2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)最新文献_第4页

Feature extraction of heart sounds using velocity and acceleration of MFCCs based on support vector machines 基于支持向量机的mfccc速度和加速度心音特征提取

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257736

M. Azmy

引用次数: 4

Configurable-accuracy approximate adder design with light-weight fast convergence error recovery circuit 具有轻量化快速收敛误差恢复电路的可配置精度近似加法器设计

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257753

Khaled Al-Maaitah, Issa Qiqieh, A. Soltan, A. Yakovlev

{"title":"Configurable-accuracy approximate adder design with light-weight fast convergence error recovery circuit","authors":"Khaled Al-Maaitah, Issa Qiqieh, A. Soltan, A. Yakovlev","doi":"10.1109/AEECT.2017.8257753","DOIUrl":"https://doi.org/10.1109/AEECT.2017.8257753","url":null,"abstract":"Approximate computing has recently introduced a new era of low-power and high-speed circuit designs. Recent efforts in the domain of configurable-accuracy approximate designs have proposed substantial performance gains and energy savings by allowing performance-energy-accuracy trade-offs. In this paper, we propose a configurable-accuracy approximate adder with new light-weight error detection technique. This is followed by significance-driven error correction stages during run-time. The correction starts by recovering the higher magnitude errors at premier correction stages, which results in fast convergence and higher precision outputs. Compared to other equivalent approximate adders, the proposed design has drastically reduced the logic counts used for error detection process; hence, achieving lower overhead of silicon area and improving the energy-efficiency of the adder design with faster convergence to the exact results. A number of different bit-widths of the proposed adder (32-bit to 256-bit) are designed in Verilog and synthesized using Synopsys Design Compiler. Our post-synthesis experiments showed significant reductions of 12% and 10% for Dynamic and Leakage Power respectively, and 8% in the silicon area for the design with full correction stages. Moreover, the proposed adder with large bit-widths has reserved these reduction ratios while presenting better scalability overhead. Additionally, our low overhead proposed design has presented the chance to be improved in terms of increasing accuracy to reach 100% exact results as accurate conventional adder at the final correction stage.","PeriodicalId":286127,"journal":{"name":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386976","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: 7

A new visual tool to improve the effectiveness of teaching and learning CPU scheduling algorithms 提高CPU调度算法教学效果的一种新的可视化工具

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257759

Yosef Jbara

引用次数: 4

Use filtering techniques to improve the accuracy of association rules 使用过滤技术来提高关联规则的准确性

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257773

Zainab Darwish, M. Al-Akhras, Mohamed Habib

{"title":"Use filtering techniques to improve the accuracy of association rules","authors":"Zainab Darwish, M. Al-Akhras, Mohamed Habib","doi":"10.1109/AEECT.2017.8257773","DOIUrl":"https://doi.org/10.1109/AEECT.2017.8257773","url":null,"abstract":"Association rules' learning is a machine learning technique used to find interesting relations among data items and is a base to build an association rules classifier. The accuracy of the classifier highly depends on the quality and accuracy of data items. This accuracy can be affected negatively by noisy instances and this may lead to classification overfitting. This work investigates overcoming this problem by applying DROP3 or ALLKNN filtering algorithms to the datasets prior to generating association rules and building a classifier. Several experiments and comparisons were conducted to test the accuracy of the above filtering algorithms. The experiments were conducted on three noise levels: 0%, 5% and 10%. Results were more promising with ALLKNN as accuracy has improved remarkably especially with high noise ratios. With ALLKNN, average classification accuracy for the eight datasets in the 0% noise case improved from 70.47% to 73.63% compared to the base case when ALLKNN was not used. This improvement in classification accuracy was more apparent with the increase in noise ratio. In the 5% noise ratio the accuracy improved from 66.08% to 76.17%. In the 10% noise ratio the accuracy improved from 59.89% to 75.68%.","PeriodicalId":286127,"journal":{"name":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130818647","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

Embedded system implementation for material recognition using deep learning 利用深度学习实现材料识别的嵌入式系统

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257769

K. Younis, Waed Ayyad, Abdallah Al-Ajlony

引用次数: 5

A measurement study of internet services in Jordan: A case study 约旦互联网服务的测量研究:一个案例研究

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257752

M. Masoud, Yousef Jaradat, Ismael Jannoud, Esra’a A. Bashayreh

引用次数: 0

Landmark detection from sidescan sonar images 侧扫描声纳图像的地标检测

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257760

M. Al-Rawi, A. Galdran, Fredrik Elmgren, Jonathan Rodriguez, J. Bastos, M. Pinto

{"title":"Landmark detection from sidescan sonar images","authors":"M. Al-Rawi, A. Galdran, Fredrik Elmgren, Jonathan Rodriguez, J. Bastos, M. Pinto","doi":"10.1109/AEECT.2017.8257760","DOIUrl":"https://doi.org/10.1109/AEECT.2017.8257760","url":null,"abstract":"Sidescan sonars have seen wide deployment in underwater imaging. They can be used to image the seabed to a rather acceptable resolution from a few centimeters to 10 centimeters. Yet, sonar images are still of a substantially lower visual quality as they suffer from quite a few problems, e.g., acoustic shadows that vary according to vehicle heading and sonar grazing angle, speckle noise, geometric deformation due to ping variation and speed of vehicle carrying the sonar, etc. Landmark detection in sidescan sonar images is vital to find objects and locations of interest that are useful in various underwater operations. The objective of this work is proposing novel landmark detection methods for this class of images. Cubic smoothing spline fitted to the across-track signals is proposed as a method to detect the objects and their shadows. To cover a large area, experimental data has been acquired during missions performed in Melenara Bay (Las Palmas/Spain) using autonomous underwater vehicles (AUVs) equipped with Klein 3500 sidescan sonar. The AUVs have been deployed in two missions (one mission performed each day) and a total of 25 large-resolution images have been acquired. The AUV generated 12 parallel path images in the first mission and 13 parallel path images in the second mission with an angle of 70 degrees between the direction of mission #1 and mission #2. This difference in the directions of the two missions was necessary to ensure different acoustic shadows between the two sets of images, each set being generated from a different mission. Results show that the proposed methods are powerful in detecting landmarks from these challenging images.","PeriodicalId":286127,"journal":{"name":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132737217","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: 3

The effect of diacritization on Arabic speech recogntion 变音符化对阿拉伯语语音识别的影响

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257758

Fawaz S. Al-Anzi, Dia AbuZeina

{"title":"The effect of diacritization on Arabic speech recogntion","authors":"Fawaz S. Al-Anzi, Dia AbuZeina","doi":"10.1109/AEECT.2017.8257758","DOIUrl":"https://doi.org/10.1109/AEECT.2017.8257758","url":null,"abstract":"Arabic automatic speech recognition (ASR) is a successful application of natural language processing (NLP). However, Arabic formal text is generally written without diacritics, which produces different pronunciation forms. That is, the Arabic writing system allows discarding short vowels and, hence, forcing the reader to use the prior knowledge and the words context to infer the missing diacritics. For speech recognition, there are two options for textual training data; either diacritized (also called vowelized) or non-diacritized text. However, using non-diacritized text may introduce a challenge for Arabic ASR as missing the short vowels may lead to some confusion in the learning process. This ambiguity produces a less than optimal acoustic model that is one of the most important components of ASR systems. In this paper, we present the performance using diacritized and non-diacritized text. In the experiments, we used the Carnegie Mellon University (CMU) PocketSphinx speech recognizer. We also used a new “in house” modern standard Arabic (MSA) continuous speech corpus that contains 13.5 hours for training and 4.1 hours for testing. The text of the corpus was manually diacritized. For acoustic modelling, we used the phonetic tied-mixture (PTM). The experimental results show that the non-diacritized text system scored 76.4% (i.e. 1-word error rate (WER)) while the diacritized text based system scored 63.8%. Even the diacritized case has less accuracy due to the slight differences in diacritics; however, the non-diacritized case might be adequate and faultless for the Arabic native speakers.","PeriodicalId":286127,"journal":{"name":"2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131969558","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: 4

UWB antenna with corners modified patch 带转角修改贴片的超宽带天线

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257745

A. A. Shareef, Sari F. Amle, N. Awad, M. K. Abdelazeez

引用次数: 2

Heart sounds recognition using multifractal detrended fluctuation analysis and support vector machine 基于多重分形去趋势波动分析和支持向量机的心音识别

2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) Pub Date : 2017-10-01 DOI: 10.1109/AEECT.2017.8257742

M. Azmy, R. Mohamady

引用次数: 4