2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)最新文献

A survey of component carrier selection algorithms for carrier aggregation in long term evolution-advanced 基于长期进化的载波聚合组件载波选择算法综述

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-30 DOI: 10.1109/ICSIMA.2017.8311994

H. Ramli, A. L. Asnawi, F. Isa, A. Azman

{"title":"A survey of component carrier selection algorithms for carrier aggregation in long term evolution-advanced","authors":"H. Ramli, A. L. Asnawi, F. Isa, A. Azman","doi":"10.1109/ICSIMA.2017.8311994","DOIUrl":"https://doi.org/10.1109/ICSIMA.2017.8311994","url":null,"abstract":"Given that the demand for real-time multimedia contents that require significantly high data rate are getting of high popularity, a new mobile cellular technology known as Long term Evolution-Advanced (LTE-A) was standardized. The LTE-A is envisaged to support high peak data rate by aggregating more than one contiguous or non-contiguous Component Carriers (CCs) of the same or different frequency bandwidths. This paper provides a survey on the case where the LTE-A is working in backward compatible mode as well as when the system contains only LTE-A users. Note that the backward compatible mode indicates that the LTE-A contains a mixture of the legacy Long Term Evolution Release 8 (LTE) users that support packets (re)transmission on a single CC and the LTE-A users that are capable of utilizes more than one CCs for packets (re)transmission. It can be concluded from the study that the CC selection algorithms for newly-arrived LTE users can benefit from the channel diversity and the load status whereas the carrier aggregation that does not allocate all of the available CCs to the newly arrived LTE-A users shown to be more efficient.","PeriodicalId":137841,"journal":{"name":"2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885140","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

Sensor node clutter distribution in LoRa LPWAN LoRa LPWAN中传感器节点杂波分布

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-29 DOI: 10.1109/ICSIMA.2017.8312013

Nur Aishah Binti Zainal, M. H. Habaebi, Israth Jahan Chowdhury, M. R. Islam

引用次数: 6

Harris corner detector and blob analysis featuers in human activty recognetion 哈里斯角点检测器和斑点分析在人体活动识别中的特点

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-28 DOI: 10.1109/ICSIMA.2017.8312025

Mohanad Babiker, Othman Omran Khalifa, K. K. Htike, Aisha Hassan, Muhamed Zaharadeen

引用次数: 7

Investigation of various algorithms on multichannel audio compression 多声道音频压缩的各种算法研究

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-28 DOI: 10.1109/ICSIMA.2017.8311985

T. Gunawan, Siti Aisyah Abdul Rashid, M. Kartiwi

{"title":"Investigation of various algorithms on multichannel audio compression","authors":"T. Gunawan, Siti Aisyah Abdul Rashid, M. Kartiwi","doi":"10.1109/ICSIMA.2017.8311985","DOIUrl":"https://doi.org/10.1109/ICSIMA.2017.8311985","url":null,"abstract":"Multichannel audio or surround sound compression is rather more challenging to compress compare to mono and stereo audio. Nowadays, many methods and algorithms have been proposed to improve the compression performance on multichannel audio. This book focuses on performance evaluation of various algorithms on multichannel audio compression. First, we identified and investigated current state-of-the-art audio compression algorithms, both lossless and lossy compression, which can handle mono, stereo, 5.1, and 7.1 multichannel audio. Out of various algorithms available, AC3, AAC, and Ogg have been selected as lossy compression algorithms, while FLAC and MPEG-4 ALS have been chosen as lossless compression algorithms. Two performance measure were used in the experiments, i.e. compression ratio and encoding time. The results showed that among three lossy audio compression algorithms, AC3 has the fastest encoding time while Ogg Vorbis has the highest compression ratio. Furthermore, between FLAC and MPEG-4 ALS, FLAC has faster encoding time and MPEG-4 ALS has higher compression ratio. Overall, in terms of encoding time and compression ratio, it has been found that FLAC is the fastest coder while Ogg Vorbis has the highest compression ratio among five encoders evaluated.","PeriodicalId":137841,"journal":{"name":"2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122544824","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

Automated daily human activity recognition for video surveillance using neural network 基于神经网络的视频监控日常人类活动自动识别

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-28 DOI: 10.1109/ICSIMA.2017.8312024

Mohanad Babiker, Othman Omran Khalifa, K. K. Htike, Aisha Hassan, Muhamed Zaharadeen

{"title":"Automated daily human activity recognition for video surveillance using neural network","authors":"Mohanad Babiker, Othman Omran Khalifa, K. K. Htike, Aisha Hassan, Muhamed Zaharadeen","doi":"10.1109/ICSIMA.2017.8312024","DOIUrl":"https://doi.org/10.1109/ICSIMA.2017.8312024","url":null,"abstract":"Surveillance video systems are gaining increasing attention in the field of computer vision due to its demands of users for the seek of security. It is promising to observe the human movement and predict such kind of sense of movements. The need arises to develop a surveillance system that capable to overcome the shortcoming of depending on the human resource to stay monitoring, observing the normal and suspect event all the time without any absent mind and to facilitate the control of huge surveillance system network. In this paper, an intelligent human activity system recognition is developed. Series of digital image processing techniques were used in each stage of the proposed system, such as background subtraction, binarization, and morphological operation. A robust neural network was built based on the human activities features database, which was extracted from the frame sequences. Multi-layer feed forward perceptron network used to classify the activities model in the dataset. The classification results show a high performance in all of the stages of training, testing and validation. Finally, these results lead to achieving a promising performance in the activity recognition rate.","PeriodicalId":137841,"journal":{"name":"2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132911880","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: 63

Black phosphorus saturable absorber for Q-switched technique pulse generation 用于调q技术脉冲产生的黑磷饱和吸收体

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-28 DOI: 10.1109/ICSIMA.2017.8312008

L. A. Hussein, B. A. Hamida, T. Eltaif, Sheroz Khan, M. H. Habaebi, Ahmad Anwar Zainuddin, S. Harun

引用次数: 3

Off line Arabic handwritten character using neural network 使用神经网络的离线阿拉伯手写字符

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-28 DOI: 10.1109/ICSIMA.2017.8312026

E. Shamsan, Othman Omran Khalifa, Aisha Hassan, H. Hamdan

引用次数: 2

Performance analysis of IEEE 1857.2 lossless audio compression linear predictor algorithm IEEE 1857.2无损音频压缩线性预测算法性能分析

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-01 DOI: 10.1109/ICSIMA.2017.8312033

Fathiah Abdul Muin, T. Gunawan, E. Elsheikh, M. Kartiwi

{"title":"Performance analysis of IEEE 1857.2 lossless audio compression linear predictor algorithm","authors":"Fathiah Abdul Muin, T. Gunawan, E. Elsheikh, M. Kartiwi","doi":"10.1109/ICSIMA.2017.8312033","DOIUrl":"https://doi.org/10.1109/ICSIMA.2017.8312033","url":null,"abstract":"In addition to commercial consumer market, high quality and multichannel audio has become more relevant in many other fields. More lossless audio compression standards and algorithm are proposed to tackle the problem of reducing the size of a raw audio bitstream without loss of data. This paper has two objectives. First, we aim to review and analyze the performance of the IEEE 1857.2 standard. Focus is on the predictor and the pre-processing block. The predictor utilizes Linear Predictive Coding (LPC) as its main mechanism. The pre-processing block normalizes the error residue of the Linear Predictive encoder. The second objective is to present results from experimenting different wave sound file type inputs. Results are discussed, and comparisons are made to identify the effect on compression ratio of the lossless encoder. As well as this, comparison is made to analyze the entropy flatness of the error residue from the predictor and pre-processing output the predictor order in the linear predictive coding mechanism varies. We concluded that pre-processing block works well to flatten the output at lower predictor order for all the sound types, but works best at improving the residual output for music sound type.","PeriodicalId":137841,"journal":{"name":"2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126785971","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

Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier 基于多类SVM分类器的棕榈油叶病检测数字图像处理技术

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-01 DOI: 10.1109/ICSIMA.2017.8311978

Ahmad Nor Ikhwan Masazhar, M. Kamal

引用次数: 40

Comparative analysis for a real time face recognition system using raspberry Pi 基于树莓派的实时人脸识别系统对比分析

2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) Pub Date : 2017-11-01 DOI: 10.1109/ICSIMA.2017.8311984

Umm-e-laila, Muzammil Khan, Muhammad Kashif Shaikh, Syed Annas bin Mazhar, Khalid Mehboob

{"title":"Comparative analysis for a real time face recognition system using raspberry Pi","authors":"Umm-e-laila, Muzammil Khan, Muhammad Kashif Shaikh, Syed Annas bin Mazhar, Khalid Mehboob","doi":"10.1109/ICSIMA.2017.8311984","DOIUrl":"https://doi.org/10.1109/ICSIMA.2017.8311984","url":null,"abstract":"Security is a major threat to institutions that is why there is a need of several specially trained personnel to attain the desired security to overcome the declining security conditions in the country. These personnel, as human beings, make mistakes that might affect the level of security. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts them from restricted or high-security areas in real time and help in minimizing human error. Face recognition is one of the most important biometrics pattern recognition technique which is used in a broad spectrum of applications. The time and accuracy factor is considered as a major problem that specifies the performance of automatic face recognition system in real time environments. Various solutions have been proposed using multicore systems. However, harnessing current advancements is not without difficulties. Motivated by such challenge, this paper provides the architectural design, detailed design and proposes a comparative analysis for a Real Time Face Recognition System with three variant implementations of Real Time Face Recognition algorithms including Local Binary Patterns Histograms (LBP), PCA (Principal Component Analysis) and Fisher face. Finally, this paper concludes the speed obtained for the advanced implementations achieved by integrating embedded system models against the convention implementation.","PeriodicalId":137841,"journal":{"name":"2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131284568","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: 18