Exergy-based sustainability analysis of a low power, high frequency piezo-based ultrasound reactor for rapid biodiesel production

2017 ◽  
Vol 148 ◽  
pp. 759-769 ◽  
Author(s):  
Mortaza Aghbashlo ◽  
Meisam Tabatabaei ◽  
Soleiman Hosseinpour ◽  
Zahra Khounani ◽  
Seyed Sina Hosseini
Keyword(s):  
Low Power ◽  
High Frequency ◽  
Biodiesel Production ◽  
Sustainability Analysis

High frequency, large displacement, and low power consumption piezoelectric translational actuator based on an oval loop shell

2012 ◽  
Vol 176 ◽  
pp. 99-109 ◽  
Author(s):  
Taiho Yeom ◽  
Terrence W. Simon ◽  
Min Zhang ◽  
Mark T. North ◽  
Tianhong Cui
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
High Frequency ◽  
Large Displacement ◽  
Low Power Consumption

scholarly journals A Neuromorphic Spiking Neural Network Detects Epileptic High Frequency Oscillations in the Scalp EEG

2021 ◽  
Author(s):  
Karla Burelo ◽  
Georgia Ramantani ◽  
Giacomo Indiveri ◽  
Johannes Sarnthein
Keyword(s):  
Low Power ◽  
Seizure Frequency ◽  
High Frequency ◽  
Therapy Monitoring ◽  
Non Invasive ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Eeg Recordings ◽  
Active Epilepsy ◽  
Wearable Electronic Devices

Abstract Background: Interictal High Frequency Oscillations (HFO) are measurable in scalp EEG. This has aroused interest in investigating their potential as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. The demand for therapy monitoring in epilepsy has kindled interest in compact wearable electronic devices for long- term EEG recording. Spiking neural networks (SNN) have been shown to be optimal architectures for being embedded in compact low-power signal processing hardware. Methods: We analyzed 20 scalp EEG recordings from 11 patients with pediatric focal lesional epilepsy. We designed a custom SNN to detect events of interest (EoI) in the 80-250 Hz ripple band and reject artifacts in the 500-900 Hz band. Results: We identified the optimal SNN parameters to automatically detect EoI and reject artifacts. The occurrence of HFO thus detected was associated with active epilepsy with 80% accuracy. The HFO rate mirrored the decrease in seizure frequency in 8 patients (p = 0.0047). Overall, the HFO rate correlated with seizure frequency (rho = 0.83, p < 0.0001, Spearman’s correlation).Conclusions: The fully automated SNN detected clinically relevant HFO in the scalp EEG. This is a further step towards non-invasive epilepsy monitoring with a low-power wearable device.

High frequency InAs-channel HEMTs for low power ICs

2004 ◽  
Author(s):  
Y. Royter ◽  
K.R. Elliott ◽  
P.W. Deelman ◽  
R.D. Rajavel ◽  
D.H. Chow ◽  
...  
Keyword(s):  
Low Power ◽  
High Frequency

A low-power droplet manipulator employing high-frequency surface acoustic waves

2009 ◽  
Vol 92 (4) ◽  
pp. 49-55
Author(s):  
Tsutomu Sasaki ◽  
Yasutomo Tanaka ◽  
Tatsuya Omori ◽  
Ken-Ya Hashimoto ◽  
Masatsune Yamaguchi
Keyword(s):  
Low Power ◽  
Acoustic Waves ◽  
High Frequency ◽  
Surface Acoustic Waves

scholarly journals Development of integrated preamplifier for high-frequency ultrasonic transducers and low-power handheld receiver

2011 ◽  
Vol 58 (12) ◽  
pp. 2646-2658 ◽  
Author(s):  
Hojong Choi ◽  
Xiang Li ◽  
Sien-Ting Lau ◽  
Changhong Hu ◽  
Qifa Zhou ◽  
...  
Keyword(s):  
Low Power ◽  
High Frequency ◽  
Ultrasonic Transducers
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