Paulo Alexandre Crisóstomo Lopes

Dealing With Unknown Impedance and Impulsive Noise in the Power-Line Communications Channel

Power-line communication (PLC) allows establishing digital communications without adding any new wires. It will turn ones house or neighborhood grid into a smart grid. PLC has some issues, namely, high noise at low frequencies and varying characteristic impedance. This paper addresses these issues to improve the signal-to-noise ratio by increasing the signal or reducing the noise. PLC MODEMs are subject to legislations that limit the signals in the line. The radiated signal is proportional to the current, but not to the input current, since the current forms a standing wave along the line. However, better performance can be achieved if the input current is measured. The receiver circuit of the transmitting MODEM can be used to estimate the input impedance. This paper presents a technique to use this new information to achieve better performance and to follow legislation changes in the band above 30 MHz. A study of the viability of using impulsive noise reduction techniques to further increase performance is also presented. The short noise pulses result in high correlation between the noises in different carriers. Impulse position detection should result in an increase in capacity.

The behavior of the modified FX-LMS algorithm with secondary path modeling errors

In active noise control there has been some research based in the modified filtered-X least mean square (LMS) algorithm (MFX-LMS). When the secondary path is perfectly modeled, this algorithm is able to perfectly eliminate its effect. It is also easily adapted to allow the use of fast algorithms such as the recursive least square, or algorithms with good tracking performance based on the Kalman filter. This letter presents the results of a frequency domain analysis about the behavior of the MFX-LMS in the presence of secondary path modeling errors and a comparison with the FX-LMS algorithm. Namely, it states that for small values of the secondary path delay both algorithms perform the same, but that the step-size of the FX-LMS algorithm decreases with increasing delay, while the MFX-LMS algorithm is stable for an arbitrary large value for the secondary path delay, as long as the real part of the ratio of the estimated to the actual path is greater than one half (Re{S/spl circ//sub z//S/sub z/}>1/2). This means that for the case of no phase errors the estimated amplitude should be greater than half the real one and for the case of no amplitude errors the phase error should be less than 60/spl deg/. Analytical expressions for the limiting values for the step-size in the presence of modeling errors are given for both algorithms.

New Normalized LMS Algorithms Based on the Kalman Filter

While the LMS algorithm and its normalized version (NLMS), have been thoroughly used and studied. Connections between the Kalman filter and the RLS algorithm have been established however, the connection between the Kalman filter and the LMS algorithm has not received much attention. By linking these two algorithms, a new normalized Kalman based LMS (KLMS) algorithm can be derived that has some advantages to the classical one. Their stability is guaranteed since they are a special case of the Kalman filter. More, they suggests a new way to control the step size, that results in good convergence properties for a large range of input signal powers, that occur in many applications. They prevent high measurement noise sensitivity that may occur in the NLMS algorithm for low order filters, like the ones used in OFDM equalization systems. In these paper, different algorithms based on the correlation form, information form and simplified versions of these are presented. The simplified form maintain the good convergence properties of the KLMS with slightly lower computational complexity.

Modeling and optimization of the access impedance of power line channels

Digital networks can be established using the same set of wires that is used to distribute the power signal through our homes, the power-line channel (PLC). This networks have no new wires. However, the PLC channel has highly varying characteristics that need to be taken into account, namely the equivalent transmission line characteristic impedance and input impedance. The impedance seen at the input of the power-line channel varies with frequency, time and from line to line. This means that for the same voltage signal the injected current will vary. The current flowing in the channel is the main source of radiated electromagnetic interference (EMI), and imposes limits on the injected signal. The amplitude of the current in the line is not equal to the input current. The current peaks in the line can be minimized by minimizing the current of the positive traveling wave and it will be shown that this can be accomplished if the transmitter is coupled through a matched resistor (to the line impedance) or adequate signal processing. The signal processing method allows to easily change the access impedance. At the output of the channel the signal to noise ratio is independent of charge resistance. A system with input impedance estimation and minimization of the positive wave amplitude was simulated.

Auxiliary Noise Power Scheduling Algorithm for Active Noise Control with Online Secondary Path Modeling and Sudden Changes

In active noise control (ANC), online secondary path modeling can be achieved by adding a small auxiliary noise signal. If the secondary path changes are slow, then this signal can be low, but keeping the stability of the ANC system with sudden secondary path changes requires higher values for this signal, so auxiliary noise power scheduling is required. The proposed algorithm deals well with sudden (and strong) changes, due to the fast convergence of the secondary path model. It is compared with other similar algorithms in the literature. The ability to deal with different physical conditions without changing the algorithms parameters is also compared. The proposed algorithm increases the auxiliary noise to levels close to the acoustic noise when no cancelation is done and reduces it to lower levels when noise cancelation is being performed. In addition variants of the LMS that improve performance are proposed.

On the Modeling of New Tunnel Junction Magnetoresistive Biosensors

A fully integrated biochip based on a 16 × 16 scalable matrix structure of aluminum oxide magnetic tunnel junctions (MTJs) and thin-film diodes (TFDs of hydrogenated amorphous silicon) was fabricated and included as the biosensor of a portable handheld microsystem developed for biomolecular recognition detection using magnetic labels [deoxyribonucleic acid (DNA) hybridization, antibody antigen interaction, etc.]. The system uses magnetic field arraying of magnetically tagged biomolecules and can potentially be used to detect single or few biomolecules. Each biosensor matrix node is the series between a TFD (p-i-n or Schottky-barrier type) and an MTJ. In this paper, this matrix basic cell biosensor element is completely characterized and modeled. Experimental measured data are provided and compared with the proposed theoretical models results. It is shown that the diode may be used both as the matrix switching device and as an in-site temperature sensor and that the MTJ may act as the magnetoresistive sensor for detecting the fringe field of immobilized magnetic markers. Therefore, the fabricated fully integrated biochip included in the developed handheld microsystem may be used for biomolecular recognition.

A New Type of Normalized LMS Algorithm Based on the Kalman Filter

While the LMS algorithm and its normalized version (NLMS), have been thoroughly used and studied, and connections between the Kalman filter and the RLS algorithm have bean established, the connection between the Kalman filter and the LMS algorithm has not received much attention. By linking these two algorithms, a new normalized LMS algorithm can be derived that has some advantages to the classical one. Firstly, its stability is guaranteed since it is a special case of the Kalman filter. Secondly, it suggests a new way to control the step size that results in optimum convergence for a large range of input signal powers, that occur in many applications. Finally, it prevents measurement noise amplification that occurs in the NLMS algorithm for low order filters, like the ones used in OFDM equalization systems.

Active noise control algorithms with reduced channel count and their stability analysis

In narrowband active noise control systems, the dimension of the quiet zone around each error sensor is proportional to the wavelength of the noise being cancelled. In broadband active noise control systems, several error sensors per anti-noise source are used (two as a rule of thumb) in order to achieve larger cancellation regions, but the fact still remains that the size of the quiet zone will be proportional to the wavelength of the noise. In this paper, it is proposed to achieve wide quiet zones by bandlimiting the anti-noise signal. This results in techniques that do not need the extra number of error sensors. This cannot be done by placing a filter in the cancellation path, or using the anti-aliasing or reconstruction filters, unless you use a very sharp filter with long delay. Instead, several algorithms based on the MFxLMS algorithm are presented that accomplish this task. This permits systems with more anti-noise sources than error sensors, which allows a better inversion of the cancellation path while still achieving large quiet zones. The new algorithms are less sensitive to cancellation path modelling errors. Namely, the maximum error allowed in the delay estimate increases as the anti-noise bandlimiting frequency decreases.

Generic Architecture Designed for Biomedical Embedded Systems

Abstract: Embedded Systems assume an increasing importance in biomedical applicationssuch as clinical analysis and patient monitoring. The lack of generic architec-tures make the design of this type of autonomous embedded systems a cum-bersome and expensive task. This paper proposes a generic architecture for de-veloping biomedical embedded systems, considering both the hardware and thesoftware layers. A prototype of one of these systems for biomolecular recogni-tion, based on magnetoresistive sensors to detect magnetic markers, was alreadyimplemented by off-the-shelf components usage. Experimental results show theeffectiveness of the proposed architecture and the advantage of its application todevelop distributed biomedical embedded systems.Keywords: Embedded systems, biomedical applications, computing architectures, au-tonomous communication systems 1. INTRODUCTION In the last few years there has been a crescent interest on embedded systemsfor biomedical applications, increasing the demand on computing and commu-nication while, at the same time, maintaining the necessity of a portable andautonomous system. Applications such as biochemical operations for clinicalanalysis (e.g, glucose/lactate analysis), DNA analysis and proteomics analysisfor clinical diagnostics [Piedade et al., 2006] and real-time pervasive patientmonitoring [Jovanov et al., 2001] [Halteren et al., 2004] are typical exampleswhere high computing and communication requirements must be effective.

Leakage-based precoding algorithms for multiple streams per terminal MU-MIMO systems

Abstract Mobile networks rely extensively on multi-input multi-output (MIMO) communications to increase the data rate and improve signal quality. In multi-user MIMO (MU-MIMO) the signals are steered at the base station, forming multiple beams to the several user terminals (UTs). This is achieved by precoding the signals to be sent to the UTs. In leakage-based precoding, the precoding vectors are selected so that the signal to leakage plus noise ratio is maximized, where leakage is the amount of signal that is meant for a given UT but is received by the other UTs. This technique gives better results than techniques that completely eliminate the interference without regard to the signal or noise level (zero forcing solutions) like the block diagonal (BD) algorithm. However, current leakage-based algorithms are only optimal in the case of single-antenna UTs. In this paper, we propose simplified versions of these algorithms suitable for multiple streams per UTs, and compare its performance with existing solutions. Simulation results show an increase in performance. One of the versions does not require any Singular Value Decomposition (SVD) while the other does SVD of a much smaller matrix. Both still achieve better performance than the competition.