Showing papers by "Tetsuya Hirose published in 2023"
DOI•
21 May 2023
TL;DR: In this article , the authors developed a method that resulted in a shortened reconstruction time and a high reconstruction accuracy with a high compression ratio by utilizing selected EEG signals, and a compressed EEG signal with an original time length of 1 s could be recovered in only approximately 26 ms.
Abstract: The use of compressed sensing (CS) to achieve low-power consumptions in electroencephalogram (EEG) mea-surement devices has attracted considerable research interest. However, a signal processing issue in utilizing CS is the trade- off between the compression ratio (CR), reconstruction accuracy, and reconstruction time. In this study, we developed a method that resulted in a shortened reconstruction time and a high reconstruction accuracy with a high CR by utilizing selected EEG signals. When EEG signals were sorted using the mean frequency and only the most frequently occurring EEG signals were used in the basis matrix, a compressed EEG signal with an original time length of 1 s could be recovered in only approximately 26 ms, and an average normalized mean square error of 0.11 was achieved at a CR of 5.
TL;DR: In this paper , a switched-capacitor voltage buck converter (SC-VBC) with variable step-down controller (VSC) and switching frequency controller (SFC) is proposed.
Abstract: This paper proposes a switched-capacitor voltage buck converter (SC-VBC) with variable step-down controller (VSC) and switching frequency controller (SFC). The VSC and SFC change the step-down ratio and switching frequency of the SC-VBC in accordance with the input voltage and load current, respectively, enabling the converter to operate efficiently with a wide-ranging input voltage and load current. Measurements of a prototype chip demonstrated that our SC-VBC achieved a wide input voltage range and high efficiency of 1.3–2.6 V and 69%, respectively.
TL;DR: In this article , the authors proposed a fully integrated switched-capacitor voltage boost converter with a digital maximum power point tracking (MPPT) control circuit for lowvoltage and low-power energy harvesting.
Abstract: This paper proposes a fully-integrated switched-capacitor voltage boost converter with a digital maximum power point tracking (MPPT) control circuit for low-voltage and low-power energy harvesting. The proposed digital MPPT control circuit converts the analog voltage information of a PV cell into digital values and extracts the maximum power regardless of the harvester conditions and load current. Measurement results demonstrated that our proposed circuit can track the maximum power point of a PV cell successfully. The maximum voltage conversion ratio of our circuit was 5.6. The proposed power management system generated a 2.58 V output voltage from a 0.46 V input voltage. The maximum power conversion efficiency was 63.6%.
DOI•
21 May 2023
TL;DR: In this paper , the authors proposed and implemented a wireless EEG measurement device using compressed sensing, utilizing random undersampling and only a small thermoelectric generator (TEG) as the power source.
Abstract: The realization of a compact wireless electroencephalogram (EEG) measurement device that can be used in daily life without concern for power consumption has garnered considerable attention. Thus, wireless EEG measurement devices with energy harvesting have been proposed, but there have been issues with harvester size and power output. In this study, we proposed and implemented a wireless EEG measurement device using compressed sensing, utilizing random undersampling and only a $40\ \text{mm}\times 40\ \text{mm}$ small thermoelectric generator (TEG) as the power source. The results of the 4x compression experiment revealed a reduction in the power of the microcontroller from $345\ \mu\mathrm{W}$ to $97\ \mu\mathrm{W}$ at 3.3 V. This implies that a wireless EEG measurement device can operate well with a small TEG, even though the reconstructed signal is not inferior to the original signal, in which the average normalized mean square error is approximately 0.24.