such experiment are shown in Fig. 13.


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(a) Real speed = 40 ± 2 Km/h

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Fig. 12. The block diagram of the EVM platform TMS320DM6437

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Figs. 14. Graph of the speed depending on the distance, (a): calculating by vision field method, (b): calculating by stereovision technique with B = 10 cm, focal length = 948.40580 (pixels) = 7.1130 mm

Fig. 13. Result visual by TMS320 DM6437EVM

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(b) Real speed = 50 ± 2 Km/h

Experimental Results

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This section presents the results of the distance measurement and the speed estimation of a moving object by vision field and stereovision techniques. In our experiments, we utilize miniatures cameras Sony 1/3" (U2S-C911), and Easy TV MPEG acquisition card with RCA I/O. These cameras have [3]-[8] mm focal length, 0.8 Lux with horizontal resolution of 420 TVL. In the stereovision case, the two cameras are separated from each other by a baseline B of 10 cm. The results obtained are presented in Figs. 14. They represent the speed of the vehicle estimated at different distance from the camera. The results of Fig. 14(a) were obtained by using the vision field method, while those of Fig. 14(b) were obtained, by using the stereovision technique with B = 10 cm, focal length = 948.40580 (pixels) = 7.1130 mm


From the graphs that are given by Figs. 14(a) and (b), we can say that the speed estimation is more accurate for the short distances. This may be explained by the fact that the difference between two pixels near the image center gives a great distance in the real scene and vice versa. Therefore, the error of the speed calculation is caused by the erroneous results of measurement for the long distances.

VII.

Conclusion

Real word video sequences have been used to test and validate the proposed approaches for distance and speed calculation of a vehicle using a vision based system. For the detection part, we obtained very satisfactory results, by using the background subtraction method. For the measurement of the distance and the speed of a moving object, we have implemented a monocular technique and a binocular technique. The monocular approach uses the calculation of the vision field which requires prior knowledge of the real size of the object and the estimation of its size in the image. For the measurement


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by stereovision, we implemented a stereo-correlation technique combining stereovision and matching point correlation. For the real time application, we implemented the vision field method on TMS320DM6437 EVM platform. In the presence of a moving vehicle at speeds ranging from 20 to 100 km / h and for distances in the range [0-50] m, the preliminary results showed that the accuracy is about 10%.

Meriem Boumehed was born in 1982 (Algeria), completed her graduate and post-graduate studies at the University of Sciences and Technology, Mohammed Boudiaf (Oran, Algeria) where she successfully received the engineer (2004) and magister diplomas(2007). Her research interests include computer vision ,motion analysis in monocular and stereoscopic image sequences (detection, estimation, and segmentation) . Abdelaziz Ouamri was born in Algeria. He received the Engineer diploma degree in Electrical Engineering from ENSI(CAEN), the DEA degree in automatic and signal processing from the University Paris XI in 1979 the Doctor Engineer in 1981 and the Doctorat d’Etat degree in signal processing from the University Paris XI in 1986, France. He is currently a Professor at the University of Sciences and Technology of Oran, Algeria His research interests are focused on high resolution spectral array processing methods, detection and tracking moving objects. In 1990 he received the senor award from the IEEE Signal Processing Society in the Spectrum Estimation Technical area.

References

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Mokhtar Keche received the Engineering degree in Telecommunications from the Ecole Nationale Superieure des Telecommunications (ENST), Paris, France in 1978, and the Doctor of Engineering degree and PhD from the University of Rennes in France and the University of Nottingham in UK in 1982 and 1998, respectively. He is an associate professor at the Electronic Department of University of Sciences and Technology, Mohamed Boudiaf, (USTO, MB) Oran (Algeria). His research interests are in the area of Digital Communications, Array Processing, and Multitarget tracking.

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J.Morat, “vision stéréoscopique par ordinateur pour la détection et le suivi de cibles pour une application automobile”. Thèse de doctorat de l’institut national polytechnique de Grenoble, 2008. [2] S.Ching S. Cheung and C.Kamath, “Robust techniques for background subtraction in urban traffic video”. Center for Applied Scientific Computing Lawrence Livermore Laboratory,2004. [3] S.Se and P.Jasiobedzki, “Stereo-Vision Based 3D Modeling and Localization for Unmanned Vehicles”. International Journal of Intelligent Control and Systems, vol. 13, no. 1, march 2008, 4758. [4] D. Gutchessy, M. Trajkovicz, E. Cohen-Solalz, D. Lyonsz, A. K. Jainy, “A Background Model Initialization Algorithm for Video Surveillance”.Eighth IEEE International Conference on Computer Vision ICCV 2001. 0-7695-1143-0/01,2001 . [5] M.Zayed, “Véhicules Intelligents : Etude et développement d’un capteur intelligent de vision pour l’attelage virtuel”, Thèse de doctorat à l’Université des Sciences et Technologies de Lille, 2005. [6] V.Lemonde, “Stéréovision embarquée sur véhicule : de l’autocalibrage à la détection d’obstacles”, Thèse de doctorat de l’institut national des sciences appliquées de Toulouse,2005. [7] S.Gupte, O.Masoud, F.K.Martin, P.Papanikolopoulos , “Detection and Classification of Vehicles”, IEEE transactions intelligent transportation systems,vol.3,no.1,march 2002. [8] R. Hartley and A. Zisserman, Multiple View Geometry in computer vision. Cambridge University Press, 2003. [9] C. Harris and M.J. Stephens. A combined corner and edge detector. The Fourth Alvey Vision Conference (1988), pp. 147151 Key: citeulike:665979,pages 147–152, 1988. [10] Texas Instruments, TMS320DM64x Video Port to Video Port Communication, November 2006 ,www.ti.com/lit/an/spraaf3/spraaf3.pdf.

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Authors’ information

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University of Science and Technology of Oran Mohamed Boudiaf ;Faculty of Electrical Engineering, Department of Electronics ;Signal and Images Laboratory ;Oran, Algeria. E-mail: [email protected] Belal Alshaqaqi was born in 1981 (Rafah, Palestinian), completed his graduate and postgraduate studies at the University of Sciences and Technology, Mohammed Boudiaf (Oran, Algeria) where he successfully received the engineer (2007) and magister diplomas(2009). His research interests include computer vision ,motion analysis in monocular and stereoscopic image sequences (detection, estimation, segmentation, and tracking) with a focus on real time implementation using digital signal processor (DSP).



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International Review on Computers and Software (IRECOS) (continued from outside front cover) 2868

Model of SNOD Algorithm of Spatial Outlier Data Mining and Analysis Based on Entropy by He Li

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A Speckle Suppression Method for SAR Image Based on BEMD by Changjun Huang, Jiming Guo, Xiaodong Yu, Changzheng Yuan

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Group Mobility Judge Based on Node’s Neighbors Matrix in Clustering MANET by Hualong Jiang, Shuai Zhang, Guangtao Shao

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Automatic Jet Area Detection During Mitral Regurgitation by Kalpana Saini, M. L. Dewal, Manojkumar Rohit

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Application of Evolutionary Algorithm in Managing the Trade-Off between Complexity of Software and its Deliverables by Siddharth Lavania, Manuj Darbari, Neelu Jyoti Ahuja, Praveen Kumar Shukla

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A Graph Based Heuristic Towards Test Suite Reduction by Preethi Harris, Nedunchezhian Raju

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Research and Implementation of the Key Technology of Network Video Retrieval and Content Extraction by Min Li

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Formal Concept Analysis in Hybrid Relational Databases by Yuxia Lei, Yuefei Sui, Baoxiang Cao

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Moving Objects Detection Based on Gaussian Mixture Model, LBP Texture and Saliency Map by Lili Lin, Changlong Ju, Wenhui Zhou, Xiuping Wang

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Analysis on the Cooperative Fault Diagnosis Method Based on the Immune Evolutionary Strategy by Wei Li, Qiang Xu, Kai Xu

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A Method of Image Completion for Moving a Large Object by Lingxia Liu, Qiang Song

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A Kind of New Algorithm to Solve the Multi-Objective Traveling Salesman by Shuiqiang Liu, Jianhua Zhou, Juncheng Lei

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Approximation Algorithm of RNA Folding Including Pseudoknots by Zhendong Liu

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Survey of Image Fusion Algorithms by Xiaoqing Luo, Xiaojun Wu

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A Novel Detection Model for Network Attack Inspired by Immunology by Run Chen, Jiliu Zhou, Caiming Liu, Yan Zhang

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Abstracting and Indexing Information: Cambridge Scientific Abstracts (CSA/CIG) Academic Search Complete (EBSCO Information Services) COMPENDEX - Elsevier Bibliographic Database Index Copernicus (Journal Master List): Impact Factor 6.14 Autorizzazione del Tribunale di Napoli n. 59 del 30/06/2006

(continued from inside back cover) Using Sharepoint for the Control of an Economical Process by Marior Dan Adrian

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Hybrid Method for Information Retrieval Based on the Similarity between Queries by Hicham Moutachaouik, Brahim Ouhbi, Hicham Behja, Bouchra Frikh, Abdelaziz Marzak, Hassan Douzi

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Routing Scheme for Multiple Sources in Virtual Private Networks by C. Mahalakshmi, M. Ramaswamy

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1828-6003(201211)7:6;1-L Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved