2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)最新文献

Role of EEG delta and beta oscillations during problem solving tasks 在解决问题过程中脑电图δ和β振荡的作用

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824138

N. Roslan, H. Amin, L. I. Izhar, M. Saad, S. Sivapalan

{"title":"Role of EEG delta and beta oscillations during problem solving tasks","authors":"N. Roslan, H. Amin, L. I. Izhar, M. Saad, S. Sivapalan","doi":"10.1109/ICIAS.2016.7824138","DOIUrl":"https://doi.org/10.1109/ICIAS.2016.7824138","url":null,"abstract":"Problem solving is one of the higher-order thinking skills that have been studied by many researchers using Electroencephalography (EEG) brain signals. This paper concentrates on specific neural oscillations that can be observed in EEG signals which are delta (1.0–4.0 Hz) and beta (12.0–25.0 Hz) during problem solving task. Our aim is to investigate the role of the delta and beta neural oscillations during problem solving task at cortical area as compared to resting state (i.e. eyes open) using EEG. Eight volunteered healthy right-handed male students were recruited in this study. EEG 128-channel Hydro-Cel Geodesic (EGI Inc.) system was used in this study for data collection, but only 19 channels were used for data analysis. EEG recordings were taken during problem solving task (i.e. Raven's Advanced Progressive Matric (RAPM)) and during resting state (eyes open). Results showed that delta was significantly higher in almost all brain region and beta was significantly higher at prefrontal region only. Since we aim to investigate the role of delta and beta oscillation during problem solving, further investigation needs to be done with greater number of subjects in order to have more significant result to support this study. Further investigation could also help in finding quantitative indicators for intelligent assessment using brain signals.","PeriodicalId":247287,"journal":{"name":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126175794","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

Numerical modelling and simulation of dielectrophoretic based WBC sorting using sidewall electrodes 基于介电泳的侧壁电极白细胞分选的数值模拟与仿真

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824090

C. M. Yousuff, N. H. Hamid, K. Hussain, Eric Tatt Wei Ho

引用次数: 6

Beamforming in Massive MU-MIMO cellular networks: A stochastic geometry approach 大规模MU-MIMO蜂窝网络中的波束形成:随机几何方法

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824122

A. Hassan, M. Moinuddin, U. M. Al-Saggaf

引用次数: 4

Biologically inspired network on chip fault tolerant algorithm using time division multiplexing 基于分时复用的生物启发网络片上容错算法

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824058

M. A. J. Sethi, F. Hussin, N. H. Hamid

{"title":"Biologically inspired network on chip fault tolerant algorithm using time division multiplexing","authors":"M. A. J. Sethi, F. Hussin, N. H. Hamid","doi":"10.1109/ICIAS.2016.7824058","DOIUrl":"https://doi.org/10.1109/ICIAS.2016.7824058","url":null,"abstract":"Biologically inspired solutions are a novel way of solving the complex and real world problems. Due to the advanced nanoscale manufacturing processes and the complex communication requirements of the processing elements (PEs) various faults have occurred on NoC. The complexity and communication requirement of the NoC has also increased due to the heterogeneous devices. To support the complexity of NoC, the physical device sizes are scaled down, which have contributed to faults. Various fault tolerant techniques have been proposed in the literature to address the temporary faults. But all these algorithms have drawbacks in terms of adaptiveness and robustness. Bio-inspired NoC using Time division multiplexing (TDM) is based on the characteristics of biological brain. The technique is fault tolerant as it detects and bypass the faulty interconnects. With the help of TDM, multiple connections are possible between multiple sources and multiple destinations, which efficiently utilize the NoC bandwidth between PEs. To the best of our knowledge, TDM based bio-inspired NoC is the first algorithm to address the fault tolerance using the TDM approach as the average packet latency is increased by 2.45%, while the average bandwidth and throughput is reduced by 1.86% and 14.05% respectively during the recovery of faults. Also, the accepted traffic (flit/cycle/node) of the proposed bio-inspired technique is better than traditional fault tolerant techniques by 68.45%.","PeriodicalId":247287,"journal":{"name":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128688382","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

Real-time implementation of DVFS enhanced LEON3 MPSoC on FPGA DVFS增强的LEON3 MPSoC在FPGA上的实时实现

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824035

Zohaib Najam, Muhammad Yasir Qadri, Shaheryar Najam

引用次数: 3

Neural network model with particle swarm optimization for prediction in gas metering systems 基于粒子群优化的神经网络燃气计量系统预测模型

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824049

N. Rosli, R. Ibrahim, I. Ismail

引用次数: 6

EEG spectral analysis and functional connectivity during learning of science concepts 脑电频谱分析与科学概念学习过程中的功能连接

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824051

C. M. Tyng, H. Amin, A. Malik, M. Saad

引用次数: 5

Using resting state coherence to distinguish between low and high stress groups 使用静息状态一致性来区分低压力组和高压力组

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824097

A. Subhani, A. Malik, Nidal Kamil, Mohamad Naufal, M. Saad

引用次数: 2

Design of double ring resonator (DRR) for material properties measurement 材料性能测量用双环谐振器的设计

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824038

A. Alhegazi, Z. Zakaria, R. Alahnomi, E. Ruslan, S. R. A. Rashid

引用次数: 4

Towards health monitoring in visual surveillance 走向视觉监控中的健康监测

2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) Pub Date : 2016-08-01 DOI: 10.1109/ICIAS.2016.7824046

M. A. Hassan, A. Malik, N. Saad, B. Karasfi, D. Fofi, Wafa Sohail

引用次数: 5