On the structure, convergence and performance of an adaptive I/Q mismatch corrector

The I/Q mismatches in quadrature radio receivers results in finite and usually insufficient image rejection, degrading the performance greatly. In this paper we present a detailed analysis of the Blind-Source Separation (BSS) based mismatch corrector in terms of its structure, convergence and performance. The results indicate that the mismatch can be effectively compensated during the normal operation as well as in the rapidly changing environments. Since the compensation is carried out before any modulation specific processing, the proposed method works with all standard modulation formats and is amenable to low-power implementations.

Adaptive digital receivers for analog front-end mismatch correction

Phase and gain mismatches between the I and Q analog signal processing paths of a quadrature receiver are responsible for the generation of image signals which limit the dynamic range of a practical receiver. In this paper we analyse the effects these mismatches and propose a low-complexity blind adaptive algorithm to minimize this problem. The proposed solution is based on two, 2-tap adaptive filters, arranged in Adaptive Noise Canceller (ANC) set-up. The algorithm lends itself to efficient real-time implementation with minimal increase in modulator complexity.

Adaptive compensation of analog front-end I/Q mismatches in digital receivers

I and Q Channel phase and gain misniatches are of great concern in communications receiver design. In this paper we analyse the effects of I and Q channel mismatches and propose a low-complexity blind adaptive algorithm to minimize this problem. The proposed solution consists of two, 2-tap adaptive filters, arranged in Adaptive Noise Canceller (ANC) set-up, with the output of one cross-fed to the input of the other. The system works as a de-correlator eliminating I and Q mismatch errors.

A low-complexity self-calibrating adaptive quadrature receiver

In this paper digital part of a self-calibrating quadrature-receiver is described, containing a digital calibration-engine. The blind source-separation-based calibration-engine eliminates the RF-impairments in real-time hence improving the receiver's performance without the need for test/pilot tones, trimming or use of power-hungry discrete components. Furthermore, an efficient time-multiplexed calibration-engine architecture is proposed and implemented on an FPGA utilising a reduced-range multiplier structure. The use of reduced-range multipliers results in substantial reduction of area as well as power consumption without a compromise in performance when compared with an efficiently designed general purpose multiplier. The performance of the calibration-engine does not depend on the modulation format or the constellation size of the received signal; hence it can be easily integrated into the digital signal processing paths of any receiver.

Design and low-power implementation of an adaptive image rejection receiver

This paper deals with and details the design and implementation of a low-power; hardware-efficient adaptive self-calibrating image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Hybrid strength-reduced and re-scheduled data-flow, low-power implementation of the adaptive self-calibration algorithm is developed and its efficiency is demonstrated through simulation case studies. A behavioral and structural model is developed in Matlab as well as a low-level architectural design in VHDL providing valuable test benches for the performance measures undertaken on the detailed algorithms and structures.

An Evaluation of Complex Adaptive Evolvable System Simulation

Thesis (Master's)--University of Washington, 2016-03

Adaptive-threshold region merging via path scanning

Region merging algorithms commonly produce results that are seen to be far below the current commonly accepted state-of-the-art image segmentation techniques. The main challenging problem is the selection of an appropriate and computationally efficient method to control resolution and region homogeneity. In this paper we present a region merging algorithm that includes a semi-greedy criterion and an adaptive threshold to control segmentation resolution. In addition we present a new relative performance indicator that compares algorithm performance across many metrics against the results from human segmentation. Qualitative (visual) comparison demonstrates that our method produces results that outperform existing leading techniques.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Performance of microstrip antennas in the presence of EBG in an indoor localization system

Indoor localization systems in nowadays is a huge area of interest not only at academic but also at industry and commercial level. The correct location in these systems is strongly influenced by antennas performance which can provide several gains, bandwidths, polarizations and radiation patterns, due to large variety of antennas types and formats. This paper presents the design, manufacture and measurement of a compact microstrip antenna, for a 2.4 GHZ frequency band, enhanced with the use of Electromagnetic Band-Gap (EBG) structures, which improve the electromagnetic behavior of the conventional antennas. The microstrip antenna with an EBG structure integrated allows an improvement of the location system performance in about 25% to 30% relatively to a conventional microstrip antenna.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Multiagent system for adaptive strategy formulation in electricity markets

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

User modeling In adaptive hypermedia educational systems

This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling (UM), numerous research and developed systems already seem to promise good results, but some experimentation and implementation are still necessary to conclude about the utility of the UM. That is, the experimentation and implementation of these systems are still very scarce to determine the utility of some of the referred applications. At present, the Student Modeling research goes in the direction to make possible reuse a student model in different systems. The standards are more and more relevant for this effect, allowing systems communicate and to share data, components and structures, at syntax and semantic level, even if most of them still only allow syntax integration.