The next-generation wireless communication sys-tems require available bandwidth, reliability, and mobility. How-ever, the wider bandwidth and higher carrier frequency pose challenges for communication systems, including stricter peak-to-average power ratio (PAPR) requirements and more severe Doppler effects. This paper introduces an enhanced discrete Fourier transform spread (ED-OTFS) waveform that utilizes head-tail insertion sequences and frequency domain spectrum shaping (FDSS) methods to mitigate multipath delay variations and reduce high PAPR. Additionally, a reliable channel equalization algorithm for the ED-OTFS receiver is designed to mitigate the impact of the Doppler shift. Simulation results demonstrate that the proposed waveform significantly improves the bit error rate (BER) and PAPR performance compared with the existing schemes.

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm$\times16$mm$\times9$mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84-$\mu {}\text{V}_{\mathrm{ RMS}}$input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

Voxel-based methods have been widely used in point cloud 3D object detection. These methods usually transform points into voxels while suffering from information loss during point cloud voxelization. To address this problem, we propose a novel one-stage Voxelization Information Compensation Network (VIC-Net), which has the ability of loss-free feature extraction. The whole framework consists of a point branch for geometry detail extraction and a voxel branch for efficient proposals generation. Firstly, PointNet++ is adopted to efficiently encode geometry structure features from the raw point clouds. Then based on the encoded point features, two Point2Voxel (P2V) feature fusion modules are proposed to fuse point features with a voxel backbone, including Local P2V and Multi-Scale P2V. The P2V modules respectively integrate local detail features and multi-scale semantic contexts into a sparse voxel backbone. Thirdly, an auxiliary reconstruction loss is employed on the point branch to explicitly guide the point backbone to be aware of real geometry structures. In addition, we extend VIC-Net to a two-stage approach, namely VIC-RCNN, which further utilizes the fine geometry features to refine object locations. Experiments on the KITTI dataset demonstrate that our proposed VIC-Net outperforms other onestage methods and our two-stage method VIC-RCNN achieves new state-of-the-art performance.