This paper proposes a transmit beamforming strategy for the integrated sensing and communication (ISAC) systems enabled by the novel stacked intelligent metasurface (SIM) architecture, different from conventional single-layer reconfigurable intelligent surface (RIS) by cascading multiple transmissive metasurface layers, where the base station (BS) simultaneously performs downlink communication and radar target detection via fully passive wave domain beamforming, result in the significant reduction in hardware cost and power consumption. To ensure superior dual-function performance simultaneously, we design the multi-layer cascading beamformer by maximizing the sum rate of the users while optimally shaping the normalized beam pattern for detection. A dual-normalized differential gradient descent ( $\rm{D}^{3}$ ) algorithm is further proposed to solve the resulting non-convex multi-objective problem (MOP), where gradient differences and dual normalization are employed to ensure a flexible trade-off between communication and sensing objectives at the gradient level, providing finer control over the optimization process. Numerical results demonstrate the superiority of the proposed beamforming design in terms of balancing communication and sensing performance.

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.

Humidity sensors are widely used in various fields of life. In meteorological detection, the sensor must have high sensitivity and fast dynamic response time due to extreme environmental interference. However, the sensitive mechanism of the humidity sensor determines that the dynamic response time will inevitably be increased while improving the sensitivity, which undoubtedly creates difficulties for sensor design. This article takes the interdigitated capacitive humidity sensor as the research object and proposes an optimal design scheme for the sensor that considers high dynamic response time and sensitivity. By constructing the sensor’s theoretical mathematical model, the influence of each structure is analyzed. The theoretical model has been verified by finite element simulation to have an accuracy higher than 95%. The article constructs the sensor optimization objective equation based on this model. Through analysis, within the range of structural parameters set in the article, to improve the sensitivity and reduce the dynamic response time of the sensor, the width and spacing of the interdigital electrodes should have a minimum value of 3 μm and a maximum value of 14 μm, respectively. The thickness of the electrode layer and the moisture-sensitive layer should be flexibly adjusted according to the application to ensure the lowest value of the optimization objective function. To further improve the sensor’s performance, the article optimizes the electrode structure and heating strategy of the sensor heating layer, which not only enhances the uniformity of heat transfer but also increases the optimal heat transfer area by 6% compared with the traditional scheme.

The integrated sensing and communication (ISAC) technique is regarded as a key component in future vehicular applications. In this paper, we propose an ISAC solution that integrates Long Range (LoRa) modulation with frequency-modulated continuous wave (FMCW) radar in the millimeter-wave (mmWave) band, called mmWave-LoRadar. This design introduces the sensing capabilities to the LoRa communication with a simplified hardware architecture. Particularly, we uncover the dual discontinuity issues in time and phase of the mmWave-LoRadar received signals, rendering conventional signal processing techniques ineffective. As a remedy, we propose a corresponding hardware design and signal processing schemes under the compressed sampling framework. These techniques effectively cope with the dual discontinuity issues and mitigate the demands for high-sampling-rate analog-to-digital converters while achieving good performance. Simulation results demonstrate the superiority of the mmWave-LoRadar ISAC system in vehicular communication and sensing networks.