2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)最新文献_第6页

Mathematical Models for Extracellular Fluid Measurement to Detect Hydration Level Based on Bioelectrical Impedance Analysis 基于生物电阻抗分析的细胞外液检测水合水平的数学模型

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861841

Mst Farzana Khatun, Md. Sohel Rana, Tahasin Ahmed Fahim, S. T. Zuhori

{"title":"Mathematical Models for Extracellular Fluid Measurement to Detect Hydration Level Based on Bioelectrical Impedance Analysis","authors":"Mst Farzana Khatun, Md. Sohel Rana, Tahasin Ahmed Fahim, S. T. Zuhori","doi":"10.1109/CCECE.2019.8861841","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861841","url":null,"abstract":"This paper shows mathematical models for extracellular fluid (ECF) measurements for both male and female human being based on bioelectrical impedance analysis along with prediction of hydration level. In this research total 2817 (1397 male and 1420 female) data have been used. Age, height, bioelectrical impedance at 5 kHz frequency, body mass index (BMI) are used for the development of mathematical models and the hydration level has been detected by the ratio of ECF to body weight considering standard limit. The proposed models have been analyzed statistically and the results show that the correlation (Pearson) coefficients are $0.999 (mathrm{p}lt 0.001)$ for both male and female individuals which denote excellent matching with actual data. Besides intervals of LOA is only -0.29 L to 0.45 L and -0.64 L to -0.06 L for male and female data respectively and most of the errors follows limit of agreement. The root mean square errors are 0.20 L for male and 0.38 L for female people. The average accuracy of proper detection of hydration level has bound 96.11%. Comparing the results of this research with existing models it is seen that the proposed models can be more suitable for ECF measurement and hydration level detection.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965032","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: 2

Joint Iterative Training-based Hybrid Precoding and Combining for Millimeter Wave Systems 基于联合迭代训练的毫米波系统混合预编码与组合

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861976

Ali Mohebbi, Weiping Zhu, M. Ahmad

引用次数: 1

Accurate Energy Forecast in Buildings: A Data Driven Machine Learning Approach 建筑中准确的能源预测:数据驱动的机器学习方法

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861583

A. Tchagang, Araz Ashouri

引用次数: 0

Generating Heat and Power from Biomass - An Overview 从生物质中产生热能和电力-概述

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE43985.2019.9052399

Gama Ali, M. El-Hawary

引用次数: 1

Skin Lesion Segmentation Based on Improved U-net 基于改进U-net的皮肤病变分割

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861848

Lina Liu, Lichao Mou, Xiaoxiang Zhu, M. Mandal

{"title":"Skin Lesion Segmentation Based on Improved U-net","authors":"Lina Liu, Lichao Mou, Xiaoxiang Zhu, M. Mandal","doi":"10.1109/CCECE.2019.8861848","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861848","url":null,"abstract":"Melanoma is one of the most common and dangerous skin cancers, accounting for 75% of deaths associated with skin cancer. Detection of melanoma in early stages can significantly improve the survival rate. Automatic segmentation of melanoma is an important and essential step for accurate detection of melanoma. Many existing works based on traditional segmentation methods and deep learning methods have been proposed for high-resolution dermoscopy images. However, due to the intrinsic visual complexity and ambiguity among different skin conditions, automatic melanoma segmentation is still a challenging task for existing methods. Among these methods, the deep learning methods have obtained more attention recently due to its high performance by training an end-to-end framework, which needs no human interaction. U-net is a very popular deep learning model for medical image segmentation. In this paper, we propose an efficient skin lesion segmentation based on improved U-net model. Experiments conducted on the 2017 ISIC Challenge dataset towards melanoma detection shows that the proposed method can obtain state-of-the-art performance on skin lesion segmentation task.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127361205","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: 28

Learning from Mistakes: Weakly Supervised Learning of Rocks 从错误中学习:岩石的弱监督学习

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861784

Justin Szoke-Sieswerda, Matt Cross, Leo Van Kampen, K. McIsaac

{"title":"Learning from Mistakes: Weakly Supervised Learning of Rocks","authors":"Justin Szoke-Sieswerda, Matt Cross, Leo Van Kampen, K. McIsaac","doi":"10.1109/CCECE.2019.8861784","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861784","url":null,"abstract":"A standard method for teaching an object detector a new class is to fine-tune it with a fully-supervised image set. The issue with fully-supervised image sets are that they are tedious to create and rely on human annotators. In this work we demonstrate that a new class can be learned by leveraging the ‘mistakes’ of a pre-trained object detector under a weakly supervised learning (WSL) paradigm and removing the need for a fully-supervised image set. Our method iteratively cycles over four stages: observation, filtering, generation, and fine-tuning. The observation stage uses an object detector to gather inferences on an image set containing many instances of the new class. These observed inferences are passed to the filtering stage that keeps only the most frequently observed class inferences. These filtered inferences are used as object-level annotations and are passed to the generation stage. In the generation stage a training set is created by superimposing the image content of the annotations onto a set of background images. The generated training set is then used in the fine-tuning stage to create an updated object detector. We trained an object detector to recognize the novel object class, rock using this method. We compared the average precision obtained by an object detector trained using our method to an object detector trained using the fully-supervised method. We were able to achieve $sim 78$% of the average precision obtained by the fully-supervised version.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124008039","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: 0

Is It Enough to just Rely on Near-End, Middle, and Far-End Points to get Feasible Relay Coordination? 仅仅依靠近端、中端和远端来获得可行的中继协调是否足够?

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861772

Ali R. Al-Roomi, M. El-Hawary

引用次数: 1

Investigation of Performance-Complexity Tradeoff in Filtering LoRa Signals LoRa信号滤波中性能-复杂度权衡的研究

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861909

Shania Stewart, Ha H. Nguyen, Robert Barton, Jérôme Henry

{"title":"Investigation of Performance-Complexity Tradeoff in Filtering LoRa Signals","authors":"Shania Stewart, Ha H. Nguyen, Robert Barton, Jérôme Henry","doi":"10.1109/CCECE.2019.8861909","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861909","url":null,"abstract":"This paper investigates the performance-complexity tradeoff when implementing pulse shaping and matched filters in LoRa communication systems. Since LoRa gateways are expected to serve a large number of end-devices in various Internet-of-Things (IoT) applications, a high degree of spectral efficiency is an important and desirable feature. Filtering with squareroot raised cosine filters can reduce the occupied bandwidth and adjacent channel interference of transmitted signals, while minimizing the amount of inter-symbol interference (ISI) inherently introduced by practical finite-length filters. The LoRa communication system with filtering was simulated and evaluated in terms of the modulation error ratio, occupied bandwidth, and bit-error rate for signals with various LoRa and filter parameters. The obtained spectra of the filtered signals are compared to those of the unfiltered signals generated by a real LoRa device. The results showed that the occupied bandwidth and the amount of ISI experienced by LoRa signals can be reduced with no degradation in the bit-error rate. Provided that the additional filter is designed and implemented efficiently, filtering is an excellent method to enhance the performance of LoRa devices.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114276238","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

CCECE 2019 Papers by Title CCECE 2019论文标题

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/ccece.2019.8861737

引用次数: 0

Event-Triggered State Estimation of High Dimensional Nonlinear Systems With Highly Nonlinear State Space Model Using Cubature Kalman Filter 高度非线性状态空间模型下高维非线性系统的事件触发状态估计

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861943

Marzieh Kooshkbaghi, H. Marquez

引用次数: 2