VOLUME-7, ISSUE-3
Date:
International Journal of Advances in
Engineering & Technology (IJAET)
Smooth, Simple and Timely Pu...
International Journal of Advances in Engineering & Technology, Jul. 2014.
©IJAET ISSN: 2231-1963
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Ijaet volume 7 issue 3 july 2014

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IJAET is honoured to announce the publication of the volume 7 issue 3 for July 2014. Papers published in IJAET provides a glimpse into a few of the many high quality research activities conducted by the young & dynamic researchers around the world. This Issue holds outstanding papers from various disciplines submitted by scholars, researchers, engineers and scientists who have been involved in active research, scholarly, and creative activities.
The Journal is available online, please visit the following website: http://www.ijaet.org/.

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Ijaet volume 7 issue 3 july 2014

  1. 1. VOLUME-7, ISSUE-3 Date: International Journal of Advances in Engineering & Technology (IJAET) Smooth, Simple and Timely Publishing of Review and Research Articles! ISSN : 2231-1963 July—2014
  2. 2. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 i Vol. 7, Issue 3, pp. i-vi Table of Content S. No. Article Title & Authors (Volume 7, Issue 3, July-2014) Page No’s 1. Data Warehouse Design and Implementation Based on Quality Requirements Khalid Ibrahim Mohammed 642-651 2. A New Mechanism in Hmipv6 to Improve Micro and Macro Mobility Sahar Abdul Aziz Al-Talib 652-665 3. Efficiency Optimization of Vector-Controlled Induction Motor Drive Hussein Sarhan 666-674 4. Flexible Differential Frequency-to-Voltage and Voltage-to- Frequency Converters using Monolithic Analogue Reconfigurable Devices Ivailo Milanov Pandiev 675-683 5. A Review Search Bitmap Image for Sub Image and the Padding Problem Omeed Kamal Khorsheed 684-691 6. Potential Use of Phase Change Materials with Reference to Thermal Energy Systems in South Africa Basakayi J.K., Storm C.P. 692-700 7. Improving Software Quality in the Service Process Industry using Agility with Software Reusable Components as Software Product Line: An Empirical Study of Indian Service Providers Charles Ikerionwu, Richard Foley, Edwin Gray 701-711 8. Produce Low-Pass and High-Pass Image Filter in Java Omeed Kamal Khorsheed 712-722 9. The Comparative Analysis of Social Network in International and Local Corporate Business Mohmed Y. Mohmed AL-SABAAWI 723-732
  3. 3. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 ii Vol. 7, Issue 3, pp. i-vi 10. Precise Calculation Unit Based on a Hardware Implementation of a Formal Neuron in a FPGA Platform Mohamed ATIBI, Abdelattif BENNIS, Mohamed BOUSSAA 733-742 11. Temperature Profiling at Southern Latitudes by Deploying Microwave Radiometer A. K. Pradhan, S. Mondal, L. A. T. Machado and P. K. Karmakar 743-755 12. Development and Evaluation of Trolley-cum-Batch Dryer for Paddy Mohammed Shafiq Alam and V K Sehgal 756-764 13. Joint Change Detection and Image Registration Method for Multitemporal SAR Images Lijinu M Thankachan, Jeny Jose 765-772 14. Load - Settlement Behaviour of Granular Pile in Black Cotton Soil Siddharth Arora, Rakesh Kumar and P. K. Jain 773-781 15. Harmonic Study of VFDS and Filter Design: A Case Study for Sugar Industry with Cogeneration V. P. Gosavi and S. M. Shinde 782-789 16. Precipitation and Kinetics of Ferrous Carbonate in Simulated Brine Solution and its Impact on CO2 Corrosion of Steel G. S. Das 790-797 17. Performance Comparison of Power System Stabilizer with and Without Facts Device Amit Kumar Vidyarthi, Subrahmanyam Tanala, Ashish Dhar Diwan 798-806 18. Hydrological Study of Man (Chandrabhaga) River Shirgire Anil Vasant, Talegaokar S.D. 807-817 19. Crop Detection by Machine Vision for Weed Management 818-826
  4. 4. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 iii Vol. 7, Issue 3, pp. i-vi Ashitosh K Shinde and Mrudang Y Shukla 20. Detection & Control of Downey Mildew Disease in Grape Field Vikramsinh Kadam, Mrudang Shukla 826-837 21. Ground Water Status- A Case Study of Allahabad, UP, India Ayush Mittal, Munesh Kumar 838-844 22. Clustering and Noise Detection For Geographic Knowledge Discovery Sneha N S and Pushpa 845-855 23. Proxy Driven FP Growth Based Prefetching Devender Banga and Sunitha Cheepurisetti 856-862 24. Search Network Future Generation Network for Information Interchange G. S. Satisha 863-867 25. A Brief Survey on Bio Inspired Optimization Algorithms For Molecular Docking Mayukh Mukhopadhyay 868-878 26. Heat Transfer Analysis of Cold Storage Upamanyu Bangale and Samir Deshmukh 879-886 27. Localized RGB Color Histogram Feature Descriptor for Image Retrieval K. Prasanthi Jasmine, P. Rajesh Kumar 887-895 28. Witricity for Wireless Sensor Nodes M. Karthika and C. Venkatesh 896-904 29. Study of Swelling Behaviour of Black Cotton Soil Improved with Sand Column Aparna, P.K. Jain and Rakesh Kumar 905-910 30. Effective Fault Handling Algorithm for Load Balancing using Ant Colony Optimization in Cloud Computing 911-916
  5. 5. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 iv Vol. 7, Issue 3, pp. i-vi Divya Rastogi and Farhat Ullah Khan 31. Handling Selfishness over Mobile Ad Hoc Network Madhuri D. Mane and B. M. Patil 917-922 32. A New Approach to Design Low Power CMOS Flash A/D Converter C Mohan and T Ravisekhar 923-929 33. Optimization and Comparative Analysis of Non-Renewable and Renewable System Swati Negi and Lini Mathew 930-937 34. A Feed Forward Artificial Neural Network based System to Minimize DOS Attack in Wireless Network Tapasya Pandit & Anil Dudy 938-947 35. Improving Performance of Delay Aware Data Collection Using Sleep and Wake Up Approach in Wireless Sensor Network Paralkar S. S. and B. M. Patil 948-956 36. Improved New Visual Cryptographic Scheme Using One Shared Image Gowramma B.H, Shyla M.G, Vivekananda 957-966 37. Segmentation of Brain Tumour from Mri Images by Improved Fuzzy System Sumitharaj.R, Shanthi.K 967-973 38. Implementation of Classroom Attendance System Based on Face Recognition in Class Ajinkya Patil, Mrudang Shukla 974-979 39. A Tune-In Optimization Process of AISI 4140 in Raw Turning Operation using CVD Coated Insert C. Rajesh 980-990 40. A Modified Single-frame Learning based Super-Resolution and its Observations Vaishali R. Bagul and Varsha M. Jain 991-997
  6. 6. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 v Vol. 7, Issue 3, pp. i-vi 41. Design Modification and Analysis of Two Wheeler Cooling Fins-A Review Mohsin A. Ali and S.M Kherde 998-1002 42. Virtual Wireless Keyboard System with Co-ordinate Mapping Souvik Roy, Ajay Kumar Singh, Aman Mittal, Kunal Thakral 1003-1008 43. Secure Key Management in Ad-Hoc Network: A Review Anju Chahal and Anuj Kumar, Auradha 1009-1017 44. Prediction of Study Track by Aptitude Test using Java Deepali Joshi and Priyanka Desai 1018-1026 45. Unsteady MHD Three Dimensional Flow of Maxwell Fluid Through a Porous Medium in a Parallel Plate Channel under the Influence of Inclined Magnetic Field L. Sreekala, M. Veera Krishna, L. Hari Krishna and E. Kesava Reddy 1027-1037 46. Video Streaming Adaptivity and Efficiency in Social Networking Sites G. Divya and E R Aruna 1038-1043 47. Intrusion Detection System using Dynamic Agent Selection and Configuration Manish Kumar, M. Hanumanthappa 1044-1052 48. Evaluation of Characteristic Properties of Red Mud For Possible use as a Geotechnical Material in Civil Construction Kusum Deelwal, Kishan Dharavath, Mukul Kulshreshtha 1053-1059 49. Performance Analysis of IEEE 802.11e EDCA with QoS Enhancements Through Adapting AIFSN Parameter Vandita Grover and Vidusha Madan 1060-1066 50. Data Derivation Investigation S. S. Kadam, P.B. Kumbharkar 1067-1074
  7. 7. International Journal of Advances in Engineering & Technology, Jul. 2014. ©IJAET ISSN: 2231-1963 vi Vol. 7, Issue 3, pp. i-vi 51. Design and Implementation of Online Patient Monitoring System Harsha G S 1075-1081 52. Comparison Between Classical and Modern Methods of Direction of Arrival (DOA) Estimation Mujahid F. Al-Azzo, Khalaf I. Al-Sabaaw 1082-1090 53. Modelling Lean, Agile, Leagile Manufacturing Strategies: An Fuzzy Analytical Hierarchy Process Approach For Ready Made Ware (Clothing) Industry in Mosul, Iraq Thaeir Ahmed Saadoon Al Samman 1091-1108 Members of IJAET Fraternity A - N
  8. 8. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 642 Vol. 7, Issue 3, pp. 642-651 DATA WAREHOUSE DESIGN AND IMPLEMENTATION BASED ON QUALITY REQUIREMENTS Khalid Ibrahim Mohammed Department of Computer Science, College of Computer, University of Anbar, Iraq. ABSTRACT The data warehouses are considered modern ancient techniques, since the early days for the relational databases, the idea of the keeping a historical data for reference when it needed has been originated, and the idea was primitive to create archives for the historical data to save these data, despite of the usage of a special techniques for the recovery of these data from the different storage modes. This research applied of structured databases for a trading company operating across the continents, has a set of branches each one has its own stores and showrooms, and the company branch’s group of sections with specific activities, such as stores management, showrooms management, accounting management, contracts and other departments. It also assumes that the company center exported software to manage databases for all branches to ensure the safety performance, standardization of processors and prevent the possible errors and bottlenecks problems. Also the research provides this methods the best requirements have been used for the applied of the data warehouse (DW), the information that managed by such a applied must be with high accuracy. It must be emphasized to ensure compatibility information and hedge its security, in schemes domain, been applied to a comparison between the two schemes (Star and Snowflake Schemas) with the concepts of multidimensional database. It turns out that Star Schema is better than Snowflake Schema in (Query complexity, Query performance, Foreign Key Joins),And finally it has been concluded that Star Schema center fact and change, while Snowflake Schema center fact and not change. KEYWORDS: Data Warehouses, OLAP Operation, ETL, DSS, Data Quality. I. INTRODUCTION A data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions. The data warehouse contains granular corporate data. Data in the data warehouse is able to be used for many different purposes, including sitting and waiting for future requirements which are unknown today [1]. Data warehouse provides the primary support for Decision Support Systems (DSS) and Business Intelligence (BI) systems. Data warehouse, combined with On-Line Analytical Processing (OLAP) operations, has become and more popular in Decision Support Systems and Business Intelligence systems. The most popular data model of Data warehouse is multidimensional model, which consists of a group of dimension tables and one fact table according to the functional requirements [2]. The purpose of a data warehouse is to ensure the appropriate data is available to the appropriate end user at the appropriate time [3]. Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing tools, decision makers navigate through and analyze multidimensional data [4]. Data warehouse uses a data model that is based on multidimensional data model. This model is also known as a data cube which allows data to be modeled and viewed in multiple dimensions [5]. And the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to memorize measures about situations or events. Dimensions are used to analyze these measures, particularly through aggregations operations (counting, summation, average, etc.) [6, 7]. Data Quality (DQ) is the crucial factor in data warehouse creation and data integration. The data warehouse must fail and cause a great economic loss and decision fault without insight analysis of
  9. 9. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 643 Vol. 7, Issue 3, pp. 642-651 data problems [8]. The quality of data is often evaluated to determine usability and to establish the processes necessary for improving data quality. Data quality may be measured objectively or subjectively. Data quality is a state of completeness, validity, consistency, timeliness and accuracy that make data appropriate for a specific use [9]. The paper is divided into seven sections. Section 1 introduction, Definition of Data Warehouse and The Quality of Data Warehouse. Section 2 presents related work, Section 3 presents Data Warehouse Creation and the main idea is that a Data warehouse database gathers data from an overseas trading company databases. Section 4describes Data Warehouse Design For this study, we suppose a hypothetical company with many branches around the world, each branch has so many stores and showrooms scattered within the branch location. Each branch has a database to manage branch information. Section 5 describes our evaluation Study of Quality Criteria for DW, which covers aspects related both to quality and performance of our approach, and the obtained results, and work on compare between star schema and snowflake schema. Section 6 provides conclusions. Finally, Section 7 describes open issues and our planned future work. 1.1 Definition of Data Warehouse A data warehouse is a relational database that is designed for query and analysis rather than for transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources. In addition to a relational database, a data warehouse environment can include an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, client analysis tools, and other applications that manage the process of gathering data and delivering it to business users. A common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by William Inmon [10]: 1. Subject Oriented. 2. Integrated. 3. Nonvolatile. 4. Time Variant. 1.2 The Quality of Data Warehouse Data quality has been defined as the fraction of performance over expectancy, or as the loss imparted to society from the time a product is shipped [11]. The believe was the best definition is the one found in [12, 13, and 14]: data quality is defined as "fitness for use". The nature of this definition directly implies that the concept of data quality is relative. For example, data semantics is different for each distinct user. The main purpose of data quality is about horrific data - data which is missing or incorrect or invalid in some perspective. A large term is that, data quality is attained when business uses data that is comprehensive, understandable, and consistent, indulging the main data quality magnitude is the first step to data quality perfection which is a method and able to understand in an effective and efficient manner, data has to satisfy a set of quality criteria. Data gratifying the quality criterion is said to be of high quality [9]. II. RELATED WORK In this section we will review related work in Data Warehouse Design and Implementation Based on Quality Requirements. We will start with the former. The paper introduced by Panos Vassiladis, Mokrane Bouzegeghoub and Christoph Quix, (2000), the proposed approach covers the full lifecycle of the data warehouse, and allows capturing the interrelationships between different quality factors and helps the interested user to organize them in order to fulfill specific quality goals. Furthermore, they prove how the quality management of the data warehouse can guide the process of data warehouse evolution, by tracking the interrelationships between the components of the data warehouse. Finally, they presented a case study, as a proof of concept for the proposed methodology [15]. The paper introduced by Leo Willyanto Santoso and Kartika Gunadi, (2006), this paper describes a study which explores modeling of the dynamic parts of the data warehouse. This metamodel enables data warehouse management, design and evolution based on a high level conceptual perspective, which can be linked to the actual structural and physical aspects of the data
  10. 10. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 644 Vol. 7, Issue 3, pp. 642-651 warehouse architecture. Moreover, this metamodel is capable of modeling complex activities, their interrelationships, the relationship of activities with data sources and execution details [16]. The paper introduced by Amer Nizar Abu Ali and Haifa Yousef Abu-Addose, (2010), The aim of this paper is to discover the main critical success factors (CSF) that leaded to an efficient implementation of DW in different organizations, by comparing two organizations namely: First American Corporation (FAC) and Whirlpool to come up with a more general (CSF) to guide other organizations in implementing DW efficiently. The result from this study showed that FAC Corporation had greater returns from data warehousing than Whirlpool. After that and based on them extensive study of these organizations and other related resource according to CSFs, they categorized these (CSF) into five main categories to help other organization in implementing DW efficiently and avoiding data warehouse killers, based on these factors [17]. The paper introduced by Manjunath T.N, Ravindra S Hegadi, (2013), The proposed model evaluates the data quality of decision databases and evaluates the model at different dimensions like accuracy derivation integrity, consistency, timeliness, completeness, validity, precision and interpretability, on various data sets after migration. The proposed data quality assessment model evaluates the data at different dimensions to give confidence for the end users to rely on their businesses. Author extended to classify various data sets which are suitable for decision making. The results reveal the proposed model is performing an average of 12.8 percent of improvement in evaluation criteria dimensions with respect to the selected case study [18]. III. DATA WAREHOUSE CREATION The main idea is that a Data warehouse database gathers data from an overseas trading company databases. For each branch of the supposed company we have a database consisting of the following schemas:  Contracting schema consists a contract and contractor date.  Stores schema managing storing information.  Showrooms schema to manage showrooms information for any branch of the supposed company.  At the top of the above schemas, an accounting schema was installed which manages all accounting operations for any branch or the while company. All information is stored into fully relational tables according to the known third normal form. The data integrity is maintained by using a foreign keys relationship between related tables, non-null constraints, check constraints, and oracle database triggers are used for the same purpose. Many indexes are created to be used by oracle optimizer to minimize DML and query response time. Security constraints are maintained using oracle privileges. Oracle OLAP policy is taken in consideration. IV. DATA WAREHOUSE DESIGN As mentioned above a warehouse home is installed on the same machine. The data warehouse is stored on a separate oracle table spaces and configured to use the above relational online tables as a source data. So the mentioned schemas are treated as data locations. Oracle warehouse builder is a java program, which are used warehouse managements. The locations of data sources are: 1. Accounting schema. 2. Stores schema. 3. Contracting schema. 4. Showrooms schema. For this study, we suppose a hypothetical company with many branches around the world, each branch has so many stores and showrooms scattered within the branch location. Each branch has a database to manage branch information. Within each supposed branch database there are the following schemas which work according to OLAP policies and maintain securities and data integrity. The schemas are: Accounting schema, Contracting schema, Stores schema and showrooms schema. All branches databases are connected to each other over WAN.
  11. 11. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 645 Vol. 7, Issue 3, pp. 642-651 Figure 1. Distributed database for hypothetical company. overseas company, as a base (or a source) for warehouse database. This paper suppose that each node belongs to one company branch, so each branch controls its own data. The main office of the company, controls the while data also with the central node. The warehouse database could be at the central node, as the company needs. We suppose that all nodes use the same programs, which are applied the database(s). Within each node, each activity is represented by a database schema, i.e. stores, showrooms, contracting, and other schemas. The core of all schemas is the accounting schema. According to jobs load, each schema could be installed on a separate database or on the same database. All related databases around company branches are cooperated within the same WAN. V. STUDY OF QUALITY CRITERIA FOR DW In this study, we will carry out some of the criteria, and these criteria are: 5.1. Data warehouse using snowflake schema Using oracle warehouse policies, each database has the following snow flaking modules: 1. Sales module. 2. Supplying module. 5.1.1. Sales module It consist of the following relational tables Table 1. Explain the relational table Table name Table type Oracle schema (owner) Sales_sh Fact table Showrooms showrooms Dimensional table Showrooms Items Dimensional table Accounting Currencies Dimensional table Accounting Customers Dimensional table Showrooms Locations Dimensional table Accounting The following diagram depicts the relations between the above dimensional and fact tables Figure 2. Sales module.
  12. 12. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 646 Vol. 7, Issue 3, pp. 642-651 Figure 2 above, represents all entities within the sales module. Any entity is designed using the Third Normal Form (3NF) rule, so it has a primary key. The most important tools used to implement integrity and validations are oracle constraints. After supplying data to the above module, and transferring it to oracle warehouse design center, retrieving data (557,441 rows) from fact table sales which are shown in the following figure 3, which mentions the detailed information for each single sales within each showroom, and location. It consists of: Voucher (doc) no. And date, the sold item, sold quantity and price. This data is available at the corresponding node (branch) and the center. Of course, the same data would be transferred to warehouse database for historical purpose. Figure 3. Sales data. Customers dimensional table consists of some personal date about the customer, like mobile, email, and location which is useful to contact him. Also it indicates the date of last purchase, and the total amount purchased for last year. This data is available for the corresponding node and the center; also it refers to the warehouse database. (See figure 4 customer data) The dimensional table of customers would is shown below. Figure 4. Customers data. 5.1.2. Supplying module Supplying the company by materials according to company usual contracts is managed by this module according to snowflake design. It consists of the following relational tables. Table 2. Within supplying module Table name Table type Oracle schema (owner) STR_RECIEVING Fact table Stores Contracts Dimensional table contracting Items Dimensional table Accounting Currencies Dimensional table Accounting Stores Dimensional table Stores Locations Dimensional table Accounting Daily_headers Dimensional table Accounting
  13. 13. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 647 Vol. 7, Issue 3, pp. 642-651 The following diagram Fig 5 depicts the relations between the above dimensional and fact tables. They are obey 3NF rule, so they have their primary key constraints, and constrained to each other using foreign keys constraints. The fact table STR_RECIEVING consists of all charges information received at company stores (contented by stores table owned by stores schema), according to the contracts (contented by contracts table owned by contracting schema). Daily headers dimensional table represent the accounting information for each contract. Using oracle triggers when new record inserted into the STR_RECIEVING fact table, some other accounting data would be created into details table row related (through foreign key) to Daily headers dimensional table. Also any charge value could be converted to the wanted currency using the data maintained by currencies dimensional table owned by accounting schema. Figure 5. Supplying module. For security reasons, direct excess to object fact table is not allowed, un imaginary view is created (named str_recieving_v) , then all users are allowed to generate a DML (data manipulation language instructions) on this view. A certain piece of code (oracle trigger) is written to manipulate data, according to server policies (data integrity and consistency) as user supplies data to the imaginary view. After supplying data to the above module, and transferring it to oracle warehouse design center, retrieving data (415,511rows) from fact table str_recieving as shown in the following figure. Figure 6. Received charges on str_recieving fact table. During charges insertion, a background process (oracle trigger) should update the stock dimension data to reflect the latest information about quantities in stock at each node and the center. The stock data contains the quantity balance, quantity in and out for the current year. It’s available at the
  14. 14. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 648 Vol. 7, Issue 3, pp. 642-651 corresponding node and the center, at the online database, and at warehouse database for previous years. Stock data could be like figure 7 as viewed by oracle SQL developer. Figure 7. Stock dimensional table data. 5.2. Data warehouse using star schema As a study case using warehouse star schema, we have: 1. Stocktaking module. 2. Accounting module. 5.2.1. Stocktaking module Its manage for the current stock within any store for the company, its data is determined by the following table. Table 3. Cooperated within stocktaking module Table name Table type Oracle schema (owner) Stock Fact table Stores Items Dimensional table Accounting Stores Dimensional table Stores Currencies Dimensional table Accounting showrooms Dimensional table Showrooms Locations Dimensional table Accounting contracts Dimensional table Contracting Str_recieving Fact table Stores To_show_t Fact table Stores The stock fact table stands for the actual stock balances within each store belongs to each branch, and the whole company at the center. The following diagram depicts the relations between the below dimensions. Figure 8. Stocktaking module as a warehouse star schema.
  15. 15. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 649 Vol. 7, Issue 3, pp. 642-651 DML (data manipulation language instructions) is done on stock fact table through oracle triggers which are the most trusted programs to maintain the highest level of integrity and security, so the imaginary view ( named stock_v) was created, users are allowed to supply data to that view, then the server would process the supplied data using oracle trigger. Querying the renormalized stock fact table within the star schema module, using oracle design center is depicted as below (no. of rows on stock table within our study case is 15,150). This figure 9 query execution is allowed for all users (public). Figure 9. Stocktaking on oracle warehouse design center. 5.2.2. Accounting module One of the most importance aspects of accounting functions is the calculations of the daily cash within each showroom belongs to the company. The daily totals for each branch and the grand total could be calculated. Timely based cash could be accumulated later on demand. Table 4. The tables needed for this activity Table name Type Oracle schema (owner) Daily cash Fact table Accounting Show_sales Fact table Accounting Showrooms Dimensional table Showroom Currencies_tab Dimensional table Accounting Locations Dimensional table Accounting Customers Dimensional table showrooms The daily cash is a view used to reflect the actual cash with each showroom on daily base. Figure 10. Daily cash using warehouse star schema.
  16. 16. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 650 Vol. 7, Issue 3, pp. 642-651 Using inner SQL joins, one could retrieve data about daily cash as follows. Figure 11. Grand daily cash as depicted by Oracle warehouse design center. VI. CONCLUSIONS The following expected conclusions have been drawn: 1. Reduce the query response time and Data Manipulation Language and using many indexes which are created to be used by oracle optimizer. 2. Star Schema is best of them Snowflake Schema the following points are reached:  Query complexity: Star Schema the query is very simple and easy to understand, while Snowflake Schema is more complex query due to multiple foreign key which joins between dimension tables .  Query performance: Star Schema High performance. Database engine can optimize and boost the query performance based on predictable framework, while Snowflake Schema is more foreign key joins; therefore, longer execution time of query in compare with star schema.  Foreign Key Joins: Star Schema Fewer Joins, while Snowflake Schema has higher number of joins. And finally it has been concluded that Star Schema center fact and change, while Snowflake Schema center fact and not change. VII. FUTURE WORKS 1. Using any other criteria in development implementation of the proposed system. 2. Using statistical methods to implement other criteria of Data Warehouse. 3. Applying algorithm Metadata and comparing between bitmap index and b-tree index. 4. Applying this work for a real organization not prototype warehouse. 5. Take advantage of the above standards in improving the performance of the use of the data warehouse and institutions according to their environment. ACKNOWLEDGEMENTS I would like to thank my supervisor Assist. Prof. Dr. Murtadha Mohammed Hamad for his guidance, encouragement and assistance throughout the preparation of this study. I would also like to extend thanks and gratitude to all teaching and administrative staff in the College of Computer – University of Anbar, and all those who lent me a helping hand and assistance. Finally, special thanks are due to members of my family for their patience, sacrifice and encouragement.
  17. 17. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 651 Vol. 7, Issue 3, pp. 642-651 REFERENCES [1] H. William Inmon, “Building the Data Warehouse”. Fourth Edition Published by Wiley Publishing, Inc., Indianapolis, Indiana.2005. [2] R. Kimball, “The data warehouse lifecycle toolkit”. 1st Edition ed.: New York, John Wiley and Sons, 1998. [3] K.W. Chau, Y. Cao, M. Anson and J. Zhang, “Application of Data Warehouse and Decision Support System in Construction Management”. Automation in Construction 12 (2) (2002) 213-224. [4] Nicolas Prat, Isabelle Comyn-Wattiau and Jacky Akoka , “Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems”. Data & Knowledge Engineering 70 (2011) 732–752. [5] Singhal, Anoop, “Data Warehousing and Data Mining Techniques for Cyber Security”. Springer. United states of America.2007. [6] Bhanali, Neera,”Strategic Data Warehousing: Achieving Alignment with Business”. CRC Prees . United State of America. 2010. [7] Wang, John. “Encyclopedia of Data Warehousing and Mining “.Second Edition. Published by Information Science Reference. United States of America. 2009. [8] Yu. Huang, Xiao-yi. Zhang, Yuan Zhen , Guo-quan. Jiang, “A Universal Data Cleaning Framework Based on User Model “, IEEE,ISECS International Computing, Communication, Control , and Management, Sanya, China, Aug 2009. [9] T.N. Manjunath, S. Hegadi Ravindra, G.K. Ravikumar, “Analysis of Data Quality Aspects in Data Warehouse Systems”. International Journal of Computer Science and Information Technologies, Vol. 2 (1), 2011, 477-485. [10] Paul Lane, “Oracle Database Data Warehousing Guide”. 11g Release 2 (11.2), September 2011. [11] D.H. Besterfield, C. Besterfield-Michna, G. Besterfield, and M. Besterfield-Sacre, ” Total Quality Management”. Prentice Hall, 1995. [12] G.K. Tayi, D.P. Ballou, “Examining Data Quality”. In Communications of the ACM, 41(2), 1998. pp. 54-57. [13] K. Orr,” Data quality and systems theory”. Communications of the ACM, 41(2), 1998. pp. 66-71. [14] R.Y. Wang, D. Strong, L.M. Guarascio, “Beyond Accuracy: What Data Quality Means to Data Consumers”. Technical Report TDQM-94-10, Total Data Quality Management Research Program, MIT Sloan School of Management, Cambridge, Mass., 1994. [15] Panos vassiladis, Mokrane Bouzegeghoub and Christoph Quix. “Towards quality-oriented data warehouse usage and evolution”. Information Systems Vol. 25, No. 2, pp. 89-l 15, 2000. [16] Leo Willyanto Santoso and Kartika Gunadi. “A proposal of data quality for data warehouses environment”. Journal Information VOL. 7, NO. 2, November 2006: 143 – 148 [17] Amer Nizar AbuAli and Haifa Yousef Abu-Addose. “Data Warehouse Critical Success Factors”. European Journal of Scientific Research ISSN 1450-216X Vol.42 No.2 (2010), pp.326-335. [18] T.N. Manjunath, S. Hegadi Ravindra, “Data Quality Assessment Model for Data Migration Business Enterprise”. International Journal of Engineering and Technology (IJET) ISSN: 0975-4024, Vol 5 No 1 Feb- Mar 2013. AUTHOR PROFILE Khalid Ibrahim Mohammed was born in BAGHDAD, IRAQ, in 1981, received his Bachelor’s Degree in computer Science from AL-MAMON UNIVERSITY COLLEGE, BAGHDAD, IRAQ during the year 2006 and M. Sc. in computer Science from College of Computer Anbar University, ANBAR , IRAQ during the year 2013. He is having total 8 years of Industry and teaching experience. His areas of interests are Data Warehouse & Business Intelligence, multimedia and Databases.
  18. 18. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 652 Vol. 7, Issue 3, pp. 652-665 A NEW MECHANISM IN HMIPV6 TO IMPROVE MICRO AND MACRO MOBILITY Sahar Abdul Aziz Al-Talib Computer and Information Engineering Electronics Engineering, University of Mosul, Iraq ABSTRACT The paper proposes new mechanism in Hierarchical Mobile IPv6 (HMIPv6) implementation for utilization in IPv6 Enterprise Gateway. This mechanism provides seamless mobility and fast handover of a mobile node in HMIPv6 networks. Besides it provides continuous communication among a member of HMIPv6 components while roaming. The mechanism anticipates future movement of mobile node and accordingly provides an effective updating mechanism. A bitmap has been proposed to help in applying the new mechanism as will be explained throughout this paper. Instead of scanning the prefix bits which might be up to 64 bits for subnet discovery, about 10% of this length will be sufficient to determine the hierarchical topology. This will enable fast Micro and Macro HMIPv6 in Enterprise Gateway. KEYWORDS: Hierarchical Mobile IPv6, bitmap, Enterprise Gateway, Mobility Anchor Point. I. INTRODUCTION IPv6 Enterprise Gateway is an apparatus or device which intercommunicate the public network with enterprise network. The enterprise network includes many sub-networks with different access routers or home anchors. For flexibility, HMIPv6 is used to organize these enterprise networks in hierarchal way. HMIPv6 stands for Hierarchical Mobile Internet Protocol Version 6. HMIPv6 is provisioning the different access areas by assigning mobile anchor point (MAP) to each area. The MAP stands on behalf of the enterprise gateway (home agent) in its particular coverage area. Besides, it will reduce the control messages exchanged and decrease the time when the mobile node roaming to another network. The mobile node can use the local MAP to keep the communication on without the need to communicate with enterprise gateway. It is known that Hierarchical Mobile IPv6 (HMIPv6) provides a flexible mechanism for local mobility management within visited networks. The main problem in hierarchical mobility is that the communication between the correspondent node (CN) and the mobile node (MN) suffers from significant delay in case of the mobile node roaming between different MAPs or between different access routers (ARs) in the same MAP coverage area. This happens because the MN in the visited network acquires a local care of address (LCoA), therefore it receives the router advertisement (RA) and uses its prefix to build its new LCoA. This process causes a communication delay between the CN and the MN for a while, especially when the roaming happens between different MAPs. This process involves more binding update messages to be sent not just to the local MAP, but also to the enterprise gateway as shown in Figure 1, the messages flow to acquire new CoA starts after MN reached the foreign network. The patent in [1] anticipates the probable CoA’s based on the number of vicinity Access Routers, which means the number of probable CoA in each single movement, and then the MAP should multicast the traffic to all of these addresses. In this paper, the Home Anchor creates the anticipated CoA based on the received bitmap from MN, this Bitmap refers to the strongest signal that the MN
  19. 19. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 653 Vol. 7, Issue 3, pp. 652-665 was triggered by the foreign router, this means Home Anchor have a single probable address to tunnel the packet to, besides the current CoA. RFC4260 [2] proposed the idea of anticipating the new CoA and tunnel the packets between the previous Access Router and the New Access Router. The differences between the work in this paper and in [2] is that packet loss will likely occur if the process is performed too late or too early with respect to the time in which the mobile node detaches from the previous access router and attach to the new one, or the packet loss is likely occur in case the BU cache in the home anchor is updated but the layer 2 handover does not complete yet. The work in [3] proposed a scheme that reduced the 82% of the total cost of the macro mobility handover of the original HMIPv6. Kumar et al. in [4] proposed an analytical model which shows the performance and applicability of MIPv6 and HMIPv6 against some key parameters in terms of cost. An adaptive MAP selection based on active overload prevention (MAP-AOP) is proposed in [5]. The MAP periodically evaluates the load status by using dynamic weighted load evaluation algorithm, and then sends the load information to the covered access routers (AR) by using the expanded routing advertisement message in a dynamic manner. In this paper, a solution to the explained problem has been proposed in which the mobile node sends binding update request at the moment when it initiates the movement to the foreign network, and to add a bitmap field to the binding update request to recognize different components of the HMIPv6. The paper is organized as follows. Section 2 describes the methodology of the proposed mechanism supported by figures; section 3 illustrates the new operation of micro and macro-mobility. Binding update message format is introduced in section 4 where the bitmap idea comes from. Finally, section 5 concludes the work and presents the future work. II. METHODOLOGY The objective of this paper is provided by a method for multi-cell cooperative communication comprising: detecting a beacon from a new access router, translating the beacon received from the new access router to a bitmap, sending a binding update request message that contains the bitmap to a current mobile access point, assessing the bitmap, tunneling data packets from a correspondent node through the enterprise gateway to the current destination and to the new destination simultaneously, sending an update message once the mobile node reaches the new destination, refreshing binding cache tables and tunneling the data packets only to the new destination according to the new address of mobile node.
  20. 20. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 654 Vol. 7, Issue 3, pp. 652-665 Figure 1 (a): Flow Control of Micro and Macro Mobility Process (part 1)
  21. 21. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 655 Vol. 7, Issue 3, pp. 652-665 Figure 1 (b): Flow Control of Micro and Macro Mobility Process (part 2) Some of the most important key points in this paper:  Adding a bitmap which is a number of binary bits proposed to be used instead of the subnet prefix to specify the subnet.
  22. 22. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 656 Vol. 7, Issue 3, pp. 652-665  Propose to update the binding update message to include a bitmap field; this field is occupied by a lowest part which refers to the router’s prefix and a highest part which refers to the home anchor’s address, the proposed message can be called binding update request which should be sent as soon as the MN initiates the movement to the anticipated foreign network.  The mobile node has the ability to measure the beacon strength in order to decide which anchor point to join as the future home agent.  Binding Update message: it is mentioned in HMIPv6 standard [6, 7], this message is sent by the mobile node to the home anchor or the home agent (in some cases it is sent to correspondent node), it is used to register the MN’s Care of Address in its home anchor or its home agent, but this message is sent after the MN acquires a CoA in its foreign network.  Binding cache Table: it is a mapping table that resides in the Enterprise Gateway (home agent) and home anchor points to translate the value of the bitmap to its equivalent prefix. Also it is used to track the MN’s movement.  The proposed solution contributes to both inter-domain (macro-mobility) and intra-domain (micro-mobility) in terms of handover delay. This will improves multimedia applications. 1. The New Operation of Micro and Macro-mobility: a) Operation of Micro Mobility: As shown in Figure 1, the proposed solution suggested sending a binding update request (BU req.) when the MN initiates its movement to the foreign network. The MN will sense the strong signal from the foreign network router. It translates this to the bitmap field by setting up the corresponding bit. The contributed bitmap in this paper requires updating the present binding update message by using the reserved bits already exist for future use. Then the MN sends the updated BU req. message to the upstream Home Anchor. When the home anchor receives the updated BU req., it will check the highest bits in the bitmap, as we can see in the decision box (Figure 1), if these bits refers to the home anchor itself, the home anchor will learn that this is a micro-mobility and create a new address (CoA) for the MN according to the lowest bits of the bitmap. Now, the Home Anchor cache table has two CoAs for a particular MN one for the previous MN’s location and the other for the new location. It starts to tunnel the packets to both CoAs for a while till the handover preparation is finalized. Therefore the MN will have a seamless mobility and continuous communication, even before obtaining the new CoA from the future foreign router. When the MN reaches the foreign router area and obtains the CoA, it will send a Binding update message which includes the HoA and the CoA to the upstream Home Anchor, then the Home Anchor will refresh its binding cache and delete the old CoA. Finally, the Home Anchor will tunnel the packets only to the new CoA. b) Operation of Macro Mobility This section explains the mobility between two routers each of them belongs to different Home Anchor or different domains. The process flow is shown in Figure 1. Assume the MN initiates the movement to a router that is connected to different home Anchor. The MN receives the beacon from the foreign router and recognizes it as different from the old one from the prefix. It reflects this in the higher bits of the bit map. It updates the BU request message accordingly with the new bitmap then sends the message to the upstream Home Anchor. When the Home Anchor receives the BU req., it will check the highest bits in the bitmap, in this case it will recognize that these bits refers to another Anchor, therefore it forwards the BU req. to the Enterprise Gateway (home agent). At the same time, the home anchor translates the lowest part of the bitmap to its prefix and adds it to the MAC address to form the new CoA. The Home Anchor will tunnel the packets to the current CoA and the new CoA at the same time. On the other hand, the Enterprise Gateway receives the BU req. and translates the highest bits of the bitmap to the equivalent Home Anchor address. Besides, it refreshes the cache table with this new value and starts to tunnel the packets to the current Home Anchor and the new Home Anchor. But the traffic which is destined to the new Anchor will not be forwarded to the MN till the MN reaches the particular foreign router and obtained a new CoA. At this time the MN will send a BU message to the Enterprise Gateway. As a result, the Home Agent refreshes its cache table to delete the previous Anchor and includes the
  23. 23. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 657 Vol. 7, Issue 3, pp. 652-665 updated one then tunnels the traffic to the new Home Anchor. The new home anchor in turn will tunnel the packets to the new CoA of the mobile node in the new location. 2. Binding update message format The main contribution in this work is based on the idea of using the reserved bits in the BU message shown in Figure 2. As can be seen in Figure 2 the binding update message has reserved bits starting from bit 5 up to bit 15, so some or all of these bits can be used to implement the proposed mechanism in this paper. For example, if 6 bits are used, the highest 2- bits can be assigned to the Home Anchors with possibilities of (00, 01, 10, 11), the lowest 4-bits can be assigned to the access routers (ARs) which can handle 24 or 16 routers starting from 0000 to 1111. 0 1 2 3 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | Sequence # | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ |A|H|L|K|M| Reserved | Lifetime | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | | | | +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ Figure 2: Binding update message format [3] Figure 3 shows the system architecture for the proposed mechanism, here are some abbreviations: CN: Correspondent Node. MN: Mobile Node. BU req.: Binding Update request message. ACK: Acknowledgement message HoA: MN’s Home Address. CoA: MN’s Care of Address in the foreign network. Based on the architecture, the MN initiates its movement from the home network to Home Anchor-1. As shown in Figure 3, MN receives the beacon from R2 in Home Anchor1‘s coverage area and it translates this signal to the bitmap by setting the correspondent bit. The MN will send a BU request message which contains the bitmap to the Home Anchor1. Home Anchor1 builds its bitmap mapping table by translating the received bitmap to CoA2, then it replies back an acknowledgment message to MN. The MN receives the acknowledgment and sends another BU request message to the Enterprise Gateway (home Agent). The Enterprise Gateway receives the BU req. and checks the highest bits in the bitmap field and translates it to the equivalent home Anchor’s addresses. In this architecture, the highest bits refer to the Home Anchor-1, then the Home Agent refreshes its bitmap mapping table to add the Home Anchor’s addresses in the table.
  24. 24. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 658 Vol. 7, Issue 3, pp. 652-665 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4040::/64 2407:4050::/64 2407:4060::/64 MN HoA: 2407:3000:200d:312::b6:d2 CoA2: 2407:4020::b6:d2 1. Movement 2. BU req. (HoA: 2407:3000:200d:312::b6:d2, Bit Map: 000010) Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Address 2407:4020::b6:d2 3. Binding cache Table Lowest bit represents->R 4. Ack 5. BU (HoA: 2407:3000:200d:312::b6:d2, Bit Map: 000010) 2407:4000::2 (HA1) Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 00 Binding cache Table The Highest bits represent Home Anchors address 6. 5. MN Figure 3: MN moves from Home Network to the R2’s Network Assume there is a correspondent node (CN) in the Home Network vicinity as shown in Figure 4, the CN will send the packets to the home agent address (HoA), and the Home Agent receives the traffic and checks the binding cache table. It will find out the HoA refers to the Home Anchor-1, therefore the Home Agent tunnels the packets to Home Anchor-1, and the Home Anchor-1 in turn tunnels the packets to CoA2 according to its cache table. Finally the MN receives the packets from R2 as shown in Figure 4. Figure 5 shows the Micro-mobility scenario in the proposed mechanism, where MN indicates the augmentation of the signal received from R3 while it initiates its movement towards R3, thus it translates the beacon received from R3 to the equivalent bitmap. Then, MN sends a BU req. message including the bitmap field to the Home Anchor-1 which checks the highest bits of the received bitmap, the highest bits refers to the same Home Anchor-1. Therefore Home Anchor-1 translates the received bitmap to the equivalent Care of Address which is CoA3 (new CoA) in this case, and it adds it to the Home Anchor-1’s binding cache table.
  25. 25. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 659 Vol. 7, Issue 3, pp. 652-665 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4040::/64 2407:4050::/64 2407:4060::/64 MN HoA: 2407:3000:200d:312::b6:d2 CoA2: 2407:4020::b6:d2 Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Address 2407:4020::b6:d2 2407:4000::2 (HA1) Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 000010 CN 1. Packet destined to HoA 2. Packet Tunneled to HA1 3. Packet Tunnel to CoA2 4. MN receives and decapsulate the packet Figure 4: CN sends Packets to the MN in its Foreign Network (R2) So far, the Home Anchor-1 tunnels the packets to both CoA2 and CoA3 at the same time, this will provide a continuous communication between CN and MN. MN still can receive the traffic even before sending a BU message to Home Anchor-1 through R3. Figure 6 shows that when MN obtains CoA3 from R3, it will send a BU message to Home Anchor-1 through R3, then Home Anchor-1 checks the Binding cache table again and refreshes it. It will exclude CoA2 from the table and tunnels the packet only to CoA3 according to the new address of MN.
  26. 26. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 660 Vol. 7, Issue 3, pp. 652-665 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4040::/64 2407:4050::/64 2407:4060::/64 MN HoA: 2407:3000:200d:312::b6:d2 CoA2: 2407:4020::b6:d2 3. Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Adress 2407:4020::b6:d2 2407:4000::2 (HA1) Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 000010 CN Packet destined to HoA Packet Tunneled to HA1 4. Tunneling the packets to CoA2 and Probable CoA3 5. MN receives and decapsulate the packet 1. Movement MN 2 2. BU req. (HoA: 2407:3000:200d:312:b6:d2, Expected bitmap: 000011) 000011 2407:4030::b6:d2 1. Initiate the movement toward R3 Figure 5: MN starts to move to R3 Figure 7 shows the macro-mobility between two different home anchors. MN initiates its movement toward R4, the same procedure previously explained will be followed, and MN will decry the augmentation of R4’s signal and translate it to the equivalent bitmap. MN sends a BU req. message to Home Anchor-1; here the difference is that the Home Anchor checks the received bitmap, it finds out that the highest bits of bitmap refers to another home anchor. Therefore, Home Anchor-1 will forward the BU req. to the upstream Enterprise Gateway (Home Agent) and at the same time it translates the lowest bits to the equivalent CoA (in this case CoA4). Now, Home Anchor-1 tunnels the packets to CoA3 and CoA4 as shown in Figure 7, Enterprise gateway receives the BU req. from Home Anchor-1, and translates the highest bit to the equivalent home anchor’s address, as shown in figure 7. The Enterprise gateway will add the address of Home Anchor-2, and then it will start to tunnel the packets to Home Anchor-2. Home Anchor-2 still does not have a record in its binding cache table to track the new MN’s CoA.
  27. 27. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 661 Vol. 7, Issue 3, pp. 652-665 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4060::/64 2407:4070::/64 2407:4080::/64 HoA: 2407:3000:200d:312::b6:d2 CoA3: 2407:4030::b6:d2 2. Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Adress 2407:4020::b6:d2 2407:4000::2 (HA1) Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 000010 CN Packet destined to HoA Packet Tunneled to HA1 MN receives and decapsulate the packet 1. BU (HoA: 2407:3000:200d:312:b6:d2, CoA3: 2407:3030::b6:d2) 000011 2407:4030::b6:d2 MN 2. Refresh the table Figure 6: MN obtained CoA3 from R3 As shown in figure 8, MN acquired CoA4 and sends a BU message to Home Anchor-2 and Home Anchor-1 simultaneously. Home Anchor-1 updates its table and sends packets to CoA4 only and at the same time Home Anchor-2 starts to forward the traffic to CoA4. The MN will send a BU message to the Enterprise Gateway (Home Agent) telling about CoA4. Home Agent receives the BU message, checks the MN’s CoA then excludes Home Anchor-1 from the table and sends the traffic to Home Anhcor-2 only which is the next hop to reach CoA4.
  28. 28. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 662 Vol. 7, Issue 3, pp. 652-665 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4060::/64 2407:4070::/64 2407:4080::/64 HoA: 2407:3000:200d:312::b6:d2 CoA3: 2407:4030::b6:d2 3. Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Adress 2407:4020::b6:d2 2407:4000::2 (HA1) 6. Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 0010 CN Packet destined to HoA 2. BU request (HoA: 2407:3000:200d:312:b6:d2, Bitmap: 100110) 000011 2407:4030::b6:d2 MN 1. Initiates movement toward R4 100110 2407:4060::b6:d2 4. checking the Highest bit 5. a)Forward BU request (HoA: 2407:3000:200d:312:b6:d2, Bitmap: 100110) 2407:5000::2 (HA2)100110 7. Tunnel the packet to HA1 & HA2 5. b)Tunnel packets to CoA3 & CoA4 MN Figure 7: MN initiates movement to Home Anchor-2 (R4)
  29. 29. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 663 Vol. 7, Issue 3, pp. 652-665 100110 EnterpriseGateway /HomeAgent 2407:3000:200d:312::2/64 HomeAnchor1 2407:4000::2/64 HomeAnchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4060::/64 2407:4070::/64 2407:4080::/64 HoA:2407:3000:200d:312::b6:d2 CoA4:2407:4060::b6:d2 2.HomeAddress Bitmap 2407:3000:200d:312:b6:d2 000010 CareofAdress 2407:4020::b6:d2 2407:4000::2(HA1) HomeAddress Bitmap 2407:3000:200d:312:b6:d2 HomeAnchor 000010 CN PacketdestinedtoHoA 1.BU (HoA:2407:3000:200d:312:b6:d2, CoA4:2407:4060::b6:d2) 000011 2407:4030::b6:d2 MN 100110 2407:4060::b6:d2 2.a)Refreshthetable 2407:5000::2(HA2) TunnelthepackettoHA1&HA2 HomeAddress Bitmap 2407:3000:200d:312:b6:d2 0010 HomeAddress 2407:4060::b6:d2 2.a)Refreshthetable 3.ForwardsthepacketfromHomeAgent toCoA4 Figure 8: Home Anchor-2 forwards the traffic to CoA4
  30. 30. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 664 Vol. 7, Issue 3, pp. 652-665 100110 Enterprise Gateway /Home Agent 2407:3000:200d:312::2/64 Home Anchor1 2407:4000::2/64 Home Anchor2 2407:5000::2/64 R1 R2 R3 R4 R5 R6 2407:4010::/64 2407:4020::/64 2407:4030::/64 2407:4060::/64 2407:4070::/64 2407:4080::/64 HoA: 2407:3000:200d:312::b6:d2 CoA4: 2407:4060::b6:d2 2. Home Address Bitmap 2407:3000:200d:312:b6:d2 000010 Care of Adress 2407:4020::b6:d2 2407:4000::2 (HA1) 2. Home Address Bitmap 2407:3000:200d:312:b6:d2 Home Anchor 000010 CN Packet destined to HoA 1. BU (HoA: 2407:3000:200d:312:b6:d2, CoA4: 2407:4060::b6:d2) 000011 2407:4030::b6:d2 MN 100110 2407:4060::b6:d2 2. a) Refresh the table 2407:5000::2 (HA2) Home Address Bitmap 2407:3000:200d:312:b6:d2 0010 CoA 2407:4060::b6:d2 Forwards the packet from Home Agent to CoA4 2. refresh the Home Agent table Packet tunnel to HA2 Figure 9: Updating the Home Agent Table III. CONCLUSIONS AND FUTURE WORK From the proposed and explained scenarios, the MN will have a seamless mobility by anticipating the new location and new CoA and take some actions as soon as it initiates the movement. On the other hand, this mechanism will save the network resources by updating the cache table of the server. Which means the traffic will not keep multicasting to multi probable addresses but only to one probable address in each single movement. As a result, the real time (multimedia) applications will experience better service flow. A future work will study how the multimedia applications are affected when applying the proposed mechanism. ACKNOWLEDGEMENT The author would like to thank Mr. Aus S. Matooq for his help reviewing, commenting and his feedback on this paper.
  31. 31. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 665 Vol. 7, Issue 3, pp. 652-665 REFERENCES [1]. Omae K., Okajima I., Umeda N., “Mobility Management System, and Mobile Node Used in the System, Mobility Management Method, Mobility Management Program, And Mobility Management Node”, International Patent US 7,349,364 B2, published date: 25 March 2008. [2]. McCann P., “Mobile IPv6 Fast Handovers for 802.11 Networks”, Lucent Technologies, RFC4260, 2005. [3]. Lee K. et. al., “A Macro Mobility Handover Performance Improvement Scheme for HMIPv6”, Lecture Notes in Computer Science, Volume 3981, 2006, pp 410-419. [4]. Kumar V., Lall G C and Dahiya P., “Performance Evaluation of MIPv6 and HMIPv6 in terms of Key Parameters”, International Journal of Computer Applications 54(4):1-3, September 2012. [5]. Tao M., Yuan H. and Wei W., “Active overload prevention based adaptive MAP selection in HMIPv6 networks”, Journal of Wireless Networks, February 2014, Volume 20, Issue 2, pp 197-208. [6]. Soliman H., Castelluccia C.,El Malki K., Bellier L., “Hierarchical Mobile IPv6 Mobility Management (HMIPv6)”, RFC 4140, 2005. [7]. Soliman H., Castelluccia C.,El Malki K., Bellier L., “Hierarchical Mobile IPv6 (HMIPv6) Mobility Management”, RFC 5380, 2008. AUTHORS BIOGRAPHY Sahar A. Al-Talib received her BSc. in Electrical and Communication Engineering from University of Mosul in 1976, her MSc, and PhD in Computer and Communications Engineering from University Putra Malaysia (UPM), in 2003 and 2006 respectively. She served at MIMOS Berhad, Technology Park Malaysia, for about 4 years as Staff Researcher where she enrolled in infrastructure mesh network project (P000091) adopting IEEE 802.16j/e/d wireless technology. She became a lecturer at the Department of Computer and Information Engineering, Electronics Engineering, University of Mosul, Iraq in 2011. She was the main inventor of 10 patents and published more than 20 papers in local, regional, and international journals and conferences. She certified CISCO-CCNA, WiMAX RF Network Engineer, WiMAX CORE Network Engineer, IBM “Basic Hardware Training Course”- UK, Cii-Honeywell Bull “Software Training Course on Time Sharing System and Communication Concept”- France, Six-Sigma white belt, Harvard Blended Leadership Program by Harvard Business School Publishing – USA, Protocol Design and Development for Mesh Networks by ComNets – Germany.
  32. 32. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 666 Vol. 7, Issue 3, pp. 666-674 EFFICIENCY OPTIMIZATION OF VECTOR-CONTROLLED INDUCTION MOTOR DRIVE Hussein Sarhan Department of Mechatronics Engineering, Faculty of Engineering Technology, Amman, Jordan ABSTRACT This paper presents a new approach that minimizes total losses and optimizes the efficiency of a variable speed three-phase, squirrel-cage, vector-controlled induction motor drive through optimal control, based on combined loss-model control and search control. The motor power factor is used as the main control variable. An improvement of efficiency is obtained by online adjusting the stator current according to the value of power factor corresponding to the minimum total losses at a given operating point. The drive system is simulated using Matlab SIMULINK models. A PIC microcontroller is used as minimum loss power factor controller. Simulation results show that the proposed approach significantly improves the efficiency and dynamic performance of the drive system at all different operating conditions. KEYWORDS: Efficiency Optimization, Induction Motor Drive, Optimal Power Factor, Simulation, Vector Control. I. INTRODUCTION Squirrel-cage three-phase induction motors IMs are the workhorse of industries for variable speed applications in a wide power range. However, the torque and speed control of these motors is difficult because of their nonlinear and complex structure. In general there are two strategies to control these drives: scalar control and vector control. Scalar control is due to magnitude variation of the control variable only. The stator voltage can be used to control the flux, and frequency or slip can be adjusted to control the torque. Different schemes for scalar control are used, such as: constant V/f ratio, constant slip, and constant air-gap flux control. Scalar controlled drives have been widely used in industry, but the inherent coupling effect (both torque and flux are function of stator voltage or current and frequency) give sluggish response and system is easily prone to instability [1-3]. To improve the performance of scalar-controlled drives, a feedback by angular rotational speed is used. However, it is expensive and destroys the mechanical robustness of the drive system. Performance analysis of scalar- controlled drives shows that scalar control can produce adequate performance in variable speed drives, where the precision control is not required. These limitations of scalar control can be overcome by implementing vector (field oriented) control. Vector control was introduced in 1972 to realize the characteristics of separately-excited DC motor in induction motor drives by decoupling the control of torque and flux in the motor. This type of control is applicable to both induction and synchronous motors. Vector control is widely used in drive systems requiring high dynamic and static performance. The principle of vector control is to control independently the two Park components of the motor current, responsible for producing the torque and flux respectively. In that way, the IM drive operates like a separately-excited DC motor drive (where the torque and the flux are controlled by two independent orthogonal variables: the armature and field currents, respectively) [4-8]. Vector control schemes are classified according to how the field angle is acquired. If the field angle is calculated by using stator voltage and currents or hall sensors or flux sensing winding, then it is known as direct vector control DVC. The field angle can also be obtained by using rotor position measurement and partial estimation with only machine parameters, but not any other variables, such
  33. 33. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 667 Vol. 7, Issue 3, pp. 666-674 as voltages or currents. Using this field angle leads to a class of control schemes, known as indirect vector control IVC [1, 9]. From energetic point of view, it is well known that three-phase induction motors, especially the squirrel-cage type are responsible for most of energy consumed by electric motors in industry. Therefore, motor energy saving solutions by increasing its efficiency has received considerable attention during the last few decades due to the increase in energy cost. Energy saving can be achieved by proper motor selection and design, improvement of power supply parameters and utilizing a suitable optimal control technique [3-5, 10,11] Induction motor operation under rated conditions is highly efficient. However, in many applications, when the motor works at variable speed, it has more losses and less efficiency, so it operates far from the rated point. Under these circumstances, it is not possible to improve the motor efficiency by motor design or by supply waveform shaping technique. Therefore, a suitable control algorithm that minimizes the motor losses will rather take place. Minimum-loss control schemes have can be classified into three categories: search method, loss model, power factor control. The power factor control scheme has the advantage that the controller can be stabilized easily and the motor parameter information is not required. However, analytical generation of the optimal power factor commands remains tedious and restrictive because empirical, trial and error methods are generally used [4,5]. For this reason, search control using digital controllers is preferable. In this paper, a combined minimum-loss control and search control approach is used to find the power factor, corresponding to the minimum losses in the drive system at a specified operating point. A PIC microcontroller is used as an optimal power factor controller to online adjust the stator current (voltage) to achieve the maximum efficiency of the drive system. II. EFFICIENCY OPTIMIZATION OF VECTOR-CONTROLLED DRIVE SYSTEM The generalized block diagram of vector-controlled induction motor drive is shown in Figure 1. Figure 1. Generalized block diagram of vector-controlled induction motor drive. To perform vector control, the following steps are required [1]: 1. Measurements of motor phase voltages and currents. 2. Transformation motor phase voltages and currents to 2-phase system ( , ) using Clarke transformation, according to the following equation:
  34. 34. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 668 Vol. 7, Issue 3, pp. 666-674 )( 2 3 ], 2 1 2 1 [ scsbs scsbsas iiki iiiki     (1) where: sai actual stator current of the motor phase A sbi actual stator current of the motor phase B sci actual current of the motor phase C constk  3. Calculation of rotor flux space vector magnitude and position angle through the components of rotor flux. 4. Transformation of stator currents to qd  coordinate system using Park transformation, according to: fieldsfieldssq fieldsieldfssd iii iii     cossin ,sincos   (2) where field is the rotor flux position. The component sdi is called the direct axis component (the flux-producing component) and sqi is called the quadrature axis component (the torque-producing component). They are time invariant; flux and torque control with them is easy. The values of fieldsin and fieldcos can be calculated by: rd r field rd r field           cos ,sin (3) where:  rrrd 22  (4) The rotor flux linkages can be expressed as:   smrrr smrrr iLiL iLiL   , (5) 5. The stator current torque- sqi and flux- sdi producing components are separately controlled. 6. Calculation of the output stator voltage space vector using decoupling block. 7. Transformation of stator space vector back from qd  coordinate system to 2-phase system fixed with the stator using inverse Park transformation by: fieldsqfieldsds fieldsds iii fieldisqii     cossin ,sincos   (6) 8. Generation of the output 3-phase voltage using space modulation. The developed electromagnetic torque of the motor T can be defined as: qsdr r m i L L PT  4 3  (7) where P is the number of poles of the motor. From Equation (7) it is clear that the torque is proportional to the product of the rotor flux linkages and q-component of the stator current. This resembles the developed torque expression of the DC motor, which is proportional to the product of the field flux linkages and the armature current. If the
  35. 35. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 669 Vol. 7, Issue 3, pp. 666-674 rotor flux linkage is maintained constant, then the torque is simply proportional to the torque producing component of the stator current, as in the case of the separately excited DC machine with armature current control, where the torque is proportional to the armature current when the field is constant. The power factor p.f is also a function of developed torque, motor speed and rotor flux, and can be calculated as the ratio of input power to apparent power [3-4]: dsqsdsqs dsdsqsqs iivv iviv fp 2222 .    (8) The power factor can be used as a criterion for efficiency optimization. The optimal power factor corresponding to minimum power losses in the drive system can be found analytically or by using search method of control. To avoid tedious analytical calculations of power factor, a search control is implemented in this paper. The stator voltage is incrementally adjusted until the controller detects the minimum total losses at a given operating point. The total power losses P can be calculated as the difference between the input power inP and the output mechanical power outP : TivivP dsdsqsqs  )( 2 3 (9) The block diagram of proposed efficiency optimization system for vector-controlled induction motor is shown in Fig. 2. The system consists of 400V,4kW, 1430 rpm, 50Hz three-phase, squirrel-cage induction motor. The motor is fed from three phase AC-to-AC energy converter, based on voltage controlled pulse-width modulated inverter. A PIC 16f877A microcontroller is used as a controller in the system. Figure 2. Block diagram of vector-controlled optimized system. To investigate the proposed optimized system, two Matlab SIMULINK models were constructed. The first model, which is shown in Figure 3, represents the original vector-controlled drive system without efficiency optimization. The second model, Figure 4, illustrates the efficiency-optimized, vector- controlled system.
  36. 36. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 670 Vol. 7, Issue 3, pp. 666-674 Figure 3. Matlab SIMULINK model of vector–controlled drive system. Figure 4. Matlab SIMULINK model of efficiency-optimized, vector-controlled drive system. The PIC microcontroller was linked to the Matlab model by interface circuit shown in Figure 5. Figure 5. Block diagram of interface circuit between Matlab model and PIC microcontroller model.
  37. 37. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 671 Vol. 7, Issue 3, pp. 666-674 III. SIMULATION RESULTS To show the effect of proposed efficiency optimization technique based on power factor optimization, a comparative analysis between original drive system and optimized one has been done. Investigation was provided under the same operating conditions: frequency (speed) and load torque. Figures (6-8) show the relationship between efficiency and load torque under constant frequency. Figure 6. Relationship between efficiency and load torque at constant frequency Hzf 50 . It is clear that the implemented efficiency optimization technique improves the efficiency of the drive system for the whole frequency range. Improvement is significant at light loads. Figure 7. Relationship between efficiency and load torque at constant frequency Hzf 35 . Figure 8. Relationship between efficiency and load torque at constant frequency Hzf 15 .
  38. 38. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 672 Vol. 7, Issue 3, pp. 666-674 The relationship between efficiency and frequency at constant load torque is presented in Figures (9- 10). At light load torques the efficiency improvement is very significant for the whole frequency range. Figure 9. Relationship between efficiency and frequency at constant load torque mNT .12 Figure 10. Relationship between power factor and frequency at constant load torque mNT .2 Figure (11) is an example of the relationship between power factor and frequency at constant load. It is clear that the optimized system has better power factor for all frequencies. Figure 11. Relationship between power factor and frequency at constant load torque mNT .10 It was noticed that the dynamic response of the optimized system has been improved. The oscillations in electromagnetic torque and angular speed disappeared and the response becomes faster. Examples
  39. 39. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 673 Vol. 7, Issue 3, pp. 666-674 of dynamic response at load torque N.m5T and frequency Hz30f are shown in Figures (12, 13). Figure 12. Dynamic response of electromagnetic torque at load torque N.m5T and frequency Hz30f . Figure 13. Dynamic response of angular speed at load torque N.m5T and frequency Hz30f .
  40. 40. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 674 Vol. 7, Issue 3, pp. 666-674 IV. CONCLUSIONS An online efficiency optimization control technique based on detecting optimal power factor, which minimizes the total operational power losses in vector-controlled induction motor drive is proposed in this paper. The power factor is used as the main control variable and manipulates the stator current in order for the motor to operate at its minimum-loss point. Simulation results show that the implemented method significantly improves the efficiency and dynamic performance of the drive system, especially when the system operates at light loads and low frequencies. V. FUTURE WORK The obtained simulation (theoretical) results should be experimentally tested and validated for industrial drive systems, operating at variable speeds with different load types. Also, the results should be compared with that, for scalar-controlled drive system to insure that vector control approach gives better results and ease to implement. REFERENCES [1] Feng-Chieh Lin and Sheng-Ming Yang (2003), “On-line tuning of an efficiency-optimized vector controlled induction motor drive”, Tamkang Journal of Science and Engineering, Vol. 6, No. 2, pp. 103-110. [2] C. Thanga Raj, S. P. Srivastava and Pramod Agarwal (2009), “Energy efficient control of three-phase induction motor- a review”, International Journal of Computer and Electrical Engineering, Vol. 1, No. 1, pp. 1793-8198. [3] Hussein Sarhan (2011), "Energy efficient control of three-phase induction motor drive", Energy and Power Engineering, Vol. 3, pp. 107-112. [4] Hussein Sarhan (2011), "Online energy efficient control of three-phase induction motor drive using PIC microcontroller", International Review on Modeling and Simulation (I.RE.MO.S), Vol. 4, No. 5, pp. 2278- 2284. [5] Hussein Sarhan, (2014) "Effect of high-order harmonics on efficiency-optimized three-phase induction motor drive system performance", International Journal of Enhanced Research in Science Technology and Engineering, Vol. 3, No. 4, pp. 15-20. [6] Seena Thomas and Rinu Alice Koshy (2013), “Efficiency optimization with improved transient performance of indirect vector controlled induction motor drive”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Special Issue 1, pp. 374-385. [7] K. Ranjith Kumar, D. Sakthibala and Dr. S. Palaniswami (2010), “Efficiency optimization of induction motor drive using soft computing techniques”, International Journal of Computer Applications, Vol. 3, No. 1, pp. 8875-8887. [8] Branko D. Blanusa, Branko L. Dokic and Slobodan N. Vukosavic (2009), “Efficiency optimized control of high performance induction motor drive” Electronics, Vol. 13, No. 2, pp. 8-13. [9] G. Kohlrusz and D. Fodor (2011), “Comparison of scalar and vector control strategies of induction motors, Hungarian Journal of Industrial Chemistry, Vol. 39, No. 2, pp. 265-270. [10] Rateb H. Issa (2013), “Optimal efficiency controller of AC drive system”, International Journal of Computer Applications, Vol. 62, No. 12, pp. 40-46. [11] A Taheri and H. Al-Jallad (2012), “Induction motor efficient optimization control based on neural networks”, International Journal on “Technical and Physical Problems of Engineering, Vol. 4, No. 2, pp. 140- 144. AUTHORS Hussein S. Sarhan was born in Amman, Jordan, in 1952. He received the Master and Ph.D degrees in Electric Drive and Automation from Moscow Power Engineering Institute, USSR, in 1978 and 1981, respectively.His research areas are induction motor optimization techniques and energy efficient control of electric drives.Dr. Sarhan is a faculty member/ associate professor in Mechatronics Engineering Department/ Faculty of Engineering Technology. Sarhan is a member of Jordanian Engineering Association.
  41. 41. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 675 Vol. 7, Issue 3, pp. 675-683 FLEXIBLE DIFFERENTIAL FREQUENCY-TO-VOLTAGE AND VOLTAGE-TO-FREQUENCY CONVERTERS USING MONOLITHIC ANALOGUE RECONFIGURABLE DEVICES Ivailo Milanov Pandiev Department of Electronics, Technical University of Sofia, Sofia, Bulgaria ABSTRACT This paper presents differential frequency-to-voltage (F/V) and voltage-to-frequency (V/F) converters employing single CMOS Field Programmable Analog Array (FPAA) device. The proposed electronic circuits are based on charge-balance conversion method. The F/V converter basically consists of analogue comparator, differentiator that detects the rising and falling edges of the shaped waveform, voltage-controlled switch and output low-pass filter, working as an averaging circuit. The V/F conversion circuit includes the same modules. The input signal for the V/F circuit is applied to an integrator (realized with low-pass filter) through two- position voltage-controlled switch and the positive feedback is closed by external electrical connections. The proposed converters can be redesigned during operation, in dependence on the amplitude, frequency range and noise level of the input signal, without using of external components or applying of external current or voltage. The functional elements of the converters are realized by employment of the available configurable analogue modules (CAMs) of the FPAA AN231E04 from Anadigm. The converters have wide-band frequency response and can operate with single supply voltage of 3.3V. The experimental results show that the linearity error is less than 0.5% at the frequency range of 0 to 20…25kHz and differential input range operation of 0 - 3V. KEYWORDS: Mixed-signal circuits, Charge-balance voltage-to-frequency conversion method, Frequency-to- voltage converter (FVC), Voltage-to-frequency converter (VFC), FPAA, System prototyping. I. INTRODUCTION The voltage-to-frequency converters (VFC) are voltage-controlled oscillators whose frequency is linearly proportional to a control input voltage. The VFCs are useful devices for variety of mixed- signal (analogue and digital) processing systems, such as A/D conversion systems with a microcontroller, temperature meters, long-term integrators with infinite hold and telemetry systems [1-4]. The dual process of the voltage-to-frequency (V/F) conversion is the frequency-to-voltage (F/V) conversion. Besides, its independent applications to a speed indicator and motor speed control system (tachometer), the FVC paired with the VFC is used for high noise immunity data transmission systems and FSK generation and decoding. The basic electrical parameters that define the quality of the F/V and the V/F converters are maximum working frequency, sensitivity and linearity error. For the commercial monolithic F/V and the V/F converters, such as AD650 (from Analog Devices), LM2907 (from Texas Instruments) and LM331 (from Texas Instruments) the transfer function and the related electrical parameters are formed by the internal active building blocks and a group of external passive components. Moreover, the accuracy of the electrical parameters is largely determined by the manufacturing tolerances and the temperature drift of the values of the external passive components. A variety of F/V and the V/F converter prototype circuits for mixed-signal signal processing, are available in the literature [5-15]. Furthermore, some of them realize only V/F conversion or F/V conversion, while others implement both functions of conversion [5]. The majority of the published circuits of F/V and the V/F converters have fixed value of the sensitivity and possibility for modification of this value is not demonstrated. In many cases the amplification of small signals (such as bio-signals) requires modification of the
  42. 42. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 676 Vol. 7, Issue 3, pp. 675-683 sensitivity according to the variation of the amplitude, the bandwidth or the noise level. To solve this problem, it can be used Field Programmable Analog Array (FPAA) devices. The FPAA are programmable CMOS analogue integrated circuits based on SC technology, which can be configured not only in the design process of a device, but also during the operation. As well as FPGA the programmable analogue arrays provide cost-efficient and relatively fast design of complex electronic circuits. The use of these reconfigurable analogue devices provides the F/V and the V/F converters with ability to modify the electrical parameters according to the parameters of the input signals. Furthermore, by using of FPAAs the noise level can be obtained with significantly lower values due to the differential inputs and outputs. Among different devices and technologies in the market [16- offers dynamically programmed FPAAs (or dynamically programmed analogue signal processors - dpASPs), in particular the AN231E04 that is a reconfigurable programmable analogue device based on switched- configurable analogue blocks (CABs), surrounded by programmable interconnect resources and analogue input/output cells. The VFC in [10] is realized by using single FPAA AN221E04 and convert the input voltage from 0 to 3V into square waves within frequency range from 0 to 7kHz. Thus, the sensitivity of [10] is 2kHz/V and the achieved relative linearity error is approximately equal to 2%. As a flexible differential building block, however, it is desirable to realize the V/F converter in a form of compatible with the F/V converter. With this in mind, novel FPAA-based circuits are developed for F/V and V/F conversion. The organization of the paper is as follows: the structures and the principles of operation of the F/V and the V/F converters are described in section II; for the proposed electronic circuits in section II the transfer functions and methods for modification of the transmission coefficients are given; in section III are illustrated the experimental test of the proposed converters based on AN231K04-DVLP3 – development board [20], which is built around the AN231E04 device and finally in section IV the concluding remarks and directions for future work are discussed. Nomenclature ost Pulse with duration, determined by the master clock frequency for all CAMs inf Frequency of input signal cf Corner frequency of low-pass filter 2K Transmission coefficient of the SumFilter1 CAM refU Reference voltage VK Sensitivity of F/V converter outf Frequency of output signal inU Input voltage intt os out t f  1 - amount of time required to reach comparator threshold fK Sensitivity of V/F converter dc Direct current II. CIRCUITS DESCRIPTION 2.1. F/V converter The circuit diagram of the F/V converter is shown on Fig. 1. It is built by selection among the available configurable analogue modules (CAMs) of the AN231E04, shown in Table 1. The differential input signal is applied between terminals 11 and 12 and average output voltage is obtained between terminals 15 and 16. After shaping of the input signal by a Comparator1, the Differentiator1 detects the rising and falling edges of the shaped waveform in synchronism with the non-overlapping the master clock quartz stabilized signal, used for the proper work of the whole FPAA. The
  43. 43. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 677 Vol. 7, Issue 3, pp. 675-683 differentiator produces short pulses with duration ost , determined by the master clock frequency for all CAMs, and the polarity depending on the direction of change of the comparator’s output voltage. When the output of the differentiator is equal to zero, the voltage steering switch GainSwitch1 diverts differential voltage equal to 0V to the output of the gain stage; this is called the integration period. When the differentiator produces short pulse with negative value, the switch GainSwitch1 diverts the voltage K2Uref (Uref produces by the voltage source Voltage1 CAM with fixed value equal to +2V) from the SumFilter1 CAM to the output of the gain stage; this is called the reset period. The output low-pass filter implemented by FilterBilinear1 CAM works as an averaging circuit ( cin ff  , where cf is the corner frequency). As the frequency inf of the input signal increases, the amount of charge injected into the averaging circuit increases proportionally. The average output voltage avoU , , obtained between terminals 17 and 18, that is proportional to the input frequency is given as (1) inrefos t ref in avo fUtKdtUK T U os 2 0 2, . 1   , where inin fT /1 is the period of the input signal. Therefore, based on the formula (1), the sensitivity of the proposed F/V converter is obtained as (2) refosV UtKK 2 . Output voltage avoU , VMR Input signal VMR inf SumFilter1 Voltage1 GainSwitch1 Comparator1 Differentiator1 Comparator2 FilterBilinear1 Figure 1. FPAA configuration of F/V converter. The analysis of equation (2) shows that the change of the F/V converter’s sensitivity can be accomplished in two ways. First, for a fixed value of ost , for example equal to s20 (at master clock frequency equal to kHz50 ), for modification of the transmission coefficient 2K from 0.1 to 1 the sensitivity will vary from kHzmV /4 to kHzmV /40 . Second, for a fixed value of the 2K , for example equal to 0.5 when change the clock frequency from kHz10 ( stos 100 ) to kHz100 ( stos 10 ) the sensitivity varies from kHzmV /100 to kHzmV /10 . Moreover, the change of the coefficients of the CAMs can be implemented in the operating mode of the F/V converter.
  44. 44. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 678 Vol. 7, Issue 3, pp. 675-683 Table 1. FPAA CAMs for F/V and V/F conversion. Name Options Parameters Clocks SumFilter1 Output changes On: Phase 1 Input 1: non-inverting Input 2: inverting Corner frequency [kHz]: 1 Gain1 (Upper input): K1=1 Gain 2: (Lower input): K2 Clock A: 50kHz (Clock 3) Voltage1 Polarity: Positive (+2V) - - GainSwitch1 Compare control to: Signal ground Select input 1 when: Control low Comparator sampling phase: Phase 2 Gain stage: half cycle Gain1 (Upper input): 1.00 Gain 2: (Lower input): 1:00 Clock A: 50kHz (Clock 3) Comparator1 Compare to: Dual input Input sampling: Phase 1 Output polarity: Non-inverted Hysteresis: 0mV - Clock A: 50kHz (Clock 3) Differentiator1 - Differentiation constant [us]: 1 Clock A: 50kHz (Clock 3) FilterBilinear1 Filter type: Low pass Input sampling phase: Phase 2 Polarity: Non-inverted Corner frequency [kHz]: 0.055 Gain: 1.00 Cint1=Cint2=Cint=7.9pF Clock A: 50kHz (Clock 3) Comparator2 Compare to: Signal ground Input sampling: Phase 2 Output polarity: Non-inverted Hysteresis: 0mV - Clock A: 50kHz (Clock 3) The stability of the VK in equation (2) is determined by the stability of the master clock frequency, which is derived from the crystal oscillator and the accuracy of the parameters of the CAMs, whose values are derived from the same clock frequency. Therefore, the variation of the sensitivity VK of the proposed F/V converter will not cause changing of the nonlinearity error. For normal operation of the F/V converter the inverting input (terminals 23 and 24) of the comparator and the input (terminals 01 and 02) of the SumFilter1 are connected to voltage mid-rail (VMR) equal to V5.1 . 2.2. V/F converter The circuit diagram of the V/F converter is shown on Fig. 2. As can be seen, for the implementation of the V/F converter is used the F/V converter shown on Fig. 1. For this purpose, the input voltage inU is applied between terminals 01 and 02, terminals 11 and 12 are connected to a VMR and the voltage between terminals 17 and 18 is applied directly to the terminals 24 and 23. Thus it closes the positive feedback between the output of the averaging circuit and the inverting input of the Comparator1. The output differential signal with frequency outf is formed by the Comparator2 and can be obtained between terminals 19 and 20. When the circuit operated as a V/F converter, the transformation from voltage to frequency is based on a comparison of the input voltage’s value inU to the internal voltage equal to V2 , implemented by Voltage1 CAM. The principle of operation of the circuit shown on Fig. 2 can be described as follows. At the beginning of a cycle, the input voltage is applied to the input of the FilterBilinear1 and its output voltage decreases. This is the integration period. When the FilterBilinear1 output voltage (terminals 17 and 18) crosses signal ground, the Comparator1 triggers a one-shot whose time period ost is determined by the clock frequency. During this period, a voltage of ( refin UKU 2 ) is applied to the input of the FilterBilinear1 and the output voltage starts to increase. This is the reset period of the circuit. After the reset period has ended, the circuit begins another integration period, and starts ramping downward again. The amount of time required to reach the comparator threshold is given as
  45. 45. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 679 Vol. 7, Issue 3, pp. 675-683 (3) int 2 int 1817 1817 int )( C U UKU C t dt dU U t in refin os        , where the pFC 9.7int  is the internal capacitor of the FilterBilinear1. The output frequency can be found as (4) refos in os out UKt U tt f 2int 1    . The integration capacitor intC has no effect on the transfer function, but merely determines the amplitude of the sawtooth signal out of the averaging circuit. Output signal - outf Input voltage VMR inU SumFilter1 Voltage1 GainSwitch1 Comparator1 Differentiator1 Comparator2 FilterBilinear1 Figure 2. FPAA configuration of V/F converter. From (4) for the instability of output frequency is found (5) ref ref os os in in out out U U K K t t U U f f          2 2 . The first term in formula (5) is the regulating effect. The second, third and fourth term reflect confusing influence in the circuit. The output frequency is linearly dependent on the input voltage. The transmission coefficient 2K is interrelated with the corner frequency value of the CAM and the ratios of the two switched capacitors with values pFCC outin 9.7 . The voltage refU is produced by a dc voltage source, which making a connection to the on-chip voltage references. There are no capacitors or dynamic switches. It produces a dc output so that the output is continuous and valid during the both phases of the clock signals [20]. According to the formula (4), it is concluded that the sensitivity of the proposed V/F converter is governed by the following equation (6) refosf UtKK 2/1 . Similar to equation (2), the analysis of equation (6) shows that the variation of the sensitivity can be achieved by changing the value of the 2K or by changing the master clock frequency of the circuit. III. EXPERIMENTAL RESULTS AND DISCUSSION The experimental test that has been used for validation of the proposed converters is based on AN231K04-DVLP3 –Development board [20], which is built around the AN231E04 device. The power supply voltage of the AN231E04 is equal to +3.3V.
  46. 46. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 680 Vol. 7, Issue 3, pp. 675-683 The measured average output voltages of the prototype V/F converter are plotted on Fig. 3 against the frequency of the output signal at two values of the conversion sensitivity. The conversion sensitivities 1VK and 2VK are obtained for gain 2K equal to 0.5 and 1, respectively. The clock frequency of the circuit is chosen to be 50kHz. However, for the frequency change from 0 to 25kHz the average output voltage varies from 0 to 0.5 V (or 1V). In this operating range an error of less than 0.5% is achieved. In comparison with the F/V converters, presented in [5] and [7], the proposed converter has higher operating frequency and greater sensitivity. The F/V converters reported in [6] and [8] has a wider bandwidth (> 1MHz) and good linearity, but the sensitivity is a fixed value and the modification of the electrical parameters required redesigning of the electronic circuits. Also the F/V converter presented in [6] can use only digital input signals, while the created F/V converter can operate with both analogue and digital signals. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 5 10 15 20 25 measured calculated VU avo ,, kHz mV KV 201  kHz mV KV 402  kHzfin, Figure 3. Average output voltage versus the frequency of the input signal at two values of the conversion sensitivity obtained by the F/V converter. The oscillation frequency outf of the prototype V/F converter against the input voltage is plotted on Fig. 4. The gain 2K is set to 0.5 and 1, respectively. The clock frequency of the circuit is set equal to 50kHz. When the input voltage changes from 0 to 0.4 V (or 0.8 V) the oscillation frequency varies from 0 to 20kHz, the linearity error is again not higher than 0.5%. Thus, a figure of merit (FOM = sensitivity (MHz) / error (%)) is equal to 0.1. The result is in a good agreement with the calculated by (4). Fig. 5 and 6 present the measured output waveform at the input voltage equal to mV20 and mV400 , respectively. The clock frequency is set equal to kHz50 and the sensitivity is VkHzK f /25 . The output frequencies at these input voltages are 500.572Hz and 10.0013kHz, respectively. For the both waveforms to channel 2 (CH2) the output signal from the inverting output is applied (terminal 19 of the FPAA) and to the channel 1 (CH1) the signal from the non-inverting output is applied (terminal 20 of the FPAA). The differential output signal (M) is the difference of the voltages applied to the both channels. Notably, that with variation of the input voltage the output frequency varies proportionally, as the pulse duration is unaffected ( stos  20 ) and the error is less than 0.2%.
  47. 47. International Journal of Advances in Engineering & Technology, July, 2014. ©IJAET ISSN: 22311963 681 Vol. 7, Issue 3, pp. 675-683 0 5 10 15 20 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 measured calculated measured calculated VUin, kHzfout, V kHz K f 251  V kHz K f 502  V kHz K f 502  Figure 4. The oscillation frequency versus the input voltage at two values of the conversion sensitivity obtained by the V/F converter. Figure 5. The output waveform of the V/F converter at VkHzK f /25 and mVUin 20 . Figure 6. The output waveform of the V/F converter at VkHzK f /25 and mVUin 400 .

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