Compact tunable bandstop filters using defected microstrip structure for multi-standard wireless systems

A novel compact tunable bandstop filter using Defected Microstrip Structure (DMS) is presented in this paper. The structure utilizes a modified T shaped DMS which helps in miniaturization of the filter. To verify the concept, one such filter was simulated, designed, fabricated and tested. Measurements on a fabricated tunable filter confirm the accuracy of the design procedure. The tuning range of 20% is achieved, ranging from 2.26 GHz to 2.747 GHz. Tuning is achieved by using NXP BB179 varactor diode. A nonlinear distortion evaluation in a tunable filter was experimentally verified. Experimental verification shows the filter is highly linear.

Development of Compact Microwave Filters Using Microstrip Loop Resonators

Study of the characteristics of planar loop resonators and their use in the construction of filters at microwave frequencies are presented in this thesis.A detailed investigation of parameters affecting the strength of coupling and the resonant frequency are also carried out .Techniques for size reduction in bandstop and bandpass filters using planar loop resonators are developed.Different configurations of bandstop and bandpass filters using loop resonators are simulated and experimental results on optimal filter configurations are presented.

Optically reconfigurable CPW filters for UWB applications

A miniature optically reconfigurable ultra-wideband CPW bandpass filter is proposed. With the optical switch in the ON state (200W), the circuit behaves as a bandpass filter while in the OFF state (0W), the circuit behaves as a bandstop filter within the same frequency band. The simulation results of the proposed bandpass/bandstop filter are presented.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Toothless tiger, sleeping dragon : implied freedoms, internet filters and the growing culture of internet censorship in Australia

The internet by its very nature challenges an individual’s notions of propriety, moral acuity and social correctness. A tension will always exist between the censorship of obscene and sensitive information and the freedom to publish and/or access such information. Freedom of expression and communication on the internet is not a static concept: ‘Its continual regeneration is the product of particular combinations of political, legal, cultural and philosophical conditions’.