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A computationally efficient algorithm is proposed for modeling and coding the time-varying spectra of musical sounds. The aim is to encode individual data sets and not the statistical properties of the sounds. A given sequence of acoustic feature vectors is modeled by finding such a set of “states” (anchor points in the feature space) that the input data can be efficiently represented by interpolating...
Sparse non-negative matrix factorization (sNMF) allows for the decomposition of a given data set into a mixing matrix and a feature data set, which are both non-negative and fulfill certain sparsity conditions. In this paper it is shown that the employed projection step proposed by Hoyer has a unique solution, and that it indeed finds this solution. Then indeterminacies of the sNMF model are identified...
Ultrawideband (UWB) or impulse radio wireless communication systems are based on the transmission of extremely narrow pulses, with a duration inferior to a nanosecond. By design, Transmit-Reference (TR) UWB systems can avoid channel estimation at the receiver, while different users can share the same available bandwidth by using different spreading codes, similar to CDMA systems. This allows the receiver...
An extension of Independent Component Analysis (ICA) to the situation when the mixture of signals is contaminated by multiplicative noise is proposed in this paper. The ICA methods search for the most independent output after a linear transformation of the data vector. If the ICA model is followed by these data, the result of this search is the inverse of the unknown mixture. On the other hand, if...
Iterative controller design for planar Poiseuille flow by model unfalsification and controller redesign is the topic of the paper. The main contribution is to show that model-unfalsification-based iterative design can be useful in flow control problems. The a priori knowledge on the dynamics of the sampled system is obtained from the analytic approximation of the Navier-Stokes equations by a Galerkin...
This paper is concerned with dynamical population models obtained from short and long-term changes in size and age composition due to demographic processes such as births, deaths, migration, etc. Both deterministic and stochastic models are presented. The parameters which are embedded in the models may be either unavailable or noisy, therefore system identification methods are invoked to estimate...
In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for economical reasons, leads to natural ventilation as a standard tool. The nonlinear relationship between ventilation and temperature can be captured by Volterra models. These models represent the simple and logical extension of convolution models and can be successfully applied in nonlinear model-based...
The Anaerobic Digestion Model No1 (ADM1) is accepted widely as a common platform for anaerobic process modelling and simulation. However, this model has an excessive complexity and a large number of parameters and states. This hinders its calibration and its use in control applications, and may render it inadequate for development of state observers. Therefore, a technique of model reduction for biological...
A novel class of linear time-varying models is proposed for nonlinear system identification purposes. These models are linear in the parameters, which are time-varying according to a nonlinear dynamic law. A specific parameter tuning algorithm is presented, which is based only on input/output measurements, but which also provides an estimate of the time-varying behaviour of the parameters. So, a Linear...
The bootstrap technique is a well-known method to generate multiple versions of predictors with the same structure. In this paper two different nonlinear structures are considered: neural networks and regression trees. They are both applied on real data related to the problem of predicting state bond price on the basis of the value of the previous auction and some financial indicators. Bootstrap is...
A primary concern of future high performance systems is the way data movement is managed; the sheer scale of data to be processed directly affects the achievable performance these systems can attain. However, the increasingly complex but inherently symbiotic relationships between upcoming scientific applications and high-performance architectures necessitate increasingly informative and flexible tools...
To quantify spectrum usage, many outdoor and indoor measurement campaigns have already been conducted in different parts of the world. These studies assist policy makers in optimizing spectrum management policies by providing necessary information about the usage patterns of wireless services in different spectrum bands. Furthermore, the spectrum usage measurements help researchers to build a mechanism...
Grid systems have emerged as a means of sharing computational resources and information. Providing services for accessing, sharing and modifying large databases is a crucial task for grid management systems. This paper proposes an artificial neural network (ANN) prediction mechanism that provides an enhancement to data replication solutions within grid systems. Current replication services often exhibit...
The integration of time and space is the key to research on the spatio-temporal database and the study on spatio temporal data model still more concerned with the theory research. This paper aims to provide a comprehensive study on developed and suggested spatiotemporal data model and highlights those areas which are currently receiving or requiring further investigations. Now, more and more demands...
This paper considers the problem for reconstructing handwriting character fonts based on the so-called dynamic font method. In particular, supposing that we are given such character fonts with incorrect stroke order, we develop a scheme for correctly modifying the stroke order of characters. Such a scheme is developed by utilizing the so-called starting point fixation method and the dynamic font method...
In recent past a lot of scientific attention is paid on recognizing the emotional state of the speaker from his speech. Emotion recognition is a challenging task as human emotions are complex, subtle and emotive state in human speech does not persist long. So it is important to study the presence of emotion identifiable information in smaller segments of speech. This study is aimed at studying the...
This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective...
Emotional facial expression transfer involves sequence-to-sequence mappings from an neutral facial expression to another emotional facial expression, which is a well-known problem in computer graphics. In the graphics community, current considered methods are typically linear (e.g., methods based on blendshape mapping) and the dynamical aspects of the facial motion itself are not taken into account...
Vote count (VC) is a fast search algorithm originally designed for similarity search on large scale data set. VC can be efficiently implemented using simple modification to the Random Access Memory (RAM) or other memory structures such as NOR or NAND Flash memory, such that the search complexity reduces to O(1) regardless of the dimensionality of data or the size of the data set. This paper proposes...
In recent times we have witnessed the emergence of large online markets with two-sided preferences that are responsible for businesses worth billions of dollars. Recommendation systems are critical components of such markets. It is to be noted that the matching in such a market depends on the preferences of both sides, consequently, the construction of a recommendation system for such a market calls...
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