Accurate decoding of lower-limb motion from EEG signals is essential for advancing brain-computer interface (BCI) applications in movement intent recognition and control. This study presents NeuroDyGait, a two-stage, phase-aware EEG-to-gait decoding framework that explicitly models temporal continuity and domain relationships. To address challenges of causal, phase-consistent prediction and cross-subject variability, Stage I learns semantically aligned EEG-motion embeddings via relative contrastive learning with a cross-attention-based metric, while Stage II performs domain relation-aware decoding through dynamic fusion of session-specific heads. Comprehensive experiments on two benchmark datasets (GED and FMD) show substantial gains over baselines, including a recent 2025 model EEG2GAIT. The framework generalizes to unseen subjects and maintains inference latency below 5 ms per window, satisfying real-time BCI requirements. Visualization of learned attention and phase-specific cortical saliency maps further reveals interpretable neural correlates of gait phases. Future extensions will target rehabilitation populations and multimodal integration.

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.

Nowadays, couriers are still the main solution to address the "last mile" problem in logistics. They are usually required to record the delivery time of each parcel manually, which is essential for delivery insurances, delivery performance evaluations, and customer available time discovery. Stay points extracted from couriers' trajectories provide a chance to fill the delivery time automatically to ease their burdens. However, it is challenging due to inaccurate delivery locations and various stay scenarios. To this end, we propose the improved Delivery Time Inference (DTInf+), to infer the delivery time of waybills based on couriers' trajectories. Our solution is composed of three steps: 1) Data Pre-processing, which organizes waybills and stay points by delivery trips, 2) Delivery Location Mining, which obtains the delivery location for each address and each Geocoded waybill location by mining historical delivery caused stay points, and 3) Delivery Event-based Matching, which jointly selects the best-matched stay point for waybills at the same delivery location based on Pointer Network-like model SPSelector to infer the delivery time. Extensive experiments and case studies based on real-world datasets from JD Logistics confirm the effectiveness of our approach. Finally, a system powered by DTInf+ is deployed in JD Logistics.

Predicting gait trajectories from electroencephalography (EEG) is useful for lower limb rehabilitation. The temporal dynamics of EEG pertaining to individual subjects while they are walking require a personalized treatment inside the deep neural network. Also, the EEG data collected from different sessions or days may exhibit varied distributions, further hindering the model generality. Therefore, we propose the Multi-model ATtention Network (MATN) to address these issues. MATN exploits the self-attention mechanism on the spatio-temporal EEG encoding to learn the subject-specific temporal dynamics. To adapt to varied data distributions, MATN employs knowledge distillation, which includes a teacher and student model. The latter is supervised by the intermediate feature maps from the trained teacher and the labels. Experiments of the regression of bilateral joint angles on the legs (hip, knee, and ankle) are carried out using the MoBI dataset in a subject-specific manner. Our MATN achieves more than 18% improvement on the Pearson's correlation coefficient compared to five other state-of-the-art methods.

In this paper, we introduce a new robust 1-bit compressive sensing reconstruction algorithm in the noisy environment, where a lot of signs are flipped. By calculating the number of inconsistencies, this algorithm can adaptively detect the positions where sign flips occur. Subsequently, the sparse signals can be recovered by using "corrected" measurements without a priori knowledge of the noise level. Numerical experiments show the high performance of signal recovery for the proposed algorithm in the presence of both measurement noise and transmission noise.