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检索条件"机构=Department of Eletrical Engineering and Computer Science"

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Introducing Machine Learning to First-year Undergraduate engineering Students Through an Authentic and Active Learning Labware

Introducing Machine Learning to First-year Undergraduate Eng...

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Frontiers in Education (FIE) Conference

作者： Lei Huang Kuo-Sheng Ma Department of Eletrical Engineering and Computer Science Loyola Marymount University Los Angeles USA

ISBN: (纸本)9781538611753;9781538611746

This innovative practice work in progress paper presents our pilot experiment of teaching machine learning to first-year multidisciplinary undergraduate engineering students using an authentic and active learning tool, which consists of a public Google site repository and a course project. The preliminary evaluation we conducted by pre-post survey shows user experiences and effectiveness of this teaching/learning tool. Analysis of the evaluation results will be used to guide us for further development of the tool.

关键词： Machine learning Tools engineering students Programming Google Data models

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NoSQL database performance tuning for IoT data cassandra case study 3

NoSQL database performance tuning for IoT data cassandra cas...

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3rd International Conference on Internet of Things, Big Data and Security, IoTBDS 2018

作者： Dias, Lucas B. Holanda, Maristela Huacarpuma, Ruben C. De Sousa, Rafael T. Brasília Brazil Department of Computer Science University of Brasília Brasília Brazil Cybersecurity INCT Unit 6 LATITUDE Dept. of Eletrical Engineering University of Brasília Brasília Brazil

ISBN: (纸本)9789897582967

Data provided by Internet of Things (IoT) are time series and have some specific characteristics that must be considered with regard to storage and management. IoT data is very likely to be stored in NoSQL system databases where there are some particular engine and compaction strategies to manage time series data. In this article, two of these strategies found in the open source Cassandra database system are described, analyzed and compared. The configuration of these strategies is not trivial and may be very time consuming. To provide indicators, the strategy with the best time performance had its main parameter tested along 14 different values and results are shown, related to both response time and storage space needed. The results may help users to configure their IoT NoSQL databases in an efficient setup, may help designers to improve database compaction strategies or encourage the community to set new default values for the compaction strategies. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Information management

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PARALLEL APPROACH TO EXPEDITE MORPHOLOGICAL FEATURE EXTRACTION OF REMOTE SENSING IMAGES FOR CBIR SYSTEM

PARALLEL APPROACH TO EXPEDITE MORPHOLOGICAL FEATURE EXTRACTI...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Sandeep Kumar Swati Jain Tanish Zaveri Computer Science and Engineering Department Nirma University Ahmedabad India Eletrical Engineering Department Nirma University Ahmedabad India

In this paper, we have proposed a parallel approach to the morphological feature extraction process and demonstrated a good computational speedup. Remote sensing images have a typical property of incrementing constantly and each image being very large. Since the images are acquired constantly and hence added into the database regularly in good numbers, hence there is a need to make the feature extraction work more efficient. Moreover morphological features are good texture descriptors and are extremely compute-intensive as well. It is hence attempted to utilize the power of multi-core architecture and expedite the process of feature extraction. These feature descriptors are tested on UC Merced Land Use Land Cover Data set. Experimentation shows that with the use of parallel programming and architecture speed up of as good as 20X is obtained for CCH and RIT feature sets.

关键词： Feature extraction Remote sensing Graphics processing units Merging Image retrieval Histograms

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Prediction of protein structural class using PSI-BLAST profile based collocation of amino acid pairs

Prediction of protein structural class using PSI-BLAST profi...

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2007 1st International Conference on Bioinformatics and Biomedical engineering, ICBBE

作者： Chen, Ke Kurgan, Lukasz Ruan, Jishou University of Alberta Department of Eletrical and Computer Engineering Edmonton T6G 2V4 Canada Chern Institute of Mathematics College of Mathematical Science Nankai University Tianjin 300071 China

ISBN: (纸本)1424411203

Knowledge of structural classes is useful in understanding folding patterns in proteins. Numerous structural class prediction methods were proposed in the past. Although virtually all state-of-the-art classifiers were already tried, many of these methods use very simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation, which is based on PSI-BLAST profile based collocation of AA pairs. We used two benchmark datasets constructed by Zhou (J. of Prot Chem. 1998, 17(8):729-38) to test the proposed representation with five representative classifiers. The two best classifiers, which include a support vector machine and an instance base learner, achieved 88% and 96% accuracy on the two datasets, respectively. Our results were compared with five recently proposed methods. The comparison shows superiority of the proposed method, which reduces the error rates by 30% and 21% on the two datasets when compared with the best-performing ensemble of boosted logistic regression classifier. Finally, the new sequence representation is compared with AA composition when using support vector machine classifier. The error rate reduction due to application of the new representation equals 40% and 25% for the two datasets, respectively. In short, the PSI-BLAST profile based collocation of AA pairs is shown to be a promising feature-based sequence representation. © 2007 IEEE.

关键词： Protein folding

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CovidSense: A Smartphone-based Initiative for Fighting COVID-19 Spreading 9

CovidSense: A Smartphone-based Initiative for Fighting COVID...

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9th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-Exclusion, DSAI 2020

作者： Tsoumalis, Georgios Bampos, Zafeirios Papazoglou, Athanasios Iakovakis, Dimitrios Hadjidimitriou, Stelios Apostolidis, Georgios Charisis, Vasileios Jelinek, Herbert Khandoker, Ahsan Khalaf, Kinda Damiani, Ernesto Hadjileontiadis, Leontios Aristotle University of Thessaloniki Grc SingularLogic Grc Department of Eletrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Greece Khalifa University of Science and Technology Abu Dhabi Ae Aristotle University of Thessaloniki Greece Greece

ISBN: (纸本)9781450389372

Coronavirus Disease COVID-19 is a new strain of coronavirus, first identified in clusters with pneumonia-like symptoms with manifestation mainly of fever (>37.8oC), runny nose, dry cough, anosmia, loss of taste, and fatigue. Testing for COVID-19 can be carried out on samples obtained by various methods, including nasopharyngeal swab or sputum sample. Results are generally available within a few hours to one day. Taking into consideration the worldwide COVID-19 pandemic impact (more than 35.500.000 incidences and more than 1.000.000 deaths so far), it is clear that there is an immediate need to devise an effective tool that can be used both at the hospital and home sites to provide a reliable risk index of confronting COVID-19. Here, the potentialities of a smartphone-based app, namely CovidSense, that could provide a reliable COVID-19 risk index to the user before performing any relevant clinical test, monitor his/her symptoms and provide guidance for further actions are presented. CovidSense is a holistic approach for COVID-19 risk assessment that captures both the status of the respiratory system, via acquisition and AI-based analysis of breathing and coughing sounds, along with the Autonomic Nervus System/Immune System status, via the capturing and analysis of the Heart Rate Variability. The analytical description and functionality of the CovidSense included here manifests the usefulness of such approach in continuous symptoms monitoring, allowing people with early symptoms, quarantined and sensitive groups, such as older adults and patients with comorbidities, to become active participants in the effort of fighting the spread of COVID-19, by monitoring their status and get engaged in appropriate preventative actions. CovidSense paves the way for solutions using technological artifacts, such as smartphones, to foster inclusiveness and joint efforts in successfully addressing global health problems, such as the COVID-19 pandemic. © 2020 ACM.

关键词： COVID-19

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A noise-tolerant group delay estimator applied to dispersion measurement in gases

A noise-tolerant group delay estimator applied to dispersion...

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IEEE Symposium (IUS) Ultrasonics

作者： J.E. Carlson F. Sjoberg P.-E. Martinsson EISLAB Department of Computer Science and Electrical Engineering Luleå University of Technology Lulea Sweden Division of Signal Processing Department of Computer Science and Eletrical Engineering Luleå University of Technology Lulea Sweden

In this paper we present a model-based group velocity estimator, that can be used to measure speed of sound in ultrasonic pulse-echo systems as a function of ultrasound frequency. The estimation of group velocities involves numerical differentiation of the phase difference. In the presence of noise, this becomes numerically unstable. The model-based approach presented herein, shows better tolerance to experimental noise. The performance of the estimator is evaluated with simulations as function of pulse bandwidth and SNR. Finally, the estimator applied to real data and compared with other methods for measuring speed of sound in Ethane and Oxygen.

关键词： Delay estimation Dispersion Gases Acoustic noise Frequency estimation Velocity measurement Pulse measurements Frequency measurement Ultrasonic variables measurement Ultrasonic imaging

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Code density optimization for embedded DSP processors using data compression techniques

Code density optimization for embedded DSP processors using ...

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Conference on Advanced Research in VLSI

作者： S.Y. Liao S. Devadas K. Keutzer Department of Eletrical Engineering and Computer Science and Engineering MIT Cambridge MA USA Synopsys Inc. Mountain View CA USA

We address the problem of code size minimization in VLSI systems with embedded DSP processors. Reducing code size reduces the production cost of embedded systems. We use data compression methods to develop code size minimization strategies. We present a framework for code size minimization where the compressed data consists of a dictionary and a skeleton. The dictionary can be computed using popular text compression algorithms. We describe two methods to execute the compressed code that have varying performance characteristics and varying degrees of freedom in compressing the code. Experimental results obtained with a TMS320C25 code generator are presented.

关键词： Digital signal processing Dictionaries Very large scale integration Production systems Costs Embedded system Data compression Minimization methods Skeleton Compression algorithms

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On a pursuit-evasion problem related to motion coordination of mobile robots

On a pursuit-evasion problem related to motion coordination ...

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Annual Hawaii International Conference on System sciences (HICSS)

作者： K. Sugihara I. Suzuki Department of Information and Computer Sciences University of Hawaii Manoa Honolulu HI USA Department of Eletrical Engineering and Computer Science University of Wisconsin Milwaukee Milwaukee WI USA

A pursuit-evasion problem is discussed in which a team of mobile robots (called searchers) is required to capture a mobile robot (called a fugitive) in an N*N grid graph G/sub N/ under various conditions. An algorithm for the problem is presented under the condition that the fugitive is not allowed to stop anywhere or change direction while on an edge. This algorithm is shown to be optimal with respect to both the number of searchers and the time required to capture the fugitive. A second algorithm is presented under the condition that the fugitive can stop on a vertex but cannot change direction while on the edge. A third algorithm, with no restrictions, is presented and shown to be optimal with respect to the number of searchers required and to be optimal within a constant factor with respect to the time required in the worst case.< >

关键词： Mobile robots Robot kinematics Pursuit algorithms Motion planning Grid computing computer science Manipulators Polynomials

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Comparative Analysis of ML Algorithms for Fraud Detection

Comparative Analysis of ML Algorithms for Fraud Detection

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara Abitova Miras Abalkanov Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Binghamton NY USA Dep. of Eletrical Engineering and Computer Science Dep. of Computer Engineering State University of New York Astana IT University Astana Kazakhstan Department of Computer Engineeering Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

The challenge of credit card fraud presents a significant threat to both consumers and financial institutions alike, leading to substantial economic detriment. In response, this research applies various machine learning strategies to sift through transactional data for the identification of fraudulent activities. Specifically, we employ logistic regression, decision trees, and random forest algorithms to analyze a compiled dataset of genuine and deceptive transactions. This dataset undergoes segmentation into training and testing phases, allowing for a comprehensive assessment of each algorithm's effectiveness. The determination of accuracy scores across these phases serves as a metric for model evaluation. The findings lay down an essential groundwork for ongoing exploration into leveraging machine learning for the mitigation of credit card fraud. The integration of these analytical models promises to bolster the fraud detection capabilities of both financial entities and individual cardholders, significantly reducing the incidence of financial loss.

关键词： Training Logistic regression Machine learning algorithms Accuracy Sensitivity Credit cards Fraud

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Hamiltonian-driven Adaptive Dynamic Programming for Nonlinear Discrete-Time Dynamic Systems

Hamiltonian-driven Adaptive Dynamic Programming for Nonlinea...

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International Joint Conference on Neural Networks

作者： Yongliang Yang Donald Wunsch Yixin Yin School of Automation and Eletrical Engineering University of Science and Technology Beijing Beijing 100083 Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla Missouri 65409

ISBN: (纸本)9781509061839

In this paper, based on the Hamiltonian, an alternative interpretation about the iterative adaptive dynamic programming (ADP) approach from the perspective of optimization is developed for discrete time nonlinear dynamic systems. The role of the Hamiltonian in iterative ADP is explained. The resulting Hamiltonian driven ADP is able to evaluate the performance with respect to arbitrary admissible policies, compare two different admissible policies and further improve the given admissible policy. The convergence of the Hamiltonian ADP to the optimal policy is proven. Implementation of the Hamiltonian-driven ADP by neural networks is discussed based on the assumption that each iterative policy and value function can be updated exactly. Finally, a simulation is conducted to verify the effectiveness of the presented Hamiltonian-driven ADP.

关键词： Mathematical model Optimal control Nonlinear dynamical systems Discrete-time systems Linear programming Dynamic programming

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