Improve flexibility Reduce complexity Improve efficiency NETWORK AND INVENTORY AUDIT SERVICES FOR NETWORK PLANNING AND OPTIMIZATION For lots of different reasons, many carrier networks today are too complicated. Rapid growth, merger and acquisition, and increased demand for high-bandwidth services led to network-on-top-of-network, creating inefficiency and inflexibility. Now, many net- work infrastructures are too complex to update without causing major upheaval. With unnecessary complexity comes unwar- ranted costs. So it makes good business sense to improve the efficiency of existing opera- tions to reduce both capital and operating expenses. The problem is that many networks are so complicated that it’s hard to know what really exists in the existing operation. That also makes it hard to plan for the future. That’s where ADC can help. Our Network and Inventory Audit services are designed to help unravel complicated and often convoluted networks to improve efficiency and reduce operational costs. Our proven, process-driven approach uncovers and details network archi- tectures, legacy records, processes and data- bases that have an immediate and direct effect on the success of a carrier’s operations. ADC Network and Inventory Audit services ensure accurate and detailed reports that affect operational areas including: • Asset verification and recovery • Engineering and planning • Provisioning • Trouble administration • Facilities optimization ADC Network and Inventory Audit services are a critical first step to maximize efficiency and realize cost savings, and to ensure that your network can support both near- and long-term business strategies, no matter what these might be. At ADC, we’re experienced in building a wide variety of networks, using multiple technolo- gies from multiple vendors and delivering multiple services. So no matter what type of network, ADC has the expertise to accurately detail its physical and logical aspects. ADC employs a proven ISO 9001 certified process, comprised of: • Project design • Audit and data collection • Audit results • Operational suggestions for change management NETWORK AND INVENTORY AUDIT SERVICES FOR NETWORK PLANNING AND OPTIMIZATION NETWORK AND INVENTORY AUDIT PHASE ONE - PROJECT DESIGN Since each audit project is unique, we ensure upfront that the results will fully meet our cus- tomer’s needs. The first part of this phase deter- mines the goals and objectives, timeline and deliverables. Our audit teams conduct site visits to examine the facility, records, equipment status and the scope of the network. The result- ing design plan includes a complete scope of work, schedule and pricing information. PHASE TWO - AUDIT AND DATA COLLECTION Using customer-specific worksheets and best- practice methods, teams of ADC professional auditors are deployed to gather data from both active and passive equipment. All infor- mation is verified through a quality-assurance process to ensure highly accurate results. The data collected includes: • Mounting positions • Dimensions • Interconnections • Port connections • Equipment model, serial number and version number PHASE THREE - DELIVERABLES The result of ADC’s Network and Inventory Audit services is a professional set of deliver- ables (in hard and soft copy formats) as defined in the project design phase, and may include: • Rack elevation drawings • Chapter Algorithms for Query Processing and Optimization Chapter Outline Introduction to Query Processing Translating SQL Queries into Relational Algebra Algorithms for External Sorting Algorithms for SELECT and JOIN Operations Algorithms for PROJECT and SET Operations Implementing Aggregate Operations and Outer Joins Combining Operations using Pipelining Using Heuristics in Query Optimization Using Selectivity and Cost Estimates in Query Optimization Overview of Query Optimization in Oracle Semantic Query Optimization Introduction to Query Processing Query optimization: the process of choosing a suitable execution strategy for processing a query Two internal representations of a query – Query Tree – Query Graph Typical steps when processing a high-level query Translating SQL Queries into Relational Algebra (1) Query block: the basic unit that can be translated into the algebraic operators and optimized A query block contains a single SELECT-FROMWHERE expression, as well as GROUP BY and HAVING clause if these are part of the block Nested querieswithin a query are identified as separate query blocks Aggregate operators in SQL must be included in the extended algebra Algorithms for External Sorting (1) External sorting : refers to sorting algorithms that are suitable for large files of records stored on disk that not fit entirely in main memory, such as most database files Sort-Merge strategy : starts by sorting small subfiles (runs ) of the main file and then merges the sorted runs, creating larger sorted subfiles that are merged in turn – Sorting phase: nR = (b/nB) – Merging phase: dM = Min(nB-1, nR); nP= (logdM(nR)) nR: number of initial runs; b: number of file blocks; nB: available buffer space; dM: degree of merging; nP: number of passes g 24 a 19 d 31 c 33 b 14 e 16 r 16 d 21 m3 p d a 14 a 19 d 31 g 24 b 14 c 33 e 16 d 21 m r 16 a 14 d p Tạo run Buffer-size = 3, a b c d e g 19 14 33 31 16 24 a 14 d d 21 m3 p r 16 trộn pass-1 record/block a 14 a 19 b 14 c 33 d d 21 d 31 e 16 g 24 m3 p r 16 trộn pass-2 Algorithms for External Sorting (2) set i 1, j b; /* size of the file in blocks */ k nB; /* size of buffer in blocks */ m (j/k); /*number of runs */ {Sort phase} while (inChannels+0] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2])/3;... [j * image- >nChannels + 0]; if (k < mean) k = 0; else k = 255; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+0] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2] = (UCHAR) k; } One final window is created, and the final thresholded image is displayed and saved cvNamedWindow( “thresh“); cvShowImage(... window and display the grey image in it cvNamedWindow( “grey“, CV_WINDOW_AUTOSIZE); cvShowImage( “grey“, image ); cvWaitKey(0); // wait for a key Finally, compute the mean level for use as a threshold and pass through the image again, setting pixels less than the mean to 0 and those greater to 255; mean = mean/count; for (i=0; i height; i++) for (j=0; j width; j++) { k= (image- >imageData+i *image- >widthStep)... + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] + (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2])/3; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+0] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+1] = (UCHAR) k; (image- >imageData+i *image- >widthStep)[j *image- >nChannels+2] = (UCHAR) k; Chapter 1 ■ Practical Aspects of a Vision System At this point in the loop, count and sum the pixel values so... Finding Images by Example 395 Chapter 11 High-Performance Computing for Vision and Image Processing 425 Index 465 xi Contents Preface Chapter 1 Chapter 2 xxi Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls OpenCV The Basic OpenCV Code The IplImage Data Structure Reading and Writing Images Image Display An Example Image Capture Interfacing with the AIPCV Library... window Before anyone can modify this code in a knowledgeable way, the data structures and functions need to be explained 1.2.1 The IplImage Data Structure The IplImage structure is the in-memory data organization for an image Images in IplImage form can be converted into arrays of pixels, but IplImage also contains a lot of structural information about the image data, which can have many forms For example,... to as indirect access in OpenCV documentation and is slower than other means of accessing pixels It is, on the other hand, clean and clear 1.2.2 Reading and Writing Images The basic function for image input has already been seen; cvLoadImage reads an image from a file, given a path name to that file It can read images in JPEG, BMP, PNM, PNG, and TIF formats, and does so automatically, without the need... argv[]) { IplImage *image = 0; int i,j,k; int mean=0, count=0; char c; image = cvLoadImage(“C:/AIPCV/marchA062.jpg“); At this point, there should be image data Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2007, Article ID 89103, 9 pages doi:10.1155/2007/89103 Research Article Radar Sensor Networks: Algorithms for Waveform Design and Diversity with Application to ATR with Delay-Doppler Uncertainty Qilian Liang Department of Electrical Engineering, University of Texas at Arlington, Room 518, 416 Yates Street, Arlington, TX 76019-0016, USA Received 30 May 2006; Revised 28 November 2006; Accepted 29 November 2006 Recommended by Xiuzhen Cheng Automatic target recognition (ATR) in target search phase is very challenging because the target range and mobility are not yet perfectly known, which results in delay-Doppler uncertainty. In this paper, we firstly perform some theoretical studies on radar sensor network (RSN) design based on linear frequency modulation (LFM) waveform: (1) the conditions for waveform coexis- tence, (2) interferences among waveforms in RSN, (3) waveform diversity in RSN. Then we apply RSN to ATR with delay-Doppler uncertainty and propose maximum-likeihood (ML) ATR algorithms for fluctuating targets and nonfluctuating targets. Simulation results show that our RSN vastly reduces the ATR error compared to a single radar system in ATR with delay-Doppler uncertainty. The proposed waveform design and diversity algorithms can also be applied to active RFID sensor networks and underwater acous- tic sensor networks. Copyright © 2007 Qilian Liang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION AND MOTIVATION The goal for any target recognition system is to give the most accurate interpretation of what a target is at any given point in time. There are two classes of motion models of targets, one for maneuvering targets and one for nonmaneuvering (constant velocity and acceleration) targets. The area that is still lacking in target recognition is the ability to detect reli- ably when a target is beginning a maneuver where its speed and range are uncertain. The tracking system can switch the algorithms applied to the problem from a nonmaneuvering set to the maneuvering set when a target is beginning a ma- neuver. But when the tracker does finally catch up to the tar- get after the maneuver and then perform ATR, the latency is too high. In time-critical mission situation, such latency in ATR is not tolerable. In this paper, we are interested in study- ing automatic target recognition with range and speed uncer- tainty, that is, delay-Doppler uncertainty, using radar sensor networks (RSN). The network of radar sensors should oper- ate with multiple goa ls managed by an intelligent platform network that can manage the dynamics of each radar to meet the common goals of the platform rather than each radar to operate as an independent system. Therefore, it is significant to perform signal design and processing and networking co- operatively within and between platforms of radar sensors and their communication modules. In this paper, we are interested in studying algorithms on radar sensor network (RSN) design based on linear frequency modulation (LFM) waveform: (1) the conditions for waveform coexistence, (2) interferences Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2007, Article ID 98938, 10 pages doi:10.1155/2007/98938 Research Article Tree-Based Distributed Multicast Algorithms for Directional Communications and Lifetime Optimization in Wireless Ad Hoc Networks Song Guo, 1 Oliver W. W. Y ang, 2 and Victor C. M. Leung 1 1 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada V6T 1Z4 2 School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada K1N 6N5 Received 1 June 2006; Revised 29 October 2006; Accepted 30 October 2006 Recommended by Xiuzhen Cheng We consider the problem of maximizing the network lifetime in WANETs (wireless ad hoc networks) with limited energy re- sources using omnidirectional or directional antennas. Unlike most solutions that use a centralized multicast algorithm, we use graph-theoretic approach to solve the problem in a distributed manner. After providing a globally optimal solution for the special case of single multicast session using omnidirectional antenna, this approach leads us to a group of distributed algorithms for multiple multicast in WANETs using directional antennas. Experimental results show that our distributed multicast algorithms for directional communications outperform other centralized multicast algorithms significantly in terms of network lifetime for both single-session and multiple-session scenarios. Copyright © 2007 Song Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION There is an increasing interest in wireless ad hoc networks in many application domains where instant infrastructure is needed and no central backbone system and administration (like base stations and wired backbone in a cellular system) exist. Each communicating node in these networks acts as a router in addition to a host in order to communicate with each other over a limited number of shared radio channels. A communication session can be achieved either through a single-hop transmission if the communicating nodes are close enough to each other, or through multiple hops by re- laying through intermediate nodes. Since each node in such a network is usual ly powered by a battery with limited amount of energy, the wireless ad hoc network will become unus- able after the batteries are drained. Consequently, energy ef- ficiency is an important design consideration for wireless ad hoc networks. Over the last few years, energy efficient communication in wireless ad hoc networks with directional antennas has re- ceived more and more attention. This is because directional communications can save transmission power by concentrat- ing RF energy where it is needed [1, 2]. On the other hand, the broadcast/multicast communication is also an important issue as many routing protocols for wireless ad hoc networks need this mechanism to maintain the routes between nodes. Therefore, one would be interested in finding an algorithm that would provide the maximum lifetime to the multicast session. The optimization metric is typically defined as the duration of the network operation time until the battery de- ... Introduction to Query Processing Translating SQL Queries into Relational Algebra Algorithms for External Sorting Algorithms for SELECT and JOIN Operations Algorithms for PROJECT and SET Operations... Semantic Query Optimization Introduction to Query Processing Query optimization: the process of choosing a suitable execution strategy for processing a query Two internal representations of a query. .. Operations and Outer Joins Combining Operations using Pipelining Using Heuristics in Query Optimization Using Selectivity and Cost Estimates in Query Optimization Overview of Query Optimization