Due to the recent advances in semiconductor technology, the Radio Frequency Identification (RFID) technology is approaching the critical point for inventory application in global supply chain logistics. Its unique advantage to identify multiple tags simultaneously can remove large amount of labor-intensive scanning for inventory control and checkout process. To realize multiple-tag-identification, the tag collision problem, which is due to the signal collision of simultaneous transmission by multiple tags, needs to be solved. / Over the years, many different RFID systems and anti-collision algorithms were proposed, but a uniform theory which fully analyzes the anti-collision system is still lacking. Most of the previous work treated the RFID system as a special kind of Random Access (RA) system and designed anti-collision algorithms to maximize the temporary throughput. In this thesis, we provide a theoretical framework for the RFID anti-collision system. We differentiate the RFID system with the RA system and propose a general model for all types of the RFID systems. We also provide a general method for algorithm optimization in different RFID systems. As special examples, we analyze some popular RFID systems and derive the optimal algorithms under the system constraints. These optimal results are verified by computer simulation. / Zhu, lei. / Adviser: Tak-shing Peter Yum. / Source: Dissertation Abstracts International, Volume: 73-03, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 152-156). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Li, Kin Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 95-99). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction / Motivation --- p.1 / Chapter 2 --- Literature Review --- p.7 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Dynamic Spectrum Access Networks --- p.8 / Chapter 2.3 --- Reliability --- p.10 / Chapter 2.3.1 --- Reliability in Wireless Networks --- p.10 / Chapter 2.3.2 --- Reliability in Wireline Networks --- p.11 / Chapter 2.4 --- Capacity Planning in Wireless Mesh Networks --- p.14 / Chapter 2.4.1 --- Interference Model --- p.14 / Chapter 2.4.2 --- Link Capacity Constraint --- p.15 / Chapter 2.4.3 --- Feasible Path --- p.16 / Chapter 2.4.4 --- Optimal Capacity Allocation in DSA Net- works and Wireless Mesh Networks --- p.16 / Chapter 2.5 --- Chapter Summary --- p.18 / Chapter 3 --- Lifetime Aware Routing without Backup --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- System Model --- p.20 / Chapter 3.3 --- Lifetime Distribution of a Path without Backup Protection --- p.22 / Chapter 3.3.1 --- Exact Lifetime Distribution --- p.23 / Chapter 3.3.2 --- The Chain Approximation --- p.24 / Chapter 3.4 --- Route Selection without Backup Protection --- p.26 / Chapter 3.4.1 --- NP-Hardness of Finding Maximum Lifetime Path --- p.26 / Chapter 3.4.2 --- The Minimum Weight Algorithm --- p.28 / Chapter 3.4.3 --- Greedy Algorithm --- p.28 / Chapter 3.4.4 --- GACA - The Greedy Algorithm using the Chain Approximation --- p.32 / Chapter 3.5 --- Simulation Results --- p.33 / Chapter 3.5.1 --- Tightness of the Chain Approximation Bound for Vulnerable Area --- p.33 / Chapter 3.5.2 --- Comparison between Greedy and GACA using Guaranteed Lifetime --- p.36 / Chapter 3.5.3 --- Factors impacting the performance of GACA --- p.37 / Chapter 3.6 --- Chapter Summary --- p.43 / Chapter 4 --- Prolonging Path Lifetime with Backup Channel --- p.44 / Chapter 4.1 --- Introduction --- p.44 / Chapter 4.2 --- Non-Shared Backup Protection --- p.45 / Chapter 4.2.1 --- Lifetime of a Path with Non-Shared Backup --- p.45 / Chapter 4.2.2 --- Route Selection for paths with Non-Shared Backup --- p.46 / Chapter 4.3 --- Shared Backup Protection --- p.47 / Chapter 4.3.1 --- Sharing of Backup Capacity --- p.48 / Chapter 4.3.2 --- Lifetime of a Path with Shared Backup --- p.48 / Chapter 4.3.3 --- Route Selection for paths with Shared Backup --- p.50 / Chapter 4.4 --- Simulation Results --- p.50 / Chapter 4.4.1 --- Tightness of Failure Probability Upper Bound for Backup Protection --- p.51 / Chapter 4.4.2 --- Comparison between the Shared Backup and Non Shared Backup schemes --- p.53 / Chapter 4.5 --- Chapter Summary --- p.54 / Chapter 5 --- Finding Capacity-Feasible Routes --- p.55 / Chapter 5.1 --- Introduction --- p.55 / Chapter 5.2 --- Constructing an Edge graph --- p.56 / Chapter 5.3 --- Checking Capacity Feasibility under each Protec- tion Scheme --- p.58 / Chapter 5.3.1 --- No Backup Protection --- p.59 / Chapter 5.3.2 --- Non-Shared Backup Protection --- p.59 / Chapter 5.3.3 --- Shared Backup Protection --- p.60 / Chapter 5.4 --- Chapter Summary --- p.62 / Chapter 6 --- Performance Evaluations and Adaptive Protec- tion --- p.63 / Chapter 6.1 --- Introduction --- p.63 / Chapter 6.2 --- Tradeoffs between Route Selection Algorithms --- p.64 / Chapter 6.3 --- Adaptive Protection --- p.66 / Chapter 6.3.1 --- Route Selection for Adaptive Protection --- p.67 / Chapter 6.3.2 --- Finding a Capacity-Feasible Path for Adaptive Protection --- p.68 / Chapter 6.4 --- Comparison between No Protection and Adaptive Protection --- p.69 / Chapter 6.5 --- Chapter Summary --- p.71 / Chapter 7 --- Restoration Capacity Planning and Channel Assignment --- p.72 / Chapter 7.1 --- Introduction --- p.72 / Chapter 7.2 --- System Model --- p.74 / Chapter 7.2.1 --- Channel Assignment Model --- p.74 / Chapter 7.2.2 --- Presence of Primary Users --- p.75 / Chapter 7.2.3 --- Link Flow Rates --- p.76 / Chapter 7.2.4 --- Problem Formulation --- p.77 / Chapter 7.3 --- Simulation Results --- p.79 / Chapter 7.3.1 --- "Comparison between ""Shared Backup"" and “No Restore Plan"" using Guarantee Percentage and Reduced Capacity" --- p.80 / Chapter 7.3.2 --- Comparison using Traffic Demand Scaling Factor g and Guarantee Fraction p --- p.81 / Chapter 7.3.3 --- Comparison between Optimal Channel Assignment and Random Channel Assignment --- p.84 / Chapter 7.4 --- Chapter Summary --- p.86 / Chapter 8 --- Conclusion and Future Works --- p.87 / Chapter A --- Proof of Theorem (3.1) in Chapter3 --- p.90 / Chapter B --- Proof of Theorem (4.1) in Chapter4 --- p.92 / Bibliography --- p.95

No description available.

Doctor of Philosophy (PhD) / This thesis addresses the issues arising in range-only localisation and tracking using Radio Frequency Received Signal Strength Indicator measurements. One of the key issues in Radio Frequency (RF) based localisation and tracking applications is to obtain an accurate sensor representation. Such a sensor model is one of the prerequisites to achieve high accuracy and precision in the localisation and tracking task. The sensor models used at present for this task are very simplistic, and as a consequence are unable to achieve highly accurate and precise localisation. While such an accurate sensor description is desirable it has not been presented for RF sensors. This thesis addresses the task of obtaining an accurate sensor model for RF sensors. The major drawbacks of the most commonly used model, the nth power model, are demonstrated. A new model to satisfy the necessary requirements for high accuracy localisation is developed. This model is based on theoretical considerations and experimental data. It depicts the real occurring behaviour of RF sensors more closely than the models used so far for RF based range-only localisation. The use of this better sensor representation offers the possibility of achieving more accurate localisation. The expected performance of the alternative sensor model is compared to the commonly used nth power model. Furthermore, the inherent properties of the new sensor model are presented and their ramifications with regards to the goal of achieving highly accurate localisation are discussed. In addition to the sensor model development, the well-known probabilistic filtering techniques Kalman Filter, Particle Filter and Histogram Filter are compared and used to implement 1-dimensional and 2-dimensional range-only trackers. The filtering techniques are evaluated with respect to their suitability for appropriately handling the new multi-modal sensor model and the resulting multi-modal state distributions, and to provide correct and conclusive localisation and tracking results. Results from experiments using real data obtained in outdoor environments with a prototype RF localisation system as well as results obtained from simulations are presented in this thesis to validate the theoretical findings and the newly developed sensor model.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2007. / Title from title screen (site viewed Dec. 5, 2007). PDF text: xi, 166 p. : ill. ; 9 Mb. UMI publication number: AAT 3273922. Includes bibliographical references. Also available in microfilm and microfiche formats.

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

No description available.

In high-frequency circuit design, performance is often limited by the quality of the passive components available for a particular process. Specifically, spiral inductors can be a major bottle-neck for Voltage-Controlled Oscillators (VCOs), Low-Noise Amplifiers (LNAs), mixers, etc. For designers to correctly optimize a circuit using a spiral inductor, several frequency-domain characteristics must be known including the quality factor (Q), total inductance, and the self-resonant frequency. This information can be difficult to predict for spirals built on lossy silicon substrates because of the complicated frequency-dependent loss mechanisms present. The first part of this research addresses the need for a scalable, predictive model for obtaining the frequency domain behavior of spiral inductors on lossy silicon substrates. The technique is based on the Partial Element Equivalent Circuit (PEEC) method and is a flexible approach to modelling spiral inductors. The basic PEEC technique is also enhanced to efficiently include the frequency dependent eddy-currents in the lossy substrate through a new complex-image method. This enhanced PEEC approach includes all of the major non-ideal effects including the conductor-skin and proximity effects, as well as the substrate-skin effect. The approach is applied to octagonal spiral inductors and comparisons with measurements are presented. To complement the scalable enhanced-PEEC model, a new wide-band compact equivalent circuit model is presented which is suitable for time-domain simulations. This model achieves wide-band accuracy through the use of "transformer-loops" to model losses caused by the magnetic field. A fast extraction technique based on a least squares fitting procedure is also described. Results are presented for a transformer-loop compact model extracted from measurements. The combination of an accurate scalable model and a wide-band compact equivalent-circuit model provides a complete modelling methodology for spiral inductors on lossy silicon. / Graduation date: 2003

Thesis (Ph. D.)--Ohio State University, 2006. / Full text release at OhioLINK's ETD Center delayed at author's request

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