ppxscal/embedding_network huggingface dataset:
Embedded Multiprocessors: Scheduling and Synchronization, 2nd edition
Authors: Sundararajan Sriram, Shuvra S. Bhattacharyya
Venue:  (2009)

Showing how to design multiprocessor computer systems that are streamlined for multimedia applications, this work presents architectures and design methodologies for parallel systems in embedded DSP applications. It describes unique techniques for optimizing communication and synchronization and provides several examples of practical applications that demonstrate the relevance of the techniques presented. This second edition updates the background material on existing embedded multiprocessors, including single-chip multiprocessors. It also summarizes the new research on dataflow models for signal processing that has been carried out since the publication of the first edition.
Phase-Lock Basics, 2nd edition
Authors: William F. Egan
Venue:  (2007)

Broad-based and hands-on, Phase-Lock Basics, Second Edition is both easy to understand and easy to customize. The text can be used as a theoretical introduction for graduate students or, when used with MATLAB simulation software, the book becomes a virtual laboratory for working professionals who want to improve their understanding of the design process and apply it to the demands of specific situations. This second edition features a large body of new statistical data obtained from simulations and uses available experimental data for confirmation of the simulation results. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
A new technique for detecting dental diseases by using high speed neuro-computers
Authors: Hazem M. El-Bakry, Nikos Mastorakis
Venue: Proceedings of the 2nd conference on European computing conference (2008)

In this paper, a new fast algorithm for dental diseases detection is presented. Such algorithm relies on performing cross correlation in the frequency domain between input image and the input weights of fast neural networks (FNNs). It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations. One of the limitations of Direct Digital Radiography (DDR) is noise. Some recent publications have indicated that Digital Subtraction Radiography (DSR) might significantly aid in the clinical diagnosis of dental diseases, once various clinical logistic problems limiting its widespread use have been over come. Noise in digital radiography may result from sources other than variation in projection geometry during exposure. Structure noise consists of all anatomic features other than those of diagnostic interest. Limitations of plain radiographs in detecting early, small bone lesions are also due to the presence of structure noise. This research work has been under - taken in an attempt to minimize structure noise in digital dental radiography by using digital subtraction radiography. By minimizing the structure noise, the validity of the digitized image in detecting diseases is enhanced.
Incentives for BGP Guided IP-Level Topology Discovery
Authors: Benoit Donnet
Venue: Proceedings of the First International Workshop on Traffic Monitoring and Analysis (2009)

Internet topology discovery has been an attractive research field during the past decade. In particular, the research community was interested in modeling the network as well as providing efficient tools, mostly based on traceroute, for collecting data. In this paper, we follow this track of rendering traceroute-based exploration more efficient. We discuss incentives for coupling passive monitoring and active measurements. In particular, we show that high-level information, such as BGP updates, might be used to trigger targeted traceroutes. As a result, the network dynamics might be better capture. We also provide a freely available tool for listening to BGP feeds and triggering dedicated traceroutes.
Photoshop CS3 in Easy Steps: For Windows and Mac
Authors: Robert Shufflebotham
Venue:  (2007)

Because of its steep learning curve, many users work with only a few of Photoshops features. This book helps them take advantage of its many exciting tools. Beginning with a detailed tutorial on essential concepts and terms, it then guides readers step-by-step through every aspect of the application, including new and enhanced features such as Smart Filters, Quick Selection, and Vanishing Point. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Dimensioning and Location Planning for Wireless Networks under Multi-level Cooperative Relaying
Authors: Bin Lin, Pin-Han Ho
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

This paper studies the problem of network dimensioning and location planning in multi-hop wireless networks. A key technology of wireless multi-level cooperative relaying (CR) is incorporated, which has been recognized as an effective design paradigm for achieving throughput enhancement. A mathematical formulation is presented to capture the nature of the problem, and characterize the behavior of multi-level CR. The tasks of dimensioning, relay placement, relay allocation, and relay sequence design have been considered in a unified optimization framework. The formulation is a nonlinear integer program. To avoid the intractable computation complexity in solution, an efficient two-phase algorithm is developed. We conduct a series of case studies to verify the proposed algorithm, in which the results demonstrate the efficiency of our proposed algorithm and the significant benefits in terms of deployment cost reduction under multi-level CR.
Performance assessment of search management agent under asymmetrical problems and control design applications
Authors: Jukkrit Kluabwang, Deacha Puangdownreong, Sarawut Sujitjorn
Venue: WSEAS Transactions on Computers (2009)

The article presents the performance evaluation of the management agent (MA) containing the adaptive tabu search (ATS) as its search core. In particular, asymmetrical surface optimization problems have been considered. It has been found that symmetrical property of the problems has a significant effect on search performance of the ATS, but MA(ATS). As an average, the MA(ATS) is about 2 times faster than the ATS under both symmetrical and asymmetrical problems. The article also gives reviews on the ATS and the MA(ATS) algorithms. An application on controller design for a coupled system with three-degree-of-freedom is also elaborated.
A model to evaluate the industrial readiness for technology transfer: "a case-study of Iranian automobile industry"
Authors: M. Manteghi, M. Samiei Nasr
Venue: Proceedings of the 1st WSEAS international conference on Nanotechnology (2009)

Today, automotive industry plays a significant role in the economic development of countries around the world. But the results of numerous studies have revealed that despite the substantial efforts made to employ efficient models for effective technology transfer, the effective implementation of such models in developing countries including Iran has faced many challenges due to the inadequacy of related infrastructure. Therefore, it is of great importance for the managers and experts in the field of automotive industry to carefully assess the present state and to develop an appropriate model to evaluate the readiness of the industry for effective transfer of necessary technologies that will ultimately facilitate the growth of this industry in the competitive markers. For this purpose, the necessary indices for the evaluation of readiness of Iranian automotive industry for the effective technology transfer and the therapeutic strategies to reach the ideal state were identified and formed into a questionnaire that was administered to experts inside and outside Iran. Data obtained from the questionnaire were codified and entered into Concept System for concept mapping and into LISREL for path analysis. Then, the criteria that are believed to be important in the evaluation of the readiness of the automotive industry for an effective technology transfer were identified along with their significant coefficients and total values. And finally, based on the determining categories, criteria, and indices, a model was developed to evaluate the capabilities of the automotive industry for the effective technology transfer. The results of the causal analysis revealed that the model enjoyed a high index of goodness-of-fit (X2 = 0.0128, PMSR = 0.00) at the 0.05 level of significance.
On-line coordination among discrete-event agents
Authors: Manh Tung Pham, Kiam Tian Seow
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

This paper addresses a novel coordination problem for distributed agents in a discrete-event setting. We introduce and study a predicate coordination problem as the problem of distributed agents interacting and communicating between themselves to satisfy (the invariance of) a global predicate specifying an inter-agent constraint. We then develop an optimal coordination policy by which the agents can coordinate to satisfy the predicate constraint. To implement the optimal policy, we develop two on-line coordination strategies including one that can achieve significant savings in communication bandwidth.
An empirical determination of samples for decision trees
Authors: Hyontai Sug
Venue: Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases (2009)

Because it is not known to determine a?proper sample size for data mining tasks, the task of determining proper sample sizes for decision trees that are one of the best data mining algorithms is arbitrary, and as the size of samples grows, the size of generated decision trees grows with some improvement in error rates. But we cannot use larger and larger samples, because it's not easy to understand large decision trees and data overfitting problem can happen with limited target data set. This paper suggests an objective approach in determining a proper sample size to generate good decision trees with respect to generated tree size and error rates. Experiments with two representative decision tree algorithms, CART and C4.5 show very promising results.
Deriving ontological structure from a folksonomy
Authors: Saurabh Lalwani, Michael N. Huhns
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

In this paper we describe our investigation of tagging systems and the derivation of ontological structure in the form of a folksonomy from the set of tags. Tagging systems are becoming popular, because the amount of information available on some websites is becoming too large for humans to browse manually and the types of information (multimedia data) is unsuitable for the indexers used by conventional search engines to organize. However, tag-based search is very inaccurate and incomplete (low precision and recall), because the semantics of the tags is both weak and ambiguous. The basic problem is that tags are treated like keywords by search engines, which consider individual tags in isolation. However, there is additional semantics implicit in a collection of tagged data. In this paper, we innovate and investigate techniques to make the implicit semantics explicit, so that search can be improved in both precision and recall and additional utility can be derived from the tags that people associate with multimedia items (pictures, blogs, videos, etc.). Our approach is to propose hypotheses about the ontological structure inherent in a collection of tags and then attempt to verify the hypotheses statistically. We conducted more than one hundred experimental searches on Flickr with different tags and discovered by statistical analysis information about how tags are assigned by users and what ontological knowledge is implicit in these tags that can be made explicit, and ultimately, exploited.
Human-robot interactions to promote play and learning
Authors: Byron Lahey, Natalie Freed, Patrick Lu, Camilla Nørgaard Jensen, Kasia Muldner, Winslow Burleson
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

Research shows that children construct much of their knowledge through active manipulation of the environment, which allows them to connect abstract concepts to observable outcomes. Despite these findings, although the integration of novel pedagogical technologies into classroom settings has begun, the technologies predominantly have focused on instruction in virtual contexts. To date, however, little is known about novel technologies that step outside of the virtual realm into the physical classroom, thereby leveraging findings on embodied mathematical cognition to influence educational practices. As a first step in filling this gap, we present the Active Learning Environment with Robotics Tangibles (ALERT) framework. Our system relies on human-robot interaction and tangible instruction to motivate and trigger learning in students through a variety of activities that integrate play and instruction in mixed reality environments. Here we describe some of the activities supported by ALERT, and discuss plans for evaluating the pedagogical utility of the system.
Recovering Social Networks From Massive Track Datasets
Authors: Christopher I. Connolly, J. Brian Burns, Hung H. Bui
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.
Quality of protection analysis and performance modeling in IP multimedia subsystem
Authors: An'an Luo, Chuang Lin, Kai Wang, Lei Lei, Chanfang Liu
Venue: Computer Communications (2009)

IP Multimedia Subsystem (IMS) is widely considered as the main solution for the next generation multimedia rich communication. In order to provide multi-level security service in IMS to mobile users for multimedia applications, it is insufficient to take the security benefits into consideration, but adequately analyzing the impact of security policies in IMS on the performance cost quantitatively is also necessary and significant. In this paper, we first propose a novel study of IMS performance by a QPN model to much more precisely describe SIP signaling in IMS, and made performance evaluation. Then we defined totally seven levels of IMS security policies according to IMS specifications in 3GPP and proposed QoP partition model which quantitatively reflects strength of protection of SIP signaling and users' security needs. Further more, we did our original contributions to use the QPN model to evaluate the impacts of security mechanism on system performance cost quantitatively. With the multi-view security partition introduced, different security policies could be adjusted according to the application and users' security requirements, so that multi-level security service can be provided to diverse users and applications for a better tradeoff between security requirements and system performance in IMS.
Identification and control of nonlinear systems by a time-delay recurrent neural network
Authors: Hong-Wei Ge, Wen-Li Du, Feng Qian, Yan-Chun Liang
Venue: Neurocomputing (2009)

In this paper, we first present a novel time-delay recurrent neural network (TDRNN) model by introducing the time-delay and recurrent mechanism. The proposed TDRNN model has special advantages such as simple structure, deeper depth and higher resolution ratio in memory. Thereafter, we develop the dynamic recurrent back-propagation algorithm for the TDRNN. To guarantee the fast convergence, the optimal adaptive learning rates are also derived in the sense of discrete-type Lyapunov stability. More specifically, a TDRNN identifier and a TDRNN controller are constructed to perform the identification and control of the nonlinear systems. Numerical experiments show that the TDRNN model has good effectiveness in the identification and control for dynamic systems.
Systematic search within an optimisation space based on Unified Transformation Framework
Authors: Shun Long, Grigori Fursin
Venue: International Journal of Computational Science and Engineering (2009)

Modern compilers have limited ability to exploit the performance improvement potential of complex transformation compositions due to their ad-hoc nature. Unified Transformation Framework (Kelly and Pugh, 1993a) provides a unified representation of different iteration reordering transformations. It results in a large and complex space to explore. This paper presents a heuristic search algorithm capable of efficiently locating good optimisations within this space. Preliminary experimental results on Java show that it can achieve an average speedup of 1.14 and 1.10 on two different platforms, and more than 75&#37; of the maximum performance available can be obtained within 20 evaluations.
Computational Intelligence: An Introduction, 2nd edition
Authors: Andries P. Engelbrecht
Venue:  (2007)

Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments. The main focus of this text is centred on the computational modelling of biological and natural intelligent systems, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation. Engelbrecht provides readers with a wide knowledge of Computational Intelligence (CI) paradigms and algorithms; inviting readers to implement and problem solve real-world, complex problems within the CI development framework. This implementation framework will enable readers to tackle new problems without any difficulty through a single Java class as part of the CI library. Key features of this second edition include: A tutorial, hands-on based presentation of the material. State-of-the-art coverage of the most recent developments in computational intelligence with more elaborate discussions on intelligence and artificial intelligence (AI). New discussion of Darwinian evolution versus Lamarckian evolution, also including swarm robotics, hybrid systems and artificial immune systems. A section on how to perform empirical studies; topics including statistical analysis of stochastic algorithms, and an open source library of CI algorithms. Tables, illustrations, graphs, examples, assignments, Java code implementing the algorithms, and a complete CI implementation and experimental framework. Computational Intelligence: An Introduction, Second Edition is essential reading for third and fourth year undergraduate and postgraduate students studying CI. The first edition has been prescribed by a number of overseas universities and is thus a valuable teaching tool. In addition, it will also be a useful resource for researchers in Computational Intelligence and Artificial Intelligence, as well as engineers, statisticians, operational researchers, and bioinformaticians with an interest in applying AI or CI to solve problems in their domains. Check out http://www.ci.cs.up.ac.za for examples, assignments and Java code implementing the algorithms.
Detecting Community Structure in Networks by Propagating Labels of Nodes
Authors: Chuanjun Pang, Fengjing Shao, Rencheng Sun, Shujing Li
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Community structure is a common property of many networks. Automatically finding communities in networks can provide invaluable help in understanding the structure and the functionality of networks. Many algorithms to find communities have been developed in recent years. Here we devise a new algorithm to detect communities in networks--propagation algorithm. By propagating the labels of nodes in networks, detecting communities are transformed into analyzing the labels of nodes in networks in this algorithm. Real-world and computer-generated networks are used to verify this algorithm. The results of our experiments indicate that it is sensitive to community structure and effective at discovering communities in networks.
Identification of LPI radar signals by higher order spectra and neural network techniques
Authors: L. Anjaneyulu, N. V. S. N. Sarma, N. S. Murthy
Venue: International Journal of Information and Communication Technology (2009)

LPI radars use continuous wave, wide bandwidth low power signals of the order of a few watts making its detection difficult. The important advantage of LPI radar is to go undetected, while maintaining a strong battlefield awareness. Common spectral analysis and conventional methods fail to detect emissions of LPI radars and even normal radars in noisy environments. This leads us to use higher order spectral analysis (HOSA) techniques enabling us to extract much more information from the same intercept and hence facilitating detection. This paper reports the results of HOSA techniques (bi-spectrum, bi-coherence and tri-spectrum) and artificial neural networks (ANNs), applied to LPI radar signals. Bi-phase Barker coded signals of different lengths, P1, P2, P3 and P4 polyphase coded signals and Frank signal are analysed using HOSA techniques to produce 2-D signatures of these signals. An artificial neural network (ANN) is trained on these signatures so that it will be able to detect and identify the LPI radar signal whose type is unknown when received. The results obtained clearly indicate the promising capability of these techniques to identify the type of LPI signal even with SNRs as low as &minus;3 dB.
An Efficient Student Model Based on Student Performance and Metadata
Authors: Arndt Faulhaber, Erica Melis
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

This paper describes a new student model technology that combines evidences and knowledge about pedagogical and domain structure. Its structure is generated from the metadata available in the content representation of the adaptive web-based learning platform ACTIVEMATH (or other contents). The evidences are processed with Item Response Theory and Transferable Belief Model uncertainty methodologies. We summarize evaluation results for this student model.
Patriotic Information Systems
Authors: Todd Loendorf, Todd Loendorf, G. David Garson
Venue:  (2007)

Database technology can be used for various ends, ranging from promotion of democracy to strengthening of nationalism to shoring up authoritarian regimes through misinformation. Its use affects every layer of society: from individuals to households to local governments, and is a consuming issue in the United States Government s stance on privacy, security, and technology. Patriotic Information Systems discusses how, with its non-participatory enforcement ethos, its inherent bias against freedom of information, and its massive claims on IT budget resources, the information technology security system of the future may be even less hospitable to the democratic visions which some theorists once anticipated would be among the most important contributions of information technology to society.