• Journal of Power Electronics
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• v.16 no.3
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• pp.1226-1235
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• 2016

For wearable health monitoring systems, a fundamental problem is the limited space for storing energy, which can be translated into a short operational life. In this paper, a highly efficient active voltage doubling rectifier with a wide input range for micro-piezoelectric energy harvesting systems is proposed. To obtain a higher output voltage, the Dickson charge pump topology is chosen in this design. By replacing the passive diodes with unbalanced-biased comparator-controlled active counterparts, the proposed rectifier minimizes the voltage losses along the conduction path and solves the reverse leakage problem caused by conventional comparator-controlled active diodes. To improve the rectifier input voltage sensitivity and decrease the minimum operational input voltage, two low power common-gate comparators are introduced in the proposed design. To keep the comparator from oscillating, a positive feedback loop formed by the capacitor C is added to it. Based on the SMIC 0.18-μm standard CMOS process, the proposed rectifier is simulated and implemented. The area of the whole chip is 0.91×0.97 mm², while the rectifier core occupies only 13% of this area. The measured results show that the proposed rectifier can operate properly with input amplitudes ranging from 0.2 to 1.0V and with frequencies ranging from 20 to 3000 Hz. The proposed rectifier can achieve a 92.5% power conversion efficiency (PCE) with input amplitudes equal to 0.6 V at 200 Hz. The voltage conversion efficiency (VCE) is around 93% for input amplitudes greater than 0.3 V and load resistances larger than 20kΩ.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.4 s.346
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• pp.56-65
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• 2006

The Quantum-dot Cellular Automata (QCA) can be considered as a candidate for the next generation digital logic implementation technology due to their small feature sizes and ultra low power consumption. Up to now, several designs using Uh technology have been proposed. However, we found not all of the designs function properly. Furthermore, no general design guidelines have been proposed so far. A straightforward extension of a simple functional design pattern may fail. This makes designing a large scale circuits using QCA technology an extremely time-consuming process. In this paper, we show several critical vulnerabilities related to unbalanced input paths to QCA gates and sneak noise paths in QCA interconnect structures. In order to make up the vulnerabilities, a disciplinary guideline will be proposed. Also, we present a fast adder which has been designed by the discipline, and verified to be functional by the simulation.