18,165 research outputs found
Robust Sampling Clock Recovery Algorithm for Wideband Networking Waveform of SDR
A novel technique for sampling clock recovery in a wideband networking waveform of a software defined radio is proposed. Sampling clock recovery is very important in wideband networking radio operation as it directly affects the Medium Access adaptive time slot switching rate. The proposed Sampling clock recovery algorithm consists of three stages. In the first stage, Sampling Clock Offset (SCO) is estimated at chip level. In the second stage, the SCO estimates are post-filtered to improve the tracking performance. We present a new post-filtering method namely Steady-State State-Space Recursive Least Squares with Adaptive Memory (S4RLSWAM). For the third stage of SCO compensation, a feedforward Lagrange interpolation based algorithm is proposed. Real-time hardware results have been presented to demonstrate the effectiveness of the proposed algorithms and architecture for systems requiring high data throughput. It is shown that both the proposed algorithms achieve better performance as compared to existing algorithms
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Comparison of Current Gravity Estimation and Determination Models
This paper will discuss the history of gravity estimation and determination models while analyzing methods that are in development. Some fundamental methods for calculating the gravity field include spherical harmonics solutions, local weighted interpolation, and global point mascon modeling (PMC). Recently, high accuracy measurements have become more accessible, and the requirements for high order geopotential modeling have become more stringent. Interest in irregular bodies, accurate models of the hydrological system, and on-board processing has demanded a comprehensive model that can quickly and accurately compute the geopotential with low memory costs. This trade study of current geopotential modeling techniques will reveal that each modeling technique has a unique use case. It is notable that the spherical harmonics model is relatively accurate but poses a cumbersome inversion problem. PMC and interpolation models, on the other hand, are computationally efficient, but require more research to become robust models with high levels of accuracy. Considerations of the trade study will suggest further research for the point mascon model. The PMC model should be improved through mascon refinement, direct solutions that stem from geodetic measurements, and further validation of the gravity gradient. Finally, the potential for each model to be implemented with parallel computation will be shown to lead to large improvements in computing time while reducing the memory cost for each technique.Aerospace Engineering and Engineering Mechanic
A Stochastic Majorize-Minimize Subspace Algorithm for Online Penalized Least Squares Estimation
Stochastic approximation techniques play an important role in solving many
problems encountered in machine learning or adaptive signal processing. In
these contexts, the statistics of the data are often unknown a priori or their
direct computation is too intensive, and they have thus to be estimated online
from the observed signals. For batch optimization of an objective function
being the sum of a data fidelity term and a penalization (e.g. a sparsity
promoting function), Majorize-Minimize (MM) methods have recently attracted
much interest since they are fast, highly flexible, and effective in ensuring
convergence. The goal of this paper is to show how these methods can be
successfully extended to the case when the data fidelity term corresponds to a
least squares criterion and the cost function is replaced by a sequence of
stochastic approximations of it. In this context, we propose an online version
of an MM subspace algorithm and we study its convergence by using suitable
probabilistic tools. Simulation results illustrate the good practical
performance of the proposed algorithm associated with a memory gradient
subspace, when applied to both non-adaptive and adaptive filter identification
problems
Cache-aware Performance Modeling and Prediction for Dense Linear Algebra
Countless applications cast their computational core in terms of dense linear
algebra operations. These operations can usually be implemented by combining
the routines offered by standard linear algebra libraries such as BLAS and
LAPACK, and typically each operation can be obtained in many alternative ways.
Interestingly, identifying the fastest implementation -- without executing it
-- is a challenging task even for experts. An equally challenging task is that
of tuning each routine to performance-optimal configurations. Indeed, the
problem is so difficult that even the default values provided by the libraries
are often considerably suboptimal; as a solution, normally one has to resort to
executing and timing the routines, driven by some form of parameter search. In
this paper, we discuss a methodology to solve both problems: identifying the
best performing algorithm within a family of alternatives, and tuning
algorithmic parameters for maximum performance; in both cases, we do not
execute the algorithms themselves. Instead, our methodology relies on timing
and modeling the computational kernels underlying the algorithms, and on a
technique for tracking the contents of the CPU cache. In general, our
performance predictions allow us to tune dense linear algebra algorithms within
few percents from the best attainable results, thus allowing computational
scientists and code developers alike to efficiently optimize their linear
algebra routines and codes.Comment: Submitted to PMBS1
Recursive least squares for online dynamic identification on gas turbine engines
Online identification for a gas turbine engine is vital for health
monitoring and control decisions because the engine electronic
control system uses the identified model to analyze the performance
for optimization of fuel consumption, a response to the pilot
command, as well as engine life protection. Since a gas turbine engine
is a complex system and operating at variant working conditions, it
behaves nonlinearly through different power transition levels and at
different operating points. An adaptive approach is required to capture
the dynamics of its performance
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