Wenbin Li
Beijing Forestry University

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Simulation Analysis of Interface Circuits for Piezoelectric Energy Harvesting with Damped Sinusoidal Signals and Random Signals Shuai Pang; Wenbin Li; Jiangming Kan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.2093

Abstract

Various interface circuits for collecting the energy of piezoelectric cantilever beams have been widely investigated. Such circuits include the standard interface, series synchronized switch harvesting interface circuit, parallel synchronized switch harvesting interface, synchronized charge extraction interface, and others. Most studies focus on the performance of different interface circuits with standard sinusoidal excitations. However, in real situations where constant harmonic vibrations are not present, the equivalent voltage from piezoelectric cantilever beams with excitations is not necessarily sinusoidal. In the present study, a simulation analysis of four different interface circuits with signal sources that are both damped sinusoidal and random was performed using Matlab and PSpice. The results show that the interface circuits have improved performance under low load resistance values with damped sinusoidal signal. In addition, the parallel and series synchronized switch harvesting interface circuits may perform well in collecting piezoelectric energy with random excitations.
Image Deblurring via an Adaptive Dictionary Learning Strategy Lei Li; Ruiting Zhang; Jiangmin Kan; Wenbin Li
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.532

Abstract

Recently, sparse representation has been applied to image deblurring. The dictionary is the fundamental part of it and the proper selection of dictionary is very important to achieve super performance. The global learned dictionary might achieve inferior performances since it could not mine the specific information such as the texture and edge which is contained in the blurred image. However, it is a computational burden to train a new dictionary for image deblurring which requires the whole image (or most parts) as input; training the dictionary on only a few patches would result in over-fitting. To address the problem, we instead propose an online adaption strategy to transfer the global learned dictionary to a specific image. In our deblurring algorithm, the sparse coefficients, latent image, blur kernel and the dictionary are updated alternatively. And in every step, the global learned dictionary is updated in an online form via sampling only a few training patches from the target noisy image. Since our adaptive dictionary exploits the specific information, our deblurring algorithm shows superior performance over other state-of-the-art algorithms.