by Ng, King Kwok. / Thesis submitted in: December 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 99-104). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining and Rule Discovery --- p.1 / Chapter 1.1.1 --- Association Rule --- p.3 / Chapter 1.1.2 --- Sequential Pattern --- p.4 / Chapter 1.1.3 --- Dependence Rule --- p.6 / Chapter 1.2 --- Association Rule Mining --- p.8 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Outline of the Thesis --- p.10 / Chapter 2 --- Related Work on Association Rule Mining --- p.11 / Chapter 2.1 --- Batch Algorithms --- p.11 / Chapter 2.1.1 --- The Apriori Algorithm --- p.11 / Chapter 2.1.2 --- The DIC Algorithm --- p.13 / Chapter 2.1.3 --- The Partition Algorithm --- p.15 / Chapter 2.1.4 --- The Sampling Algorithm --- p.15 / Chapter 2.2 --- Incremental Association Rule Mining --- p.16 / Chapter 2.2.1 --- The FUP Algorithm --- p.17 / Chapter 2.2.2 --- The FUP2 Algorithm --- p.18 / Chapter 2.2.3 --- The FUP* Algorithm --- p.19 / Chapter 2.2.4 --- The Negative Border Method --- p.20 / Chapter 2.2.5 --- Limitations of Existing Incremental Association Rule Mining Algorithms --- p.21 / Chapter 3 --- A New Incremental Association Rule Mining Approach --- p.23 / Chapter 3.1 --- Outline for the Proposed Approach --- p.23 / Chapter 3.2 --- Our New Approach --- p.26 / Chapter 3.2.1 --- The IDIC_M Algorithm --- p.26 / Chapter 3.2.2 --- A Variant Algorithm: The IDIC_S Algorithm --- p.29 / Chapter 3.3 --- Performance Evaluation of Our Approach --- p.30 / Chapter 3.3.1 --- Experimental Results for Algorithm IDIC_M --- p.30 / Chapter 3.3.2 --- Experimental Results for Algorithm IDIC_S --- p.35 / Chapter 3.4 --- Discussion --- p.39 / Chapter 4 --- Related Work on Multiple_Level AR and Belief-Driven Mining --- p.41 / Chapter 4.1 --- Background on Multiple_Level Association Rules --- p.41 / Chapter 4.2 --- Related Work on Multiple-Level Association Rules --- p.42 / Chapter 4.2.1 --- The Basic Algorithm --- p.42 / Chapter 4.2.2 --- The Cumulate Algorithm --- p.44 / Chapter 4.2.3 --- The EstMerge Algorithm --- p.44 / Chapter 4.2.4 --- Using Hierarchy-Information Encoded Transaction Table --- p.45 / Chapter 4.3 --- Background on Rule Mining in the Presence of User Belief --- p.46 / Chapter 4.4 --- Related Work on Rule Mining in the Presence of User Belief --- p.47 / Chapter 4.4.1 --- Post-Analysis of Learned Rules --- p.47 / Chapter 4.4.2 --- Using General Impressions to Analyze Discovered Classification Rules --- p.49 / Chapter 4.4.3 --- A Belief-Driven Method for Discovering Unexpected Patterns --- p.50 / Chapter 4.4.4 --- Constraint-Based Rule Mining --- p.51 / Chapter 4.5 --- Limitations of Existing Approaches --- p.52 / Chapter 5 --- Multiple-Level Association Rules Mining in the Presence of User Belief --- p.54 / Chapter 5.1 --- User Belief Under Taxonomy --- p.55 / Chapter 5.2 --- Formal Definition of Rule Interestingness --- p.57 / Chapter 5.3 --- The MARUB_E Mining Algorithm --- p.61 / Chapter 6 --- Experiments on MARUB_E --- p.64 / Chapter 6.1 --- Preliminary Experiments --- p.64 / Chapter 6.2 --- Experiments on Synthetic Data --- p.68 / Chapter 6.3 --- Experiments on Real Data --- p.71 / Chapter 7 --- Dealing with Vague Belief of User --- p.76 / Chapter 7.1 --- User Belief Under Taxonomy --- p.76 / Chapter 7.2 --- Relationship with Constraint-Based Rule Mining --- p.79 / Chapter 7.3 --- Formal Definition of Rule Interestingness --- p.79 / Chapter 7.4 --- The MARUB_V Mining Algorithm --- p.81 / Chapter 8 --- Experiments on MARUB_V --- p.84 / Chapter 8.1 --- Preliminary Experiments --- p.84 / Chapter 8.1.1 --- Experiments on Synthetic Data --- p.87 / Chapter 8.1.2 --- Experiments on Real Data --- p.93 / Chapter 9 --- Conclusions and Future Work --- p.96 / Chapter 9.1 --- Conclusions --- p.95 / Chapter 9.2 --- Future Work --- p.97

by Tang Tsz-Hong. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 55-56). / APPROVAL --- p.i / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iv / Chapter CHAPTER I --- INTRODUCTION --- p.1 / What is Warehouse? --- p.1 / Common Component of the Warehouse --- p.3 / Why Implement a Warehouse? --- p.4 / Transforming Data to Information --- p.4 / Solution for End-Users --- p.5 / Fast Reaction --- p.5 / Common Problem --- p.6 / Chapter CHAPTER II --- MANAGEMENT ISSUES --- p.8 / Cost of Ownership --- p.8 / Return on Investment --- p.8 / IDC Key Findings --- p.9 / The Total Cost --- p.9 / Customer Costs --- p.11 / Time Costs --- p.11 / Make Sense of Warehouse --- p.12 / Reacting Quickly to Volatile Controls and Opportunities --- p.12 / Managing both The Macro and Micro Perspective --- p.13 / Improving Managerial Ability --- p.13 / Time Savings --- p.14 / The Total Customer Relationship --- p.14 / Have a Shared Vision within the Organization --- p.15 / Specify the Scope of Problem to solve --- p.16 / Keep the Manageable Scope --- p.17 / Keep Scalable and Support Detail Data --- p.18 / Safeguards for Warehouse --- p.19 / Industry Standards and Technology --- p.20 / Partner or Vendor --- p.22 / Skill Inventory and Staffing Requirements --- p.23 / Power User and Training --- p.25 / Chapter CHAPTER III --- ARCHITECTURAL ISSUES --- p.28 / Business Requirement Study --- p.29 / Business Case Study --- p.30 / Key Success Factors of Warehousing --- p.31 / Skill Profiles and Staffing Plan --- p.33 / Growth Plan --- p.33 / Technical Blueprint --- p.35 / Enable Data Populating and Business Analysis --- p.36 / Ease the Maintenance --- p.37 / Adapt to Requirement Evolution --- p.37 / Chapter CHAPTER IV --- SYSTEM COMPONENTS --- p.38 / Acquisition Component --- p.38 / Data Populating --- p.40 / Data Analyzers --- p.40 / Data Cleaners --- p.41 / Storage Component --- p.41 / Databases --- p.42 / Data Repository --- p.43 / Access Component --- p.44 / Data Analysis and Miner --- p.45 / Middleware --- p.46 / Other Operational Components --- p.46 / Network Infrastructure --- p.46 / Backup Utility --- p.47 / Security --- p.48 / Chapter CHAPTER V --- SUMMARY --- p.51 / APPENDIX - BIBLIOGRAPHY --- p.55

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Li Zhiheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 77-79). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Acknowledgement --- p.iii / Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.ix / List of Algorithms --- p.x / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Review of frequent itemset mining --- p.1 / Chapter 1.2 --- Data mining proxy serving for large numbers of users --- p.3 / Chapter 1.3 --- Privacy issues on proxy service --- p.4 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Frequent itemsets mining --- p.7 / Chapter 2.1 --- Preliminaries --- p.7 / Chapter 2.2 --- Data mining queries --- p.8 / Chapter 2.3 --- A running example --- p.10 / Chapter 3 --- Data Mining Proxy --- p.13 / Chapter 3.1 --- Load data for mining --- p.14 / Chapter 3.2 --- An Overview --- p.16 / Chapter 3.3 --- Tree Operations --- p.16 / Chapter 3.4 --- Data Mining Usages and Observations --- p.18 / Chapter 4 --- Implementation of Proxy --- p.23 / Chapter 4.1 --- Problems in implementation --- p.23 / Chapter 4.2 --- A Coding Scheme --- p.24 / Chapter 4.3 --- On-disk/In-Memory Tree Representations and Mining --- p.27 / Chapter 4.4 --- Tree Operation Implementations --- p.29 / Chapter 4.4.1 --- Tree Projection Operation Implementations: πd2m( )and πm2m( ) --- p.31 / Chapter 4.4.2 --- Tree Merge Operation Implementations: --- p.33 / Chapter 4.4.3 --- Frequent Itemset/Sub-itemset Tree Building Request --- p.37 / Chapter 4.4.4 --- The Tree Projection Operation π and Frequent Super- itemset Tree Building Request --- p.39 / Chapter 5 --- Performance Studies --- p.45 / Chapter 5.1 --- Mining with Different Sizes of Trees in Main Memory --- p.47 / Chapter 5.2 --- Constructing Trees in Main Memory --- p.48 / Chapter 5.3 --- Query Patterns and Number of Queries --- p.50 / Chapter 5.4 --- Testing Sub-itemset Queries with Different Memory Sizes --- p.51 / Chapter 5.5 --- Replacement Strategies --- p.51 / Chapter 6 --- Privacy Preserving in Proxy Service --- p.61 / Chapter 6.1 --- Data Union Regardless Privacy Preserving --- p.61 / Chapter 6.2 --- Secure Data Union --- p.65 / Chapter 6.2.1 --- Secure Multi-party Computation --- p.65 / Chapter 6.2.2 --- Basic Methods of Privacy Preserving in Semi-honest Envi- ronment --- p.67 / Chapter 6.2.3 --- Privacy Preserving On Data Union --- p.70 / Chapter 6.3 --- Discussions --- p.73 / Chapter 7 --- Conclusion --- p.75 / Bibliography --- p.77

Xiaolei Yuan. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 95-101). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.1.1 --- Data Mining: A New Wave of Database Applications --- p.1 / Chapter 1.1.2 --- Association Rule Mining --- p.4 / Chapter 1.2 --- Motivation --- p.7 / Chapter 1.3 --- Main Contribution --- p.8 / Chapter 1.4 --- Thesis Organization --- p.9 / Chapter 2 --- Literature Review --- p.10 / Chapter 2.1 --- Data mining and association rule mining --- p.10 / Chapter 2.2 --- Integration data mining with DBMS --- p.11 / Chapter 2.3 --- Query language design for association rule mining --- p.12 / Chapter 2.4 --- Unified data mining models --- p.15 / Chapter 2.5 --- Other topics --- p.15 / Chapter 3 --- A New Data Mining Query Language M2MQL --- p.17 / Chapter 3.1 --- Simple item-based association rule --- p.18 / Chapter 3.1.1 --- One rule set --- p.19 / Chapter 3.1.2 --- Rule set and Source data set --- p.22 / Chapter 3.1.3 --- New rule sets from existing ones --- p.24 / Chapter 3.2 --- Generalized item-based association rules --- p.25 / Chapter 3.3 --- CREATE RULE and SELECT RULE Primitive --- p.32 / Chapter 4 --- The Algebra in M2MQL --- p.33 / Chapter 4.1 --- Review of nested relations --- p.33 / Chapter 4.1.1 --- Concepts of nested relation --- p.34 / Chapter 4.1.2 --- Nested relation and association rule mining --- p.35 / Chapter 4.2 --- Nested relational algebra --- p.36 / Chapter 4.3 --- Specific data mining algebra --- p.39 / Chapter 4.3.1 --- POWERSET p --- p.40 / Chapter 4.3.2 --- SET-CONTAINMENT-JOIN xc --- p.40 / Chapter 4.3.3 --- Functional operators --- p.42 / Chapter 5 --- Mining On Top of M2MQL --- p.50 / Chapter 5.1 --- Problem statement --- p.50 / Chapter 5.2 --- Frequency Counting Phase --- p.52 / Chapter 5.3 --- Frequent Itemset Generation Phase --- p.54 / Chapter 5.4 --- Rule Generation Phase --- p.57 / Chapter 5.5 --- Summary --- p.64 / Chapter 6 --- Conclusions and Future Work --- p.65 / Chapter 6.1 --- What we have achieved --- p.65 / Chapter 6.2 --- What is ahead --- p.66 / Chapter 6.2.1 --- Issues of Query Optimization --- p.66 / Chapter 6.2.2 --- Issues of Expanding Table Forms --- p.67 / Chapter A --- General Syntax of M2MQL --- p.68 / Chapter B --- Syntax and Example for MSQL --- p.71 / Chapter B.1 --- Syntax of MSQL --- p.71 / Chapter B.2 --- Example --- p.73 / Chapter C --- Syntax and Example for MINE RULE --- p.76 / Chapter C.1 --- syntax of MINE RULE --- p.76 / Chapter C.2 --- Example --- p.77 / Chapter C.2.1 --- Counting Groups --- p.78 / Chapter C.2.2 --- Making Couples of Clusters --- p.79 / Chapter C.2.3 --- Extracting Bodies --- p.80 / Chapter C.2.4 --- Extracting Rules --- p.80 / Bibliography --- p.83

Cheung, Yin-ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-124). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining Techniques --- p.1 / Chapter 1.1.1 --- Classification --- p.1 / Chapter 1.1.2 --- Association Rules Mining --- p.2 / Chapter 1.1.3 --- Estimation --- p.2 / Chapter 1.1.4 --- Prediction --- p.2 / Chapter 1.1.5 --- Clustering --- p.2 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.2 --- Problem Definition --- p.3 / Chapter 1.3 --- Thesis Organization --- p.4 / Chapter I --- Decision Tree Classifiers --- p.6 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Introduction to Classification --- p.7 / Chapter 2.2 --- Classification Using Decision Trees --- p.8 / Chapter 2.2.1 --- Constructing a Decision Tree --- p.10 / Chapter 2.2.2 --- Related Work --- p.11 / Chapter 3 --- Strategies to Enhance the Performance in Building Decision Trees --- p.14 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.1.1 --- Related Work --- p.15 / Chapter 3.1.2 --- Post-evaluation vs Pre-evaluation of Splitting Points --- p.19 / Chapter 3.2 --- Schemes to Construct Decision Trees --- p.27 / Chapter 3.2.1 --- One-to-many Hashing --- p.27 / Chapter 3.2.2 --- Many-to-one and Horizontal Hashing --- p.28 / Chapter 3.2.3 --- A Scheme using Paired Attribute Lists --- p.29 / Chapter 3.2.4 --- A Scheme using Database Replication --- p.31 / Chapter 3.3 --- Performance Analysis --- p.32 / Chapter 3.4 --- Experimental Results --- p.38 / Chapter 3.4.1 --- Performance --- p.38 / Chapter 3.4.2 --- Test 1 : Smaller Decision Tree --- p.40 / Chapter 3.4.3 --- Test 2: Bigger Decision Tree --- p.44 / Chapter 3.5 --- Conclusion --- p.47 / Chapter II --- Mining Association Rules --- p.48 / Chapter 4 --- Background --- p.49 / Chapter 4.1 --- Definition --- p.49 / Chapter 4.2 --- Association Algorithms --- p.51 / Chapter 4.2.1 --- Apriori-gen --- p.51 / Chapter 4.2.2 --- Partition --- p.53 / Chapter 4.2.3 --- DIC --- p.54 / Chapter 4.2.4 --- FP-tree --- p.54 / Chapter 4.2.5 --- Vertical Data Mining --- p.58 / Chapter 4.3 --- Taxonomies of Association Rules --- p.58 / Chapter 4.3.1 --- Multi-level Association Rules --- p.58 / Chapter 4.3.2 --- Multi-dimensional Association Rules --- p.59 / Chapter 4.3.3 --- Quantitative Association Rules --- p.59 / Chapter 4.3.4 --- Random Sampling --- p.60 / Chapter 4.3.5 --- Constraint-based Association Rules --- p.60 / Chapter 5 --- Mining Association Rules without Support Thresholds --- p.62 / Chapter 5.1 --- Introduction --- p.63 / Chapter 5.1.1 --- Itemset-Loop --- p.66 / Chapter 5.2 --- New Approaches --- p.67 / Chapter 5.2.1 --- "A Build-Once and Mine-Once Approach, BOMO" --- p.68 / Chapter 5.2.2 --- "A Loop-back Approach, LOOPBACK" --- p.74 / Chapter 5.2.3 --- "A Build-Once and Loop-Back Approach, BOLB" --- p.77 / Chapter 5.2.4 --- Discussion --- p.77 / Chapter 5.3 --- Generalization: Varying Thresholds Nk for k-itemsets --- p.78 / Chapter 5.4 --- Performance Evaluation --- p.78 / Chapter 5.4.1 --- Generalization: Varying Nk for k-itemsets --- p.84 / Chapter 5.4.2 --- Non-optimal Thresholds --- p.84 / Chapter 5.4.3 --- "Different Decrease Factors,f" --- p.85 / Chapter 5.5 --- Conclusion --- p.87 / Chapter 6 --- Mining Interesting Itemsets with Item Constraints --- p.88 / Chapter 6.1 --- Introduction --- p.88 / Chapter 6.2 --- Proposed Algorithms --- p.91 / Chapter 6.2.1 --- Single FP-tree Approach --- p.92 / Chapter 6.2.2 --- Double FP-trees Approaches --- p.93 / Chapter 6.3 --- Maximum Support Thresholds --- p.102 / Chapter 6.4 --- Performance Evaluation --- p.103 / Chapter 6.5 --- Conclusion --- p.109 / Chapter 7 --- Conclusion --- p.110 / Chapter A --- Probabilistic Analysis of Hashing Schemes --- p.112 / Chapter B --- Hash Functions --- p.114 / Bibliography --- p.117

Choi Chi Hon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 120-126). / Abstracts in English and Chinese. / Abstract --- p.i / Abstract (Chinese) --- p.ii / Acknowledgement --- p.iii / Contents --- p.iv / List of Figures --- p.viii / List of Tables --- p.x / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Maintenance Cost View Selection Problem --- p.2 / Chapter 1.2 --- Previous Research Works --- p.3 / Chapter 1.3 --- Major Contributions --- p.4 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Data Warehouse and OLAP Systems --- p.8 / Chapter 2.1.1 --- What Is Data Warehouse? --- p.8 / Chapter 2.1.2 --- What Is OLAP? --- p.10 / Chapter 2.1.3 --- Difference Between Operational Database Systems and OLAP --- p.10 / Chapter 2.1.4 --- Data Warehouse Architecture --- p.12 / Chapter 2.1.5 --- Multidimensional Data Model --- p.13 / Chapter 2.1.6 --- Star Schema and Snowflake Schema --- p.15 / Chapter 2.1.7 --- Data Cube --- p.17 / Chapter 2.1.8 --- ROLAP and MOLAP --- p.19 / Chapter 2.1.9 --- Query Optimization --- p.20 / Chapter 2.2 --- Materialized View --- p.22 / Chapter 2.2.1 --- What Is A Materialized View --- p.23 / Chapter 2.2.2 --- The Role of Materialized View in OLAP --- p.23 / Chapter 2.2.3 --- The Challenges in Exploiting Materialized View --- p.24 / Chapter 2.2.4 --- What Is View Maintenance --- p.25 / Chapter 2.3 --- View Selection --- p.27 / Chapter 2.3.1 --- Selection Strategy --- p.27 / Chapter 2.4 --- Summary --- p.32 / Chapter 3 --- Problem Definition --- p.33 / Chapter 3.1 --- View Selection Under Constraint --- p.33 / Chapter 3.2 --- The Lattice Framework for Maintenance Cost View Selection Prob- lem --- p.35 / Chapter 3.3 --- The Difficulties of Maintenance Cost View Selection Problem --- p.39 / Chapter 3.4 --- Summary --- p.41 / Chapter 4 --- What Difference Heuristics Make --- p.43 / Chapter 4.1 --- Motivation --- p.44 / Chapter 4.2 --- Example --- p.46 / Chapter 4.3 --- Existing Algorithms --- p.49 / Chapter 4.3.1 --- A*-Heuristic --- p.51 / Chapter 4.3.2 --- Inverted-Tree Greedy --- p.52 / Chapter 4.3.3 --- Two-Phase Greedy --- p.54 / Chapter 4.3.4 --- Integrated Greedy --- p.57 / Chapter 4.4 --- A Performance Study --- p.60 / Chapter 4.5 --- Summary --- p.68 / Chapter 5 --- Materialized View Selection as Constrained Evolutionary Opti- mization --- p.71 / Chapter 5.1 --- Motivation --- p.72 / Chapter 5.2 --- Evolutionary Algorithms --- p.73 / Chapter 5.2.1 --- Constraint Handling: Penalty v.s. Stochastic Ranking --- p.74 / Chapter 5.2.2 --- The New Stochastic Ranking Evolutionary Algorithm --- p.78 / Chapter 5.3 --- Experimental Studies --- p.81 / Chapter 5.3.1 --- Experimental Setup --- p.82 / Chapter 5.3.2 --- Experimental Results --- p.82 / Chapter 5.4 --- Summary --- p.89 / Chapter 6 --- Dynamic Materialized View Management Based On Predicates --- p.90 / Chapter 6.1 --- Motivation --- p.91 / Chapter 6.2 --- Examples --- p.93 / Chapter 6.3 --- Related Work: Static Prepartitioning-Based Materialized View Management --- p.96 / Chapter 6.4 --- A New Dynamic Predicate-based Partitioning Approach --- p.99 / Chapter 6.4.1 --- System Overview --- p.102 / Chapter 6.4.2 --- Partition Advisor --- p.103 / Chapter 6.4.3 --- View Manager --- p.104 / Chapter 6.5 --- A Performance Study --- p.108 / Chapter 6.5.1 --- Performance Metrics --- p.110 / Chapter 6.5.2 --- Feasibility Studies --- p.110 / Chapter 6.5.3 --- Query Locality --- p.112 / Chapter 6.5.4 --- The Effectiveness of Disk Size --- p.115 / Chapter 6.5.5 --- Scalability --- p.115 / Chapter 6.6 --- Summary --- p.116 / Chapter 7 --- Conclusions and Future Work --- p.118 / Bibliography --- p.120

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

For A/D converters in ultra-wideband (UWB) communication systems, the flash A/D type is commonly used because of its fast speed and simple architecture. However, the number of comparators in a flash A/D converter exponentially increases with an increase in resolution; therefore, an interpolating technique is proposed in this thesis to mitigate the exponential increase of comparators in a flash converter. The proposed structure is designed to improve the system bandwidth degradation by replacing the buffers and resistors of a typical interpolating technique with a pair of transistors. This replacement mitigates the bandwidth degradation problem, which is the main drawback of a typical interpolating A/D converter. With the proposed 4-bit interpolating structure, 3.75 of effective number of bits (ENOB) and 31.52dB of spurious-free dynamic range (SFDR) are achieved at Nyquist frequency of 264MHz with 6.93mW of power consumption. In addition, a 4-bit D/A converter is also designed for the transmitter part of the UWB communication system. The proposed D/A converter is based on the charge division reference generator topology due to its full swing output range, which is attractive for low-voltage operation. To avoid the degradation of system bandwidth, resistors are replaced with capacitors in the charge division topology. With the proposed D/A converter, 0.26 LSB of DNL and 0.06 LSB of INL is obtained for the minimum input data stream width of 1.88ns. A 130 µm ×286 µm chip area is required for the proposed D/A converter with 19.04mW of power consumption. The proposed A/D and D/A converter are realized in a TSMC 0.18 µm CMOS process with a 1.8 supply voltage for the 528MHz system frequency.