Daniel F. Albuquerque

Analog Filter Bank for Cochlear Radio

In this paper, we propose a design for the analog part of an Hybrid Filter Bank (HFB) system targeted to radio-frequency (RF) signals, inspired on the mammals cochlea, with the basilar membrane mimicked by microstrip lines. A prototype is presented, operating from 840 MHz to 1470 MHz using ADCs working at 210 Msps. Simulation results revealed that it is possible to design a HFB with low values of distortion and aliasing. With this cochlear analog front-end it is possible to increase the bandwidth of the signal while keeping a high dynamic range. Further developments of this technology could fulfill the promise of Software-defined Radio to build a radio able to deal with wideband signals.

High Accuracy 3D Indoor Positioning Using Broadband Ultrasonic Signals

In this paper we propose a high accuracy indoor positioning system based on broadband ultrasonic signals and time of arrival (ToA) measurements. Using low cost transducers we were able to use acoustic chirps of between 20 and 45KHz as pulse signals. This overcomes most of the problems faced by the narrow band signals usually used with common piezo-ultrasonic transducers, which include poor resolution, low environment noise immunity, short range and low robustness to the Doppler effect. Using synchronized ultrasonic anchor nodes and time division multiplexing to share the medium, we build a GPS-like system for indoor pervasive applications. A set of experiments were performed to evaluate the proposed system. Very stable 3D position estimates were obtained (absolute standard deviation less than 2.3cm) and a position refresh rate of 350ms was achieved.

Controlling the reconstruction error in Hybrid Filter Banks

In this work we propose a Hybrid Filter Bank (HFB) design method with controlled reconstruction error. We show that this can be achieved if (i) the band-pass analog filters have a phase response that satisfy a given condition and (ii) the chosen delay for the HFB allows a correct approximation of the non-causal part of the digital synthesis filters impulse response. We are using this type of system to perform the analog to digital conversion of RF signals in a software defined radio front-end with a combined bandwidth / dynamic range beyond the current Analog to Digital Converters limits.

Characterization of the Near-Far problem in a CDMA-based acoustic localization system

In this work the Near-Far problem is characterized in the context of an acoustic local positioning system (ALPS). First, a simple energy normalization algorithm is presented to identify the Near-Far situation and also to resolve the signal detection in the case of non-overlapping receptions. Then, an improved signal interference cancellation (ISIC) algorithm is proposed to remove from the received signal most of the energy of the most powerful reception in case of overlapping receptions, thus allowing the detection of weaker signals. The performance of these algorithms has been first studied by simulating the behaviour of an ALPS with four beacons emitting BPSK-modulated Kasami codes installed in a rectangular anechoic chamber. The same study has been later conducted with signals acquired in a real scenario, using a third-generation iPad as the mobile node of the ALPS.

High energy OFDM pulse design algorithm for acoustic ToF ranging and localization

This paper proposes an algorithm to design band-limited Orthogonal Frequency Division Multiplexing (OFDM) pulses for accurate time-of-flight ranging in acoustic-based localization systems. The proposed algorithm is capable to generate OFDM pulses with higher energy when compared with other common pulse design approaches with similar characteristics. The results attained have shown that it is possible to design OFDM pulses with 17% more energy than a chirp or a BPSK pulse with the same characteristics. The proposed algorithm uses an iterative approach to increase the pulse energy while keeping the same amplitude, bandwidth and duration in each iteration. Two different OFDM pulses are provided and compared with two common pulses, namely chirp and BPSK pulses, with the same characteristics.

Doppler resilient modulation in a CDMA-based acoustic local positioning system

This paper proposes a Doppler resilient modulation approach that can be used in acoustic local positioning systems with Binary Phase-Shift Keying (BPSK) modulated signals for Time-of-Flight (ToF) estimation. The proposed approach is implemented in the receiver and can be performed in parallel with other classic techniques to improve the position estimation process. The technique is based on Differential Binary Phase-Shift Keying (DBPSK) modulation instead of the widely used BPSK modulation and presents a low computational complexity. The performance of the proposed technique is studied for BPSK-modulated Kasami codes with 63 and 255 bit length. Results have shown that the proposed technique is not only robust to Doppler effect but also to noise, multipath and multiple-access interference.

Analysis of the perceptual impact of high frequency audio pulses in smartphone-based positioning systems

The ability to locate objects and people indoors remains a substantial challenge and effective centimeter-level smartphone-based indoor positioning, is still an open problem. Conventional smartphones have recently been used for centimeter-level indoor positioning by performing acoustic Time-of-Flight (ToF) ranging. Due to limitations in the smartphone hardware, i.e. available bandwidth typically below 24 kHz, these systems use signals with both audio and ultrasonic spectral content in the ranging process. By using signals with lower frequencies (in the audio band) it is possible to measure longer distances, thus circumventing the high attenuation that ultrasounds suffer. But a system using audible signals would be quite annoying for humans. In this paper we propose a methodology to design non-invasive audio pulses, i.e. pulses with reduced perceptual impact on humans. Additionally, we have evaluated the proposed non-invasive pulses with naïve subjects in a soundproof room for different sound pressure levels.

Iterative Algorithm for High Resolution Frequency Estimation

—Compressed sensing (CS) is a theory that allows us to recover sparse or compressible signals from a much smaller number of samples or measurements than with traditional methods. The problem of detection and estimation of the frequency of a signal is more difficult when the frequencies of the signal are not present on the DFT basis. The Fourier coefficients are not exactly sparse due to the leakage effect if the frequency is not a multiple of the fundamental frequency. In this work we present a high frequency resolution spectrum estimation algorithm that explores the CS, for this type of nonperiodic signal from finite number of samples. It takes advantage of the sparsity of the signal in the frequency domain. The algorithm transforms the DFT basis into a frame with a large number of vectors by inserting columns between some of the existing ones. The proposed algorithm can estimate the amplitudes and frequencies even when the frequencies are too close together, a particularly difficult situation which are not covered by most of the known algorithms. Simulation results show good convergence and a high resolution when compared with other algorithms.

Accurate Spectral Estimation of Non-periodic Signals Based on Compressive Sensing

In this work we propose a method based on compressive sensing (CS) for estimating the spectrum of a signal written as a linear combination of a small number of sinusoids. In practice one deals with signals with finite-length and so the Fourier coefficients are not exactly sparse. Due to the leakage effect in the case where the frequency is not a multiple of the fundamental frequency of the DFT, the success of the traditional CS algorithms is limited. To overcome this problem our algorithm transform the DFT basis into a frame with a larger number of vectors, by inserting a small number of columns between some of the initial ones. The algorithm takes advantage of the compactness of the interpolation function that results from the l1 norm minimization of the Basis Pursuit (BP) and is based on the compressive sensing theory that allows us to acquire and represent sparse and compressible signals, using a much lower sampling rate than the Nyquist rate. Our method allow us to estimate the sinusoids amplitude, phase and frequency.