DESIGN OF A MINIATURIZED SOP INTEGRATED GNSS RF FRONT-END MODULE

Navigation devices used to be bulky and expensive and were not widely commercialized for personal use. Nowadays, all useful electronic devices are turning into being handheld so that they can be conveniently used anytime and anywhere. One can claim that almost any mobile phone, used today, has quite strong navigational capabilities that can efficiently work anywhere in the globe. No matter where you are, you can easily know your exact location and make your way smoothly to wherever you would like to go. This couldn’t have been made possible without the existence of efficient and small microwave circuits responsible for the transmission and reception of high quality navigation signals. This thesis is mainly concerned with the design of novel highly miniaturized and efficient filtering components working in the Global Navigational Satellite Systems (GNSS) frequency band to be integrated within an efficient Radio Frequency (RF) front-end module (FEM). A System-on-Package (SoP) integration technique is adopted for the design of all the components in this thesis. Two novel miniaturized filters are designed, where one of them is a wideband filter targeting the complete GNSS band with a fractional bandwidth of almost 50% at a center frequency of 1.385 GHz. This filter utilizes a direct inductive coupling topology to achieve the required wide band performance. It also has very good out-of-band rejection and low IL. Whereas the other dual band filter will only cover the lower and upper GNSS bands with a rejection notch in between the two bands. It has very good inter band rejection. The well-known “divide and conquer” design methodology was applied for the design of this filter to help save valuable design and optimization time. Moreover, the performance of two commercially available ultra-Low Noise Amplifiers (LNAs) is studied. The complete RF FEM showed promising preliminary performance in terms of noise figure, gain and bandwidth, where it out performed other commercial front-ends in these three aspects. All the designed circuits are fabricated and tested. The measured results are found to be in good agreements with the simulations.

Assisting End-users in Filling Out Web Services

With the quick advance of web service technologies, end-users can conduct various on-line tasks, such as shopping on-line. Usually, end-users compose a set of services to accomplish a task, and need to enter values to services to invoke the composite services. Quite often, users re-visit websites and use services to perform re-occurring tasks. The users are required to enter the same information into various web services to accomplish such re-occurring tasks. However, repetitively typing the same information into services is a tedious job for end-users. It can negatively impact user experience when an end-user needs to type the re-occurring information repetitively into web services. Recent studies have proposed several approaches to help users fill in values to services automatically. However, prior studies mainly suffer the following drawbacks: (1) limited support of collecting and analyzing user inputs; (2) poor accuracy of filling values to services; (3) not designed for service composition. To overcome the aforementioned drawbacks, we need maximize the reuse of previous user inputs across services and end-users. In this thesis, we introduce our approaches that prevent end-users from entering the same information into repetitive on-line tasks. More specifically, we improve the process of filling out services in the following 4 aspects: First, we investigate the characteristics of input parameters. We propose an ontology-based approach to automatically categorize parameters and fill values to the categorized input parameters. Second, we propose a comprehensive framework that leverages user contexts and usage patterns into the process of filling values to services. Third, we propose an approach for maximizing the value propagation among services and end-users by linking a set of semantically related parameters together and similar end-users. Last, we propose a ranking-based framework that ranks a list of previous user inputs for an input parameter to save a user from unnecessary data entries. Our framework learns and analyzes interactions of user inputs and input parameters to rank user inputs for input parameters under different contexts.