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Brazilian Symposium on Computing System engineering (SBESC)

作者： Adriano Francisco Ronszcka Cleverson Avelino Ferreira Paulo Cézar Stadzisz João Alberto Fabro Jean Marcelo Simão Graduate School in Electrical Engineering and Industrial Computer Science (CPGEI) Graduate School of Applied Computing (PPGCA) Federal University of Technology Parana (UTFPR) Curitiba Brazil

The so-called Notification-Oriented Paradigm (NOP) is a new technique to develop software. NOP is a rule-oriented approach where each rule and fact-base element is split in small entities. These entities particularly collaborate by means of precise notifications only when their state changes. This allows carrying out an inference process free of structural and temporal redundancies. Due to its peculiar properties, NOP tends to be an alternative to achieve suitable performance in program execution, easiness in codification level, and appropriate code modularity, structuration and decoupling. In order to validate these supposed features, a framework was implemented in C++. Even though it quite demonstrated the features of the paradigm in terms of execution performance and development process, it still presented a gap therein. In this context, this paper presents a preliminary programming language and compiler to explore NOP features. This new approach simplifies the development in NOP and speedup execution performance.

关键词： Redundancy C++ languages programming Collaboration Engines Couplings

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Optimizing 3D Clothing Models for VR: A Deep Learning Approach to Triangle Count Reduction

Optimizing 3D Clothing Models for VR: A Deep Learning Approa...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Surasachai Doungtap Varinya Phanichraksaphong Jenq-Haur Wang International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study presents a deep learning framework optimizing 3D clothing models for VR, using a CNN to significantly reduce the triangle count of models from DeepFashion3D and CAP-UDF datasets. Achieving a balance between efficiency and detail, it cuts triangle count from over 160,000 to below 4,000, maintaining high DPI. The approach automates optimization, promising scalability and efficiency in VR fashion, setting a foundation for future 3D content development, enhancing virtual garment realism and interactivity.

关键词： Deep learning Solid modeling Three-dimensional displays Scalability Clothing Optimization Consumer electronics

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Eigenvalue Problems: Algorithms, Software and Applications in Petascale Computing 1

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丛书名： Lecture Notes in Computational science and engineering

1000年

作者： Tetsuya Sakurai Shao-Liang Zhang Toshiyuki Imamura Yusaku Yamamoto Yoshinobu Kuramashi Takeo Hoshi

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Evaluating Conveyor Belt Health With Signal Processing applied to Inertial Sensing

Evaluating Conveyor Belt Health With Signal Processing Appli...

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2023 Symposium on Internet of Things, SIoT 2023

作者： Matos, Saulo Neves Coletti, Otavio Ferracioli Filho, Fernando Ugucioni De Carvalho, Ricardo C. C. L. Pinto, Thomas V. B. De Barros, Luiz G. D. Ranieri, Caetano Mazzoni Lopes, Bruno Eduardo Ueyama, Jo Pessin, Gustavo Institute of Mathematics and Computer Science University of Sao Paulo Brazil Sao Carlos School of Engineering University of Sao Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Universidade Federal of Minas Gerais Graduate Program in Electrical Engineering MG Belo Horizonte Brazil Vale -SA Vitoria Brazil

ISBN: (纸本)9798350329438

Conveyor belts are commonly used in the mining industry for efficient material transport. However, they are prone to failures such as idler anomalies, belt tears, and misalignment. Current monitoring systems only evaluate the conveyor or idle belt's condition at the installation point. This study proposes a method to detect faults in conveyor belts, specifically idler anomalies and belt deviation, using two IMUs installed at the lateral extremities of the belt to evaluate its health along its route. In a reduced-scale conveyor belt, we conducted experiments by collecting gyroscopes and accelerometer data in various scenarios, including normal conditions and anomaly situations. We modified the idlers' physical structure or changed the belt tracks to create these scenarios. We used similarity analysis techniques to compare the data collected and performed statistical hypothesis analysis to observe the influence of the anomaly scenarios on the gyroscope and accelerometer data. We used Dynamic Time Warping (DTW) for gyroscope data to compare ground truth with anomaly scenarios. For the acceleration data, we performed vibration analysis by converting the time domain data to the frequency using Fast Fourier Transform (FFT) and evaluating the difference in spectral density between the reference and anomaly data. Statistical hypothesis analysis revealed that anomaly scenar-ios influenced the gyroscope x-axis and z-axis more. According to the spectral density analysis of the accelerometer, it was revealed that belt misalignment on the y-axis may be detected. © 2023 IEEE.

关键词： Belt conveyors

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Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion

Calibrationless parallel imaging reconstruction based on str...

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作者： Shin, Peter J. Larson, Peder E. Z. Ohliger, Michael A. Elad, Michael Pauly, John M. Vigneron, Daniel B. Lustig, Michael Department of Radiology and Biomedical Imaging University of California at San Francisco San FranciscoCA United States UC Berkeley-UCSF Graduate Program in Bioengineering CA United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA United States Department of Computer Science Technion'Israel Institute of Technology Haifa Israel Magnetic Resonance Systems Research Laboratory Department of Electrical Engineering Stanford University StanfordCA United States

Purpose: A calibrationless parallel imaging reconstruction method, termed simultaneous autocalibrating and k-space estimation (SAKE), is presented. It is a data-driven, coil-by-coil reconstruction method that does not require a separate calibration step for estimating coil sensitivity information. Methods: In SAKE, an undersampled, multichannel dataset is structured into a single data matrix. The reconstruction is then formulated as a structured low-rank matrix completion problem. An iterative solution that implements a projection-ontosets algorithm with singular value thresholding is described. Results: Reconstruction results are demonstrated for retrospectively and prospectively undersampled, multichannel Cartesian data having no calibration signals. Additionally, non-Cartesian data reconstruction is presented. Finally, improved image quality is demonstrated by combining SAKE with wavelet-based compressed sensing. Conclusion: Because estimation of coil sensitivity information is not needed, the proposed method could potentially benefit MR applications where acquiring accurate calibration data is limiting or not possible at all. © 2013 Wiley Periodicals, Inc.

关键词： Compressed sensing

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Testing a new proposed IPv6 QoS management model in inter and intra domains

Testing a new proposed IPv6 QoS management model in inter an...

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： El-Bahlul Fgee William J. Phillips A. Elhounie School of Applied Science and Engineering Department of Electrical and Computer Engineering Academy of Graduate Studies Tripoli Libya Department of Engineering Mathematics and Internetworking Dalhousie University Canada

Network multimedia applications constitute a large part of Internet traffic and present a big challenge because of their sensitivity to delay, packet loss and higher bandwidth requirement. The need for guaranteed delivery and lower delay is caused by propagation of more the one domain. The domains used in this paper are co-operating and communicating with each other and all of them support IPv6 QoS. Therefore, there is a need for IP QoS management model that handles and manages QoS requests and cooperate with other QoS schemes. In this paper, the IPv6 QoS manager is tested when the QoS of traffic flows propagate two and three domains. The IPv6 QoS manager handles QoS requests by either processing them locally if the intended destination is located locally or forwarding them to the neighboring domains that are managed by IPv6 QoS managers. Two simulation scenarios are presented in this paper, intra domain, one domain, and inter domains, two and three domains. End-to-end delay results for the different scenarios have approved that this QoS model can work either in intra or inter domains. In addition to the delay, packets are policed and degraded to lower priority if they exceed their initial traffic rates. This proves that the IPv6 QoS model is flexible and not restricted to one domain. Also, end-to-end QoS has been achieved with one admission and management unit instead of individual and independent management and admission units as in the case of IntServ.

关键词： Testing Quality of service Traffic control Telecommunication traffic Bandwidth Communication system traffic control IP networks Diffserv networks Internet Protocols

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Data-driven analysis of kinaesthetic and tactile information for shape classification

Data-driven analysis of kinaesthetic and tactile information...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Thiago Eustaquio Alves de Oliveira Vinicius Prado da Fonseca Emanuil Huluta Paulo F. F. Rosa Emil M. Petriu School of Electrical Engineering and Computer Science University of Ottawa Faculty of Medicine Ottawa ON CA School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Defense Engineering Graduate Program The Military Institute of Engineering Rio de Janeiro Brazil

Humans sense of touch consists in a complexity of sensors and nervous system. The information inferred by this system enables the daily dexterous manipulation tasks. In biological systems, there is no conscious prioritization of sensors while performing tactile exploration and the selection of exploratory movements is driven by learning instincts and data gathered by previous movements. The development of artificial systems tries to mimic such systems with engineered sensors and strategies for movement selection. This paper presents a data-driven analysis to the problem of sensor selection in the contour following for shape discrimination task. This task consists of a 4-DOF robotic finger exploring a set of 7 synthetic shapes. The data collected from the motors, inertial measurement unit, and magnetometer was analyzed applying principal component analysis and a multilayer perceptron neural network. Results show the variation of classification rate depending on the fingertip material and sensor considered. It is worth to observe that the magnetometer was the most robust in both cases.

关键词： Shape Magnetometers Robot sensing systems Principal component analysis Magnetic sensors

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Microstructure, Stress and Optical Properties of Ge/C Multilayers

Microstructure, Stress and Optical Properties of Ge/C Multil...

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Physics of X-Ray Multilayer Structures, PXRAYMS 1994

作者： Lu, Xiang Nguyen, Tai D. Soufli, Regina Gullikson, Eric M. Underwood, James H. Center for X-ray Optics Lawrence Berkeley Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Applied Science and Technology Graduate Group University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science University of California BerkeleyCA94720 United States

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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Raman Scattering and X-Ray Diffraction Studies of Gallium Nitride Films Grown on (100) Gallium Arsenide

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MRS Online Proceedings Library 1994年第1期339卷 503-508页

作者： S. W. Brown S. C. Rand C.-H. Hong D. Pavlidis University of Michigan Applied Physics Program Ann Arbor USA Department of Electrical Engineering and Computer Science University of Michigan Solid State Electronics Laboratory Ann Arbor USA

Raman spectroscopy and x-ray diffraction are used to characterize Gallium Nitride (GaN) films grown on (100) Gallium Arsenide (GaAs) substrates. Reflection X-ray diffraction data from (200) planes of GaAs and cubic GaN are presented. The linewidth of the cubic GaN diffraction peak is shown to be a strong function of the growth temperature. Raman spectra are presented for a series of samples grown at different temperatures. Raman scattering is characterized by strong peaks at 560 cm-1 and at 736 cm-1, corresponding to TO and LO phonon modes of cubic GaN, respectively. An additional, unexplained feature at 768 cm-1 is clearly observed in Raman spectra of c-GaN samples grown at lower temperatures. The polarization dependence of the intensity of the GaN LO phonon mode is presented and compared with the GaAs LO phonon mode to establish the relative orientation of the c-GaN epitaxy on GaAs.

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