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We present an approach to the schematic design and time-dependent simulation of microfluidic systems that are composed of basic components, such as pumps, channels, mixing junctions, and valves. For the schematic capture and the simulation, the Cadence Virtuoso design framework is employed, with the components modeled by Verilog-AMS. The pressures and flow rates are calculated with the help of the hydraulic analogy between electronics and fluidics. Signal flow quantities are used to express the flow of chemical concentration. Our focus is on systems employing chemofluidic transistors, i.e., hydrogel-based microvalves that facilitate autonomous flow control without the need for an external control unit. Our approach also covers layout editing by the use of programmed cells. The utility of the approach is exemplified by the successful top-down design of a chemofluidic oscillator circuit and by the functionally correct simulation of a chemofluidic SR flip-flop. [ABSTRACT FROM AUTHOR]

In ultra-wideband (UWB) wireless propagation channels, each multipath component (MPC) can exhibit delay dispersion or, equivalently, frequency dependence; even more, this dispersion can be different for different MPCs. While this effect is understood to have important implications for UWB system design, extraction of the frequency-dependence parameters from measurements has been lacking. This paper will provide both the theoretical tools for the parameter extraction, and provide experimental results of such parameters in UWB indoor channels. We first develop a method that extracts a single MPC. Based on the image principle, the method identifies locations and intensity of radio sources including both original and image sources (corresponding to MPCs) by a distributed antenna array. We show that the method provides spatial resolution better than 0.5 m and is robust against possible measurement errors. Having applied the method to the measured channels in indoor line-of-sight scenarios, we track the detected image sources over (partially overlapping) frequency subbands to obtain frequency dependent properties of their intensity and location. The variation of the source intensity over the frequency was found to be significant. Furthermore, the locations of the image sources can vary as the frequency changes. Comparison of the results with models that do not describe varying per-path frequency dependence revealed noticeable difference in the shape of the CIRs, and as a result, in the delay spread and the number of significant delay bins for Rake reception. [ABSTRACT FROM PUBLISHER]

In this paper, algorithms for joint high-resolution direction-of-arrival (DoA) and polarization estimation using real-world arrays with imperfections are proposed. Both azimuth and elevation angles are considered. Partially correlated and coherent signals may be handled as well. Unlike most of the work available in the open literature, we consider the case when polarization sensitive antenna arrays, which may be disposed on a conformal surface, may have unknown (but fixed) geometries, be composed of elements with individual beampatterns and be subject to cross-polarization as well as mounting platform effects. Herein, recent results on steering vector modeling from noise-corrupted array calibration measurements are employed. This allows for incorporating all nonidealities of an antenna array into the estimation algorithms in a general and convenient manner. The proposed estimators can be implemented using the fast Fourier transform or polynomial rooting techniques regardless of the array configuration. The stochastic Cramér–Rao lower bound for the estimation problem at hand is established using the results from array steering vector modeling as well. The proposed expression takes into account array nonidealities, making such a bound tight even for real-world arrays. Extensive simulation results are provided using several real-world antenna arrays. The proposed algorithms outperform conventional algorithms available in the literature and have a performance close to the stochastic Cramér–Rao lower bound. [ABSTRACT FROM AUTHOR]

In this paper, we derive a unified framework for orthonormal decomposition of the array manifold on scalar fields. Such fields are encountered in cases where polarization is not considered, e.g., single polarized radio waves and acoustic pressure. The results generalize and unify different decompositions of the array manifold, found in recent literature, to arrays of arbitrary geometry including conformal arrays. The concept of equivalence matrix is introduced, establishing a link between the spherical harmonics and 2-D Fourier basis functions. Under some mild assumptions that typically hold in practice, a one-to-one relationship between spherical harmonic spectra and 2-D Fourier spectra may be established. Additionally, it is shown that the rows of the equivalence matrix and the 2-D Fourier spectra of the array manifold span the same subspace. With such results the spherical harmonic and 2-D Fourier decompositions of the array manifold vector, i.e., Wavefield Modeling and 2-D Effective Aperture Distribution Function (EADF) are shown to be equivalent. Results on the modeling capabilities of both orthonormal decompositions are obtained. Moreover, the equivalence matrix is shown to facilitate noise attenuation. A fast spherical harmonic transform with complexity \cal O(Q \log Q) can be obtained by exploiting the equivalence matrix, where Q represents the total number of points on the sphere. Finally, the equivalence matrix allows to gain more insight into the relation between rotating a function on the sphere and on the torus. These contributions facilitate high-resolution array processing both in elevation and azimuth irrespective of the array structure or imperfections. [ABSTRACT FROM PUBLISHER]

In this paper, we present and evaluate the performance of a dynamic dual-link wideband multiple-input-multiple-output (MIMO) channel sounder. The channel sounder can simultaneously measure two wideband dual-polarized links, each with a MIMO matrix size of 30 x 30 and 30 x 32. Multilink MIMO systems, including multiuser and cooperative MIMO, are essential parts of future high-throughput wireless local area networks and fourth-generation cellular systems. To fully understand such systems, the dynamic characteristics of multilink MIMO channels have to be measured. In this paper, we present a channel sounder that enables such measurements, including double-directional parameter estimation possibility for both links. The presented dual-link MIMO channel sounder does not suffer from the deficiencies of previous "virtual multiuser"measurement systems. Furthermore, system analysis and sample results from a measurement campaign with this channel sounder at 5.3 GHz in an indoor office environment are presented. [ABSTRACT FROM AUTHOR]

This paper contributes to the field of higher order (N > 2) tensor decompositions in signal processing. A novel PARATREE tensor model is introduced, accompanied with Sequential Unfolding SVD (SUSVD) algorithm. SUSVD, as the name indicates, applies a matrix singular value decomposition sequen- tially on the unfolded tensor reshaped from the right hand basis vectors of the SVD of the previous mode. The consequent PARATREE model is related to the well known family of PARAFAC tensor decomposition models. Both of them describe a tensor as a sum of rank-i tensors, but PARATREE has several advantages over PARAFAC, when it is applied as a lower rank approximation technique. PARATREE is orthogonal (due to SUSVD), fast and reliable to compute, and the order (or rank) of the decomposition can be adaptively adjusted. The low rank PARATREE approximation can be applied for, e.g., reducing computational complexity in inverse problems, measurement noise suppression as well as data compression. The benefits of the proposed algorithm are illustrated through application examples in signal processing in comparison to PARAFAC and HOSVD. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach for detection, estimation and tracking of multiple-input multiple-output (MIMO) radio propagation parameters from multidimensional channel sounding measurements. A realistic state-space model is developed for the purpose, and the extended Kalman filter (EKF) is applied in a particular computationally efficient form to track the geometrical double-directional propagation path parameters. The observation model utilizes the dense multipath component (DMC), describing the distributed scattering in the channel, as part of the underlying noise process. The DMC model assumes an exponential profile in delay, and allows for an arbitrary angular distribution. In addition, a novel dynamic state dimension estimator using statistical goodness-of-fit tests is introduced. The employed methods are supported by illustrative estimation examples from MIMO channel sounding measurements. [ABSTRACT FROM AUTHOR]

In this paper, we consider the manifold separation technique (MST), which stems from the wavefield modeling formalism developed for array processing. MST is a method for modeling the steering vector of antenna arrays of practical interest with arbitrary 2-D or 3-D geometry. It is the product of a sampling matrix (dependent on the antenna array only) and a Vandermonde structured coefficients vector depending on the wavefield only. This allows fast direction-of-arrival (D0A) algorithms designed for linear arrays to be used on arrays with arbitrary configuration. In real-world applications, the calibration measurements used to determine the sampling matrix are corrupted by noise. This impairs the performance of MST-based algorithms. In particular, we study the effect of noisy calibration measurements on subspace-based DoA algorithms using MST. Expressions describing the error in the DoA estimates due to calibration noise and truncation are derived. This allows predicting the performance of MST-based algorithms in real-world applications. The analysis is verified by simulations. We established a link between the optimal number of selected modes and the statistics of calibration noise. We analyze the modeling error when MST is used for 1-D (azimuth) DoA estimation. [ABSTRACT FROM AUTHOR]

In this paper, we derive an estimation method that jointly estimates the parameters of the concentrated propagation paths and the distributed scattering component that are frequently observed in multiple-input multiple-output (MIMO) channel sounding measurements. The joint angular-delay domain model leads to a correlation matrix with high dimensionality, which makes direct implementation of a maximum-likelihood (ML) estimator unfeasible. We derive low-complexity methods for computing approximate ML estimates that exploit the structure of the covariance matrices. We propose an iterative two-step procedure that alternates between the estimation of the parameters of the concentrated propagation paths and the parameters of the distributed scattering. For the distributed scattering, the estimator first optimizes the parameters describing their time-delay structure. Then, using the estimated time-delay parameters, the parameters of the angular distributions are optimized. We present simulation results and compare the estimated time-delay and angular distributions to the actual distributions, demonstrating that high-quality estimates are obtained. The large sample performance of the estimator is studied by establishing the Cramér-Rao lower bound (CRLB) and comparing it to the variances of the estimates. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size for most parameters, and no bias is observed. [ABSTRACT FROM AUTHOR]