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- Bhikkaji, Bharath, et al.
(författare)
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Recursive Algorithms for Estimating the Parameters in a One Dimensional Heat Diffusion System: Analysis
- 2004
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In [5], we have proposed two recursive algorithms in the frequency domain to estimate the parameters of a one dimensional heat diffusion system. There in, we have discussed in detail the construction and the implementation of the algorithms. Further in [5], we observed the convergence of the proposed algorithms using certain numerical examples. In this paper, we analyse the convergence of these algorithms from a theoretical perspective.
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| 5. |
- Lundberg, Magnus, et al.
(författare)
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A Novel Approach to High Level Switching Activity Modeling with Applications to Low Power DSP System Synthesis
- 2001
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 49:12, s. 3157-3167
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Tidskriftsartikel (refereegranskat)abstract
- We address high-level synthesis of low-power digital signal processing (DSP) systems by using efficient switching activity models. We present a technology-independent hierarchical scheme that can be easily integrated into current communications/DSP CAD tools for comparing the relative power/performance of two competing DSP designs without specific knowledge of transistor-level details. The basic building blocks considered for such systems are a full adder, a half adder, and a one-bit delay. Estimates of the switching activity at the output of these primitives are used to model the activity in more complex building blocks of DSP systems. The presented hierarchical method is very fast and simple. The accuracy of estimates obtained using the proposed approach is shown to be within 4% of the results obtained using extensive bit-level simulations. Our approach shows that the choice of multiplier/multiplicand is important when using array multipliers in a datapath. If the input signal with smaller mean square value is chosen as the multiplicand, almost 20% savings in switching activity can be achieved. This observation is verified by an analog simulation using a 16 x 16 bit array multiplier implemented in a 0.6-mu process with 3.3 V supply voltage.
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| 7. |
- Mahata, Kaushik, 1973-
(författare)
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Estimation Using Low Rank Signal Models
- 2003
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Designing estimators based on low rank signal models is a common practice in signal processing. Some of these estimators are designed to use a single low rank snapshot vector, while others employ multiple snapshots. This dissertation deals with both these cases in different contexts. Separable nonlinear least squares is a popular tool to extract parameter estimates from a single snapshot vector. Asymptotic statistical properties of the separable non-linear least squares estimates are explored in the first part of the thesis. The assumptions imposed on the noise process and the data model are general. Therefore, the results are useful in a wide range of applications. Sufficient conditions are established for consistency, asymptotic normality and statistical efficiency of the estimates. An expression for the asymptotic covariance matrix is derived and it is shown that the estimates are circular. The analysis is extended also to the constrained separable nonlinear least squares problems. Nonparametric estimation of the material functions from wave propagation experiments is the topic of the second part. This is a typical application where a single snapshot vector is employed. Numerical and statistical properties of the least squares algorithm are explored in this context. Boundary conditions in the experiments are used to achieve superior estimation performance. Subsequently, a subspace based estimation algorithm is proposed. The subspace algorithm is not only computationally efficient, but is also equivalent to the least squares method in accuracy. Estimation of the frequencies of multiple real valued sine waves is the topic in the third part, where multiple snapshots are employed. A new low rank signal model is introduced. Subsequently, an ESPRIT like method named R-Esprit and a weighted subspace fitting approach are developed based on the proposed model. When compared to ESPRIT, R-Esprit is not only computationally more economical but is also equivalent in performance. The weighted subspace fitting approach shows significant improvement in the resolution threshold. It is also robust to additive noise.
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| 8. |
- Mahata, Kaushik
(författare)
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Identification of dynamic errors-in-variables models
- 2002
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The problem of identifying dynamic errors-in-variables models is of fundamental interest in many areas like process control, array signal processing, astronomical data reduction. In recent years, this field has received increased attention of the research community. In this thesis, some time domain and frequency domain approaches for identifying the errors-in-variables model is studied. The first chapter gives an overview of various methods for identifying dynamic errors-in-variables systems. Several approaches are classified and a qualitative comparison of different existing methods is also presented. The second chapter deals with instrumental variables based approaches. The least squares and the total least squares methods of solving the Yule–Walker equation is of central interest here. The methods are compared from the view point of asymptotic performance, numerical robustness and computation. The method presented in the third chapter uses prefiltered data. The input-output data is passed through a pair of user defined prefilters and the output data from the prefilters is subjected to a least-squares like algorithm. Compared to the IV approach, the proposed method shows a significant improvement in the small-sample properties of the MA parameter estimates, without any increase in the computational load. In the fourth chapter, we show that the two-dimensional process composed of the input-output data admits a finite order ARMA representation. Then we propose a parametric identification algorithm and another non-parametric identification method based on the ARMA representation.
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| 9. |
- Mahata, Kaushik, et al.
(författare)
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On the use of flexural wave propagation experiments for identification of complex modulus
- 2001
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we investigate the estimation of the complex modulus of a viscoelastic material from flexural wave experiments. A bar specimen of uniform cross-section is subjected to lateral impact by a steel ball giving rise to flexural waves traveling along the bar. The strains due to wave propagation are registered as functions of time using strain gauges at different sections. The measured strains are transformed in to the frequency domain. A non-parametric estimation of the complex modulus is carried out for each frequency. An analysis of the quality of the non-parametric estimate is carried out. The validity of the theoretical results are confirmed by numerical studies and experimental tests.
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| 10. |
- Söderström, Torsten, et al.
(författare)
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Perspectives on errors-in-variables estimation for dynamic systems
- 2001
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The paper gives an overview of various methods for identifying dynamic errors-in-variables systems. Several approaches are classified by how the original information in time-series data of the noisy input and output measurements is condensed before further processing. For some methods, such as instrumental variable estimators, the information is condensed into a nonsymmetric covariance matrix as a first step before further processing. In a second class of methods, where a symmetric covariance matrix is used instead, the Frisch scheme and other bias-compensation approaches appear. When dealing with the estimation problem in the frequency domain, a milder data reduction typically takes place by first computing spectral estimators of the noisy input-output data. Finally, it is also possible to apply maximum likelihood and prediction error approaches using the original time-domain data in a direct fashion. This alternative will often require quite high computational complexity but yield good statistical efficiency.The paper is also presenting various properties of parameter estimators for the errors-in-variables problem, and a few conjectures are included, as well as some perspectives and experiences by the authors.
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