2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)最新文献_第3页

Audio Signal Processing and Musical Instrument Detection using Deep Learning Techniques 使用深度学习技术的音频信号处理和乐器检测

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691427

S. Elghamrawy, Shehab Edin Ibrahim

{"title":"Audio Signal Processing and Musical Instrument Detection using Deep Learning Techniques","authors":"S. Elghamrawy, Shehab Edin Ibrahim","doi":"10.1109/JAC-ECC54461.2021.9691427","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691427","url":null,"abstract":"The advance of deep learning and audio signal processing techniques has led to serious development on Musical Information retrieval (MIR). Effective audio processing can improve speed, reduce errors, and sometimes increase the accuracy of detecting musical instrument. Spectrographic data is also necessary for many mathematical tools common across Musical Information retrieval. A major aspect of MIR is the categorization of pieces of music. One of the main tools used for categorization tasks in recent years is deep learning, which has led to many advancements in MIR. One such categorization task that deep learning is useful for is the recognition of instruments in a piece of music. In this paper, a new architecture is proposed for audio processing and musical instrument detection using Multilayer Perceptron (MLPs), Convolution Neural Networks (CNN), and Recurrent Neural Networks - Long Short Term Memory (RNN-LSTM). In addition, a number of experiments are implemented using real dataset that contains 20,000 recording. The three deep learning techniques are implemented and compared to present potential new solutions. The usage of processing techniques unique to the field of deep learning is also discussed.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132305022","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

Autonomous Detection of Stair Dimensions for the Motion Planning of Stair Climbing Robots 面向爬楼梯机器人运动规划的楼梯尺寸自主检测

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691423

Yasser Ashraf, Ahmed Abdallah, Abdelhaleem Osman, Ezz El-Din Nehad, M. Fanni

引用次数: 0

FPGA Based Hardware Accelerator for Sorting Data 基于FPGA的数据排序硬件加速器

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691432

Maher Abdelrasoul, Ahmed Sayed Shaban, H. Abdel-Kader

引用次数: 1

Missing Value Imputation for Remote Healthcare Data: A Case study of Portable Health Clinic System 远程医疗数据缺失值的估算:以移动医疗诊所系统为例

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691308

Yosuke Imamura, N. Abedin, Lu Sixian, Shaira Tabassum, Ashir Ahmed

{"title":"Missing Value Imputation for Remote Healthcare Data: A Case study of Portable Health Clinic System","authors":"Yosuke Imamura, N. Abedin, Lu Sixian, Shaira Tabassum, Ashir Ahmed","doi":"10.1109/JAC-ECC54461.2021.9691308","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691308","url":null,"abstract":"This study aims to investigate the best method for imputing missing values in remote healthcare data set. Missing value means an empty field in a health record. It may occur for three major reasons- (i) the parameter was not measured (ii) measured but not recorded and (iii) lost during communications. Our case study, Portable Health Clinic (PHC) data has been collected from multiple regions, by different authorities in different time. PHC data contains manual errors too. Missing and erroneous data are problematic for data analysis and for making accurate predictions. Hence, it is necessary to detect and eliminate error data and also fill the empty fields. Missing value imputation methods are widely known for processing numerical data. PHC data has both numerical and categorical data which makes it difficult to impute. We came up with a new data processing mechanism to feed into existing machine learning algorithm. To test our idea, we used a complete PHC data set (numerical only) without any missing values. Then we generated missing values by randomly erasing a part of the data set. We used several existing imputation methods and our proposed method on the same target data set to compare their performances. It is found that the Mean Imputer, kNN and MissForest are not effective. Iterative Imputer predicted best in 7 features and ours in 4 cases. Therefore, it can be concluded that the effectiveness of imputation methods may vary depending on the specific data set and features. Our future work is to include the categorical data and monitor the performance.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121345289","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

Binary Block-Based Neural Network Decoder for F-GSM Signal Detection 基于二进制块的神经网络解码器用于F-GSM信号检测

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691454

Amer Ahmed H. Albarqi, Hany S. Hussein

{"title":"Binary Block-Based Neural Network Decoder for F-GSM Signal Detection","authors":"Amer Ahmed H. Albarqi, Hany S. Hussein","doi":"10.1109/JAC-ECC54461.2021.9691454","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691454","url":null,"abstract":"Fully-generalised spatial modulation (F-GSM) was presented as an energy and spectral efficiency modulation scheme. In F-GSM, almost all possible transmitted antenna indices combinations are utilized in the data transmission process, which improves its spectral and energy efficiency on one side. On the other side, this adds more challenges to the F-GSM decoder due to using maximum Likelihood Estimator (MLE). Although MLE achieves optimum performance, its computational complexity (CC) increases exponentially with the F-GSM achievable rate. Therefore, this paper proposes a simple binary block-based neural networks (BBNN) F-GSM decoder with lower CC. In particular, a simple binary classifier neural network architecture is proposed to detect the activation status independently of each antenna or spatial bit, which reduces the offline training time and required training data size. Moreover, it makes the F-GSM decoder more reliable and improves adaptation capability when the number of antennas is changed. After that, a low complexity Euclidean distance-based estimator is used to detect the signal constellation. Simulation results show that the bit error rate (BER) performance of the proposed BBNN decoder outperforms the performance of the conventional block zero-forcing (BZF) and block minimum mean squared error (BMMSE) systems. In contrast, it archives a comparable ABER to that of traditional MLE but with lower CC, as it achieves on average ~81.5 % CC reduction compared to MLE.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893184","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

Detection of Micro Movements of Facial Muscles in Facial Videos 人脸视频中面部肌肉微运动的检测

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691309

G. Fahmy

引用次数: 0

The Effects of surface plasmon polariton on Silicon Thin Film Solar Cell Array 表面等离子体激元对硅薄膜太阳能电池阵列的影响

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691446

Khalil ElKhamisy, Hamdy Abdelhamid, S. Elagooz, El-Sayed M. El-Rabaie

引用次数: 0

An Efficient Algorithm for Analysis of Handwriting Task for the Detection of Parkinson's disease 一种用于帕金森病检测的手写任务分析的高效算法

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691312

Sara A. Elazazy, M. Eldesoky, M. El-Wakad, A. Soliman

引用次数: 1

Multi-Beam Radiation in Time-modulated SIW-DRA linear array 时调制SIW-DRA线性阵列中的多波束辐射

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691310

A. S. Zainud-Deen, H. Malhat

引用次数: 0

Remote Monitoring System Dedicated To COVID-19 Patients Healthcare 专用于COVID-19患者医疗保健的远程监控系统

2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC) Pub Date : 2021-12-13 DOI: 10.1109/JAC-ECC54461.2021.9691450

M. Abo-Zahhad, Aya Taghian, Mayar Hossam, Nora Atef, Sarah Reda

{"title":"Remote Monitoring System Dedicated To COVID-19 Patients Healthcare","authors":"M. Abo-Zahhad, Aya Taghian, Mayar Hossam, Nora Atef, Sarah Reda","doi":"10.1109/JAC-ECC54461.2021.9691450","DOIUrl":"https://doi.org/10.1109/JAC-ECC54461.2021.9691450","url":null,"abstract":"In 2019, COVID-19 posed a challenge to the whole world. Till today, it confuses the scientists as it has no defined medication. This paper aims to design a cost-efficient system that can monitor mild and serious cases of COVID-19 patients by continuously measuring and recording their health status throughout studying the obvious symptoms of COVID-19. The collected data can then be evaluated by nearby health centers and proper medical intervention will take place in times of emergency. This system shall protect doctors from too much contact with the patients. It will also alleviate the financial and emotional pressure that patients face during their stay in hospitals. In addition, it can act as one of the solutions to improve the low-standard healthcare system in Egypt [1]. In comparison to the prior applied systems, it has been found that the designed system is cost efficient, simpler in structure and more dedicated to COVID-19 virus. The proposed system is designed to include a transmitter, a receiver, and a virtual communication channel adopting IoT technology. The implemented part of the system is centered on the sensory and the patients’ interactions with the system. The obtained results revealed its efficiency compared to the state of the art systems.","PeriodicalId":354908,"journal":{"name":"2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115400994","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