Some performance results of the Cartesian feedback and feedforward linearization techniques applied to a class-A power amplifier operating at 28 GHz are presented. The performance of the combination of HMMC-5040 (driver) and HMMC-5033 (power amplifier) is used as benchmark for simulation analysis. This analysis is addressed to show the key aspects on spectral regrowth due to phase margin and loop gain for Cartesian feedback, and time delay, phase and gain mismatch for feedforward. A 16 QAM digital signal at 10 Mbits/s filtered with a squared raised cosine filter with /spl alpha/=0, 25 is used as test signal.

In this paper, results related with the assessment of the capabilityto detect forest degradation by analyzing NDFI time series through the BFAST algorithm are presented. Recent studies have shown the potential of the BFAST algorithm applied to a time-series of satellite-derived spectral indices such as NDVI or EVI to detect unambiguous and subtle perturbations of the forest cover canopy both positive (e.g. regeneration) and negative (e.g. deforestation). Similarly, these results suggest the feasibility to distinguish between several types of forest degradation and their causal agents such as selective logging and forest fire. In this context, the results derived from this research show that using NDFI as a data source in the BFAST algorithm improves the detection of forest degradation, and additionally provides information to understand both temporal and spatial approaches related with the dynamics of perturbations of the forest canopy

The application of the passivity theory to the radio-frequency (RF) power-amplifier (PA) linearization problem leads to a new structure that is capable to being built completely with analog circuitry. In this paper, the basis of the passivity (or hyperstability) theory are revised and applied to the linearization of an RF power amplifier, proposing the resulting linearizer structure and experimenting with it from simulations based on the RF PA experimentally validated by Saleh and from a real model of a Class A PA operating at 28 GHz.

In this work, a multi-sensor approach to extract misclassified oil palm plantations from forest cover map using a modified Pauli Decomposition technique is presented. The proposed method includes the generation of a primary forest cover map built using a Landsat-based Normalized Difference Fraction Index, and then the palm oil plantation is filtered out using scattering mechanisms through the Modified Pauli Decomposition technique based on the fusion of Sentinel 1 and Alos Palsar data. Accuracy assessment of the final product, produces accuracy values of 0.946 for forest class, while the classification accuracy for non-forest class is 0.92.

Characterization of a physical system is an important issue to approach some applied physics and engineering problems. The complex natural resonance frequencies of the system which are included in its impulsive response are characteristic of such system and are part of its description. Few works written in English language show a comparison among discrete methods that extract natural complex frequencies from a system impulsive response. Much less common is to find works written in Spanish language about this important research topic. Given this situation, important discrete numeric methods to estimate the complex natural resonance frequencies of a system through its impulsive response are described, tested and compared in different simulation scenarios in this document. According to the obtained results, the matrix pencil method with a SVD filter is the less sensitive method to the noise, while the Prony method and its different versions are the fastest ones. Scenarios that could be more suitable for each method are discussed.