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This article presents a fully integrated multi-phase (MP) buck converter for on-chip power management units. A peak-and-valley differential sensing (PVDS) scheme based on a flying capacitor is proposed to evenly balance the multiple inductor currents without significant power overhead even under mismatches in inductances, parasitic series resistances, and control-signal skews among phases. In addition, dynamic re-allocation of on-chip capacitors enables an even more optimal frequency response and output ripple based on the number of activated phases. It also improves the chip area efficiency, resulting in a higher power density. The proposed DLL-based MP clock generation provides high granularity in the phase-shedding control to achieve high efficiency over a wide load range. The proposed fully integrated buck converter using six bond-wire inductors (1 nH) was fabricated in a 28-nm CMOS process. The chip running at a frequency of 400 MHz/phase can finely adjust the number of active phases by an integer step ranging from 1 to 6 through sensing a load current. A maximum power density of 1.23 W/mm2 and a peak efficiency of 83.7% were measured. This work achieved an ultra-fast dynamic voltage scaling (DVS) rate of 75 mV/ns. [ABSTRACT FROM AUTHOR]

This article presents an energy-harvesting (EH) interface chip system for triboelectric nanogenerators (TENGs). Despite TENG’s numerous advantages, its high open-circuit voltage and large parasitic capacitance ($C_{T}$) give a considerable burden to design TENG-EH integrated circuits (ICs). In this work, a scalable multi-chip-stacked bias-flip (MCS-BF) technique is proposed to harvest TENG energy with ultra-high extraction voltage, exceeding an LDMOS transistor’s voltage-rating limit (~70 V). Even in multi-chip-stacked structures, the MCS-BF utilizing a series inductor cancels the energy loss caused by $C_{T}$. Furthermore, a practical solution to synchronize chip-to-chip control signals without over-voltage stress is also presented, even when many chips are daisy-chained together. The proposed TENG-EH chips were fabricated in a 0.18- $\mu \text{m}$ BCD process. In experiments with a real wind-TENG, an extremely high extraction voltage of 130 V (195 V) was successfully demonstrated with two (three) MCS-BF chips. Also, a harvested power per TENG area was measured to be $96.4 \mu \text{W}$ /cm2 with a two-chip solution, which is $4\times $ higher than previous state-of-the-art TENG-EH chips. The conversion from rectified input to regulated (battery voltage) output attained a measured peak efficiency of 70.7%. The proposed TENG-EH multi-chip system offers a $3.14\times $ power extraction gain (figure of merit). [ABSTRACT FROM AUTHOR]

This article proposes an online maximum torque-per-Ampere (MTPA) control of an interior permanent-magnet synchronous motor (IPMSM) based on the square-wave voltage signal injection. Because the proposed method does not rely on a position sensor, it is applicable to sensorless IPMSMs. The proposed algorithm consists of a dynamic inductance estimator, frequency-adaptive flux observer, and an MTPA tracking controller. The proposed inductance estimation utilizes the pulsating square-wave voltage injection, which can maximize the injection frequency, and the dynamic performance can be preserved during the signal injection. Through the online flux and inductance estimation, the magnetic saturation of the IPMSM and the temperature variation can be fully considered in the MTPA control. The conventional rotor position estimator is replaced with the MTPA tracking controller, which does not rely on offline commissioning parameters. The proposed algorithm can operate under both the torque control and speed control mode, and accurate torque control on MTPA curve can be achieved. The simulation and experimental results demonstrate that the proposed method accurately tracks the MTPA point up to 200% of the rated torque in the mid- and high-speed region. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable dc–dc converter for maximum thermoelectric generator (TEG) energy harvesting in a battery-powered duty-cycling wireless sensor node. The proposed dc–dc converter adopts discontinuous energy harvesting, which operates in single-input dual-output (SIDO) boost, battery-TEG pile-up buck (BTPB), dual-phase buck–boost (DPBB), and battery supplied buck modes with a single shared inductor. Fabricated in a 65-nm CMOS process, the converter adopts an adaptive dead-time controller for zero-voltage switching (ZVS) and an adaptive switch size (ASS) with maximum power point tracking (MPPT) to maximize efficiency in a wide TEG voltage range. The SIDO boost mode achieves 88.5% and 81.1% peak end-to-end efficiency with maximum and no-load conditions (only charging the battery), respectively. The combination of BTPB and DPBB modes to maximize the TEG power extraction during the battery-powered operation results in up to 44% saving in battery power. The battery-supplied buck mode achieves 93.3% peak efficiency. [ABSTRACT FROM AUTHOR]

Recently, deep learning-based methods for blind reverberation time estimation have been proposed, and outperform those based on conventional signal processing. The signal processing approaches extract the reverberant environmental features of sound by statistical analysis, while deep learning approaches train a network to capture the relationship between acoustic features and the reverberation time. In this letter, we propose a method for blind reverberation time estimation that explicitly reflects physical properties of reverberation by combining the deep learning approach, attentive pooling, and statistical characteristics of reverberant speech obtained using a signal processing method, i.e., spectral decay rates (SDRs). The results obtained with the proposed blind reverberation time estimation method are superior to the previously published state-of-the-art results for the EVAL dataset of the ACE Challenge. This work can be considered a good example of the collaboration between signal processing expertise and deep learning approach. [ABSTRACT FROM AUTHOR]

Detecting microcalcifications (MCs) in real time is important in the guidance of many breast biopsies. Due to its capability in visualizing biopsy needles without radiation hazards, ultrasound imaging is preferred over X-ray mammography, but it suffers from low sensitivity in detecting MCs. Here, we present a new nonionizing method based on real-time multifocus twinkling artifact (MF-TA) imaging for reliably detecting MCs. Our approach exploits time-varying TAs arising from acoustic random scattering on MCs with rough or irregular surfaces. To obtain the increased intensity of the TAs from MCs, in MF-TA, acoustic transmit parameters, such as the transmit frequency, the number of focuses and f-number, were optimized by investigating acoustical characteristics of MCs. A real-time MF-TA imaging sequence was developed and implemented on a programmable ultrasound research system, and it was controlled with a graphical user interface during real-time scanning. From an in-house 3D phantom and ex vivo breast specimen studies, the MF-TA method showed outstanding visibility and high-sensitivity detection for MCs regardless of their distribution or the background tissue. These results demonstrated that this nonionizing, noninvasive imaging technique has the potential to be one of effective image-guidance methods for breast biopsy procedures. [ABSTRACT FROM AUTHOR]

This article analyzes the effect of discretization in the frequency-adaptive flux observer (FAO). Although FAO has structural simplicity and ease of implementation, the discrete-time FAO has phase and gain errors in fundamental flux estimation. As a result, the estimated flux might be erroneous, especially in the high-speed region. For accurate flux estimation, this article analyzes how the voltage synthesis in the pulsewidth-modulation affects the stator flux. Through the analysis, the backward Euler method is selected for the primitive stator flux estimation. To mitigate the FAO's discretization error, the proposed FAO utilizes the integrators constructed at the rotor reference frame (RRF). Since the discretization error comes from the approximation error of the continuous-time integrator at the operating frequency, the proposed FAO conducts the fundamental flux extraction at the RRF, where the operating frequency components are treated as a dc signal. Thanks to the coordinate transformation, the FAO can be discretized with no error while keeping the structure of double integrators. The proposed method is verified through the computer simulation and experimental test. [ABSTRACT FROM AUTHOR]