Tuning techniques for kinetic MEMS energy harvesters

Todorov, Georgi; Todorov, T.; Ivanov, Ivan; Valtchev, Stanimir; Klaassens, Ben

doi:10.1109/intlec.2011.6099874

Cited by 10 publications

(5 citation statements)

References 7 publications

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“…The forty participants were divided into openand closed-eye resting-state groups using discriminant analysis and autoregressive (AR) model features [11,12]. When just half of the data was used for training, accuracy only reached 80%, but when all of the data was used, it reached 100% (study) [13]. To get the same result, however uses a big AR order and several epochs.…”

Section: Introductionmentioning

confidence: 99%

Detecting Optimal Regions for a Single EEG Channel Biometric System

Venkatesan,

Mamidala,

Rani B

2022

IJSMEM

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Innovative security systems are increasingly making use of biometric modalities as an authentication method. However, the biometric technology that is presently available on the market provides solutions to a significant number of these difficulties. The widespread use of bogus biometrics in today’s society is one of the most significant reasons for concern. The results of an electroencephalogram (EEG) can provide some interesting information on the matter. This is a highly challenging endeavour since reproduction calls for careful preparation on your part. Several different investigations have shown that the procedure may be trusted to provide accurate results. Nonetheless, the collecting of data necessitates a large expenditure of time in addition to the sensors. In this study, we provide a biometric technique that takes use of EO resting-state EEG recordings that were taken from a single-channel electrode placement on the scalp. These recordings were generated in order to determine the precision of the method. The electroencephalograms (EEGs) of all nine persons who were examined yielded a total of 45 different signals. The interval of time that passed between each EEG wave segment was under five seconds. This specific piece of study focused its attention on the

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Section: Introductionmentioning

confidence: 99%

Detecting Optimal Regions for a Single EEG Channel Biometric System

Venkatesan,

Mamidala,

Rani B

2022

IJSMEM

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“…By making the elastic behaviour of a vibrating mass non-linear, the device frequency response exhibits a chaotic behaviour (duffing mode resonance) (Halvorsen 2013), extending the frequency range of operability and, in turn, the level of extracted power (Hajati et al 2011;Goldschmidtboeing et al 2009). Moreover, tuning of EHs resonant frequency is also studied to maximise extraction in the widest possible range of operability (Todorov et al 2011).…”

Section: Introductionmentioning

confidence: 99%

MEMS-based multi-modal vibration energy harvesters for ultra-low power autonomous remote and distributed sensing

et al. 2018

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In this contribution, we discuss the implementation of a novel microelectromechanical-systems (MEMS)-based energy harvester (EH) concept within the technology platform available at the ISAS Institute (TU Vienna, Austria). The device, already presented by the authors, exploits the piezoelectric effect to convert environmental vibrations energy into electricity, and presents multiple resonant modes in the frequency range of interest (i.e. below 10 kHz). The experimental characterisation of a sputter deposited aluminium nitride piezoelectric thin-film layer is reported, leading to the extraction of material properties parameters. Such values are then incorporated in the finite element method model of the EH, implemented in Ansys Workbench TM , in order to get reasonable estimates of the converted power levels achievable by the proposed device solution. Multiphysics simulations indicate that extracted power values in the range of several lW can be addressed by the EH-MEMS concept when subjected to mechanical vibrations up to 10 kHz, operating in closed-loop conditions (i.e. piezoelectric generator connected to a 100 kX resistive load). This represents an encouraging result, opening up the floor to exploitations of the proposed EH-MEMS device in the field of wireless sensor networks and zeropower sensing nodes.

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“…In the literature, different methods have been suggested to overcome the problem of narrow bandwidth harvesters. For example, Todorov et al [2] illustrated a method for varying the resonance frequency of a piezoelectric beam, changing the beam's stiffness through capacitors. Cottone et al [3] instead showed that the use of nonlinear bistable systems improves the performance of an energy harvester.…”

Section: Introductionmentioning

confidence: 99%

A C-Battery Scale Energy Harvester: Part A — System Dynamics

Nico

Boco

Frizzell³

et al. 2015

Volume 2: Integrated System Design and Implementation; Structural Health Monitoring; Bioinspired Smart Materials and Systems; E

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In recent years, the development of small and low power electronics has led to the deployment of Wireless Sensor Networks (WSNs) that are largely used in military and civil applications. Vibrational energy harvesting can be used to power these sensors in order to obviate the costs of battery replacement. Vibrational energy harvesters (VEHs) are devices that convert the kinetic energy present in the ambient into electrical energy using three principal transduction mechanisms: piezoelectric, electromagnetic or electrostatic. The investigation presented in this paper specifically aims to realize a device that converts vibrations from different ambient sources to electrical energy for powering autonomous wireless sensors. A “C-battery” scale (25.5 mm diameter by 57.45 mm long, 29.340 cm3) two Degree-of-Freedom (2-DoF) nonlinear electromagnetic energy harvester, which employs velocity amplification, is presented in this paper. Velocity amplification is achieved through sequential collisions between two free-moving masses, a primary (larger) and a secondary (smaller) mass. The nonlinearities are due to the use of multiple masses and the use of magnetic springs between the primary mass and the housing, and between the primary and secondary masses. Part A of this paper presents a detailed experimental characterization of the system dynamics, while Part B describes the design and verification of the magnet/coil interaction for optimum prototype power output. The harvester is characterized experimentally under sinusoidal excitation for different geometrical configurations and also under the excitation of an air-compressor. The maximum output power generated under sinusoidal excitation of arms = 0.4 g is 1.74 mW across a resistive load of 9975 Ω, while the output rms voltage is 4.2 V. Under the excitation of the compressor, the maximum peak power across a load resistance of 8660 Ω is 1.37 mW, while the average power is 85.5 μW.

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Tuning techniques for kinetic MEMS energy harvesters

Cited by 10 publications

References 7 publications

Detecting Optimal Regions for a Single EEG Channel Biometric System

Detecting Optimal Regions for a Single EEG Channel Biometric System

MEMS-based multi-modal vibration energy harvesters for ultra-low power autonomous remote and distributed sensing

A C-Battery Scale Energy Harvester: Part A — System Dynamics

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