2019 27th Iranian Conference on Electrical Engineering (ICEE)最新文献_第9页

Designing SAW Assisted Multi-Mode Plasmonic Sensor SAW辅助多模等离子体传感器的设计

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786459

Mohammad Mahdi Mehrnegar, M. Moravvej-Farshi, S. Darbari

引用次数: 0

Correlation-Based Regularized Common Spatial Patterns for Classification of Motor Imagery EEG Signals 基于相关的正则化公共空间模式运动图像脑电信号分类

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786490

Khatereh Darvish Ghanbar, T. Y. Rezaii, M. Tinati, A. Farzamnia

引用次数: 2

Proposal of an Analytical Model for Performance Evaluation of WR -Resolvers under Short Circuit Fault 短路故障下WR -Resolvers性能评估分析模型的提出

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786676

H. Lasjerdi, Z. Nasiri-Gheidari, F. Tootoonchian

引用次数: 6

Bipolar Resistive Switching of an Al/ZnO/Ti-based Memristor Al/ZnO/ ti基忆阻器的双极电阻开关

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786428

Armin Alizadeh, F. Hossein-Babaei

引用次数: 0

Forward Contract Negotiation Equilibrium in Uncongested Electricity Markets 非拥塞电力市场的远期合同谈判均衡

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786756

M. Banaei, M. Oloomi‐buygi

引用次数: 3

A 5-6.2GHz Variable Bandwidth Frequency Synthesizer for IEEE 802.11ac Applications 一种用于IEEE 802.11ac应用的5-6.2GHz变带宽频率合成器

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786591

M. Nikandish, M. Ehsanian

引用次数: 0

Transfer Learning based Motor Imagery Classification using Convolutional Neural Networks 基于迁移学习的卷积神经网络运动意象分类

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786636

Milad Parvan, A. Ghiasi, T. Y. Rezaii, A. Farzamnia

{"title":"Transfer Learning based Motor Imagery Classification using Convolutional Neural Networks","authors":"Milad Parvan, A. Ghiasi, T. Y. Rezaii, A. Farzamnia","doi":"10.1109/IranianCEE.2019.8786636","DOIUrl":"https://doi.org/10.1109/IranianCEE.2019.8786636","url":null,"abstract":"Nowadays, classification of signals is considered as the crucial role of motor imagery brain computer interface. Moreover, deep learning approaches show acceptable performance in image recognition applications as well as speech recognition. However, practicality of the aforementioned technique is not generally deployed on motor imagery tasks. Hence, the goal of this paper is to apply convolutional neural networks to classify the motor imagery EEG signals. In addition, data augmentation along with excusive transfer learning strategy are used to overcome the problem of few trials in motor imagery tasks. On the other hand, analytical regression assessments are also applied to the raw data for mitigating the stress of EOG on EEG. Consequently, the simulation results clearly convey the contribution of the proposed algorithm via testing on BCI competition IV dataset 2b. Applying EOG artifact removal and data augmentation methods resulted in 0.07 improvement in kappa coefficient. Furthermore, using our proposed transfer learning method led to 0.06 improvement in terms of kappa coefficient.","PeriodicalId":6683,"journal":{"name":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","volume":"83 1","pages":"1825-1828"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78147387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Modeling of Graphene Effect as a Transparent Anode on the Absorption of Perovskite Solar Cells 石墨烯作为透明阳极对钙钛矿太阳能电池吸收的模拟

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786532

Z. Arefinia, M. Vafadar

引用次数: 1

Sensor Single and Multiple Anomaly Detection in Wireless Sensor Networks for Healthcare 医疗保健无线传感器网络中传感器单、多异常检测

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786588

Mozhgan Mohammadi Nezhad, M. Eshghi

引用次数: 10

A Graph Based Approach to Analyse Metabolic Networks for Strain Engineering 基于图的应变工程代谢网络分析方法

2019 27th Iranian Conference on Electrical Engineering (ICEE) Pub Date : 2019-04-01 DOI: 10.1109/IranianCEE.2019.8786434

Leila Hassani, M. Moosavi, P. Setoodeh

{"title":"A Graph Based Approach to Analyse Metabolic Networks for Strain Engineering","authors":"Leila Hassani, M. Moosavi, P. Setoodeh","doi":"10.1109/IranianCEE.2019.8786434","DOIUrl":"https://doi.org/10.1109/IranianCEE.2019.8786434","url":null,"abstract":"Recently, there has been great interest in the field of metabolic engineering. Engineered microorganisms such as bacteria can be exploited to synthesize value-added bioproducts. Conversely, optimal genetic manipulations to reach to operative engineered cells is very costly; hence, regarding whole-cell functionalities, a number of proficient simulation algorithms have been developed and widely applied for systems-level analyses, and as a consequence, systems metabolic engineering. However, most of the currently-used in silico algorithms are usually time-consuming. In other words, it is requisite that computer scientists gain prior knowledge of molecular biology and biochemistry to be able to develop more effective algorithms in this field. Here, in the current study, we regard the metabolic network of interest as a general graph to present algorithmic perspective of the complex metabolic systems. A cell's metabolism is modeled in the form of a metabolic network. This way, the major problems in the field of metabolic engineering can be tackled using graph theory and graph mining algorithms. For this purpose, we reconstruct the metabolic network as a general graph, redefine the metabolic engineering problems accordingly, and finally, solve one of them as a sample.","PeriodicalId":6683,"journal":{"name":"2019 27th Iranian Conference on Electrical Engineering (ICEE)","volume":"24 1","pages":"1839-1843"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76890380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1