Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Moving Objects Tracking in Video by Graph Cuts and Parameter Motion Model"

Transcription

1 International Journal of Comuter Alications ( ) Moving Objects Tracking in Video by Grah Cuts and Parameter Motion Model Khalid Housni, Driss Mammass IRF SIC laboratory, Faculty of sciences Agadir -Morocco Youssef Chahir GREYC-UMR CNRS 607, University of Caen Caen France ABSTRACT The tracking of moving objects in a video sequence is an imortant task in different domains such as video comression, video surveillance and object recognition. In this aer, we roose an aroach for integrated tracking and segmentation of moving objects from image sequences where the camera is in movement. This aroach is based on the calculation of minimal cost of a cut in a grah Grah Cuts and the D arametric motion models estimated between successive images. The algorithm takes advantage of smooth otical flow which is modeled by affine motion and grah cuts in order to reach maximum recision and overcome inherent roblems of conventional otical flow algorithms. Our method is simle to imlement and effective. Exerimental results show the good erformance and robustness of the roosed aroach. General Terms Tracking, Video Analysis, Comuter Grahic. Keywords Grah Cuts, Motion Models, Tracking, Video Analysis, Overlaing Objects. 1. INTRODUCTION Tracking of moving objects is one of the major areas of research, several aroaches have been develoed. We can classify these aroaches according to the rincile used in two great classes: Model-based aroach, these aroaches use the result obtained in the revious frame as a rediction rocess initialization in the current frame [1,, 3, 4, 5, 6]. Motion-based aroach (also called intensity based) [7, 8, 9, 10], the rediction of the tracked object is done by the motion model estimated between the revious and current frame. Whatever the method used, model-based or motion-based, we can classify these aroaches according to the object shae reresentations commonly emloyed for tracking into three categories (the reader is referred to [11] for details): Point: The object is reresented by a oint, that is, the centroid [1] or by a set of oints [13, 1]. Primitive geometric shaes: Object shae is reresented by a rectangle, ellise, etc [14]. Object silhouette and contour: the object is reresented by a contour [5] or by silhouette (region inside the contour) [6, 4]. The methods of the last category are often based on dynamic segmentation technique. These methods are used for tracking comlex non-rigid shaes and when we want to extract the silhouette of the object at any time, without rior knowledge of its new shae. To summarize, a robust aroach to tracking moving objects must solve three major classes of roblems. Problem of large dislacements: the methods based on the modification of contour (like [5]) or shae (like [4]) redicted in the revious ste cannot suort the great movement of objects. The simlest way to solve this roblem is to use the motion model of the object to track in the ste of redicting. Problem of toology change: Several aroaches in the literature namely [1,, 3] cannot handle toology change due to contour or silhouette used in the initialization or due to dominant motion model used that doesn t always cover all ixels of the object to track. This is the case for comlex non-rigid shaes (Namely the movement of the feet). Problem of overlaing objects: the classification of ixels of overlaing objects is the most difficult roblem and esecially when we want to extract the shae of objects at any time. In this aer, to manage the objects dislacement, we used a rediction based on motion model comuted between the current and revious frame. That will take into account the large dislacement. And we have used a dynamic segmentation ste based on otimal boundary diction by grah cuts to solve the roblem of toological change and the movement with a comlex model (such as when arts of an object have different models). This stage of refinement by grah cuts cannot classify the ixels of objects that are overlaed. We overcome this limitation through the use of a similarity measure based on the observed intensities. This aer is organized as follows: In the next section we resent the basic definition and terminology of grah cuts and how they are used for image segmentation. In sections 3 and 4, we introduce the algorithm of object extraction under hard constraints and we exlain how to use a grah cuts algorithm in order to refine a border of redicted objects, to obtain rich objects descrition and to overcome inherent roblems of conventional otical flow algorithms, and we give a solution for case of overlaing objects. In section 5, we give the results obtained. Lastly, we give a conclusion and future works.. GRAPH CUTS METHODS IN VISION Grah cuts have been used to efficiently solve a wide variety of vision roblems like stereo [15] image segmentation [16, 17], image restoration [15, 18], texture synthesis [19] and many others that can be formulated in terms of energy minimization. This energy is reresented in this form: 0

2 International Journal of Comuter Alications ( ) E( L) E P data D E ( L smooth ) q 1.. n N V q ( L, L ) Where E is a iecewise smoothness term, smooth E is a data data term, P is the set of ixels in the image, V L, L ) is a q q ( q smoothness enalty function to imose a satial consistency, D(l P ) is a data enalty function and (L ) =1... n is a vector of variables in finite sace L L { s, t} (L for label). Every ixel is associated with a variable L. N designates the set of the neighbors of the ixel. The main idea of a grah cuts is to bring back the roblem of minimization of energy to a roblem of a minimal cut in a grah. Greig and al. have shown that the minimization (such as estimating the maximum a osterior Markov random field) can be achieved by the minimum cut of a grah with two secific nodes "source" and "sink" for the restoration of binary images [18]. Later, the aroach has been extended to non-binary roblems known as "S-T Grah Cuts" [15].V.Kolmogorov [0] have given a necessary and sufficient condition, known as submodularity condition for a function to be minimized by grah cuts. In the case of energy (1), V q (L, L q ) is a term of vicinity, which makes it ossible to check the condition, and the construction of grah is done as follows: As shown in figure 1, each ixel P ij in the image corresonds to a node (vertices) v ij in the grah. Two additional nodes are the source and sink, resectively the object and the background. Each node is linked to its neighbors by edges n-link, with connectivity chosen (in our imlementation we used 8- connected lattice as our neighborhood structure), and whose caacities deend on differences in intensity, this allows to encourage the segmentation (cut) which asses through regions where the image gradient is strong enough. Each node is also connected by edges, t-link to the terminals (source and sinks). A cut C of grah G (V, E) is a artition of the vertices V into two sets V 1 and V, such that : V V 1 1 V V V source V 1 and sink V In grah theory, the cost of a cut is usually defined as the sum of the weights corresonding to the edges it serves as is formulated in (). (1) The value of E (x) is equal to same constant lus the cost of the ST minimum cut C. We say that E is exactly reresented by G if the constant = 0. To calculate the minimum cut, several algorithms have been develoed: increasing ath [1], Pushing-relabel [] and the new algorithm of Boykov - Kolmogrov [3]. In our alication, we used the algorithm of Boykov-Kolmogrov for its seed. Sink Source Fig 1: Construction of grah for a 3x3 image with the connectivity N8. Each ixel corresonds to a node, and all ixels are connected to the source and the sink. 3. OUR APPROACH The simle way for tracking an object in movement is to detect motion by the subtraction of each two successive frames, but this necessitates that the background is stationary (fixed camera). In this aer, we resent an aroach of tracking moving objects in a dynamic frame (camera in movement). In the first ste, we use the grah cuts to select the objects to be tracked (section 4-1). The second ste consists of calculating the motion model for each object (section 4 -), which corresonds to the selected objects (or redicted objects), between the current frame and the following one. These models of movement will be used to redict each object in the next frame. Thereafter, we will use the grah cuts to obtain recise contours (section 4-3). Figure gives the rincial stes of our algorithm. To classify the ixels of overlaing objects we used the multi may cuts algorithm (section 4-4). 4. ALGORITHM IMPLEMENTATIONS In this section, we discuss the imlementation details of the roosed algorithm deicted in figure.the algorithm consists of three major stes : Selection of objects to track by Grah cuts (ste of initializatio, Prediction ste by visual motion model, and ste of borders objects refinement. C S qt (, q) C w(, q) () 1

3 International Journal of Comuter Alications ( ) Selection of objects to track Prediction ste Refinement ste Estimation of motion model of each object between the frame i and frame i+1 Prediction of all objects in the frame i+1 Refining the objects border Automatic initialization of objects to track in the next frame Case of overlaing objects No Use of multi-way cuts to classify the ixels of the overlaing objects Number of objects refined == initial number of objects Yes Last frame of the video sequence? No Yes End Fig : General outline of our aroach. 4.1 Selection of Objects to Track To select the objects to track, we used the interactive segmentation method based on detection of the boundary by Grah Cuts [16]. The extraction is done by the exloitation of the rior information entered by the user (seed of the object and background) (see figure 4 -a). These ixels selected by the user are then connected to the terminals (source and sink) by the edges with infinite weight (4) (5). This will satisfy the constraints (3), while all other ixels (nodes) will have no links to the terminals (6) It is about using some toological constraints, known as hard constraints which can indicate that certain seeds of image a rior identified as a art of the Object or the Background All neighboring ixels are connected by edges, n-links, whose caacities deend on intensity differences I Iq (7). (see figure 3). O : L 1 B : L 0 The cost of the edges is given by: W (, S) if O W (, T) 0 W (, S) 0 if B W (, T) W(, S) W(, T) 0 if O B I I q W (, q) ex( * 1 ) dist(, q) : Standard deviation of the intensities of the seeds of object S: source, T: sink, O: seeds of object, B: seeds of background. Fig 3: A simle D otimal boundary for 3x3 image. Boundary conditions are given by object seeds O and background seeds B rovided by the user.

4 International Journal of Comuter Alications ( ) (a) (a) (b) (b) (c) Fig 4: (a) First frame with hard constraints, (b) Objects selected by grah cuts- to be tracked, (c) identification of selected objects by algorithm of detection of connected comonent. 4. Visual Motion Model The whole key of the moving objects tracking, using motion based aroach, resides in the choice of the motion model for each object because this one must reresent the movement of all ixels in the object. Our aim is to control the dislacement of the objects to track with a rior knowledge of the objects and the background in the revious frame. As we have already introduced, a motion model is used to aroximate the objects in the courant frame. In the literature, several models of motion have been resented like rigid, homograhy, quadratic, etc. Affine Model (8) is a reasonable comromise between the low comlexity and fairly good aroximation of the non-comlex motions of objects at about the same deth. A u( i ) c1 a1 a xi ( i ) * v( i ) c a3 a4 yi In other words, affine motion model is a arametric modeling to six arameters (c 1,c, a 1, a, a 3, a 4 ) of the movement defining the transformation T: Ω t Ω t+1 which ermits to ass the ixel x to the ixel x'. Several methods have been roosed to estimate a arameter motion model. The most famous method in this field could be using multi-resolution aroach [4]. This method allows calculating a arameter motion model in real times between the two inut frames. In our alication, we used this algorithm for its seed. An imlementation of this algorithm between two successive images can be found in MotionD software, develoed at Irisa/INRIA Rennes [5]. (c) Fig 5: (a) First frame with hard constraints, (b) Object selected by grah cuts -to be tracked, (c), (d) The object redicted in the frames, 3 of the rond-oint sequence using motion model. 4.3 Refinement of Border Objects (d) We can clearly see that in the figure 5, that a art of the road background at summer covered by the redicted object and a art of the car object is not covered. Figure 6 & 8 shows this. Fig 6: This figure illustrates that a art of the rode at summer covered by the redicted object and a art of the car is not covered. To obtain a rich object descrition with the aim of accomlishing advanced scene interretation tasks we used the grah cuts aroach described in section 3.1. therefore, as we have already introduced, grah cuts method for segmentation requires the user to mark a few ixels as object (seeds of object) and few others as background (seeds of background) and to generate automatically this information, we used the mathematical morhology (see figure7): Seeds of object: Four-time erosion of object detected using motion model (figure 7-b). Seeds of background: Seven-time dilatation of the object detected minus five-time dilatation of the object detected (figure 7-c). 3

5 International Journal of Comuter Alications ( ) (a) (c) (b) Fig 7: (a) object redicted using motion model (b), (c) hard constraint (seeds of object and background). The area between the blue (seeds of object) and the red line (seeds of background) is a band of incertitude around the borders. A grah is then built on this band to otimize the contours of the object. This otimizes the borders object with a recision of ± 9 ixels, thus the number of ixels an object aroaching the camera will increase and the reverse for the object away from camera. Fig 8: First image gives the result obtained using only motion model, second image give the result obtained after the refinement by grah cuts. 4.4 Case of Overlaing Objects Before calculating the motion model for each object, we use the algorithm of detection of a connected comonent to identify the selected objects (labeling). This algorithm will classify the image ixels according to their belonging to object_1, object_ object_n or to background. However, the image ixels classification is reliable when the objects to track are searated and consequently, multi-objects tracking may be ambiguous in some cases, like in the case of overlaing objects. We overcome this limitation through the introduction of a visual aearance constraint, where a classification according to the observed intensities is to be considered. To this end, we will use the multi-way cuts algorithm [15] to classify the ixels of overlaing objects. We define the cost functions for multi-way cuts as follows: (d) I objecti ( i1,... and if I' Ai ( ) objecti ( i1,... W ( Ai ( ), Li( i1,... ) 0 W ( Ai ( ), B) 0 I object i( i1,... and if I' Ai ( ) object i( i1,... W Ai ( ), L ) ex I I i ( i 1,... W ( Ai ( ), B) 0 if I' ( Ai ( ) W (, Li( i1,... n Background W (, B) ) ) 0 Where object i(i=1, is one of the objects to track, I is the image ixel in the frame I, ω Ai is affine motion model of object i, I ωai() is the match of ixel in the frame I using motion model, L i(i=1 is the terminal i (corresonds to the object i), B is the terminal corresonds to the background. (9), (10) and (11) are used to calculate the costs of the edges connecting vertices (ixels) with terminals. All neighboring vertices are connected by edges, n-links, whose caacities is calculated by (7). 5. EXPERIMENTATIONS AND RESULTS 5.1 Data The method has been tested on five sequences (taxit, rondoint, walk, croisement and Coastguarder). The first sequence has 41 frames of 56 x 191. The next sequence has 33 frames of 56 x 4. The third sequence has 17 frames of 300 x 00. The fourth sequence has 7 frames of 300 x 00.The last sequence has 80 frames of 300 x 40. In rond-oint, croisement and coastguarder sequence, the camera is in movement, and it is fixed in two other sequences. 5. Results and Comlexity The grah cuts aroach is often used to solve a labeling roblem. Knowing that, the comlexity to solve such a roblem deends on the number of ixels being matched. And in order to reduce a comlexity of grah cuts labeling, we only used the rectangle which contains the object to treat instead of using the entire image, this method allows us to reduce the size of the grah. By this reduction in data size, and by using the fast algorithm described in [3], the grah cut is made in a realtime. On figure 9, we can on the one hand see that the curve reresenting the number of ixels detected in the rediction ste is below the curve reresents the number of ixels of object refinement. On the other hand, the curve reresenting the number of ixels rejected by the refinement ste never touches the x-axis, which shows that the motion model (called dominant motio does not always cover all the ixels of the tracked object. Then the object redicted in the first ste, rediction ste, is only used as initialization to the next ste (refinement ste). 4

6 number of ixels numbre of ixels International Journal of Comuter Alications ( ) Figure 10 shows the change in the size of the object between the first and the last frame. This change in size may be due to rarochement or being away of the object from the camera or due to the change in the toology. We have overcome these roblems, the roblem of the change in the size and the roblem of the motion model that does not cover all the ixels of the object, by the stage of refinement. We have imlemented our aroach in C++. And for all tests that we did, we used x GHz Intel Core Duo with GB in memory. Table 1: means execution time er frame (note that for reducing the comlexity of grah cuts labeling, we only used the rectangle which contains the object to refine instead of using the entire image that reduces the size of grah). Comutation of Solve a grah Build a grah motion model cut s s 0.01 s In case of the objects tracking that are not overlaing, total time er frame is s. In this case, the tracking can be obtained in real time. In the other case, to this cost we must add the comutation time required to classify the ixels of overlaing objects (=number of objects * time to build and solve a grah cuts) for examle, for croisement sequence (two objects overla) means execution time er frame is s. At first sight, we note that we have a great difference between the results (figure 8); the tracking resulting from combining Motion-model with Grah cuts is visually more satisfactory than those obtained by using only a arameter motion model modeled by affine motion. Figure 11 and 1 gives some examles of the exerimental results. We can see in the last frame of croisement sequence (figure 1) the reaearance of the object (black car) while the rogram could not detect it. This is normal because the object has disaeared in the revious frame and in the algorithm which we roosed the first ste uses the object selected by the user or the object detected in the revious ste statistical results obtained from rond-oint sequence Figure 9: statistical results obtained shows the number of true ixels detected in the first ste (tracking using only motion model), number of ixels added and rejected by ste of refining the objects border number of ixels of refined object number of true ixels detected by the first ste number of ixels rejected by the ste of refining number of ixels added by the ste of refining frames object size (in number of ixels) in the first and last frames first frame rond-oint walk taxi laste frame Fig 10: This figure shows the change in the size of the tracked objects. This change is taken into account in the ste of refinement of objects. 6. CONCLUSION AND FUTURE WORK In this aer we resented how we can track moving objects in video using the grah cuts aroach and otical flow modeled by affine motion. We showed how to refine the border of redicted objects. The combined aroaches roduce dense and accurate otical flow fields. It rovides a owerful and closed reresentation of the local brightness structure. Globally, the evaluation showed that the roosed aroach gives good results according to our test. Further research will concentrate on imroving the algorithm by defining criteria for identifying what roerties characterize objects and distinguish them from other objects and from the background that will allow us to find the disaeared object. We will also try using more sohisticated initialization of seeds (automatic initializatio. Fig 11: Result obtained in frames 7, 13, 18 and from rond-oint sequence 5

7 International Journal of Comuter Alications ( ) Taxi sequence Walk sequence Coastguarder sequence Croisement sequence Fig 1: Exerimental results. We can see in the last frame of croisement sequence the reaearance of the object (black car) while the rogram could not detect it. This is normal because the object is disaeared in the revious frame and in the algorithm which we roosed the first ste uses the object selected by the user or the object detected in the revious ste 6

8 International Journal of Comuter Alications ( ) 7. REFERENCES [1] D. Terzooulos, R. Szeliski Tracking with kalman snakes. Active vision,.3 0. [] M. Isard, A. Blake Condensation conditional density roagation for visual tracking. Int. J. Comuter Vision, 9(1) :5 8. [3] J. MacCormick, A. Blake A robabilistic exclusion rincile for tracking multile objects. Int. J. Comuter Vision, 39(1) : [4] C.R. Wren, A. Azarbayejani, T. Darrell, A.P. Pentland Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal.Mach. Intell. 19(7), [5] N. Xu, N. Ahuja. 00. Object contour tracking using grah cuts based active contours. Proc. Int. Conf. Image Processing. [6] B. Rosenhahn, U. Kersting, S. Andrew, T. Brox, R. Klette and H-P. Seidel A silhouette based human motion tracking system Technical Reort (The University of Auckland, New Zealand: CITR) ISSN: [7] B. K..P. Horn, B. G. Schunck Determining Otical Flow. AI 17, [8] J. Barron, D. Fleet, S. Beauchemin Performance of otical flow techniques. Int. J. of Comut. Vis. 1(1), [9] A.R. Mansouri, J. Konrad Multile motion segmentation with level sets. IEEE Trans. Image Process. 1(), [10] G. Adiv Determining 3D motion and structure from otical flow generated by several moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 7(4), [11] A. Yilmaz, O. Javed, M. Shah Object tracking: A survey. ACM Comut.Surv. 38, 13. [1] C. Veenman, M. Reinders, and E. Backer Resolving motion corresondence for densely moving oints. IEEE Trans. Patt. Analy. Mach. Intell. 3, 1, [13] D. Serby, S. Koller-Meier, and L. V. Gool Probabilistic object tracking using multile features. In IEEE International Conference of Pattern Recognition (ICPR) [14] D. Comaniciu, V. Ramesh and P. Meer Kernelbased object tracking. IEEE Trans. Patt. Analy.Mach. Intell. 5, [15] Y. Boykov, O. Veksler, and R. Zabih Fast aroximate energy minimization via grah cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(11): [16] Y. Boykov, M.P. July Interactive Grah Cuts for Otimal Boundary & Region Segmentation of Objects in N-D Images. In: roceedings of the International Conference on Comuter Vision, Vancouver Canada. [17] K. Housni, D. Mammass and Y. Chahir Interactive ROI Segmentation using Grah Cuts, GVIP-ICGST Journal,Volume 9, Issue 6, [18] D. Greig, B. Porteous, and A. Seheult Exact maximum a osteriori estimation for binary images. Journal of the Royal Statistical Society, Series B, 51(): [19] V. Kwatra, A. Schodl, I. Essa, and A. Bobick Grahcut textures: image and video synthesis using grah cuts. In Proceedings of SIGGRAPH. [0] V. Kolmogorov What Energy Functions Can Be Minimized via Grah Cuts?. IEEE transactions on attern analysis and machine intelligence, vol. 6, NO.. [1] L. Ford and D. Fulkerson Flows in Networks. Princeton University Press. [] A. Goldberg and R. Tarjan October A new aroach to the maximum flow roblem. Journal of the Association for Comuting Machinery, 35(4): [3] Y. Boykov and V. Kolmogorov An exerimental comarison of min-cut/max-flow algorithms for energy minimization in vision. 3rd. International Worksho on EMMCVPR. Sringer-Verla, Setember. [4] J.-M. Odobez, P. Bouthemy Robust multiresolution estimation of arametric motion models. Journal of Visual Communication and Image Reresentation, 6(4): [5] Vista team, htt:// 7

CABRS CELLULAR AUTOMATON BASED MRI BRAIN SEGMENTATION

CABRS CELLULAR AUTOMATON BASED MRI BRAIN SEGMENTATION XI Conference "Medical Informatics & Technologies" - 2006 Rafał Henryk KARTASZYŃSKI *, Paweł MIKOŁAJCZAK ** MRI brain segmentation, CT tissue segmentation, Cellular Automaton, image rocessing, medical

More information

The Online Freeze-tag Problem

The Online Freeze-tag Problem The Online Freeze-tag Problem Mikael Hammar, Bengt J. Nilsson, and Mia Persson Atus Technologies AB, IDEON, SE-3 70 Lund, Sweden mikael.hammar@atus.com School of Technology and Society, Malmö University,

More information

Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information

Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information Heiko Hirschmüller Institute of Robotics and Mechatronics Oberfaffenhofen German Aerosace Center (DLR) P.O. Box 1116,

More information

Forensic Science International

Forensic Science International Forensic Science International 214 (2012) 33 43 Contents lists available at ScienceDirect Forensic Science International jou r nal h o me age: w ww.els evier.co m/lo c ate/fo r sc iin t A robust detection

More information

A New Method for Eye Detection in Color Images

A New Method for Eye Detection in Color Images Journal of Comuter Engineering 1 (009) 3-11 A New Method for Eye Detection in Color Images Mohammadreza Ramezanour Deartment of Comuter Science & Research Branch Azad University, Arak.Iran E-mail: Mr.ramezanoor@gmail.com

More information

Point Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11)

Point Location. Preprocess a planar, polygonal subdivision for point location queries. p = (18, 11) Point Location Prerocess a lanar, olygonal subdivision for oint location ueries. = (18, 11) Inut is a subdivision S of comlexity n, say, number of edges. uild a data structure on S so that for a uery oint

More information

Alpha Channel Estimation in High Resolution Images and Image Sequences

Alpha Channel Estimation in High Resolution Images and Image Sequences In IEEE Comuter Society Conference on Comuter Vision and Pattern Recognition (CVPR 2001), Volume I, ages 1063 68, auai Hawaii, 11th 13th Dec 2001 Alha Channel Estimation in High Resolution Images and Image

More information

Web Application Scalability: A Model-Based Approach

Web Application Scalability: A Model-Based Approach Coyright 24, Software Engineering Research and Performance Engineering Services. All rights reserved. Web Alication Scalability: A Model-Based Aroach Lloyd G. Williams, Ph.D. Software Engineering Research

More information

A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION

A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION 9 th ASCE Secialty Conference on Probabilistic Mechanics and Structural Reliability PMC2004 Abstract A MOST PROBABLE POINT-BASED METHOD FOR RELIABILITY ANALYSIS, SENSITIVITY ANALYSIS AND DESIGN OPTIMIZATION

More information

Load Balancing Mechanism in Agent-based Grid

Load Balancing Mechanism in Agent-based Grid Communications on Advanced Comutational Science with Alications 2016 No. 1 (2016) 57-62 Available online at www.isacs.com/cacsa Volume 2016, Issue 1, Year 2016 Article ID cacsa-00042, 6 Pages doi:10.5899/2016/cacsa-00042

More information

DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON

DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON DAY-AHEAD ELECTRICITY PRICE FORECASTING BASED ON TIME SERIES MODELS: A COMPARISON Rosario Esínola, Javier Contreras, Francisco J. Nogales and Antonio J. Conejo E.T.S. de Ingenieros Industriales, Universidad

More information

Machine Learning with Operational Costs

Machine Learning with Operational Costs Journal of Machine Learning Research 14 (2013) 1989-2028 Submitted 12/11; Revised 8/12; Published 7/13 Machine Learning with Oerational Costs Theja Tulabandhula Deartment of Electrical Engineering and

More information

Failure Behavior Analysis for Reliable Distributed Embedded Systems

Failure Behavior Analysis for Reliable Distributed Embedded Systems Failure Behavior Analysis for Reliable Distributed Embedded Systems Mario Tra, Bernd Schürmann, Torsten Tetteroo {tra schuerma tetteroo}@informatik.uni-kl.de Deartment of Comuter Science, University of

More information

A Brief Introduction to Design of Experiments

A Brief Introduction to Design of Experiments J. K. TELFORD D A Brief Introduction to Design of Exeriments Jacqueline K. Telford esign of exeriments is a series of tests in which uroseful changes are made to the inut variables of a system or rocess

More information

Solutions to Problem Set 3

Solutions to Problem Set 3 Massachusetts Institute of Technology 6.042J/18.062J, Fall 05: Mathematics for Comuter Science October 3 Prof. Albert R. Meyer and Prof. Ronitt Rubinfeld revised October 8, 2005, 979 minutes Solutions

More information

A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm

A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm International Journal of Comuter Theory and Engineering, Vol. 7, No. 4, August 2015 A Virtual Machine Dynamic Migration Scheduling Model Based on MBFD Algorithm Xin Lu and Zhuanzhuan Zhang Abstract This

More information

A Multivariate Statistical Analysis of Stock Trends. Abstract

A Multivariate Statistical Analysis of Stock Trends. Abstract A Multivariate Statistical Analysis of Stock Trends Aril Kerby Alma College Alma, MI James Lawrence Miami University Oxford, OH Abstract Is there a method to redict the stock market? What factors determine

More information

Pinhole Optics. OBJECTIVES To study the formation of an image without use of a lens.

Pinhole Optics. OBJECTIVES To study the formation of an image without use of a lens. Pinhole Otics Science, at bottom, is really anti-intellectual. It always distrusts ure reason and demands the roduction of the objective fact. H. L. Mencken (1880-1956) OBJECTIVES To study the formation

More information

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. This document is downloaded from DR-NTU, Nanyang Technological University Library, Singaore. Title Automatic Robot Taing: Auto-Path Planning and Maniulation Author(s) Citation Yuan, Qilong; Lembono, Teguh

More information

An Analysis of Reliable Classifiers through ROC Isometrics

An Analysis of Reliable Classifiers through ROC Isometrics An Analysis of Reliable Classifiers through ROC Isometrics Stijn Vanderlooy s.vanderlooy@cs.unimaas.nl Ida G. Srinkhuizen-Kuyer kuyer@cs.unimaas.nl Evgueni N. Smirnov smirnov@cs.unimaas.nl MICC-IKAT, Universiteit

More information

Local Connectivity Tests to Identify Wormholes in Wireless Networks

Local Connectivity Tests to Identify Wormholes in Wireless Networks Local Connectivity Tests to Identify Wormholes in Wireless Networks Xiaomeng Ban Comuter Science Stony Brook University xban@cs.sunysb.edu Rik Sarkar Comuter Science Freie Universität Berlin sarkar@inf.fu-berlin.de

More information

Factoring Variations in Natural Images with Deep Gaussian Mixture Models

Factoring Variations in Natural Images with Deep Gaussian Mixture Models Factoring Variations in Natural Images with Dee Gaussian Mixture Models Aäron van den Oord, Benjamin Schrauwen Electronics and Information Systems deartment (ELIS), Ghent University {aaron.vandenoord,

More information

Large-Scale IP Traceback in High-Speed Internet: Practical Techniques and Theoretical Foundation

Large-Scale IP Traceback in High-Speed Internet: Practical Techniques and Theoretical Foundation Large-Scale IP Traceback in High-Seed Internet: Practical Techniques and Theoretical Foundation Jun Li Minho Sung Jun (Jim) Xu College of Comuting Georgia Institute of Technology {junli,mhsung,jx}@cc.gatech.edu

More information

D.Sailaja, K.Nasaramma, M.Sumender Roy, Venkateswarlu Bondu

D.Sailaja, K.Nasaramma, M.Sumender Roy, Venkateswarlu Bondu Predictive Modeling of Customers in Personalization Alications with Context D.Sailaja, K.Nasaramma, M.Sumender Roy, Venkateswarlu Bondu Nasaramma.K is currently ursuing her M.Tech in Godavari Institute

More information

Concurrent Program Synthesis Based on Supervisory Control

Concurrent Program Synthesis Based on Supervisory Control 010 American Control Conference Marriott Waterfront, Baltimore, MD, USA June 30-July 0, 010 ThB07.5 Concurrent Program Synthesis Based on Suervisory Control Marian V. Iordache and Panos J. Antsaklis Abstract

More information

Problem Set 6 Solutions

Problem Set 6 Solutions ( Introduction to Algorithms Aril 16, 2004 Massachusetts Institute of Technology 6046J/18410J Professors Erik Demaine and Shafi Goldwasser Handout 21 Problem Set 6 Solutions This roblem set is due in recitation

More information

Learning Human Behavior from Analyzing Activities in Virtual Environments

Learning Human Behavior from Analyzing Activities in Virtual Environments Learning Human Behavior from Analyzing Activities in Virtual Environments C. BAUCKHAGE 1, B. GORMAN 2, C. THURAU 3 & M. HUMPHRYS 2 1) Deutsche Telekom Laboratories, Berlin, Germany 2) Dublin City University,

More information

On the (in)effectiveness of Probabilistic Marking for IP Traceback under DDoS Attacks

On the (in)effectiveness of Probabilistic Marking for IP Traceback under DDoS Attacks On the (in)effectiveness of Probabilistic Maring for IP Tracebac under DDoS Attacs Vamsi Paruchuri, Aran Durresi 2, and Ra Jain 3 University of Central Aransas, 2 Louisiana State University, 3 Washington

More information

ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS

ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS ENFORCING SAFETY PROPERTIES IN WEB APPLICATIONS USING PETRI NETS Liviu Grigore Comuter Science Deartment University of Illinois at Chicago Chicago, IL, 60607 lgrigore@cs.uic.edu Ugo Buy Comuter Science

More information

Static and Dynamic Properties of Small-world Connection Topologies Based on Transit-stub Networks

Static and Dynamic Properties of Small-world Connection Topologies Based on Transit-stub Networks Static and Dynamic Proerties of Small-world Connection Toologies Based on Transit-stub Networks Carlos Aguirre Fernando Corbacho Ramón Huerta Comuter Engineering Deartment, Universidad Autónoma de Madrid,

More information

An Efficient NURBS Path Generator for a Open Source CNC

An Efficient NURBS Path Generator for a Open Source CNC An Efficient NURBS Path Generator for a Oen Source CNC ERNESTO LO VALVO, STEFANO DRAGO Diartimento di Ingegneria Chimica, Gestionale, Informatica e Meccanica Università degli Studi di Palermo Viale delle

More information

SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID

SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID International Journal of Comuter Science & Information Technology (IJCSIT) Vol 6, No 4, August 014 SQUARE GRID POINTS COVERAGED BY CONNECTED SOURCES WITH COVERAGE RADIUS OF ONE ON A TWO-DIMENSIONAL GRID

More information

Mean shift-based clustering

Mean shift-based clustering Pattern Recognition (7) www.elsevier.com/locate/r Mean shift-based clustering Kuo-Lung Wu a, Miin-Shen Yang b, a Deartment of Information Management, Kun Shan University of Technology, Yung-Kang, Tainan

More information

An Associative Memory Readout in ESN for Neural Action Potential Detection

An Associative Memory Readout in ESN for Neural Action Potential Detection g An Associative Memory Readout in ESN for Neural Action Potential Detection Nicolas J. Dedual, Mustafa C. Ozturk, Justin C. Sanchez and José C. Princie Abstract This aer describes how Echo State Networks

More information

Object Tracking System Using Motion Detection

Object Tracking System Using Motion Detection Object Tracking System Using Motion Detection Harsha K. Ingle*, Prof. Dr. D.S. Bormane** *Department of Electronics and Telecommunication, Pune University, Pune, India Email: harshaingle@gmail.com **Department

More information

A Hand-held Photometric Stereo Camera for 3-D Modeling

A Hand-held Photometric Stereo Camera for 3-D Modeling A Hand-held Photometric Stereo Camera for 3-D Modeling Tomoaki Higo 1, Yasuyuki Matsushita 2, Neel Joshi 3, Katsushi Ikeuchi 1 1 The University of Tokyo, 2 Microsoft Research Asia, 3 Microsoft Research

More information

High Quality Offset Printing An Evolutionary Approach

High Quality Offset Printing An Evolutionary Approach High Quality Offset Printing An Evolutionary Aroach Ralf Joost Institute of Alied Microelectronics and omuter Engineering University of Rostock Rostock, 18051, Germany +49 381 498 7272 ralf.joost@uni-rostock.de

More information

The Optimal Sequenced Route Query

The Optimal Sequenced Route Query The Otimal Sequenced Route Query Mehdi Sharifzadeh, Mohammad Kolahdouzan, Cyrus Shahabi Comuter Science Deartment University of Southern California Los Angeles, CA 90089-0781 [sharifza, kolahdoz, shahabi]@usc.edu

More information

Service Network Design with Asset Management: Formulations and Comparative Analyzes

Service Network Design with Asset Management: Formulations and Comparative Analyzes Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with

More information

From Simulation to Experiment: A Case Study on Multiprocessor Task Scheduling

From Simulation to Experiment: A Case Study on Multiprocessor Task Scheduling From to Exeriment: A Case Study on Multirocessor Task Scheduling Sascha Hunold CNRS / LIG Laboratory Grenoble, France sascha.hunold@imag.fr Henri Casanova Det. of Information and Comuter Sciences University

More information

Topology of the Prism Model for 3D Indoor Spatial Objects

Topology of the Prism Model for 3D Indoor Spatial Objects Toology of the Prism Model for 3D Indoor Satial Objects Joon-Seok Kim, Hye-Young Kang, Tae-Hoon Lee, Ki-Joune Li Deartment of Comuter Engineering Pusan National University joonseok@nu.edu, hyezero@nu.edu,

More information

Design of A Knowledge Based Trouble Call System with Colored Petri Net Models

Design of A Knowledge Based Trouble Call System with Colored Petri Net Models 2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China Design of A Knowledge Based Trouble Call System with Colored Petri Net Models Hui-Jen Chuang, Chia-Hung

More information

A Modified Measure of Covert Network Performance

A Modified Measure of Covert Network Performance A Modified Measure of Covert Network Performance LYNNE L DOTY Marist College Deartment of Mathematics Poughkeesie, NY UNITED STATES lynnedoty@maristedu Abstract: In a covert network the need for secrecy

More information

Multiperiod Portfolio Optimization with General Transaction Costs

Multiperiod Portfolio Optimization with General Transaction Costs Multieriod Portfolio Otimization with General Transaction Costs Victor DeMiguel Deartment of Management Science and Oerations, London Business School, London NW1 4SA, UK, avmiguel@london.edu Xiaoling Mei

More information

Comparing Dissimilarity Measures for Symbolic Data Analysis

Comparing Dissimilarity Measures for Symbolic Data Analysis Comaring Dissimilarity Measures for Symbolic Data Analysis Donato MALERBA, Floriana ESPOSITO, Vincenzo GIOVIALE and Valentina TAMMA Diartimento di Informatica, University of Bari Via Orabona 4 76 Bari,

More information

Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting

Softmax Model as Generalization upon Logistic Discrimination Suffers from Overfitting Journal of Data Science 12(2014),563-574 Softmax Model as Generalization uon Logistic Discrimination Suffers from Overfitting F. Mohammadi Basatini 1 and Rahim Chiniardaz 2 1 Deartment of Statistics, Shoushtar

More information

Improved Algorithms for Data Visualization in Forensic DNA Analysis

Improved Algorithms for Data Visualization in Forensic DNA Analysis Imroved Algorithms for Data Visualization in Forensic DNA Analysis Noor Maizura Mohamad Noor, Senior Member IACSIT, Mohd Iqbal akim arun, and Ahmad Faiz Ghazali Abstract DNA rofiles from forensic evidence

More information

Confidence Intervals for Capture-Recapture Data With Matching

Confidence Intervals for Capture-Recapture Data With Matching Confidence Intervals for Cature-Recature Data With Matching Executive summary Cature-recature data is often used to estimate oulations The classical alication for animal oulations is to take two samles

More information

Service Network Design with Asset Management: Formulations and Comparative Analyzes

Service Network Design with Asset Management: Formulations and Comparative Analyzes Service Network Design with Asset Management: Formulations and Comarative Analyzes Jardar Andersen Teodor Gabriel Crainic Marielle Christiansen October 2007 CIRRELT-2007-40 Service Network Design with

More information

Pressure Drop in Air Piping Systems Series of Technical White Papers from Ohio Medical Corporation

Pressure Drop in Air Piping Systems Series of Technical White Papers from Ohio Medical Corporation Pressure Dro in Air Piing Systems Series of Technical White Paers from Ohio Medical Cororation Ohio Medical Cororation Lakeside Drive Gurnee, IL 600 Phone: (800) 448-0770 Fax: (847) 855-604 info@ohiomedical.com

More information

where a, b, c, and d are constants with a 0, and x is measured in radians. (π radians =

where a, b, c, and d are constants with a 0, and x is measured in radians. (π radians = Introduction to Modeling 3.6-1 3.6 Sine and Cosine Functions The general form of a sine or cosine function is given by: f (x) = asin (bx + c) + d and f(x) = acos(bx + c) + d where a, b, c, and d are constants

More information

Synopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE

Synopsys RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Development FRANCE RURAL ELECTRICATION PLANNING SOFTWARE (LAPER) Rainer Fronius Marc Gratton Electricité de France Research and Develoment FRANCE Synosys There is no doubt left about the benefit of electrication and subsequently

More information

Interaction Expressions A Powerful Formalism for Describing Inter-Workflow Dependencies

Interaction Expressions A Powerful Formalism for Describing Inter-Workflow Dependencies Interaction Exressions A Powerful Formalism for Describing Inter-Workflow Deendencies Christian Heinlein, Peter Dadam Det. Databases and Information Systems University of Ulm, Germany {heinlein,dadam}@informatik.uni-ulm.de

More information

X How to Schedule a Cascade in an Arbitrary Graph

X How to Schedule a Cascade in an Arbitrary Graph X How to Schedule a Cascade in an Arbitrary Grah Flavio Chierichetti, Cornell University Jon Kleinberg, Cornell University Alessandro Panconesi, Saienza University When individuals in a social network

More information

POISSON PROCESSES. Chapter 2. 2.1 Introduction. 2.1.1 Arrival processes

POISSON PROCESSES. Chapter 2. 2.1 Introduction. 2.1.1 Arrival processes Chater 2 POISSON PROCESSES 2.1 Introduction A Poisson rocess is a simle and widely used stochastic rocess for modeling the times at which arrivals enter a system. It is in many ways the continuous-time

More information

Multi-Channel Opportunistic Routing in Multi-Hop Wireless Networks

Multi-Channel Opportunistic Routing in Multi-Hop Wireless Networks Multi-Channel Oortunistic Routing in Multi-Ho Wireless Networks ANATOLIJ ZUBOW, MATHIAS KURTH and JENS-PETER REDLICH Humboldt University Berlin Unter den Linden 6, D-99 Berlin, Germany (zubow kurth jr)@informatik.hu-berlin.de

More information

STUDIES ON DYNAMIC VISCOSITY CHANGES OF THE ENGINE S LUBRICATION OIL DEPENDING ON THE PRESSURE

STUDIES ON DYNAMIC VISCOSITY CHANGES OF THE ENGINE S LUBRICATION OIL DEPENDING ON THE PRESSURE Journal of KONES Powertrain and Transort, Vol. 20, No. 4 2013 STUDIES ON DYNAMIC VISCOSITY CHANGES OF THE ENGINE S LUBRICATION OIL DEPENDING ON THE PRESSURE Grzegorz Sikora Gdynia Maritime University Deartment

More information

2D Modeling of the consolidation of soft soils. Introduction

2D Modeling of the consolidation of soft soils. Introduction D Modeling of the consolidation of soft soils Matthias Haase, WISMUT GmbH, Chemnitz, Germany Mario Exner, WISMUT GmbH, Chemnitz, Germany Uwe Reichel, Technical University Chemnitz, Chemnitz, Germany Abstract:

More information

Automatic Search for Correlated Alarms

Automatic Search for Correlated Alarms Automatic Search for Correlated Alarms Klaus-Dieter Tuchs, Peter Tondl, Markus Radimirsch, Klaus Jobmann Institut für Allgemeine Nachrichtentechnik, Universität Hannover Aelstraße 9a, 0167 Hanover, Germany

More information

CSI:FLORIDA. Section 4.4: Logistic Regression

CSI:FLORIDA. Section 4.4: Logistic Regression SI:FLORIDA Section 4.4: Logistic Regression SI:FLORIDA Reisit Masked lass Problem.5.5 2 -.5 - -.5 -.5 - -.5.5.5 We can generalize this roblem to two class roblem as well! SI:FLORIDA Reisit Masked lass

More information

The impact of metadata implementation on webpage visibility in search engine results (Part II) q

The impact of metadata implementation on webpage visibility in search engine results (Part II) q Information Processing and Management 41 (2005) 691 715 www.elsevier.com/locate/inforoman The imact of metadata imlementation on webage visibility in search engine results (Part II) q Jin Zhang *, Alexandra

More information

Migration to Object Oriented Platforms: A State Transformation Approach

Migration to Object Oriented Platforms: A State Transformation Approach Migration to Object Oriented Platforms: A State Transformation Aroach Ying Zou, Kostas Kontogiannis Det. of Electrical & Comuter Engineering University of Waterloo Waterloo, ON, N2L 3G1, Canada {yzou,

More information

Computing the Most Probable String with a Probabilistic Finite State Machine

Computing the Most Probable String with a Probabilistic Finite State Machine Comuting the Most Probable String with a Probabilistic Finite State Machine Colin de la Higuera Université de Nantes, CNRS, LINA, UMR6241, F-44000, France cdlh@univ-nantesfr Jose Oncina De de Lenguajes

More information

Branch-and-Price for Service Network Design with Asset Management Constraints

Branch-and-Price for Service Network Design with Asset Management Constraints Branch-and-Price for Servicee Network Design with Asset Management Constraints Jardar Andersen Roar Grønhaug Mariellee Christiansen Teodor Gabriel Crainic December 2007 CIRRELT-2007-55 Branch-and-Price

More information

A Study of Active Queue Management for Congestion Control

A Study of Active Queue Management for Congestion Control In IEEE INFOCOM 2 A Study of Active Queue Management for Congestion Control Victor Firoiu Marty Borden 1 vfiroiu@nortelnetworks.com mborden@tollbridgetech.com Nortel Networks TollBridge Technologies 6

More information

Microscopic Simulation of Pedestrian Flows

Microscopic Simulation of Pedestrian Flows Hoogendoorn 1 Microscoic Simulation of Pedestrian Flows Serge P. Hoogendoorn (s.hoogendoorn@ct.tudelft.nl) Transortation and Traffic Engineering Section Faculty of Civil Engineering and Geosciences Delft

More information

Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images

Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images Proceedings of Internation Conference on Computer Vision, Vancouver, Canada, July 2001 vol.i, p.105 Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images Yuri Y. Boykov

More information

MATHEMATICAL MODEL OF THE TEMPERATURE REGIME IN A FURNACE FOR FOAM GLASS GRANULES PRODUCTION. Georgi E. Georgiev, Luben Lakov, Krasimira Toncheva

MATHEMATICAL MODEL OF THE TEMPERATURE REGIME IN A FURNACE FOR FOAM GLASS GRANULES PRODUCTION. Georgi E. Georgiev, Luben Lakov, Krasimira Toncheva Journal Journal of hemical of hemical Technology and and Metallurgy, 50, 4, 50, 2015, 4, 2015 486-492 MATHEMATIAL MODEL OF THE TEMPERATURE REGIME IN A FURNAE FOR FOAM GLASS GRANULES PRODUTION Georgi E.

More information

Risk in Revenue Management and Dynamic Pricing

Risk in Revenue Management and Dynamic Pricing OPERATIONS RESEARCH Vol. 56, No. 2, March Aril 2008,. 326 343 issn 0030-364X eissn 1526-5463 08 5602 0326 informs doi 10.1287/ore.1070.0438 2008 INFORMS Risk in Revenue Management and Dynamic Pricing Yuri

More information

Principles of Hydrology. Hydrograph components include rising limb, recession limb, peak, direct runoff, and baseflow.

Principles of Hydrology. Hydrograph components include rising limb, recession limb, peak, direct runoff, and baseflow. Princiles of Hydrology Unit Hydrograh Runoff hydrograh usually consists of a fairly regular lower ortion that changes slowly throughout the year and a raidly fluctuating comonent that reresents the immediate

More information

Illumination-Invariant Tracking via Graph Cuts

Illumination-Invariant Tracking via Graph Cuts Illumination-Invariant Tracking via Graph Cuts Daniel Freedman and Matthew W. Turek Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180 Abstract Illumination changes are a ubiquitous

More information

Buffer Capacity Allocation: A method to QoS support on MPLS networks**

Buffer Capacity Allocation: A method to QoS support on MPLS networks** Buffer Caacity Allocation: A method to QoS suort on MPLS networks** M. K. Huerta * J. J. Padilla X. Hesselbach ϒ R. Fabregat O. Ravelo Abstract This aer describes an otimized model to suort QoS by mean

More information

Multistage Human Resource Allocation for Software Development by Multiobjective Genetic Algorithm

Multistage Human Resource Allocation for Software Development by Multiobjective Genetic Algorithm The Oen Alied Mathematics Journal, 2008, 2, 95-03 95 Oen Access Multistage Human Resource Allocation for Software Develoment by Multiobjective Genetic Algorithm Feng Wen a,b and Chi-Ming Lin*,a,c a Graduate

More information

lecture 25: Gaussian quadrature: nodes, weights; examples; extensions

lecture 25: Gaussian quadrature: nodes, weights; examples; extensions 38 lecture 25: Gaussian quadrature: nodes, weights; examles; extensions 3.5 Comuting Gaussian quadrature nodes and weights When first aroaching Gaussian quadrature, the comlicated characterization of the

More information

Efficient Training of Kalman Algorithm for MIMO Channel Tracking

Efficient Training of Kalman Algorithm for MIMO Channel Tracking Efficient Training of Kalman Algorithm for MIMO Channel Tracking Emna Eitel and Joachim Seidel Institute of Telecommunications, University of Stuttgart Stuttgart, Germany Abstract In this aer, a Kalman

More information

Modeling and Simulation of an Incremental Encoder Used in Electrical Drives

Modeling and Simulation of an Incremental Encoder Used in Electrical Drives 10 th International Symosium of Hungarian Researchers on Comutational Intelligence and Informatics Modeling and Simulation of an Incremental Encoder Used in Electrical Drives János Jób Incze, Csaba Szabó,

More information

Edge tracking for motion segmentation and depth ordering

Edge tracking for motion segmentation and depth ordering Edge tracking for motion segmentation and depth ordering P. Smith, T. Drummond and R. Cipolla Department of Engineering University of Cambridge Cambridge CB2 1PZ,UK {pas1001 twd20 cipolla}@eng.cam.ac.uk

More information

Discrete Stochastic Approximation with Application to Resource Allocation

Discrete Stochastic Approximation with Application to Resource Allocation Discrete Stochastic Aroximation with Alication to Resource Allocation Stacy D. Hill An otimization roblem involves fi nding the best value of an obective function or fi gure of merit the value that otimizes

More information

4 Perceptron Learning Rule

4 Perceptron Learning Rule Percetron Learning Rule Objectives Objectives - Theory and Examles - Learning Rules - Percetron Architecture -3 Single-Neuron Percetron -5 Multile-Neuron Percetron -8 Percetron Learning Rule -8 Test Problem

More information

Optimal Routing and Scheduling in Transportation: Using Genetic Algorithm to Solve Difficult Optimization Problems

Optimal Routing and Scheduling in Transportation: Using Genetic Algorithm to Solve Difficult Optimization Problems By Partha Chakroborty Brics "The roblem of designing a good or efficient route set (or route network) for a transit system is a difficult otimization roblem which does not lend itself readily to mathematical

More information

NOISE ANALYSIS OF NIKON D40 DIGITAL STILL CAMERA

NOISE ANALYSIS OF NIKON D40 DIGITAL STILL CAMERA NOISE ANALYSIS OF NIKON D40 DIGITAL STILL CAMERA F. Mojžíš, J. Švihlík Detartment of Comuting and Control Engineering, ICT Prague Abstract This aer is devoted to statistical analysis of Nikon D40 digital

More information

Franck Cappello and Daniel Etiemble LRI, Université Paris-Sud, 91405, Orsay, France Email: fci@lri.fr

Franck Cappello and Daniel Etiemble LRI, Université Paris-Sud, 91405, Orsay, France Email: fci@lri.fr MPI versus MPI+OenMP on the IBM SP for the NAS Benchmarks Franck Caello and Daniel Etiemble LRI, Université Paris-Sud, 945, Orsay, France Email: fci@lri.fr Abstract The hybrid memory model of clusters

More information

A Novel Architecture Style: Diffused Cloud for Virtual Computing Lab

A Novel Architecture Style: Diffused Cloud for Virtual Computing Lab A Novel Architecture Style: Diffused Cloud for Virtual Comuting Lab Deven N. Shah Professor Terna College of Engg. & Technology Nerul, Mumbai Suhada Bhingarar Assistant Professor MIT College of Engg. Paud

More information

Simulation and Verification of Coupled Heat and Moisture Modeling

Simulation and Verification of Coupled Heat and Moisture Modeling Simulation and Verification of Couled Heat and Moisture Modeling N. Williams Portal 1, M.A.P. van Aarle 2 and A.W.M. van Schijndel *,3 1 Deartment of Civil and Environmental Engineering, Chalmers University

More information

Finding a Needle in a Haystack: Pinpointing Significant BGP Routing Changes in an IP Network

Finding a Needle in a Haystack: Pinpointing Significant BGP Routing Changes in an IP Network Finding a Needle in a Haystack: Pinointing Significant BGP Routing Changes in an IP Network Jian Wu, Zhuoqing Morley Mao University of Michigan Jennifer Rexford Princeton University Jia Wang AT&T Labs

More information

One-Chip Linear Control IPS, F5106H

One-Chip Linear Control IPS, F5106H One-Chi Linear Control IPS, F5106H NAKAGAWA Sho OE Takatoshi IWAMOTO Motomitsu ABSTRACT In the fi eld of vehicle electrical comonents, the increasing demands for miniaturization, reliability imrovement

More information

The risk of using the Q heterogeneity estimator for software engineering experiments

The risk of using the Q heterogeneity estimator for software engineering experiments Dieste, O., Fernández, E., García-Martínez, R., Juristo, N. 11. The risk of using the Q heterogeneity estimator for software engineering exeriments. The risk of using the Q heterogeneity estimator for

More information

Storage Basics Architecting the Storage Supplemental Handout

Storage Basics Architecting the Storage Supplemental Handout Storage Basics Architecting the Storage Sulemental Handout INTRODUCTION With digital data growing at an exonential rate it has become a requirement for the modern business to store data and analyze it

More information

PRIME, COMPOSITE, AND SUPER-COMPOSITE NUMBERS

PRIME, COMPOSITE, AND SUPER-COMPOSITE NUMBERS PRIME, COMPOSITE, AD SUPER-COMPOSITE UMBERS Any integer greater than one is either a rime or comosite number. Primes have no divisors excet one and itself while comosites will have multile divisors. It

More information

United Arab Emirates University College of Sciences Department of Mathematical Sciences HOMEWORK 1 SOLUTION. Section 10.1 Vectors in the Plane

United Arab Emirates University College of Sciences Department of Mathematical Sciences HOMEWORK 1 SOLUTION. Section 10.1 Vectors in the Plane United Arab Emirates University College of Sciences Deartment of Mathematical Sciences HOMEWORK 1 SOLUTION Section 10.1 Vectors in the Plane Calculus II for Engineering MATH 110 SECTION 0 CRN 510 :00 :00

More information

A Third Generation Automated Teller Machine Using Universal Subscriber Module with Iris Recognition

A Third Generation Automated Teller Machine Using Universal Subscriber Module with Iris Recognition A Third Generation Automated Teller Machine Using Universal Subscriber Module with Iris Recognition B.Sundar Raj Assistant rofessor, Det of CSE, Bharath University, Chennai, TN, India. ABSTRACT: This aer

More information

Stability Improvements of Robot Control by Periodic Variation of the Gain Parameters

Stability Improvements of Robot Control by Periodic Variation of the Gain Parameters Proceedings of the th World Congress in Mechanism and Machine Science ril ~4, 4, ianin, China China Machinery Press, edited by ian Huang. 86-8 Stability Imrovements of Robot Control by Periodic Variation

More information

Provable Ownership of File in De-duplication Cloud Storage

Provable Ownership of File in De-duplication Cloud Storage 1 Provable Ownershi of File in De-dulication Cloud Storage Chao Yang, Jian Ren and Jianfeng Ma School of CS, Xidian University Xi an, Shaanxi, 710071. Email: {chaoyang, jfma}@mail.xidian.edu.cn Deartment

More information

Tracking of Multiple Objects under Partial Occlusion

Tracking of Multiple Objects under Partial Occlusion Tracking of Multiple Objects under Partial Occlusion Bing Han, Christopher Paulson, Taoran Lu, Dapeng Wu, Jian Li Department of Electrical and Computer Engineering University of Florida Gainesville, FL

More information

Software Cognitive Complexity Measure Based on Scope of Variables

Software Cognitive Complexity Measure Based on Scope of Variables Software Cognitive Comlexity Measure Based on Scoe of Variables Kwangmyong Rim and Yonghua Choe Faculty of Mathematics, Kim Il Sung University, D.P.R.K mathchoeyh@yahoo.com Abstract In this aer, we define

More information

Robotic Exploration Under the Controlled Active Vision Framework

Robotic Exploration Under the Controlled Active Vision Framework Robotic Exloration Under the Controlled Active Vision Framework Christoher E. Smith Scott A. Brandt Nikolaos P. Paanikolooulos chsmith@cs.umn.edu sbrandt@cs.umn.edu naas@cs.umn.edu Artificial Intelligence,

More information

Real-Time Multi-Step View Reconstruction for a Virtual Teleconference System

Real-Time Multi-Step View Reconstruction for a Virtual Teleconference System EURASIP Journal on Alied Signal Processing 00:0, 067 087 c 00 Hindawi Publishing Cororation Real-Time Multi-Ste View Reconstruction for a Virtual Teleconference System B. J. Lei Information and Communication

More information

An effective multi-objective approach to prioritisation of sewer pipe inspection

An effective multi-objective approach to prioritisation of sewer pipe inspection An effective multi-objective aroach to rioritisation of sewer ie insection L. Berardi 1 *, O.Giustolisi 1, D.A. Savic 2 and Z. Kaelan 2 1 Technical University of Bari, Civil and Environmental Engineering

More information

A Complete Operational Amplifier Noise Model: Analysis and Measurement of Correlation Coefficient

A Complete Operational Amplifier Noise Model: Analysis and Measurement of Correlation Coefficient IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: FUNDAMENTAL THEORY AND APPLICATION, VOL. 47, NO. 3, MARCH 000 40 for ractical alication, oening the ath for widesread adotion of the clock-gating technique

More information

Tracking Groups of Pedestrians in Video Sequences

Tracking Groups of Pedestrians in Video Sequences Tracking Groups of Pedestrians in Video Sequences Jorge S. Marques Pedro M. Jorge Arnaldo J. Abrantes J. M. Lemos IST / ISR ISEL / IST ISEL INESC-ID / IST Lisbon, Portugal Lisbon, Portugal Lisbon, Portugal

More information