Latin American applied research

Discrete-time Controllers Based On The Internal Model Principle For Shunt Active Power Filters

This paper presents an analysis and design of discrete-time controllers based on the internal model principle, applied to shunt active power filters. The presented control strategies are aimed to compensate the current harmonics produced by nonlinear loads connected to the point of common coupling in low voltage power grids. The proposed current controllers are based on the internal model principle operating with different sampling frequencies and for even and odd or only odd harmonics. It is demonstrated that the use of a downsampled rate, and fewer poles in the internal model, results in a straightforward digital implementation; improvement of the transient response; increasing of the stability margin of the closed-loop system and it is possible to obtain source currents with reduced total harmonic distortion. To validate the above claims and demonstrate the steady-state and transient performance, simulation results are presented

Classification of asr word hypotheses using prosodic information and resampling of training data

In this work, we propose a novel resampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%

Performance of a simplified soft-distance decoding algorithm for LDPC codes over the rayleigh fading channel

In this paper, we investigate the performance of a Soft-Input soft-Output decoding algorithm for LDPC codes that uses Euclidean distance as its metric, in the Rayleigh fading channel. It is found that its Bit Error Rate performance is close to that of traditional decoding algorithms like the Sum-Product algorithm and its logarithmic version. Main characteristics of the proposed algorithm and its modification to perform over the Rayleigh channel are described. This algorithm uses squared Euclidean distance as the metric, does not require knowledge of the signal-to-noise ratio of the received signal, and is less complex to implement than other soft-input, soft-output algorithms

Evaluation of a new model for vowels synthesis with perturbations in acoustic parameters

Voice signal contains intrinsic irregularities which become more evident in the presence of pathologies. The acoustic parameters are very useful for the clinical assessment of voice and pathologies detection. Most existing voice models handle irregularities as additive noise and not as information carriers. In this work, a new model is proposed allowing to generate synthesized voices with previously selected acoustical parameters shimmer and jitter. Artificial voices are generated from a glottal source signal, obtained by conveniently disturbing amplitudes and periods, and then filtered using an auto-regressive linear filter. Models were developed for amplitude and period perturbations based on statistical methods. Several signals were generated and the performance of the model was analyzed. The quality of synthesized voices was evaluated using an objective quality measurement. The obtained jitter and shimmer values mostly agreed with the theoretically predicted values. These results suggest that this model is useful for artificial voices generation

Voice conversion using k-histograms and residual averaging

The main goal of a voice conversion system is to modify the voice of a source speaker, in order to be perceived as if it had been uttered by another specific speaker. Many approaches found in the literature convert only the features related to the vocal tract of the speaker. Our proposal is to convert those characteristics, and to process the signal passing through the vocal chords. Thus, the goal of this work is to obtain better scores in the voice conversion results

Adaptive blind interference cancellation and spatial scheduling schemes for closed loop multiuser mimo systems

To improve the spectrum efficiency in wireless communication, two techniques are commonly used: adaptive digital signal processing and resource allocation. The aim of both techniques is to reduce the interference level. In this paper we study the performance improvement of using jointly these techniques for closed loop multiuser MIMO systems. We propose a closed loop spatial multiuser scheduling scheme that enables code-reuse without significantly degrading the performance of an Adaptive Blind Receiver (ABR)

Insulin dependent diabetes mellitus control

This work considers the problem of automatically controlling the glucose level in Insulin Dependent Diabetes Mellitus (IDDM) patients. The objective is to include several important and practical issues in the design: model uncertainty, time variations, nonlinearities, measurement noise, actuator delay and saturation, and real-time implementation. These are fundamental issues to be solved in a device implementing this control. A simulator of the well known Sorensen 19-th state model has been built. It has been found that this compartmental model although nolinear, is almost Linear Time Invariant (LTI) in practice. To this end, a robust controller is designed and tested against the simulator in order to check all the previous practical issues

Reinforcement learning using gaussian processes for discretely controlled continuous processes

In many application domains such as autonomous avionics, power electronics and process systems engineering there exist discretely controlled continuous processes (DCCPs) which constitute a special subclass of hybrid dynamical systems. We introduce a novel simulation-based approach for DDCPs optimization under uncertainty using Reinforcement Learning with Gaussian Process models to learn the transitions dynamics descriptive of mode execution and an optimal switching policy for mode selection. Each mode implements a parameterized feedback control law until a stopping condition triggers. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Throughput maximization in a buffer tank subject to an uncertain schedule of several inflow discharges is used as case study addressing supply chain control in manufacturing systems.

Carrier frequency offset compensation for ofdma systems using circular banded matrices

Orthogonal frequency division multiple access (OFDMA) is a multiuser communication technique that allocates to each user a set of orthogonal carriers. In the presence of carrier frequency offset (CFO) the orthogonality among carriers is lost and it is impossible to recover the information of the users without CFO compensation. The resulting multiple access interference (MAI) can be described as an interference matrix of large dimensions. In order to compensate for the CFO, this matrix must be inverted, what is computationally complex. Therefore, a banded matrix approximation is usually introduced. In this paper we propose a circular banded matrix which is a better approximation to the actual interference matrix. Also, by means of numerical simulation, we show that neither banded nor circular banded matrices approximations work well for normalized CFO close to 0.5

An efficient spectral shaping method for ofdm systems using correlated interpolation of symbols

The generation of suitable orthogonal frequency division multiplexing (OFDM) signals on the grounds of fully digital signal processing is considered. The main objective is to obtain a discrete-time signal with adequate allocation of power emissions in both, in-band portion (i.e., the allocated band for communication) and out-of-band portion (i.e., the band allocated to adjacent channels) of the spectrum. The proposed method prevents the transmitter from using traditional filtering techniques, to keep under control power emissions in the system. In addition, the adaptability feature of our proposal makes its implementation attractive within cognitive radio (CR) and software defined radio (SDR) OFDM-based systems. Our spectral shaping approach is based on an optimum interpolation, obtained from the combination of an inverse fast Fourier transform (IFFT) and a spectral precoding operation, both of them transparent from the perspective of a conventional (legacy) OFDM receiver.

Set-membership estimation theory for coupled mimo Wiener-like models

In this paper, an approach for identifying coupled multiple-input, multiple-output (MIMO) Wiener-like models is presented. Each multiple-input, single-output (MISO) model structure contained in the MIMO model is parameterized using FInite sets of discrete Laguerre transfer functions followed by High Level Canonical Piecewise Linear (HLCPWL) that represents the static memoryless nonlinear block. For each MISO model, the parameters of the HLCPWL functions are found via Set-membership (SM) estimation theory, under mild error constraints. In this way, each MISO Wiener-like model is described as a set of parameters for the nonlinear static subsystem, whose values are obtained by solving a linear programming problem. The MIMO Wiener-like model structure is then represented as a set of coupled input-output MISO models, converting the identification of a coupled MIMO system into the identification of MISO systems. In order to validate the proposed identification algorithm, an illustrative example is provided.

Correlation-based inter and intra-band predictions for lossless compression of multispectral images

We present a new lossless compressor for multispectral images having few bands. The mentioned compressor takes into account variations in spectral correlation in order to determine the appropriate spectral and spatial prediction to be performed. The algorithm exploits 2 different facts. On one hand, highly correlated bands may be efficiently compressed with fast computations. On the other hand, a class-conditioned wavelet-based compressor, which is more time-consuming, has given very high compression ratios, even in the case of lowly correlated bands. Our correlation dependent hybrid algorithm yields high compression ratios, outperforming state-of-the-art lossless compressors, and has reasonable execution times

Homography-based pose estimation to guide a miniature helicopter during 3d-trajectory tracking

This work proposes a pose-based visual servoing control, through using planar homography, to estimate the position and orientation of a miniature helicopter relative to a known pattern. Once having the current flight information, the nonlinear underactuated controller presented in one of our previous works, which attends all flight phases, is used to guide the rotorcraft during a 3D-trajectory tracking task. In the sequel, the simulation framework and the results obtained using it are presented and discussed, validating the proposed controller when a visual system is used to determine the helicopter pose information

Post-compensation of a CT first-order ΣΔ ADC using PWL dynamic system

An approach for compensating non-linearities in a continuous-time first-order sigma-delta converter with one bit quantization is presented. The proposed compensators are parallel nonlinear dynamic systems using piecewise linear static functions. A reduction of an order of magnitude is obtained in the measured squared error when compared to the uncompensated sigma-delta converter. Our results confirm a significant improvement in signal to noise and distortion ratio for single tone input signals and in spurious free dynamic range for multi-tone inputs