Edgar Alejandro Andrade-Gonzalez

IMPROVING FREQUENCY RESPONSE OF MICROSTRIP FILTERS USING DEFECTED GROUND AND DEFECTED MICROSTRIP STRUCTURES

In this work, the introduction of Defected Microstrip Structures and Defected Ground Structures is presented to improve the performance of a traditional stepped-impedance microstrip lowpass fllter. The attenuation in the stop-band is enhanced by more than 15dB and selectivity is increased, without modifying the insertion loss in the pass band. A comparison of characteristics in fllters is made when the combination of Defected Ground and Defected Microstrip Structures, as well as when only the flrst one are used. A model of the Defected Microstrip Structure and the corresponding equations to obtain the equivalent lumped and distributed element values are also given.

A Novel Active Load Linearizer for HBT Low Noise Amplifier at 2.4 GHz

A novel active load linearizer is introduced to enhance the non-linear performance of a HBT Low Noise Amplifier. This active load is configured as a Current Source. The results of this structure are compared to an equally biased non-active loaded device, obtaining a higher third order Input Intercept Point (IIP3) around 17 dBm, and a higher compression point (G1 dB) around -1 dBm.

Novel Dual Band Resonant Cylindrical Hat-Covered Monopole for Personal Communications

A cylindrical hat-loaded method is employed to make monopoles behave as dual band resonators. The two frequency bands are obtained by perturbing higher propagation modes by modifying the hat length. The longer the cylindrical hat is, the lower the resonant frequencies are obtained. The hat length behaves as an inductive load, also increasing the electric length of the monopole. As a result, a compact resonant structure is got, working at two different frequencies without modifying the omnidirectional radiation pattern at both bands.

Three Comparative Methods for Mammograms Segmentation: by Master-Slave Algorithm and by Two Parallel Fusion Algorithms

In this paper we compare three different segmentation schemes, in order to segment mammograms (taken from MIAS database): the first one is our proposed Master-Slave method, the second one is the parallel data fusion algorithms such as Bayes formalism and the third one is Dempster-Shafer theory. The Master-Slave algorithm works under the following logic: there are two classifiers (Master or slave), if Master classifier rejects the feature vector (bad classification), then at the same pixel centered-window there are a second features estimation taking a bigger or a smaller window, this second feature vector is now classified by Slave classifier (the decision could be the reject class). Both classifier decisions (Gaussian or KNN) pass to the reconstruction task giving the final mammography segmentation. For the parallel data fusion algorithms we have to estimate at the same time two vector features (one for bigger window and other for smaller window), both feature vector are classified; their decisions could be well or bad classification. From the classifier decisions, we have implemented the Bayes fusion which is based on the Belief function taking the probability to belong a given class. We have implemented also the fusion by Dempster-Shafer theory.