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10
An Inertial Measurement Unit for User Interfaces
, 2000
"... Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant param ..."
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Cited by 15 (4 self)
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Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.
Blind equalization of a nonlinear satellite system using MCMC simulation methods
, 2001
"... This paper proposes the use of Markov Chain MonteCarlo (MCMC) simulation methods for equalizing a satellite communication system. The main di#culties encountered are the nonlinear distorsions caused by the amplifier stage in the satellite. Several processing methods manage to take into account the ..."
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Cited by 3 (1 self)
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This paper proposes the use of Markov Chain MonteCarlo (MCMC) simulation methods for equalizing a satellite communication system. The main di#culties encountered are the nonlinear distorsions caused by the amplifier stage in the satellite. Several processing methods manage to take into account the nonlinearity of the system but they require the knowledge of a training/learning input sequence for updating the parameters of the equalizer. Blind equalization methods also exist but they require a Volterra modelization of the system. The aim of the paper is also to blindly restore the emitted message. To reach the goal, we adopt a Bayesian point of view. We jointly use the prior knowledge on the emitted symbols, and the information available from the received signal. This is done by considering the posterior distribution of the input sequence and the parameters of the model. Such a distribution is very di#cult to study and thus motivates the implementation of MCMC methods. The presentation of the method is cut into two parts. The first part solves the problem for a simplified model; the second part deals with the complete model, and a part of the solution uses the algorithm developed for the simplified model. The algorithms are illustrated and their performance is evaluated using Bit Error Rate versus SignaltoNoise Ratio curves. Keywords: Traveling Wave Tube Amplifier, Bayesian inference, Markov Chain MonteCarlo simulation methods, Gibbs sampling, HastingsMetropolis algorithm.
DualTransceiver Quantization Can Improve Error Performance in CDMA
"... Abstract—A Kuser directsequence spreadspectrum codedivision multipleaccess (CDMA) system with (q <
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Abstract—A Kuser directsequence spreadspectrum codedivision multipleaccess (CDMA) system with (q <<log 2 K)bit baseband signal quantization at the demodulator is considered. It is shown that additionally quantizing the K +1 level output signal of the CDMA modulator into q bits improves significantly the average biterror performance in a nonnegligible regime of noise variance, σ 2, and user load, β, under various system settings, like additive white Gaussian noise (AWGN), Rayleigh fading, singleuser detection, multiuser detection, random and orthogonal spreading codes. For the case of singleuser detection in random spreading AWGNCDMA, this regime is identified explicitly as σ < γ(q) √ β, where γ(q) is a certain prefactor depending on q, and the associated BER improvement is derived analytically for q =1, 2. For the other examined system settings, computer simulations are provided, corroborating this interesting behavior. I.
unknown title
, 2011
"... EPJ manuscript No. (will be inserted by the editor) Nonparametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data. ..."
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EPJ manuscript No. (will be inserted by the editor) Nonparametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data.
Connexions module: m16338 1 Algorithms for Data with Restrictions ∗
"... Many applications involve processing real data. It is ine cient to simply use a complex FFT on real data because arithmetic would be performed on the zero imaginary parts of the input, and, because of symmetries, output values would be calculated that are redundant. There are several approaches to d ..."
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Many applications involve processing real data. It is ine cient to simply use a complex FFT on real data because arithmetic would be performed on the zero imaginary parts of the input, and, because of symmetries, output values would be calculated that are redundant. There are several approaches to developing special algorithms or to modifying complex algorithms for real data. There are two methods which use a complex FFT in a special way to increase e ciency [4], [14]. The rst method uses a lengthN complex FFT to compute two lengthN real FFTs by putting the two real data sequences into the real and the imaginary parts of the input to a complex FFT. Because transforms of real data have even real parts and odd imaginary parts, it is possible to separate the transforms of the two inputs with 2N4 extra additions. This method requires, however, that two inputs be available at the same time. The second method [14] uses the fact that the last stage of a decimationintime radix2 FFT combines two independent transforms of length N/2 to compute a lengthN transform. If the data are real, the two half length transforms are calculated by the method described above and the last stage is carried out to calculate the total lengthN FFT of the real data. It should be noted that the halflength FFT does not have to be calculated by a radix2 FFT. In fact, it should be calculated by the most e cient complexdata algorithm possible, such as the SRFFT or the PFA. The separation of the two halflength transforms and
AFast Algorithm for Nonstationary Delay Estimation
, 1998
"... Indexing terms: adaptive algorithm, time delay estimation Abstract: A computationally e cient algorithm for estimating time delay between signals received at two spatially separated sensors is proposed. The delay estimate is adapted directly to maximize the mean product of the rst sensor output and ..."
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Indexing terms: adaptive algorithm, time delay estimation Abstract: A computationally e cient algorithm for estimating time delay between signals received at two spatially separated sensors is proposed. The delay estimate is adapted directly to maximize the mean product of the rst sensor output and the ltered output of the second received signal. Convergence behaviour and variance of the delay estimate are derived. Itisshown that the proposed algorithm outperforms the explicit time delay estimator at low signaltonoise ratio. 1
FFT Spectrum Analyzer using Goertzel Filter
"... In this paper, the implementation of DSP algorithms on FPGA devices are taken into consideration and the FFT spectral analysis as a real time application was tested in MATLAB System Generator. It integrates two separate fields Digital Signal Processing (DSP) and Very Large Scale Integration (VLSI). ..."
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In this paper, the implementation of DSP algorithms on FPGA devices are taken into consideration and the FFT spectral analysis as a real time application was tested in MATLAB System Generator. It integrates two separate fields Digital Signal Processing (DSP) and Very Large Scale Integration (VLSI). The structure and chronological procedure followed focuses on the sophisticated DSP design and implementation of a Fast Fourier Transform (FFT) spectrum analyzer. The entire system was implemented in MATLAB Simulink Xilinx System generator (SG) toolbox. After simulation, the verilog coding was extracted and implemented on FPGA Virtex II device. As a part of betterment, FIR filter of the analyzer was replaced with Goertzel filter in order to improve the area efficiency of the FPGA device. It provides better frequency resolution and helps in extracting the amplitude component of the signal, thus aiding in improved spectral analysis.
Compound precoding: a preequalisation technique for the bandlimited Gaussian channel
, 2009
"... Abstract: Compound precoding is a stateoftheart technique for combining trellis coding and decisionfeedback equalisation (DFE) prior to upstream transmission on the telephoneline channel, and is an option in the International Telecommunications Union (ITU)T V.92 standard. In this paper, we pro ..."
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Abstract: Compound precoding is a stateoftheart technique for combining trellis coding and decisionfeedback equalisation (DFE) prior to upstream transmission on the telephoneline channel, and is an option in the International Telecommunications Union (ITU)T V.92 standard. In this paper, we provide a detailed overview of this technique. We demonstrate that compound precoding combines in a straightforward manner with most practical trellis codes. We show that compound precoding exhibits a signaltonoise ratio (SNR) gain with respect to other schemes which combine with trellis coding, and quantify this gain. We also show that, for stability of the compound precoder, the precoder feedforward filter must be decomposed into its constituent minimum phase (MP) and all pass (AP) components. Finally, a simulation study of V.92 upstream transmission demonstrates the shaping advantage of compound precoding over competitor techniques for preequalisation in this setting. 1
Bayesian Structure Learning for Functional Neuroimaging
"... Predictive modeling of functional neuroimaging data has become an important tool for analyzing cognitive structures in the brain. Brain images are highdimensional and exhibit large correlations, and imaging experiments provide a limited number of samples. Therefore, capturing the inherent statistic ..."
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Predictive modeling of functional neuroimaging data has become an important tool for analyzing cognitive structures in the brain. Brain images are highdimensional and exhibit large correlations, and imaging experiments provide a limited number of samples. Therefore, capturing the inherent statistical properties of the imaging data is critical for robust inference. Previous methods tackle this problem by exploiting either spatial sparsity or smoothness, which does not fully exploit the structure in the data. Here we develop a flexible, hierarchical model designed to simultaneously capture spatial block sparsity and smoothness in neuroimaging data. We exploit a function domain representation for the highdimensional smallsample data and develop efficient inference, parameter estimation, and prediction procedures. Empirical results with simulated and real neuroimaging data suggest that simultaneously capturing the block sparsity and smoothness properties can significantly improve structure recovery and predictive modeling performance. 1