ppxscal/embedding_network huggingface dataset:
Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm
Authors: J. Tang, L. Wang, Z. Yao
Venue: International Journal of Remote Sensing (2007)

The landscape pattern of Daqing City, China, has undergone a significant change over the past 20 years, as a result of the rapid urbanization process. To understand how urbanization has influenced the landscape in Daqing City, the largest base of the petrochemical industry in China, we conducted a series of spatial analyses with landscape pattern maps obtained from Landsat images in 1979, 1990 and 2000. Results indicate that a substantial urban area has been extended during the past two decades, along with the shrinking of wetland and woodland. Spatio-temporal optimization is not a trivial task in developing landscape models. In previous studies, the optimization of spatial and temporal factors was achieved separately, because of the difficulty in formulating them together in a single model. In this study, we adapted the traditional Markov model by obtaining model parameters and neighbourhood rules from a modified genetic algorithm (GA). Model performance was evaluated between the empirical landscape map from the Landsat image and the simulated landscape map from the models. Over three simulation runs, the global deviation (GD) for the three models was 1.37, 1.10 and 1.15, respectively. This result shows that the Markov model and the GA together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization for this region. The future landscape distribution in 2010, 2030 and 2050 was derived using a spatial Markov model (SMM) for further urban change and planning research.
A State Feedback Strategy for Decoupling a Class of Multivariable Systems
Authors: Masoud Karimi-Ghartemani, Mohammad Mobed
Venue: Proceedings of the Third International Conference on Systems (2008)

Here a special state variable feedback or state feedback for short is introduced with the main objective of providing a convenient method for decoupling a MIMO transfer matrix. Decoupling in general and decoupling via state feedback in particular offer obvious advantages in the design and synthesis of controllers for MIMO systems. The early parts of this paper deal with the eigenvalues, poles, and zeros as well as the controllability and observability properties of the proposed closed-loop system as compared with those of the open-loop system. The proposed method is tested on a model ofa realistic physical system and results are illustrated.
Time series analysis and forecasting techniques for correlated observations
Authors: R. Voget, A. Tinnirello
Venue: Proceedings of the 5th WSEAS International Conference on Applied Mathematics (2004)

This work deals with nonstationary signals processing of blood cells. The signals under study are represented in a time-scale and statistical techniques are used to characterize their behavior and to identify a data pattern, then assuming that it will continue in the future, this data pattern is extrapolated in order to produce forecasts. The resulting fitted ARMA models allowed us to know in advance future values into a confidence range.
Probabilistic theory of multi-models
Authors: Miroslav Svítek
Venue: Proceedings of the 5th WSEAS/IASME International Conference on Systems Theory and Scientific Computation (2005)

The paper presents the mathematical theory of probabilistic multi-models composition set together by predefined probabilistic model components selected by designer. Each probabilistic component is composedd of phase parameter that characterizes the composition rules of multi-models assemble and the mixture parameter that characterizes component significance to capture the features of studied system. The presented approach results in introduction of complex probabilities being represented in spectral area with similar attributes like e.g. Fourier series. Presented theory is exemplified on numerical example to demonstrate its practical use.
Maximize desktop speed
Authors: Federico Kereki
Venue: Linux Journal (2008)

Linux is known for its speed, even on older hardware, but you will appreciate it even more after making these performance-enhancing tweaks.
Automatic Test Generation for Model-Based Code Generators
Authors: Sergey V. Zelenov, Denis V. Silakov, Alexander K. Petrenko, Mirko Conrad, Ines Fey
Venue: Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (2006)

This paper presents a novel testing approach for model-based design tools, termed GraphOTK, and applies it to the optimizing component of a code generator for Simulink and Stateflow models.
Noise-Aware On-Chip Power Grid Considerations Using a Statistical Approach
Authors: Daniel A. Andersson, Lars J. Svensson, Per Larsson-Edefors
Venue: Proceedings of the 9th international symposium on Quality Electronic Design (2008)

We analyze the correlation between different parameters of the on-chip power distribution grid and their impact on noise. By using factor analysis we are able to uncover correlations between power grid design variables and power supply noise. We derive the correlation between design variables and noise from an analysis of 300 different grids in a 65-nm process technology, and manage to find the impact that a change in power grid design variables will have on noise. The results from this analysis can be used as guidelines when designing a robust power distribution grid.
Designing the perfect auction
Authors: Hal R. Varian
Venue: Communications of the ACM (2008)

Distributed algorithmic mechanism design is a field at the intersection of computer science and economics.
Theoretical researches on the molecular structures of DNA crosslinks induced by chloroethylnitrosoureas and QSAR analysis
Authors: Ru-Gang Zhong, Li-Jiao Zhao, Yan Zhen
Venue: Proceedings of the 1st WSEAS International Conference on Computational Chemistry (2007)

Chloroethylnitrosoureas (CENUs) are important clinical antitumor agents. Their cytotoxicity is associated with the generation of DNA interstrand crosslinks. In this work, QM/MM computations are carried out to investigate DNA crosslinks by CENUs with ONIOM hybrid method. The crosslinked DNA are subdivided into three layers, each of which are described at B3LYP/6-311+G(d,p), AM1 and UFF level of theory respectively. The result shows that the deformation of DNA with dG(N1)-dC(N3) crosslink is much less than the other crosslinks, which indicate that the most favorable crosslink is between the N1 atom of guanine and the N3 atom of the complementary cytosine. The quantitative structure-activity relationship (QSAR) of CENUs is studied by ab initio computations at MP2/6-311G(d,p) level of theory. 37 kinds of CENUs with experimental anticancer therapeutic index (TI) are selected as models. Their activation energies of formation of the electrophilic centers on the α- and the β-carbon, and the octanol-water partition coefficient, are selected as structural parameters. Through numerical fitting of the computational data with the experimental anticancer therapeutic index, a formula is established to predict the anticancer activity of CENUs. The correct discrimination ratio between the computing and the experimental anticancer activity comes up to 94.6% through a five degrees classification.
A Regression Based Technique for ATE-Aware Test Data Volume Estimation of System-on-Chips
Authors: Rajesh Tiwari, Abhijeet Shrivastava, Mahit Warhadpande, Srivaths Ravi, Rubin Parekhji
Venue: Proceedings of the 26th IEEE VLSI Test Symposium (2008)

Conventional methods to assess the test data volume (TDV) of logic in system-on-chips (SoCs) use intuitive formulae that are often agnostic of the target automatic test equipment (ATE) hardware or the ATE test program compilation process. In this paper, we first show that such ATE-unaware approaches lead to a significant gap between these estimates and the actual tester memory consumed. We also provide a generic solution to this problem by using statistical regression techniques to build an ATE-aware TDV model that accurately estimates test program memory consumption as a function of the design and test pattern characteristics. We have implemented this methodology using an off-the-shelf regression solver in the context of a production test flow. We show that the estimator can be used to compute TDV with very high accuracy for logic tests of various industrial IPcores and SoCs.
Evaluation Experiment on Eye-to-Eye Contact in Remote Communication with Tiled Displays Environments
Authors: Yasuo Ebara, Nobuyuki Kukimoto, Koji Koyamada
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

In remote communication over the Internet, we consider that non-verbal communications such as eye-to-eye contact in the real world in addition to the existence of core searchers by transmission of a video in real scene is important. However, it is difficult to realize communication by eye-to-eye contact because camera which used to shoot participant in remote communication is located at the side or top of display equipment. In this paper, we have constructed the scalable tiled displays environment in two points via LAN. In these environment, we have conducted experimental verification on the effect for eye-to-eye contact by changing the position of camera on frame of LCDs on tiled displays. In these experiments, we have found to have a high tendency to discriminate gazer’s direction of eyes in the case that camera is located at close position for direction that an estimator faces on the front. In addition, the accuracy rate in case that camera is bottom position became lower than case of top position. From above results, we have showed experimentally that each participant enablesto turn direction of eyes to a camera in a natural way become important factor for realization of eye-to-eye contact in remote communication.
Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents
Authors: Elif Derya Übeyli
Venue: Digital Signal Processing (2008)

In this paper, the multiclass support vector machines (SVMs) with the error correcting output codes (ECOC) were presented for the multiclass time-varying biomedical signals (ophthalmic arterial Doppler signals, internal carotid arterial Doppler signals and electrocardiogram signals) classification problems. Decision making was performed in two stages: feature extraction by computing the Lyapunov exponents and classification using the classifier trained on the extracted features. The purpose was to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The research demonstrated that the Lyapunov exponents are the features which well represent the studied time-varying biomedical signals and the multiclass SVMs trained on these features achieved high classification accuracies.
Character recognition using statistical parameters
Authors: Nadeem Abbas Zaidi, Noor Muhammad Shiekh
Venue: Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing (2006)

Object recognition is an important task in many image processing and pattern recognition applications. Character recognition is one of the most successful applications of automatic pattern recognition techniques and essentially involves identification and classification of two dimensional (2D) signal structures. The ability to identify machine printed characters in an automated or a semi-automated manner has practical significance and different techniques are applied to get the desired recognition results. The process of character recognition involves several steps including pre-processing, feature extraction and classification. Preprocessing includes the removal of noise from the image, convert into binary image and then segment into individual character. After segmentation, each character is resized according to its aspect ratio and then boundary of the character is extracted. Feature extraction is done by applying central moment invariant technique to extract the relevant features. Classification is performed by minimum distance classifier in a 9-dimension Euclidian feature space.
An Eyes-Free In-car User Interface Interaction Style Based on Visual and Textual Mnemonics, Chording and Speech
Authors: Frode Eika Sandnes, Yo-Ping Huang, Yeh-Min Huang
Venue: Proceedings of the 2008 International Conference on Multimedia and Ubiquitous Engineering (2008)

Modern cars are equipped with advanced technology which often requires complex operation that is cognitively complex and reliant on the user’s visual attention. It is therefore hazardous for drivers to operate such devices while driving. In this paper a user interface interaction style for in-car user interfaces are proposed. Users interact with the devices using three chording keys and chording pattern sequences are derived based on visual mnemonics. The technique is illustrated for an in-car multimedia system, mobile phone and a GPS-navigation system.
Using AADL to Model a Protocol Stack
Authors: Didier Delanote, Stefan Van Baelen, Wouter Joosen, Yolande Berbers
Venue: Proceedings of the 13th IEEE International Conference on on Engineering of Complex Computer Systems (2008)

In recent trends, the Architecture Analysis and Design Language (AADL) has received increasing attention from safety-critical software development industries. Specific about the AADL is its strong syntactic and semantic support for the description of both hardware and software architectures. Considering the existing range of software architectures, we study the support AADL offers for the description of software architecture. As a case study we use an implementation of a UDP/IP protocol stack. Based on our experiences, our position is that a number of abstract concepts, e.g. a generic component concept, are missing in the AADL to make it well-suited for the high-level description of software architecture.
Finite-Time Bounds for Fitted Value Iteration
Authors: Rémi Munos, Csaba Szepesvári
Venue: The Journal of Machine Learning Research (2008)

In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decision processes (MDPs) under the assumption that a generative model of the environment is available. Our main results come in the form of finite-time bounds on the performance of two versions of sampling-based FVI. The convergence rate results obtained allow us to show that both versions of FVI are well behaving in the sense that by using a sufficiently large number of samples for a large class of MDPs, arbitrary good performance can be achieved with high probability. An important feature of our proof technique is that it permits the study of weighted Lp-norm performance bounds. As a result, our technique applies to a large class of function-approximation methods (e.g., neural networks, adaptive regression trees, kernel machines, locally weighted learning), and our bounds scale well with the effective horizon of the MDP. The bounds show a dependence on the stochastic stability properties of the MDP: they scale with the discounted-average concentrability of the future-state distributions. They also depend on a new measure of the approximation power of the function space, the inherent Bellman residual, which reflects how well the function space is "aligned" with the dynamics and rewards of the MDP. The conditions of the main result, as well as the concepts introduced in the analysis, are extensively discussed and compared to previous theoretical results. Numerical experiments are used to substantiate the theoretical findings.
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Authors: A. T. Tzallas, M. G. Tsipouras, D. I. Fotiadis
Venue: Computational Intelligence and Neuroscience (2007)

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.
Validation of mappings between schemas
Authors: Guillem Rull, Carles Farré, Ernest Teniente, Toni Urpí
Venue: Data Knowledge Engineering (2008)

Mappings between schemas are key elements in several contexts such as data exchange, data integration and peer data management systems. In all these contexts, the mapping design process requires the participation of a mapping designer, who needs to be able to validate the mapping that is being defined, i.e., check whether the mapping is in fact what the designer intended. However, to date very little work has directly focused on the effective validation of schema mappings. In this paper, we present a new approach for validating schema mappings that allows the mapping designer to ask whether they have certain desirable properties. We consider four properties of mappings: mapping satisfiability, mapping inference, query answerability and mapping losslessness. We reformulate these properties in terms of the problem of checking whether a query is lively over a database schema. A query is lively if there is a consistent instance of the database for which the query gives a non-empty answer. To show the feasibility of our approach, we use an implementation of the CQC method and provide some experimental results.
Adaptive sliding mode control for DC/DC buck converters
Authors: Ivan Trushev, Nikos Mastorakis, Ivan Tabahnev, Valeri Mladenov
Venue: Proceedings of the 9th International Conference on Circuits (2005)

The considered DC/DC buck converter controller is based on classical (conventional) sliding mode control. However their dynamic and steady state performance deteriorate if the loading condition differs from the nominal loading condition. So it is added an adaptive scheme (gain scheduling scheme) to the sliding mode controller, which can optimize the dynamic performance of the converter during load variations.
Average distance, minimum degree, and spanning trees
Authors: Peter Dankelmann, Roger Entringer
Venue: Journal of Graph Theory (2000)

The average distance &mu;(G) of a connected graph G of order n is the average of the distances between all pairs of vertices of G, i.e., &mu;(G) = ( n2)-1 &Sigma;{x,y}&sbquo;V(G) dG(x, y), where V(G) denotes the vertex set of G and dG(x, y) is the distance between x and y. We prove that every connected graph of order n and minimum degree &delta; has a spanning tree T with average distance at most $${n\over \delta + 1} + 5$$. We give improved bounds for K3-free graphs, C4-free graphs, and for graphs of given girth. &copy; 2000 John Wiley Sons, Inc. J Graph Theory 33: 113, 2000
Space bounds for graph connectivity problems on node-named JAGs and node-ordered JAGs
Authors: C. K. Poon
Venue: Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science (1993)

Two new models, NO-JAG and NN-JAG in order of increasing computation power, are introduced as extensions to the conventional JAG model. A space lower bound of /spl Omega/(log/sup 2/ n/log log n) is proved for the problem of directed st-connectivity on a probabilistic NN-JAG and a space upper bound of O(log n) is proved for the problem of directed st-nonconnectivity on a nondeterministic NO-JAG. It is also shown that a nondeterministic NO-JAG is nearly as powerful as a nondeterministic Turing machine.
A method for solving linear programming problems with fuzzy parameters based on multiobjective linear programming technique
Authors: M. Zangiabadi, H. R. Maleki, M. Mashinchi
Venue: Proceedings of the 9th WSEAS International Conference on Systems (2005)

In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters (FLP). Then by using the concept of comparison of fuzzy numbers we transform FLP problem into a multiobjective linear programming (MOLP) problem. To this end, we propose several theorems which are used to obtain optimal solutions of FLP. Finally an example is given to illustrate the proposed method of solving linear programming problem with fuzzy parameters (FLP).
Enhancement of remote measurement realization using Bluetooth wireless transmission and instrumentation information meta
Authors: Chih-Chieh Lin, Shyh-Jier Huang, Ming-Jong Jou
Venue: Proceedings of the 5th WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing (2006)

In this paper, the Bluetooth wireless technique is embedded into the digital instrument in order to perform the electric power measurement. With the advent of Bluetooth technology and the piconet concept, the supervision of several measurements can be reached, improving the monitoring efficiency very significantly. However, in view of the different measurements, the paper also proposed the instrumentation information meta language (I2ML) such that the information acquired from various instruments can be better integrated, decreasing the installation task under different platforms. The proposed approach has been tested through different scenarios and platforms; meanwhile, the graphical user interface was also prudently designated in anticipation of increasing the friendliness for engineers. The validated prototype confirmed the effectiveness of the proposed scheme for the instrument and measurement applications.
Feature Extraction of EEG Signals Using Power Spectral Entropy
Authors: Aihua Zhang, Bin Yang, Ling Huang
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02 (2008)

Brain-Computer Interfaces (BCI) use electroenc-ephalography (EEG) signals recorded from the scalp to create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. One of the most important components of BCI is feature extraction of EEG signals. How to rapidly and reliably extract EEG features for expressing the brain states of different mental tasks is the crucial element for exact classification. This paper presents an approach that performs EEG feature extraction during imagined right and left hand movements by using power spectral entropy (PSE). It acquires good classification results with the time-variable linear classifier. The maximal accuracy achieves 90%.The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements. The method is simple and quick and it provides a promising method for on-line BCI system.