Scavenged energy from ambient vibrations has become a promising energy supply for autonomous microsystems. However, restricted by device size, most MEMS vibration energy harvesters have much higher resonant frequencies than environmental vibrations, which reduces scavenged power and limits practical applicability. Herein, we propose a MEMS multimodal vibration energy harvester with specifically cascaded flexible PDMS and “zigzag” silicon beams to simultaneously lower the resonant frequency to the ultralow-frequency level and broaden the bandwidth. A two-stage architecture is designed, in which the primary subsystem consists of suspended PDMS beams characterized by a low Young’s modulus, and the secondary system consists of zigzag silicon beams. We also propose a PDMS lift-off process to fabricate the suspended flexible beams and the compatible microfabrication method shows high yield and good repeatability. The fabricated MEMS energy harvester can operate at ultralow resonant frequencies of 3 and 23 Hz, with an NPD index of 1.73 μW/cm³/g² @ 3 Hz. The factors underlying output power degradation in the low-frequency range and potential enhancement strategies are discussed. This work offers new insights into achieving MEMS-scale energy harvesting with ultralow frequency response.

The burgeoning internet of things and artificial intelligence technologies have prospered a variety of emerging applications. Human–machine interfaces (HMIs), for instance, enables users with intuitive, efficient, and friendly way to interact with machines, capable of instant information acquisition, processing, communication, and feedback, etc. These features require ultra-compact and high-performance transducers, and therefore self-powered sensors have become the key underlying technology for HMI applications. This review focuses on the piezoelectric, triboelectric, and hybrid self-powered sensors with particular attention to their microstructures and fabrication methods, showing that both traditional microfabrication and emerging fabrication methods like three-dimensional (3D) printing, electrospinning, and braiding have contributed to the planar, array, porous, fabric, and composite type self-powered sensors. Moreover, the integration method of piezoelectric and triboelectric sensor arrays is investigated. The crosstalk issue is highlighted, i.e. the signal interference between adjacent sensing units, and current solutions such as array design optimization, signal processing improvement, and material innovation to reduce crosstalk sensitivity have been reviewed through specific examples. Three categories of HMI applications have been outlined, including intelligent interaction, robotics, and human monitoring, with detailed explanations of how the self-powered sensors support these HMI applications. Through discussion of challenges and prospects, it is proposed that further coordinating the design and fabrication of micro devices with HMIs will potentially boost the intelligent application with even higher level of diversification, convenience, and interconnectivity.

The fractional open-circuit voltage (FOCV) method is commonly adopted to track maximal power point of photovoltaic systems due to easy implementation and cost-effectiveness. However, the FOCV method is confronted with unstable output power and limited tracking accuracy. This paper proposes a novel on-site traversal FOCV method with uninterrupted output power and increased tracking accuracy through simulation and experimental verifications. Each solar cell is connected with a bypass diode and switching circuitry, so that specific solar cell can be traced and measured consecutively for determining its maximal power point (MPP). MATLAB/Simulink simulation results show that, in the time-varying irradiance case, the proposed method achieves a low ripple factor of 0.13% in 11–13 h and 0.88% in 9–15 h, under the typical 24 h irradiance curve. In the spatial-varying irradiance case, the accuracy of the proposed method reaches 99.85%. Compared with other FOCV methods, like pilot cell and semi pilot cell methods, the proposed method is of higher accuracy with a limited ripple effect. Experimental results show that this method can effectively trace different output performance of specific solar cell while generating stable output voltage with a low ripple factor of 1.55%, proving its compatibility with distributed sensing and applicability in smart photovoltaic systems.

JPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. The optimized scheme in this paper scheme enables encoding speeds of up to 4 coefficients/clock and decoding speeds of up to 2 coefficients/clock, with a 75% reduction in encoding and decoding time. The maximum clock frequency of the entropy encoder is about 222.6 MHz, and the maximum clock frequency of the entropy decoder is about 127 MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.