Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

A D-band (114--170GHz) CMOS transceiver (TRX) chipset covering a 56GHz signal-chain bandwidth with a 640-Gb/s data rate is proposed in this work. The design includes an 8-way low-Q power-combined power amplifier (PA), a 2-way low-Q power-combined low noise amplifier (LNA), wideband-impedance-transformation mixers, and common-source-based cascaded distributed amplifiers (DA) to improve bandwidth and linearity. The proposed TRX chipset achieves a 200Gb/s SISO data rate and a 640Gb/s MIMO data rate.

Hyperspectral target detection (HTD) can provide detailed information about the objects and materials within a scene and holds significant importance in remote sensing image analysis. Traditional HTD methods often suffer from low detection accuracy when applied to hyperspectral images (HSIs) with low spatial resolution, where the target only occupies a few pixels. This can be addressed by exploiting the higher spatial resolution of multispectral images (MSIs). In addition, many cloud-based HTD methods, which rely on the distributed processing capability of cloud computing to cope with large-scale datasets, may result in long transmission delays that cannot meet real-time requirements. This article suggests a cloud-edge collaborative HTD approach based on the fusion of remotely sensed HSIs and MSIs. We first introduce an HTD algorithm that employs low-rank matrix decomposition and hierarchical constraint energy minimization (hCEM) to fuse a low-resolution HSI (LR-HIS) and a high-resolution MSI (HR-MSI). Aiming at a continuous shooting scenario, we further present a cloud-edge implementation of the HTD algorithm through the collaboration of a cloud cluster and edge servers deployed close to data acquisition devices. The overall processing flow of remotely sensed data fusion in the cloud-edge environment is formulated as a flowshop scheduling-like optimization problem. We develop a co-optimization scheduling algorithm to explore the best resource allocation solutions to the formulated problem. Experimental results on both general-purpose and real-world datasets show that the newly proposed HTD algorithm leads to significant improvements in detection accuracy over traditional methods, and the cloud-edge collaborative approach further enhances computational efficiency.