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检索条件"任意字段=2022 3rd International Conference on Signal Processing and Computer Science, SPCS 2022"

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Task Scheduling Scheme for Mobile Edge Computing via Bayesian Classifier 3

Task Scheduling Scheme for Mobile Edge Computing via Bayesia...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Wang, Yu Liu, Shixi School of Computer Science and Technology Anhui University Anhui Province Hefei230000 China School of Informatics Chuzhou University Anhui Province Chuzhou239000 China

The enormous computing tasks in the Mobile Edge Computing (MEC) beget challenges correlated with cost, energy consumption and quality of service. However, existing studies are not optimized enough for MEC in terms of the selection of task classification and scheduling schemes. Inspired by spam classification idea, a novel task scheduling scheme is proposed for MEC via Bayesian Classifier. The MEC tasks are first categorized into three priority categories using Naive Bayes. In addition, the priority-based classification task scheduling (PCTS) scheme is proposed to schedule the classified tasks with different priorities. Finally, the experimental evaluation is conducted using three metrics that include latency, processing time, and energy consumption. Simulation results show that the proposed scheme outperforms the baseline schemes in accordance with completion time and throughout rate. © Published under licence by IOP Publishing Ltd.

关键词： Mobile edge computing

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Hybrid Combination of Machine Learning Techniques for Diagnosis of Liver Impairment Disease in Clinical Decision Support System 3rd

Hybrid Combination of Machine Learning Techniques for Diagno...

引用

3rd international conference on Machine Intelligence and signal processing, MISP 2021

作者： Ganu, Likha Arun, Biri Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Yupia791110 India

ISBN: (纸本)9789811908392

Nowadays, liver impairment is one of the most complicated diseases in the world which is increasing very rapidly. Around 50 million adult population in the world are highly affected by cirrhosis liver impairment which is the 12th leading cause of death in 2020. In this paper, we survey and compare various machine learning techniques that help the clinical specialist to determine the risk of liver impairment and will aid the physician in the process of making decision. This paper will conduct a hybrid combination of machine learning techniques and was compared to other machine learning algorithms to determine the best performance. To this end, the study is carried out on the Indian liver patient dataset (ILPD). Firstly, data preprocessing is done to eliminate the irreverent feature and to identify the relevant feature using K-medoids clustering-based attribute weighting (k-MCAW), and for feature selection, PCA is integrated, and for classification, SVM is being carried out. The proposed hybrid method (k-MCAW + PCA + SVM) classifies the dataset into healthy or unhealthy classes. Then the performance is predicted by using the SVM classification technique in terms of accuracy, sensitivity analysis, F-measure, specificity, kappa statics value. The result shows that our purposed hybrid model is a prominent tool for valuable tool for the clinical decision support system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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Classification Prediction Model Based on BP Neural Network 3

Classification Prediction Model Based on BP Neural Network

引用

2021 3rd international conference on computer, Communications and Mechatronics Engineering, CCME 2021

作者： Jiang, Da Liu, Changhua Ou, Zuhao School of Mathematics and Computer Science Wuhan Polytechnic University China

In this paper, a method of plant image processing combined with BP neural network was proposed to predict the disease degree of brassica napus. First, the initial image preprocessing, the roots of brassica napus. were used by Otsu threshold segmentation algorithm is combined with morphological open operation, segment the rape root image with better effect, in order to reduce image size inconsistent on the effects of extracting feature parameters, can be reconstructed images after segmentation, and then obtain the root image multiple characteristic parameters, Through chart correlation analysis to select the feature parameter information, with the characteristics of the rape root disease was positively associated with the degree of disease information to refactor pixel area and the longest diameter, finally establish the BP neural network, two characteristic parameters extracted as input, root disease degree level as the output, training network and prediction and comparison. The experimental results showed that the mean square error of the classification network for brassica olerica was 0.049, and the accuracy rate of the classification results predicted by the model was about 93%. The prediction effect was good and feasible. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Research on Bulk Acoustic Wave Resonator Based on Phononic Crystal 3

Research on Bulk Acoustic Wave Resonator Based on Phononic C...

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2022 3rd international conference on Electronic Engineering and signal processing, EESP 2022

作者： Shuang, Wang College of Science Jiamusi University Jiamusi China

With the continuous advancement of information technology and the rapid development of mobile communication technology, from smart home to automatic driving of cars, and then to the application and development of virtual reality, communication technology has evolved from the earliest communication between people to communication between things. The frequency band supported by smart phones and various small mobile terminals is increasing, and their communication frequency is also gradually increasing, which brings some challenges to the traditional RF front-end devices. The traditional RF filter can not meet the current market demand because of its own performance and size constraints. As the best option to integrate with RF circuit or microwave circuit, bulk acoustic wave filter has great development and application prospects in modern RF communication system. This paper will focus on the solid-state assembled resonators based on one-dimensional and two-dimensional phononic crystals, analyze the influence and change law of the performance of bulk acoustic wave devices based on two-dimensional phononic crystals, then explore the main loss mechanism existing in solid-state assembled devices, and propose improvement schemes, which lays a foundation for the application of solid-state assembled resonators in today's RF front-end. © Published under licence by IOP Publishing Ltd.

关键词： Automation

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Dual-Band Pentagonal-Shape Hybrid Rectangular Dielectric Resonator Antenna for C-Band Application 3rd

Dual-Band Pentagonal-Shape Hybrid Rectangular Dielectric Res...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Ranjan, Pinku Patil, Mihir Sharma, Anand Atal Bihari Vajpayee-Indian Institute of Information Technology and Management Gwalior India Electro Science Laboratory Ohio State University ColumbusOH United States Department of Electronics and Communication Engineering MotiLal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9789811627606

Design and analysis of pentagonal antenna with rectangular dielectric resonator antennas for X-Band application has been presented in this paper. It is a dual-band antenna resonating at 3.72–5.8 GHz and 6.38–8 GHz, with total impedance bandwidth of 3.7 GHz, which is resonating at 4.42 GHz and 7.63 GHz, respectively. The dimension of the antenna is 30 × 30 × 1.6 mm3, and dimension of rectangular DR is 13 × 13 × 6 mm3 placed on the pentagonal patch. The DR is made up of Al2O3 (alumina ceramic). Since this antenna is designed for wideband applications, the DR is placed to increase the impedance bandwidth of the proposed antenna. The radiation efficiency greater than 95% and the gain of 3.13 dBi are achieved by the proposed antenna. It is a compact and low-profile antenna. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Radiation efficiency

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Low-Area, High-Throughput Field-Programmable Gate Array Implementation of Microprocessor Without Interlocked Pipeline Stages 3rd

Low-Area, High-Throughput Field-Programmable Gate Array Impl...

引用

3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： Sikka, Prateek Asati, Abhijit R. Shekhar, Chandra Electrical and Electronics Engineering Department Birla Institute of Technology and Science Vidya Vihar Campus Rajasthan Pilani India

ISBN: (纸本)9789811627606

Microprocessors without interlocked stages (MIPS) are based on the reduced instruction set computer architecture. These processors have been in use for years and remain in wide use today in applications such as automation, processing, and communication. Many of these applications must be run on smaller, low-cost target boards with limited resources. Field-programmable gate arrays (FPGAs) are also gaining importance in the very large scale integration design flows because their parallel architecture makes them very fast. Some low-cost FPGAs also offer limited on chip resources such as lookup tables and flip flops. Furthermore, high-level synthesis is gaining popularity among designers because it offers a higher level of design abstraction along with continued verification during the design flow. In this paper, we propose a low-area MIPS processor with high throughput. We use high-level synthesis to generate register transfer level code for the 32-bit MIPS core and target the same for Virtex 7 FPGA. We optimize the design for area and performance using selected high-level synthesis directives, which produces results superior to those reported in the literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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Meta-heuristic Algorithm for Energy-Efficient Task Scheduling in Fog Computing 3rd

Meta-heuristic Algorithm for Energy-Efficient Task Schedulin...

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3rd international conference on VLSI, Communication and signal processing, VCAS 2020

作者： AL-Amodi, Saeed Patra, Sudhansu Shekhar Bhattacharya, Suman Mohanty, Jnyana Ranjan Kumar, Vinay Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Applications KIIT Deemed To Be University Bhubaneswar India CAAS KIIT Deemed To Be University Bhubaneswar India Department of ECE MN National Institute of Technology Allahabad India

ISBN: (纸本)9789811627606

Due to the rapid growth of the Industrial IoT (IIoT), social media, digitization, and wireless communication technology in various sectors, the volume of data is increasing very rapidly. For handling and processing of the huge volume of data, cloud computing is an emerging solution with the assistance of fog computing. It is a soars of means to improve the quality of services provided to users through cloud computing, which has being more overwhelmed by the massive flow of data. Transmitting all the data to the cloud and getting back from cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is a sufficient amount of energy required in the fog layer which is promising area to be handled by the cloud service providers. An important factor which contributes to the energy consumption in fog servers is the task scheduling. In this paper, we proposed an energy saving task scheduling algorithm based on a meta-heuristic named Harris Hawks optimization technique to improve the QoS in parallel with service level agreement (SLA). The suggested algorithm outperforms in comparison to the other existing algorithms such as PSO, TLBO in terms of energy consumption and other QoS parameters. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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Design of XSRP-based automatic identification system for Analog modulated signals 3

Design of XSRP-based automatic identification system for Ana...

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3rd international conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Xu, Jiayue Ni, Ming Zhu, Danjiang Ding, Xiyao East China Institute of Computer Technology Shanghai201800 China College of Intelligence Science and Technology National University of Defense Technology Changsha410000 China

ISBN: (纸本)9781510668867

The automatic identification of the modulation mode of the communication signal is the main task of the intelligent receiver, which is now widely used in a variety of fields, including civil and military. In this paper, an XSRP-based analog modulated signal automatic identification system is designed, and the overall program block diagram is drawn using LabVIEW software. The design of the automatic identification system is divided into seven modules, focusing on the following two modules: the first is the extraction of each feature parameter when the modulated signals of AM, DSB, SSB and FM are subdivided layer by layer, and the second is the simulation of modulation mode prediction using the library function of MATLAB BP neural network algorithm. Simulations were carried out in conjunction with the XSRP software radio platform, with suitable parameters and sample sizes selected for validation, and improvements made to feature parameters to improve system performance. computer simulations show that the XSRP-based analogue modulated signals are eventually identified correctly at no less than 95% and can be expected to be used for the detection and fast identification of signals in practical non-collaborative communication systems. © 2023 SPIE.

关键词： MATLAB

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Application of Nonstationary Time Series Prediction to Shanghai Stock Index Based on SVM 22

Application of Nonstationary Time Series Prediction to Shang...

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3rd Asia-Pacific conference on Image processing, Electronics and computers, IPEC 2022

作者： Yang, Chun Ou, Kaiman Hong, Shaoyong School of Accounting Guangzhou Huashang College Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China

ISBN: (纸本)9781450395786

With the development of computer software and hardware system, machine learning methods are more and more used in various industries of social development. In the aspect of stock index prediction, the current prediction method has gradually changed from the traditional statistical analysis method to the artificial intelligence analysis method. Based on the original sample data, this paper uses support vector machine regression (SVR) model to predict the opening price of Shanghai stock index. The parameters of SVR model are optimized and debugged by grid search method (grid), particle swarm optimization (PSO) and genetic algorithm (GA). The analysis results show that the three types of support vector machine prediction models based on the original sample data can fully reflect the time-varying law of stock index and have high prediction accuracy. Among them, genetic algorithm support vector machine regression (GA-SVR) model shows that the minimum root mean square error (RMSE) is 14.730 and the minimum average absolute percentage error (MAPE) is 0.375%. GA-SVR model has good prediction effect and has certain significance for the prediction of stock price. © 2022 ACM.

关键词： Genetic algorithms

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