Abstract: Many-core GPUs provide high computing ability and
substantial bandwidth; however, optimizing irregular applications
like SpMV on GPUs becomes a difficult but meaningful task. In this
paper, we propose a novel method to improve the performance of
SpMV on GPUs. A new storage format called HYB-R is proposed to
exploit GPU architecture more efficiently. The COO portion of the
matrix is partitioned recursively into a ELL portion and a COO
portion in the process of creating HYB-R format to ensure that there
are as many non-zeros as possible in ELL format. The method of
partitioning the matrix is an important problem for HYB-R kernel, so
we also try to tune the parameters to partition the matrix for higher
performance. Experimental results show that our method can get
better performance than the fastest kernel (HYB) in NVIDIA-s
SpMV library with as high as 17% speedup.
Abstract: In this paper, a recursive algorithm for the
computation of 2-D DCT using Ramanujan Numbers is proposed.
With this algorithm, the floating-point multiplication is completely
eliminated and hence the multiplierless algorithm can be
implemented using shifts and additions only. The orthogonality of
the recursive kernel is well maintained through matrix factorization
to reduce the computational complexity. The inherent parallel
structure yields simpler programming and hardware implementation
and provides
log 1
2
3
2 N N-N+
additions and
N N
2 log
2 shifts which is
very much less complex when compared to other recent multiplierless
algorithms.
Abstract: The empirical mode decomposition (EMD) represents any time series into a finite set of basis functions. The bases are termed as intrinsic mode functions (IMFs) which are mutually orthogonal containing minimum amount of cross-information. The EMD successively extracts the IMFs with the highest local frequencies in a recursive way, which yields effectively a set low-pass filters based entirely on the properties exhibited by the data. In this paper, EMD is applied to explore the properties of the multi-year air temperature and to observe its effects on climate change under global warming. This method decomposes the original time-series into intrinsic time scale. It is capable of analyzing nonlinear, non-stationary climatic time series that cause problems to many linear statistical methods and their users. The analysis results show that the mode of EMD presents seasonal variability. The most of the IMFs have normal distribution and the energy density distribution of the IMFs satisfies Chi-square distribution. The IMFs are more effective in isolating physical processes of various time-scales and also statistically significant. The analysis results also show that the EMD method provides a good job to find many characteristics on inter annual climate. The results suggest that climate fluctuations of every single element such as temperature are the results of variations in the global atmospheric circulation.
Abstract: We present our ongoing work on the development
of a new quadrotor aerial vehicle which has a tilt-wing
mechanism. The vehicle is capable of take-off/landing in vertical flight mode (VTOL) and flying over long distances in horizontal flight mode. Full dynamic model of the vehicle is derived using
Newton-Euler formulation. Linear and nonlinear controllers for
the stabilization of attitude of the vehicle and control of its
altitude have been designed and implemented via simulations. In particular, an LQR controller has been shown to be quite
effective in the vertical flight mode for all possible yaw angles. A sliding mode controller (SMC) with recursive nature has also
been proposed to stabilize the vehicle-s attitude and altitude. Simulation results show that proposed controllers provide
satisfactory performance in achieving desired maneuvers.
Abstract: We present a simplified equalization technique for a
π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated
signal in a multipath fading environment. The proposed equalizer is
realized as a fractionally spaced adaptive decision feedback equalizer
(FS-ADFE), employing exponential step-size least mean square
(LMS) algorithm as the adaptation technique. The main advantage of
the scheme stems from the usage of exponential step-size LMS algorithm
in the equalizer, which achieves similar convergence behavior
as that of a recursive least squares (RLS) algorithm with significantly
reduced computational complexity. To investigate the finite-precision
performance of the proposed equalizer along with the π/4 -DQPSK
modem, the entire system is evaluated on a 16-bit fixed point digital
signal processor (DSP) environment. The proposed scheme is found
to be attractive even for those cases where equalization is to be
performed within a restricted number of training samples.
Abstract: In this paper, a robust statistics based filter to remove salt and pepper noise in digital images is presented. The function of the algorithm is to detect the corrupted pixels first since the impulse noise only affect certain pixels in the image and the remaining pixels are uncorrupted. The corrupted pixels are replaced by an estimated value using the proposed robust statistics based filter. The proposed method perform well in removing low to medium density impulse noise with detail preservation upto a noise density of 70% compared to standard median filter, weighted median filter, recursive weighted median filter, progressive switching median filter, signal dependent rank ordered mean filter, adaptive median filter and recently proposed decision based algorithm. The visual and quantitative results show the proposed algorithm outperforms in restoring the original image with superior preservation of edges and better suppression of impulse noise
Abstract: Numerous divergence measures (spectral distance, cepstral
distance, difference of the cepstral coefficients, Kullback-Leibler
divergence, distance given by the General Likelihood Ratio, distance
defined by the Recursive Bayesian Changepoint Detector and the
Mahalanobis measure) are compared in this study. The measures are
used for detection of abrupt spectral changes in synthetic AR signals
via the sliding window algorithm. Two experiments are performed;
the first is focused on detection of single boundary while the second
concentrates on detection of a couple of boundaries. Accuracy of
detection is judged for each method; the measures are compared
according to results of both experiments.
Abstract: This paper discusses the Urdu script characteristics,
Urdu Nastaleeq and a simple but a novel and robust technique to
recognize the printed Urdu script without a lexicon. Urdu being a
family of Arabic script is cursive and complex script in its nature, the
main complexity of Urdu compound/connected text is not its
connections but the forms/shapes the characters change when it is
placed at initial, middle or at the end of a word. The characters
recognition technique presented here is using the inherited
complexity of Urdu script to solve the problem. A word is scanned
and analyzed for the level of its complexity, the point where the level
of complexity changes is marked for a character, segmented and
feeded to Neural Networks. A prototype of the system has been
tested on Urdu text and currently achieves 93.4% accuracy on the
average.
Abstract: Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Abstract: Many well-known interconnection networks, such as kary n-cubes, recursive circulant graphs, generalized recursive circulant graphs, circulant graphs and so on, are shown to belong to the family of cycle composition networks. Recently, various studies about mutually independent hamiltonian cycles, abbreviated as MIHC-s, on interconnection networks are published. In this paper, using an improved construction method, we obtain MIHC-s on cycle composition networks with a much weaker condition than the known result. In fact, we established the existence of MIHC-s in the cycle composition networks and the result is optimal in the sense that the number of MIHC-s we constructed is maximal.
Abstract: An iterative definition of any n variable mean function is given in this article, which iteratively uses the two-variable form of the corresponding two-variable mean function. This extension method omits recursivity which is an important improvement compared with certain recursive formulas given before by Ando-Li-Mathias, Petz- Temesi. Furthermore it is conjectured here that this iterative algorithm coincides with the solution of the Riemann centroid minimization problem. Certain simulations are given here to compare the convergence rate of the different algorithms given in the literature. These algorithms will be the gradient and the Newton mehod for the Riemann centroid computation.
Abstract: This paper presents the convergence analysis
of a prediction based blind equalizer for IIR channels.
Predictor parameters are estimated by using the recursive
least squares algorithm. It is shown that the prediction
error converges almost surely (a.s.) toward a scalar
multiple of the unknown input symbol sequence. It is
also proved that the convergence rate of the parameter
estimation error is of the same order as that in the iterated
logarithm law.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE workand output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.