Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.
Design and testing of piezoelectric energy harvester for powering wireless sensors of electric line monitoring system J. Appl. Phys. 111, 07E510 (2012); 10.1063/1.3677771 Broadband energy-harvesting using a two degree-of-freedom vibrating body Appl. Phys. Lett. 98, 214102 (2011); 10.1063/1.3595278 Piezoelectric energy harvesting using shear mode 0.71 Pb ( Mg 1 / 3 Nb 2 / 3 ) O 3 -0.29 PbTiO 3 single crystal cantilever Appl. Phys. Lett. 96, 083502 (2010); 10.1063/1.3327330Study on structure optimization of a piezoelectric cantilever with a proof mass for vibration-powered energy harvesting systemPiezoelectric energy harvesting technologies have received a great attention during the last decade to design self-powered microelectronic devices such as wireless sensor nodes. Piezoelectric energy harvester is a resonant system that produces maximum power output when its resonant frequency matches the ambient vibration frequency. The deviation from the resonance causes significant decrease in the power output. There are two possible solutions to compensate the effect of frequency deviation: widening the operating frequency bandwidth and tuning the resonant frequency. Tuning the resonant frequency is a more efficient technique for applications with single time varying dominant frequency. This paper presents a comprehensive review of frequency tuning methods for piezoelectric energy harvesting systems. Two categories generally investigated in the literature include manual and autonomous tuning methods. The recent developments of many tuning strategies are discussed and summarized. V C 2012 American Institute of Physics.
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