Gas serves as one of the most indispensable energy sources for industrial production and household life. In order to improve the gas utilization efficiency, smart gas meters for the Internet of Things (IoT) has been designed to achieve two-way communication and remote control functions. However, the existing smart gas meter does not consider further reduction of power consumption. Thus, we designed an ultralow-power sensing framework for IoT and applied it to the smart gas meter. Based on a thorough analysis of the metering system, we propose an ultralow power system framework and a low-power peripheral management solution to reach ultralow power consumption. Also, we propose a cooperative sensing scheme to achieve stability and accuracy of gas volume detection. Finally, we implement a real smart gas manage system to evaluate our low-power smart gas meter solution. Verified by a comparative test, the proposed gas meter successfully reduces the power consumption by at least 37% compared to the baseline. The experimental results verify the innovative design and confirm that the proposed gas meter features ultralow power consumption, high precision, and high reliability.

The brain–computer interface (BCI) technology improves the communication efficiency between people and Internet of Things (IoT) devices. BCI based on the steady-state visual evoked potential (SSVEP-BCI) is the preferred scheme for controlling devices because of its convenient operation, low training requirement, and high information transmission rate (ITR). Most signal acquisition devices for BCIs are used for medical diagnosis and scientific research and utilize multiple channels and wet electrodes to obtain high-quality signals. However, the practicability, wearability, and cost of the signal acquisition devices for real-life applications need to be considered, resulting in new requirements for the acquisition mode, the number of electrodes, power consumption, and signal processing methods. This article presents a wearable low-power collaborative sensing system based on a time mask window canonical correlation analysis method (TMW-CCA). An 8-array spring dry electrode signal acquisition device based on a flexible circuit board is designed to address the shortcomings of traditional wet electrode acquisition devices, such as high-power consumption, discomfort, and being unsuitable for long-time use. The proposed TMW-CCA method, which uses a dry electrode sensor to evaluate the time domain’s signal quality dynamically, exhibits 12.5% higher steady-state visual evoked potential recognition accuracy and 40% lower average power consumption (only 740 mW) than the benchmark.

The single non-destructive testing (NDT) techniques face the problem of low resolution when simultaneously detecting different types and locations of defects. Composite inspection methods, which combine different NDT techniques, have been widely studied due to their high defect detection accuracy. However, most of the existing composite detection methods require complex sensor systems or rely on complex signal processing algorithms. Therefore, this paper proposes a novel hybrid sensor based on electromagnetic acoustic transducer (EMAT) and magnetic flux leakage (MFL) mechanism, which allows to accurately detect defects in ferromagnetic materials. This hybrid sensor employs a straightforward EMAT-MFL configuration, which only requires a single permanent magnet to generate the requisite magnetic field for the EMAT and MFL sensor components. In addition, it adopts a unique orthogonal butterfly coil design for the detection of cracks. The obtained results show a significant frequency difference between the EMAT and MFL signals, which demonstrates their independence and lack of interference. This eliminates the potential issue of signal aliasing decoupling. The hybrid sensor can fully use the advantages of the EMAT and MFL technologies to simultaneously detect defects and cracks on the top and bottom surfaces of ferromagnetic materials. Furthermore, the wall thinning defects can be detected with a maximum detection error of only 4.48%. This paper proposes a feasible approach for miniaturizing hybrid sensors and increasing their detection performance.

Autonomous driving is reshaping the way humans travel, with millimeter wave (mmWave) radar playing a crucial role in this transformation to enable vehicle-to-everything (V2X). Although chirp is widely used in mmWave radar systems for its strong sensing capabilities, the lack of integrated communication functions in existing systems may limit further advancement of autonomous driving. In light of this, we first design “dedicated chirps" tailored for sensing chirp signals in the environment, facilitating the identification of idle time-frequency resources. Based on these dedicated chirps, we propose a chirp-division multiple access (Chirp-DMA) scheme, enabling multiple pairs of mmWave radar transceivers to perform integrated sensing and communication (ISAC) without interference. Subsequently, we propose two chirp-based delay-Doppler domain modulation schemes that enable each pair of mmWave radar transceivers to simultaneously sense and communicate within their respective time-frequency resource blocks. The modulation schemes are based on different multiple-input multiple-output (MIMO) radar schemes: the time division multiplexing (TDM)-based scheme offers higher communication rates, while the Doppler division multiplexing (DDM)-based scheme is suitable for working in a lower signal-to-noise ratio range. We then validate the effectiveness of the proposed DDM-based scheme through simulations. Finally, we present some challenges and issues that need to be addressed to advance ISAC in V2X for better autonomous driving. Simulation codes are provided to reproduce the results in this paper: https://github.com/LiZhuoRan0.

Near space environment is the airspace at 20–100 km, where complex conditions such as low temperature, low pressure, high wind speed, and solar radiation exist. Temperature, as one of the most important meteorological parameters, is crucial for space activities. However, the accuracy of traditional temperature sensors is low, and the influence of complex environments makes the error of conventional temperature measurement methods more extensive. Therefore, we designed a new microbridge temperature sensor to reduce solar radiation and achieve a fast response. Additionally, through simulation analysis, we investigated the three factors influencing the temperature errors of Joule heat, solar radiation heat, and aerodynamic heat. Additionally, the influence of temperature error is reduced by optimizing the installation position of the sensor. The error value in the actual measurement value is removed through the temperature error model to realize the high-accuracy detection of the near space temperature.

In this paper, the static and dynamic characteristics of the MXene-based IDE capacitive humidity sensor are investigated through theoretical modeling and simulation. It is found that the capacitance increases according to the thickness of the sensing film within a certain range while stopping increases along with the growth of the thickness when the thickness is over a threshold. When the thickness is at a tiny level, a larger thickness does not lead to a significant increase in the response time due to the diffusion mechanism of water molecules. When the thickness increases to certain extent, there is an evident relationship between the response time and the change of thickness. For the humidity-sensitive film, under the same relative humidity conditions, the capacitance has a positive correlation with temperature, and the response time shows the opposite trend. Subsequent studies on the sensitive mechanisms of MXene materials explain these phenomena and demonstrate the accuracy of the model. This provides a more accurate method for sensor design. The properties of the MXene capacitive humidity sensor can be optimized by changing its structure and adjusting material parameters.