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检索条件"主题词=software algorithms"

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Genetic Algorithm for Scheduling Communication Networks in Time-Triggered Systems-of-Systems

Genetic Algorithm for Scheduling Communication Networks in T...

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IEEE International Conference on software Reliability Engineering Workshops (ISSRE Wksp)

作者： Setareh Majidi Roman Obermaisser Chair for Embedded Systems University of Siegen Siegen Germany

ISBN: (纸本)9781665426046

Systems engineering and management have evolved from developing distributed systems to the integration of complex adaptive systems and the advent of Systems of Systems (SoS). The predictable collaboration among constituent systems in an SoS plays a crucial role, especially in time-critical applications. In an SoS, we face independent constituent systems without global knowledge and central control. At the same time, resource reservations using appropriate scheduling algorithms are required to satisfy real-time requirements. Most of the existing scheduling solutions are defined for monolithic systems or complex systems with centralized authorities. In this paper, we present Time-Triggered Systems of Systems (TTSoS) and introduce a scheduling model for SoS applications with real-time requirements. We propose a two-level iterative genetic algorithm (GA) for scheduling the tasks and messages within and between time-triggered constituent systems. The results show the capability of temporal guarantees. Also, the results of the GA scheduler are compared with the performance of local search heuristics for the generated examples.

关键词： Scheduling algorithms software algorithms Search problems Scheduling Real-time systems software reliability Time factors

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An FPGA Accelerator for Spiking Neural Network Simulation and Training

An FPGA Accelerator for Spiking Neural Network Simulation an...

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IEEE International Symposium on Circuits and Systems

作者： Vasilis Sakellariou Vassilis Paliouras Electrical and Computer Engineering Department University of Patras Patras Greece

ISBN: (纸本)9781728192017;9781728192024

Spiking Neural Networks (SNNs) have recently been employed to solve a number of machine learning problems traditionally addressed by classical Artificial Neural Networks (ANNs). SNNs are different than ANNs as they incorporate time in their computations and information is encoded in the exact timing or frequency of discrete events, spikes. SNNs promise to deliver a higher energy efficiency than ANNs, when implemented in neuromorphic hardware, due to their event-driven nature. Naturally, this different computational model they introduce, creates different design challenges for the implementation of large-scale networks and needs to be addressed by different architectures. The spiking accelerator presented here aims to facilitate and accelerate the process of developing SNNs for ML applications that are traditionally addressed by ANNs and help bridge the accuracy gap between them. It achieves a significant speedup of up to 800× for inference and up to 500× for training compared to software SNN simulations for certain set-ups.

关键词： Training Time-frequency analysis software algorithms Life estimation Hardware Timing Testing

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Automated Testing of Android Applications Integrating Residual Network and Deep Reinforcement Learning

Automated Testing of Android Applications Integrating Residu...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Lizhi Cai Jilong Wang Mingang Cheng Jin Wang School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing&Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9781665458146

With the improvements of Deep Reinforcement Learning (DRL), there have been tremendous interests in utilizing DRL for automated application testing. However, most automated testing methods based on reinforcement learning have the problem of interacting with invalid UI areas and invalid interactions with controls. To solve this problem, this paper extracts the page features, constructs the Interactive Control Feature Diagram(ICCD); improves the DDQN network structure, adds the residual network, makes the algorithm take the picture as the input, and splits the original single output action(n*w*h) into two successive outputs: the interaction(1,n) and the position(1,w*h); a new reward function which combines the interaction times and the image similarity of ICCD is proposed to explore different UIs and ensure that there will be more than one action will be executed under the same UI. Experiments are carried out on five open source applications. The experimental results show that the proposed method is superior to other methods in code coverage and branch coverage.

关键词： Adaptation models software algorithms Reinforcement learning software quality Feature extraction software reliability Security

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Design of a Low Power Double AES Crypto-Processor SoC

Design of a Low Power Double AES Crypto-Processor SoC

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IEEE International Conference on Telecommunications and Photonics (ICTP)

作者： Sihabul Islam Md. Liakot Ali Rinath Ruhana Bangladesh University of Engineering and Technology (BUET) Dhaka-1000 Bangladesh

ISBN: (纸本)9781665417013

System on a Chip (SoC) is the state of the art in VLSI design and automation today due to its impressive performance matrix in terms of power, speed, area, and design time. This paper presents the design of a SoC implementing the double AES crypto processor for better security than its single core. AES has been proved the most secured symmetric cryptographic algorithm certified by USA Govt. So, its hardware implementation using SoC technology is expected to offer much better performance than that of software implementation. The chip is designed using industry-standard Verilog HDL and simulated in Altera Quartus II EDA environment. Two different symmetric keys are used to encrypt and decrypt plaintext. Moreover, mixed columns, and substitution byte functions are simplified to minimize the complexity of the proposed design. The experimental study shows that the proposed AES crypto-processor SoC outperforms the existing works in terms of power requirement.

关键词： Symmetric matrices software algorithms Very large scale integration software Hardware System-on-chip Telecommunications

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The Fusion of Bearing and Frequency Measurements from Multi-sensor Arrays for Underwater Target Tracking

The Fusion of Bearing and Frequency Measurements from Multi-...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： S. Koteswara Rao Kausar Jahan Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India

ISBN: (纸本)9781665438124

Estimation is the process of generating information that is not directly available from raw measurements to obtain different target motion parameters. To fulfil this aim, different measurements from various sensors are fused to obtain a more accurate and reliable estimate of the target state. This task is more complex when the sensors operate in passive mode. In this research work, passive measurements i.e., bearings from an array mounted on the hull (HMA) and array sensors towed behind the observer along with doppler shifted frequency measurements from the towed array are fused to obtain kinematic parameters of the desired target. The sub-optimal filter, Unscented Kalman filter is considered for the performance analysis. The results are compared to that of simple doppler-bearing tracking using measurements from HMA. The simulation is carried in MATLAB software using the Monte-Carlo simulation method for better analysis of the results.

关键词： Target tracking Surveillance software algorithms Sensor fusion Observers Frequency measurement Sensors

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Identifying Long-Term Deposit Customers: A Machine Learning Approach

Identifying Long-Term Deposit Customers: A Machine Learning ...

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International Informatics and software Engineering Conference (UBMYK)

作者： Mohammad Abu Tareq Rony Md. Mehedi Hassan Eshtiak Ahmed Asif Karim Sami Azam D. S. A. Aashiqur Reza Noakhali Science and Technology University Noakhali Bangladesh North Western University Khulna Bangladesh Faculty of Infomration Technology and Communication Sciences Tampere University Tampere Finland College of Engineering IT and Environment Charles Darwin University Darwin NT Australia Mathematics Discipline Khulna University Khulna Bangladesh

ISBN: (纸本)9781665407601

Majority of the revenue from the banking sector is usually generated from long term deposits by customers. It is for banks to understand customer characteristics to increase product sales. To aid this, marketing strategies are employed to target potential customers and let them interact with the banks directly, generating a large amount of data on customer characteristics and demographics. In recent years, it has been discovered that various data analysis, feature selection and machine learning techniques can be employed to analyze customer characteristics as well as variables that can impact customer decision significantly. These methods can be used to identify consumers in different categories to predict whether a customer would subscribe to a long-term deposit, allowing the marketing strategy to be more successful. In this study, we have taken a R programming approach to analyze financial transaction data to gain insight into how business processes can be improved using data mining techniques to find interesting trends and make more data-driven decisions. We have used statistical analysis like Exploratory Data Analysis (EDA), Principal Component Analysis (PCA), Factor Analysis and Correlations in the given data set. Besides, the study's goal is to use at least three typical classification algorithms among Logistic Regression, Random Forest, Support Vector Machine and K-nearest neighbors, and then make predictive models around customers signing up for long term deposits. Where we have gotten best accuracy from Logistic Regression which is 90.64 % as well the sensitivity is 99.05 %. Results were analyzed using the accuracy, sensitivity, and specificity score of these algorithms.

关键词： Support vector machines Data analysis Sensitivity software algorithms Sensitivity and specificity Programming Random forests

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Nelly: An Open-Source Package for Complex Refractive Index Extraction for Terahertz Spectroscopy on Layered Samples

Nelly: An Open-Source Package for Complex Refractive Index E...

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International Conference on Infrared and Millimeter Waves

作者： Uriel T. Tayvah Jacob A. Spies Jens Neu Gary W. Brudvig Charles A. Schmuttenmaer Yale University New Haven CT USA

As researchers have begun to apply THz spectroscopy to increasingly novel materials and THz spectrometers have become more widely available, the need has arisen for data workup techniques that both avoid approximations and are accessible to a wide range of researchers. Here, we present Nelly, a software package that uses a tree-based algorithm to perform complex refractive index extraction for any layered sample geometry without invoking approximations. It is provided as a MATLAB library and graphical user interface (GUI) for ease of use. We have tested it using CST simulations to verify that it gives accurate results for various sample geometries.

关键词： Geometry Spectroscopy software packages software algorithms Refractive index Approximation algorithms Libraries

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Feature Optimization of Motor Imagery EEG Classification using BEE Scout algorithms and Machine Learning for ALS Disease Predication

Feature Optimization of Motor Imagery EEG Classification usi...

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Advances in Technology, Management & Education (ICATME), International Conference on

作者： Dayashankar Pandey Varsha Namdeo SRK University Bhopal India

ISBN: (纸本)9781728185873

The detection of ALS disease is very critical due to control of nervous system. The nervous system cannot recognize all signal of physical behaviors of human body. For the monitoring of unusual physical behavior of human body identify with biomedical engineering process. Motor imagery EEG classification is the way to detection ALS disease and some other critical disease such as brain stroke and epilepsy. The electropherogram (EEG) is electric recoded signal stored in computer and converted into digital signal with A/D converter. The whole process of signal recoding proceeds by brain computer interface. The complex structure of EEG signal faced a problem of prediction of critical illness. In this paper proposed ensemble-based classifier for the prediction of critical disease. The extraction of features of EEG signals is also challenging task, for the extraction of features used wavelet transform function. The extracted features with transform are very-high dimension and noises. For the optimization of features applied BEE scout algorithm. The BEE scout algorithms reduce the unwanted component of features and provide better component of features for the classifier. The proposed algorithm simulated in MATLAB software and tested with BCI competition IV dataset.

关键词： Wavelet transforms Machine learning algorithms software algorithms Feature extraction Prediction algorithms Nervous system Electroencephalography

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Introduction of Spread Field Imaging—a Novel High Performance Collimation for SPECT

Introduction of Spread Field Imaging—a Novel High Performan...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Zhiping Mu Zhong Tao Frederic Fahey Argospect Technologies Inc. Foster City California USA

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Collimation plays a vital role in single photon emission computed tomography systems. Spread field imaging is a class of high-sensitivity, high-resolution collimation and imaging technologies that utilizes shared photon paths to improve collimator performance. In this article we introduce a novel design, called spread field imaging-narrow angle , that shares some characteristics of both parallel-hole and pinhole collimators. In this approach, the holes are long and narrow, similar to the holes in a parallel hole collimator; however, as opposed to parallel-hole collimators, there is space between the holes and the detector. With that spacing, the overall thickness of the collimator is around 7-11 cm. In addition, the holes are not identical, possibly varying in shape, size, etc., and may even include closed holes. The hole-detector spacing provides an extended "virtual" hole length that improves resolution. Holes with wider acceptance angle may be used to achieve higher resolution and sensitivity compared with parallel-hole collimators. An ordered subset expectation and maximization based algorithm has been developed to reconstruct the object image. Simulations were developed with three-dimensional software hot-rod and Hoffman brain phantoms to compare reconstructed results obtained with from spread field imaging collimation with conventional low-energy, high-resolution parallel hole collimators with full, half or a quarter of conventional imaging times. The results showed that the spread field imaging-narrow angle collimator yielded better or similar reconstruction compared with low-energy, high-resolution even when the imaging time was cut to a quarter of the conventional time. These results demonstrate that spread field imaging-narrow angle collimators have the potential to be high-resolution, low-dose collimators applicable to conventional single photon emission computed tomography systems, and suitable for a wide range of clinical applications.

关键词： Collimators Solid modeling Image resolution Sensitivity Shape software algorithms software

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A Modelling & Simulation Tier Design for the EMULSION IoT Platform

A Modelling & Simulation Tier Design for the EMULSION IoT Pl...

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Automatics and Informatics (ICAI), International Conference

作者： Ivan Ganchev Zhanlin Ji Máirtín O’Droma University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria Bulgarian Academy of Sciences Institute of Mathematics and Informatics Sofia Bulgaria College of Artificial Intelligence North China University of Science and Technology Caofeidian Tangshan City China Telecommunications Research Centre University of Limerick Limerick Ireland

This paper ♠ introduces some design aspects of the EMULSION IoT platform, which is an example of the new IoT platforms of horizontal type, evolving as a replacement of the existing vertical-type IoT platforms. The EMULSION’s architectural overview, main characteristics, and multi-tiered structure are presented, with attention dedicated to its modelling & simulation tier. ♠ This publication has emanated from joint research conducted with the financial support of the Bulgarian National Science Fund (BNSF) under the Grant No. (КП-06-ИП-КИТАЙ/1) and the S&T Major Project of the Science and Technology Ministry of China, Grant No. 2017YFE0135700.

关键词： Wireless networks Scalability software algorithms software Real-time systems Sensors Internet of Things

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