Sutrisno Ibrahim

A review on frequency tuning methods for piezoelectric energy harvesting systems

Piezoelectric 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.

A DWT-entropy-ANN based architecture for epilepsy diagnosis using EEG signals

Electroencephalogram (EEG) is one the most common tools for epilepsy diagnosis and analysis. Currently, epilepsy diagnosis is still mainly performed by a neurologist through manual or visual inspection of EEG signals. In this article, we develop a computer aided diagnosis (CAD) for epilepsy based on discrete wavelet transform (DWT), Shannon entropy and feed-forward neural network (FFNN). DWT decompose EEG signals into several frequency sub-bands such as delta, theta, alpha, beta and gamma. Shannon entropy extract the EEG features from each these frequency sub-bands. Finally, FFNN classifies the corresponding EEG signals into “normal” or “epileptic” class based on the extracted features. Our experimental results using publicly available University of Bonn EEG dataset show perfect accuracy (100%).

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

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.

Adaptive Epileptic Seizure Prediction Based on EEG Synchronization

Epilepsy is type of neurological disorder characterized by recurrent seizures that may cause injury to self and others. The ability to predict seizure before its occurrence, so that counter measures are considered, would improve the quality of life of epileptic patients. This research work proposes an adaptive seizure prediction approach based on electroencephalography (EEG) signals analysis. We use cross-correlation to estimate synchronization between EEG channels. Abnormal synchronization between brain regions may reveal brain condition and functionality. Two EEG synchronization baselines, normal and pre-seizure, are used to continuously monitor sliding windows of EEG recording to predict the upcoming seizure. The two baselines are continuously updated using distance-based method based on the most recent prediction outcome. Up to 570 hours continuous EEG recording taken from CHB-MIT dataset is used for validating the proposed method. An overall of 84% sensitivity (46 out of 55 seizures are correctly predicted) and 63% specificity are achieved with one hour prediction horizon. The proposed method is suitable to be implemented in mobile or embedded device which has limited processing resources due to its simplicity.

Modeling and simulation of volture piezoelectric energy harvester

Piezoelectric material is used as an active material to convert vibration energy into electrical output and so called piezoelectric energy harvesting. The harvesters dynamic model depends on several parameters such as physical dimensions, geometrical structure, mechanical and electrical properties of the piezoelectric material. The development of theoretical model for analysis and simulation is a complex task whereas complete information about these parameters is not available from the manufacturers datasheet. In this paper, the dynamic model parameters are identified experimentally to reduce the modeling effort and to develop easily the equivalent circuit for the energy harvester (Volture, V21BL). The validation is achieved by comparing the estimated power outputs using simulation model with the real time measured values. The obtained results affirmed the potential of the adopted approach for modeling and simulation.

An adaptive learning approach for EEG-based computer aided diagnosis of epilepsy

Epilepsy diagnosis is commonly performed by a neurologist through visual inspection of electroencephalography (EEG) signals. Computer aided diagnosis (CAD) system has a great potential to assist neurologist or medical expert therefore improving the accuracy and shortening the diagnosis time. In this article, we present an adaptive learning approach for EEG-based CAD system for epilepsy diagnosis. With adaptive learning, the CAD system is able to reinforce new knowledge based on the neurologist feedback to improve its performance over the time. A combination of discrete wavelet transform (DWT) and Shannon entropy is used to extract feature from the EEG signal. K-nearest neighbors)kNN) clasifies the EEG signal based on “normal” and “epileptic baseline”. Both baselines are continuously updated based on the most recent classification or diagnosis result. Our proposed method shows promising results tested using publicly available University of Bonn EEG dataset with overall accuracy up to 100%.