Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions a...
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Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering entropy of the notes distribution as a factor of fitness function and developing mutation and crossover functions based on harmonic rules and trying to keep the melodies intact during these processes would result in a musical piece pleasant to human ears and interesting for human mind. This algorithm does not have the constraints of the previous algorithms. Restraining mutation and crossover functions with a goal of producing melodies based on acceptable melodies composed by humans, this algorithm is not bound to any genre, instrument or melody. The experimental results of this approach show that it is near to the human composing and the results produced from it are more acceptable than the ones produced by its predecessors.
We present a general method to obtain convergent approximate value iteration algorithms with function approximation. The result is applicable to any arbitrary approximation architecture and generalizes existing result...
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We demonstrate a stable Sagnac quantum key distribution implementing BB84 phase-encoding protocol based on acousto-optic frequency shifters. The quantum bit error rate remains 4-6.5% for at least one hour without reca...
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The problem of wireless spectrum sharing is investigated using a multi-server queueing system that models a group of channels and a multiplexed arrival process formed by a primary and secondary group of users. Motivat...
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The problem of wireless spectrum sharing is investigated using a multi-server queueing system that models a group of channels and a multiplexed arrival process formed by a primary and secondary group of users. Motivated by under-utilized capacity in RF spectrum owned by the primary group, this work examines the impact of secondary user access patterns on blocking probabilities and achievable improvement in spectrum utilization with statistical multiplexing. The analytical framework presented allows estimation of the blocking for any renewal process characterization of secondary access times. In this work, Gamma distributed inter-access times are chosen to examine the impact of their index of dispersion (IDI) on the excess blocking probabilities of the Poisson distributed primary group. It is shown that by reducing the IDI value to be below unity, one can increase the secondary system utilization in the shared spectrum with minimal impact on the primary system. Combined with an increase in the number of excess channels available for secondary use, a feasible spectrum sharing paradigm can be proposed by shaping the access patterns of secondary systems
In this paper we examine a non-stationary statistical model of the broadband response of an indoor wireless channel. The channel impulse response is comprised of a line-of-sight component and multipath components mode...
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In this paper we examine a non-stationary statistical model of the broadband response of an indoor wireless channel. The channel impulse response is comprised of a line-of-sight component and multipath components modeled by Gaussian noise weighted by exponentially decaying function of time. The aforementioned model is applied to evaluate the variability in the energy across the spectrum at the receiver. The model exhibits temporal non-stationarity in its variance and favorably captures the variability of the spectral amplitude observed in numerical simulations. It is shown that the standard deviation of the logarithm of normalized power spectral amplitude tends to 5.54 dB as signal to noise ratio approaches zero
Brain Emotional Learning Based Intelligent controller, BELBIC, has been used for model free adaptive control of several complex industrial plants. It has proven to be very effective controller with light computational...
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We present a technique for planning sensor motions in a specified two-dimensional spatial domain in such a way as to make the Hessian of the parameter estimation cost well conditioned. The framework is based on the us...
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ISBN:
(纸本)1424401704
We present a technique for planning sensor motions in a specified two-dimensional spatial domain in such a way as to make the Hessian of the parameter estimation cost well conditioned. The framework is based on the use of Turing's measure of conditioning, whose minimization yields the confidence regions for the parameters as spherical as possible. Since this does not necessarily guarantee a high information content in the measurements, an additional constraint is imposed on the D-efficiency of the solutions. Then the approach converts the problem to an optimal control one in which both the control forces of the sensors and the initial sensor positions are optimized. Numerical solutions are then obtained using the MATLAB PDE toolbox and the RIOTS_95 optimal control toolbox which handles various constraints imposed on the sensor motions
One of the main challenges of any related paradigms in systems engineering is being able to handle complex systems under unforeseen uncertainties. A system may be called complex if its dimension (order) is too high an...
One of the main challenges of any related paradigms in systems engineering is being able to handle complex systems under unforeseen uncertainties. A system may be called complex if its dimension (order) is too high and its model (if available) is nonlinear, interconnected, and information on the system is so uncertain that classical techniques cannot easily handle the problem. A system of systems (SoS) is a "super system," or an integration of complex systems coordinated together in such a way as to achieve a wider set of goals with possible higher significance such as global warming, Mars missions, air traffic control, global earth observation system, etc. computational Intelligence or Soft Computing, a consortium of fuzzy logic (approximate reasoning), neuro-computing (learning), genetic algorithms and genetic programming (optimization), has proven to be a powerful set of tools for adding autonomy and semi-autonomy to many complex systems. For such systems the size of soft computing control architecture will be nearly infinite. In this presentation, paradigms using soft computing approaches are utilized to design autonomous controller with controller reuse for a number of space applications. The notion of adaptation in autonomous controller reuse can be handled via intelligent tools to add on additional capabilities in real-time scenarios. Learning from past experience is but one such scenario for the reuse of autonomous controllers. These applications include satellite array formations, robotic agents and the Virtual Laboratory (V-LAB®) for multi-physics modeling and simulation.
One of the most challenging problems of clustering is detecting the exact number of clusters in a dataset. Most of the previous methods, presented to solve this problem, estimate the number of clusters with model base...
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One of the most challenging problems of clustering is detecting the exact number of clusters in a dataset. Most of the previous methods, presented to solve this problem, estimate the number of clusters with model based algorithms, which are not able to detect all types of clusters and also face a problem in detecting coupled clusters in a dataset. In this paper we propose a new method for finding the number of clusters in a dataset utilizing information theory and a top-down hierarchical clustering algorithm. The algorithm starts from a large number of clusters and reduces one cluster in any iteration and then allocates its data points to the remaining clusters. Finally, by measuring information potential, the exact number of clusters in a desired dataset is detected. Our method shows high capability and stability in detecting the number of clusters even in complex datasets, as it is computational efficient too. We show the effectiveness of the proposed method by experimenting on several artificial and real datasets and comparing its results with two developed methods for finding the number of clusters in a dataset. The comparisons show superiority of the proposed method.
In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and c...
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