ppxscal/embedding_network huggingface dataset:
Technology-based analog and digital microsystem design
Authors: R. H. Caverly
Venue: Proceedings of the 26th Annual Frontiers in Education - Volume 01 (1996)

The telecommunications industry is one of the fastest growing industries in the United States. Fueling this growth is the high degree of computational power made possible by advances in microelectronic fabrication and design. To contribute to this technology upon graduation our students must have the necessary background that includes digital and both baseband and high frequency analog design skills. This paper describes a senior sequence in digital and analog microsystem design that uses a unique mix of lecture, projects and laboratory measurements to address telecommunication microsystem technology. The course material emphasizes the design and operation of key components and subsystems of telecommunication systems.
Efficient standard basis Reed-Solomon encoder
Authors: S. K. Jain
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06 (1996)

This paper presents an efficient Reed-Solomon encoder based on standard basis. The key operation in Reed-Solomon encoding is the multiplication of a feedback term with several (possibly) known terms. We present an efficient structure to implement this operation. The hardware complexity of this encoder is identical to the well-known Berlekamp encoder. It however, offers two advantages over the Berlekamp encoder-a critical path independent of the order of Reed-Solomon code being implemented and the ability to encode without any need for basis conversion.
GA with orthogonal transformation for RBFN configuration
Authors: O. Ciftcioglu
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

A genetic algorithm (GA) approach for radial basis function network (RBFN) training is proposed. Although several non-evolutionary training methods for RBFN training are available, orthogonal least squares (OLS) algorithm has a number of desirable properties for robust training. In the OLS algorithm the basis function centers are selected among the training samples and therefore they are predetermined in their totality. In contrast with this, the width parameters of the basis functions are not and in general cannot accurately be predetermined. Therefore they are generally taken to be equal for all centers. In the GA approach, next to the optimally selected centers, the covariance matrix of the width parameters of these centers can also be determined, thereby the network performance is enhanced. Training RBFN by GA can be accomplished in different ways with different merits. The main contribution of this research lies in its introduction of a novel method, which selects the subset optimally among the available centers. For this it uses orthogonal transformation method namely singular value decomposition (SVD)-QR method integrated to the algorithm. Along this line, GA approach for RBFN training as an alternative to OLS algorithm with additional desirable parameter optimization properties is discussed. By means of experiments described, competitiveness of the approach for robust RBFN training is presented.
mGovernment: A Reality Check
Authors: Tarek El-Kiki
Venue: Proceedings of the International Conference on the Management of Mobile Business (2007)

Effective management is considered a vital factor for harnessing the potential of any mService project. This paper is a further step into a research project that aims to measure the effectiveness of mGovernment services. The author reports on the results of a survey aimed at highlighing and defining the barriers to the success of mGovernment service projects. He analyses suggestions to overcome those barriers from the perspective of mobile government and mobile technology experts from nineteen countries around the world. Despite the fact that these experts identified four types of barriers in their answers to the survey, they only provided suggestions for overcoming three, which are considered the reality check for controlling the progress of any mService project.
Novel sparse adaptive algorithm in the Haar transform domain
Authors: S. D. Blunt
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

This paper considers the problem of adaptive identification of a sparse system such as in echo cancellation application. A sparse system has a small number of coefficients that are different from zero. We propose the use of the Haar transform to locate non-zero coefficients such that only the non-zero coefficients will need to be adapted. The consequence is an increase in convergence speed and a decrease in computation. The performance of the proposed algorithm is evaluated through simulations.
Linear variational principle for convex vector maximization
Authors: V. V. Semenov
Venue: Cybernetics and Systems Analysis (2007)

For infinitely dimensional convex vector maximization problems, it is proved that there exists, under certain conditions, an arbitrarily small additive perturbation of the performance criterion by a linear continuous operator such that the perturbed problem has efficient solutions.
Image interpolation using across-scale pixel correlation
Authors: Tao Chen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

A novel method is proposed for image interpolation. It is assumed that the pixel correlation between local regions across scales would remain similar. In addition, this a priori similarity could be extracted from a set of available image data that have the same content but different resolutions. A simple architecture is devised to estimate the correlation efficiently, which is then used to predict the unknown pixel values in a high-resolution image. Evaluation shows a promising performance of the proposed algorithm.
An Effective Hypergraph Clustering in Multi-Stage Data Mining of Traditional Chinese Medicine Syndrome Differentiation
Authors: Wang Bo, Zhang Ming-Wei, Zhang Bin, Wei Wei-Jie
Venue: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (2006)

Traditional Chinese Medicine is mysterious for its special diagnosis and treatment. In TCM, Syndrome differentiation is the method of recognizing and diagnosing diseases or body imbalances in TCM. In this paper, we first give a hierarch model of differentiation syndrome in Traditional Chinese Medicine According to the model data mining procedure is designed to complete it. Given special data mining schema and character of high-dimensional data sets, we introduce hypergraph based on greedy algorithm in cluster and similarity measure during clustering stage. Finally, the experiment shows that the hypergraph clustering is correct and efficient, which in return could be important for association rules and diagnosis.
Distributed Density Estimation Using Non-parametric Statistics
Authors: Yusuo Hu, Hua Chen, Jian-guang Lou, Jiang Li
Venue: Proceedings of the 27th International Conference on Distributed Computing Systems (2007)

Learning the underlying model from distributed data is often useful for many distributed systems. In this paper, we study the problem of learning a non-parametric model from distributed observations. We propose a gossip-based distributed kernel density estimation algorithm and analyze the convergence and consistency of the estimation process. Furthermore, we extend our algorithm to distributed systems under communication and storage constraints by introducing a fast and efficient data reduction algorithm. Experiments show that our algorithm can estimate underlying density distribution accurately and robustly with only small communication and storage overhead.
Monitoring and Analysis Framework for Grid Middleware
Authors: Ramon Nou, Ferran Julia, David Carrera, Kevin Hogan, Jordi Caubet, Jesus Labarta, Jordi Torres
Venue: Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2007)

As the use of complex grid middleware becomes widespread and more facilites are offered by these pieces of software, distributed Grid applications are becoming more and more popular. But as Grid middleware grows in size and offers more advanced features, they become more complex and heavier, as well as harder to tune. Since the performance of a distributed Grid application can be strongly influenced by the operation of the underlying grid middleware, it becomes of extreme importance to study and analyse its behaviour and performance. In this paper we present the eDragon Monitoring Framework (eDMF), a set of tools that can be used for the instrumentation and analysis of grid middleware, and which provides a unique environment to study the performance of Grid applications. The eDMF is composed of a set of specialised monitoring tools as well as by a flexible and powerful performance analysis platform. Additionally we also provide a practical application of the eDMF to the Globus Toolkit 4 (GT4), one of the most extended and popular Grid middleware, showing how it helped us in the detection and resolution of several job management problems observed in the GT4 middleware.
Model-based cardiac diagnosis of pulmonary embolism
Authors: C. Starfinger, C. E. Hann, J. G. Chase, T. Desaive, A. Ghuysen, G. M. Shaw
Venue: Computer Methods and Programs in Biomedicine (2007)

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care.
Economics-Driven Software Mining
Authors: Rami Bahsoon, Wolfgang Emmerich
Venue: Proceedings of the First International Workshop on The Economics of Software and Computation (2007)

Economics-driven software mining (EDSM) sifts through the re-pository data to extract information that could be useful for rea-soning about not only the technical aspects but also the econom-ics properties related to the development and/or evolution of soft-ware systems, and in relation to the environments in which they are procured, developed, evolved and used. The objective is to provide the analyst with insights into investment decisions related to the development, maintenance, and evolution of software sys-tems. EDSM can also assist the analyst in resource planning and utilization. In this position paper, we define EDSM, describe pos-sible scenarios for realizing EDSM and highlight some challenges.
Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation
Authors: Yuri Grishin
Venue: Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications (2007)

A spread spectrum signal recognition can be accomplished by exploiting the particular features of modulation presented in a received signal observed in presence of noise. These modulation features are the result of slight transmitter component variations and acts as an individual signature of a transmitter. The paper describes a spread spectrum signal classification algorithm based on using the Wigner-Ville Distribution (WVD), noise reduction procedure with using a two-dimensional filter and the RBF neural network probability density function estimator which extracts the features vector used for the final signal classification. The numerical simulation results for the P4-coded signals are presented.
Context representation using word sequences extracted from a news corpus
Authors: Hiroshi Sekiya, Takeshi Kondo, Makoto Hashimoto, Tomohiro Takagi
Venue: International Journal of Approximate Reasoning (2007)

The ambiguity in language is one of the most difficult problems in dealing with word senses using computers. Word senses vary dynamically depending on context. We need to specify the context to identify these. However, context also varies depending on specificity and the viewpoint of the topic. Therefore, generally speaking, people pay attention to the part of the attributes of the entity, which the dictionary definition of the word indicates, depending on such variant contexts. Dealing with word senses on computer can be split into two steps. The first is to determine all the different senses for every word, and the second is to assign each occurrence of a word to the appropriate sense. In this paper, we propose a method focusing on the first step, which is to generate atomic conceptual fuzzy sets using word sequences. Then, both contexts identified by word sequences and atomic conceptual fuzzy sets, which express word senses, and that are related to the contexts can be shown concretely. We used the Reuters collection consisting of 800,000 news articles, and extracted word sequences and generated fuzzy sets automatically using the confabulation model (a prediction method similar to the n-gram model) and five statistical measures as relations. We compared the compatibility between the confabulation model and each measure, and found that cogency and mutual information were the most effective in representing context. We demonstrate the usefulness of the word sequences to identify the context.
Hand-held Force Display with Spring-Cam Mechanism for Generating Asymmetric Acceleration
Authors: Tomohiro Amemiya, Hideyuki Ando, Taro Maeda
Venue: Proceedings of the Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2007)

This paper describes the development of a mechanism for a hand-held haptic display based on the force perception method, which exploits the perception of the difference between rapid and slow acceleration. Our prior force display was designed to generate asymmetric acceleration using a crankslider mechanism with a rotational actuator. Here, to generate asymmetric acceleration, we propose a new mechanism that adopts a spring and cam with a linear actuator. The spring-cam mechanism can generate acceleration profi les of any shape and is suitable for miniaturization. We discuss the characteristics of a prototype of the spring-cam mechanism.
Management of open source tools in a heterogeneous environment
Authors: Steve Oualine
Venue: Proceedings of the 3rd annual conference on Atlanta Linux Showcase - Volume 3 (1999)

There are many open source software tools that are useful in the standard UNIX environment. Many times you will find that the local user community is willing to support these tools. Management and configuration of these tools can be a major problem. The system administrator must take care of such as issues as: The packaging of tools, Distribution of tool sets, Build procedures, Handling of multiple tool versions. This paper discusses how the system administrator might handle these and other problems.
Characterizations of Local and Global Error Bounds for Convex Inequalities in Banach Spaces
Authors: Hui Hu
Venue: SIAM Journal on Optimization (2007)

This paper studies local and global error bounds for a convex inequality defined by a proper convex function in a Banach space. The concept of weak basic constraint qualification (weak BCQ) is introduced to control the normal directions at a boundary point of the solution set. Local and global error bounds are characterized by a direction-length decomposition condition, which provides a way to independently verify the weak BCQ and the length control of the subdifferential. To further characterize global error bounds, the segment extension property is proposed and studied. It is shown that the verification of the direction-length condition for global error bounds can be sufficiently carried out on any subset having the segment extension property instead of the entire boundary. This leads to a simple formula for the smallest global error bound. In the Euclidean space, the verification of the condition and the computation of the smallest global error bound can be carried out on the set of extreme points.
Approach for Reducing Control Packets in AODV-Based MANETs
Authors: David Espes, Cedric Teyssie
Venue: Proceedings of the Fourth European Conference on Universal Multiservice Networks (2007)

Movements of nodes in MANETs result in route changes, thus requiring the transmission of control packets. As MANETs have limited resources, it is important to reduce the number of control packets which consume bandwidth. We propose a AODV-based protocol which uses a backbone network to reduce control packets. The destination location is given by GPS and transmitted to the source by the backbone network. Our routing algorithm limits the route search to zones smaller than those used by the original AODV (Ad hoc On-demand Distance Vector) protocol, thus reducing the number of control packets. This routing algorithm represents a significant improvement of our previous work on AODV [1, 2]. To show the efficiency of our protocol, we present simulations using NS-2.
Problem-based learning in biomedical engineering curricula
Authors: M. C. LaPlaca
Venue: Proceedings of the Frontiers in Education Conference, 2001. 31st Annual - Volume 02 (2001)

Problem-based learning (PBL) anchors learning and instruction in concrete problems. We believe that PBL is well suited to educating undergraduate and graduate students within the interdisciplinary field of biomedical engineering (BME). BME draws upon many traditional disciplines to address a range of problems, from biotechnology to clinical medicine. A challenge for BME educators is to balance this broad base of fundamentals with the analytical, in depth problem solving necessary to be successful bioengineers. The ability to adapt, be innovative, and acquire and integrate relevant information is not efficiently learned in a lecture format, but rather in a small group setting that encourages self-directed learning, such as PBL. We have developed a graduate BME program with PBL as one of the pivotal components and are embarking on the introduction of this methodology to undergraduate sections. We have found PBL to be an effective vehicle for instruction, retention of material, and introduction of topics necessary for professional development.
Enhancing the efficiency of Bayesian network based coverage directed test generation
Authors: M. Braun, S. Fine, A. Ziv
Venue: Proceedings of the High-Level Design Validation and Test Workshop, 2004. Ninth IEEE International (2004)

Coverage directed test generation (CDG) is a technique for providing feedback from the coverage domain back to a generator, which produces new stimuli to the tested design. Recent work showed that CDG, implemented using Bayesian networks, can improve the efficiency and reduce the human interaction in the verification process over directed random stimuli. This paper discusses two methods that improve the efficiency of the CDG process. In the first method, additional data collected during simulation is used to "fine tune" the parameters of the Bayesian network model, leading to better directives for the test generator. Clustering techniques enhance the efficiency of the CDG process by focusing on sets of non-covered events, instead of one event at a time. The second method improves upon previous results by providing a technique to find the number of clusters to be used by the clustering algorithm. Applying these methods to a real-world design shows improvement in performance over previously published data.
Focus Group Methodology for Evaluating Information Visualization Techniques and Tools
Authors: Riccardo Mazza, Alessandra Berre
Venue: Proceedings of the 11th International Conference Information Visualization (2007)

This paper proposes a structured methodology that uses focus group interviews to evaluate Information Visualization techniques and tools. Focus groups are particularly suitable for the collection of qualitative data from users, and allow researchers to uncover unexpected problems that cannot be identified through analytical evaluations or controlled experiments. The approach relies on open-ended questions to explore user attitudes, feelings, and beliefs, but also indicates a number of questions designed to explore specific cognitive tasks related to Information Visualization systems. We argue that focusing the discussion on cognitive tasks allows for the generation of user comments that are more effective than informal and unstructured interviews.
Web-Based Application for Traffic Anomaly Detection Algorithm
Authors: Jun Lv, Xing Li, Tong Li
Venue: Proceedings of the Second International Conference on Internet and Web Applications and Services (2007)

Network traffic anomaly detection is a difficult problem in network management. This paper presents a Wavelet Generalized Likelihood Ratio (WGLR) algorithm and an Error Performance Detection (EPD) algorithm to solve this problem. WGLR algorithm combines Generalized Likelihood Ratio (GLR) algorithm and wavelet transform method, and captures the failure point in real time. Error performance Detection (EPD) algorithm is based on the prediction error of the traffic model and regards the error as the statistical variable, comparing with the threshold, which will detect the anomalous change in the signal without test window delay. Simulation and network traffic experiment has demonstrated that the algorithm has the better performance in fault detection
Computation of Localized Flow for Steady and Unsteady Vector Fields and Its Applications
Authors: Alexander Wiebel Christoph Garth Gerik Scheuermann
Venue: IEEE Transactions on Visualization and Computer Graphics (2007)

We present, extend, and apply a method to extract the contribution of a subregion of a data set to the global flow. To isolate this contribution, we decompose the flow in the subregion into a potential flow that is induced by the original flow on the boundary and a localized flow. The localized flow is obtained by subtracting the potential flow from the original flow. Since the potential flow is free of both divergence and rotation, the localized flow retains the original features and captures the region-specific flow that contains the local contribution of the considered subdomain to the global flow. In the remainder of the paper, we describe an implementation on unstructured grids in both two and three dimensions for steady and unsteady flow fields. We discuss the application of some widely used feature extraction methods on the localized flow and describe applications like reverse-flow detection using the potential flow. Finally, we show that our algorithm is robust and scalable by applying it to various flow data sets and giving performance figures.