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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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FPGA-Based advanced Real Traffic Light Controller System Design

FPGA-Based Advanced Real Traffic Light Controller System Des...

引用

ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： WM El-Medany MR Hussain CE Department IT College UOB Bahrain Spatial Information Research Geomatics ApS Manama Bahrain

Traffic light controller establishes a set of rules and instructions that drivers, pilots, train engineers, and ship captains rely on to avoid collisions and other hazards. Traffic control systems include signs, lights and other devices that communicate specific directions, warnings, or requirements. Traffic light controller (TLC) has been implemented using microcontroller FPGA, and ASIC design. FPGA has many advantages over microcontroller, some of these advantages are; the speed, number of input/output ports and performance which are all very important in TLC design, at the same time ASIC design is more expensive than FPGA. Most of the TLCs implemented on FPGA are simple ones that have been implemented as examples of FSM. This paper concerned with an FPGA design implementation of a low cost 24-hour advanced traffic light controller system that was built as a term project of a VLSI design subject using VHDL. The implemented traffic light is one of the real and complex traffic lights in Kingdom of Bahrain, for four roads and motorway with sensors and camera. The system has been successfully tested and implemented in hardware using Xilinx Spartan 3 FPGA. The system has many advantages over the exciting TLC.

关键词： Lighting control Control systems Field programmable gate arrays Communication system traffic control Microcontrollers Application specific integrated circuits Marine vehicles Road accidents Hazards Traffic control

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A Multi-level intelligent Selective Encryption Control Model for Multimedia Big data Security in Sensing System with Resource Constraints

A Multi-level Intelligent Selective Encryption Control Model...

引用

ieee International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Chen Xiao Lifeng Wang Zhu Jie Tiemeng Chen Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Electronic and Information Engineering Beijing Electronic Science and Technology Institute Beijing China

ISBN: (纸本)9781509009473

The multimedia big data in multimedia sensing and other IoT (Internet of Things) systems are high-volume, real-time, dynamic and heterogeneous. These characteristics lead to new challenges of data security. When computation and power resources in some IoT nodes are very scarce, these challenges become more serious that complex data security process on multimedia data is restricted by the aforementioned limited resources. Hence, the confidentiality of multimedia big data under resources constraints is investigated in this paper. Firstly, the growth trend of data volume compared with computational resources is discussed, and an analysis model for multimedia data encryption optimization is proposed. Secondly, a general-purpose lightweight speed tunable video encryption scheme is introduced. Thirdly, a series of intelligent selective encryption control models are proposed. Fourthly, the performance of proposed schemes is evaluated by experimental analyses and proves that schemes are effective enough to support real-time encryption of multimedia big data. Additionally, in the age of big data and cloud computing, the aforementioned analysis method can also be applied to other systems with limited resources.

关键词： Encryption Multimedia communication Streaming media Internet of things data models

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Using data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil

Using Data Analysis Methods for Predicting the Concentration...

引用

ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Lyazat Naizabayeva Gulnara Zakirova International Information Technology University Almaty Kazakhstan

This article is aimed at the selection of machine learning algorithms for the perception of elements in the composition of the soil. In our study, machine learning aims to track changes in the toxicity of contaminated soils in order to predict the level of human health hazard of a contaminated soil condition. For this, the following tasks were set: 1) collection and analysis of data on the types of soil toxicity; 2) study of a mathematical model of toxicity to assess the level of resistance; 3) testing the concentration of toxic elements in soils using four machine learning algorithms. Four machine learning algorithms were tested in the article to predict the content of heavy metals in the soil. The results show that the use of machine learning algorithms allows achieving a high degree of prediction of harmful elements concentrations in soils, which can be useful for making decisions on managing pollution sites. Further improvement of data accuracy and selection of the model and algorithm can also increase the accuracy of prediction. As a result of forecasting using the K-Nearest Neighbors algorithm, the accuracy of the model was achieved at the level of 68%, and using the Decision Tree - 69%. The results of the experiment make it possible to predict the measures of danger to human health from the state of contaminated soil. This will help reduce the risks of the relationship between soil pollution and human health. The obtained results can be useful for specialists in environmental protection, land resource management, and decision-making.

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Attribute reduction algorithm based on indistinguishable degree

Attribute reduction algorithm based on indistinguishable deg...

引用

ieee International Conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Baohua Liang Yong Liu Chenyang Shen The University Key Disciplines based on Recongnition and Intelligent Systems Hefei Anhui China Information Engineering Institute of Chaohu University Hefei Anhui China

In order to overcome the shortcomings of computing attribute reduction based on discernibility matrix, an indistinguishable degree definition and the corresponding properties are introduced. By utilizing indistinguishable degree to depict the relative importance of attribute in information system, it proposes an effective indistinguishable degree algorithm for attribute reduction, which does not store discernibility matrix. In addition, an effective distinguish forest algorithm for attribute reduction is proposed to enhance the intuition about algorithm, and the effect about dig process. Besides, the example and experiments are used to illustrate the efficiency and feasibility of the proposed algorithm.

关键词： Forestry Vegetation Classification algorithms Electronic mail Rough sets Kernel Sorting

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Federated Learning Empowered Resource Allocation in UAV-Assisted Edge intelligent systems

Federated Learning Empowered Resource Allocation in UAV-Assi...

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Computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Bintao Hu Matilda Isaac Olukunle Mobolaji Akinola Hadyan Hafizh Wenzhang Zhang School of Internet of Things Xi’an Jiaotong-Liverpool University suzhou china

Mobile edge computing (MEC) has been considered a promising advanced technology to support delay-sensitive tasks of user equipment (UE) in the internet of things (IoT) systems, it is necessary to allow multiple UEs to offload their computationally intensive tasks to a flexible edge computing server, such as an unmanned aerial vehicle (UAV)-assisted edge computing server. However, most existing works mainly focused on minimising energy consumption under the transmission and/or processing delay constraints while ignoring privacy-preserving, which will be challenging when dealing with large volumes of raw data. In this paper, we consider a federated learning (FL) empowered UAV-assisted edge intelligent system to minimise the maximum utility cost (which indicates the relationship between latency and energy consumption) to the selected UE for task processing. We propose to jointly optimise the FL task offloading decisions among all UEs and the communication resource allocation under each epoch. This is achieved by devising a federated learning-based edge intelligence offloading decision optimisation algorithm (FEOA). Simulation results show that our proposed schemes outperform the benchmarks in terms of the maximum cost efficiency among all UEs.

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data-driven indexed hypotheses for distorted shape matching

Data-driven indexed hypotheses for distorted shape matching

引用

ieee International Workshop on Robot and Human Communication (ROMAN)

作者： S. Deb D.D. Majumder National Centre for Knowledge-Based Computing Indian Statistical Institute Calcutta India

The problem of identification of multiple flat objects in a cluttered environment is dealt with. The objects become distorted due to occlusion, noise and other spurious effects. Unlike the previous model-based approaches involving exhaustive searching, this paper utilizes a data-driven indexing mechanism for model retrieval. Groups of consecutive segments, called super segments, are used as boundary descriptors. The whole approach is based on attributed string matching. With this technique model library can be modified as per requirement.< >

关键词： Shape Robot sensing systems intelligent robots intelligent sensors Image segmentation Humans Uncertainty Robot vision systems Machine vision Biosensors

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intelligent computing for demonstration-based robot learning

Intelligent computing for demonstration-based robot learning

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ieee/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Wei-Po Lee Tsung-Hsien Yang Jih-Shiou Jong Department of Information Management National Sun Yat-Sen University Taiwan

ISBN: (纸本)9781424477081

Robot learning is an efficient strategy to achieve control task without explicit programming. In this work, we present a learning framework in which the robot is taught how to perform desired behaviors through a demonstration procedure. During the demonstration, the behavior sequences are recorded and transferred to time series data, and intelligent computing methods are proposed to learn the behaviors from the data. To assess the performance of the proposed framework, different sets of experiments have been conducted and the results show that our framework can be used to learn robot behaviors efficiently and successfully.

关键词： Robot sensing systems Robot kinematics Trajectory Legged locomotion Leg Feature extraction

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Gas Discrimination & Quantification using Sensor Array with 3D Convolution Regression Dual Network

Gas Discrimination & Quantification using Sensor Array with ...

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ieee International Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Smart sensor system design requires intelligent data processing, which analyzes raw time-series sensor data to efficiently and precisely discriminate and quantify target gases. The work presented here utilizes the response of twin gas sensor arrays for gases such as ethanol, ethylene, methane, and carbon monoxide to discriminate and quantify the target gases. We propose a 3D convolution neural-based regression dual network (3D-CNrdN) for both gas quantification and discrimination. The spatiotemporal correlation of sensor array responses inspired us to design the deep neural network for the gas concentration estimation model. The sensor array set is spatially correlated, and all the twin array responses are temporally related. 3D-CNrdN uses raw time-series gas sensor array data. The data is fed to the network as the 3D pattern, which contains 2D spatial information varying with third dimension time to recognize patterns that eventually predict the concentration. The model evaluation shows that the proposed methods are an effective technique for gas quantification and identification with $\text{RMSE}=0.3179$ and classification accuracy 94.37%. Furthermore, the proposed method outperforms and provides higher discrimination accuracy and lower RMSE than other machine learning and deep learning methods.

关键词： Deep learning Gases Three-dimensional displays Convolution Neural networks Spatiotemporal phenomena Pattern recognition

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Deploying MIB data Mining for Proactive Network Management

Deploying MIB Data Mining for Proactive Network Management

引用

International ieee Conference on intelligent systems, IS

作者： P. G. Kulkarni S. I. McClean G. P. Parr M. M. Black School of Computing and Information Engineering University of Ulster Coleraine Northern Ire-land

Due to increasing reliance on computer communication networks, it is highly desirable that networks should have the ability to detect symptoms of oncoming exception conditions and take measures to prevent them thereby enabling a degree of proactive network management that underpins an acceptable quality of service. This paper proposes a framework for achieving congestion avoidance through proactive network management using data mining. It examines the inter-relationships between network element management information base (MIB) attributes, queue parameters (associated with a transmission link) and the level of congestion at a network node and identifies hybrid parameters that have a bearing on congestion. By employing data mining on the data pertaining to these variables, congestion at the network node can be predicted. Results from our initial experimentation with particular data mining models show that the accuracy achieved is as high as 98% in all of the cases thus rendering data mining a viable approach to proactively identity network exception conditions

关键词： data mining Computer network management Neural networks intelligent systems Conference management Quality management Communication networks Performance analysis Analytical models Computational intelligence

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Research on Enterprise Information Transmission System under Computer Big data Technology

Research on Enterprise Information Transmission System under...

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Power Electronics, Computer Applications (ICPECA), ieee International Conference on

作者： Xiuyun Lu Zhenjiang Vocational Technical College Zhenjiang China

This paper studies and designs a standard networked data acquisition and transmission system based on OPC standard and Web Services technology. The system establishes master and slave stations for FMS equipment, and conducts communication test between master station and slave station. The OPCUA server is used to store the collected and converted real-time data and interact with the OPCUA client, and at the same time upload the converted data to the cloud storage system for further data analysis. The cloud combines Redis and MySQL to realize the storage of running data. Finally, the feasibility and integrity of the proposed system are verified by experiments.

关键词： Cloud computing Frequency modulation Web services data acquisition Information processing Maintenance engineering Real-time systems

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