DoDTrack: Indoor Mobile Devices Tracking via Difference-of-Doppler

In this paper, the Doppler frequency shift (DFS) is exploited as the only sensing parameter for low-cost indoor mobile device tracking. The existing trajectory tracking methods via DFS of Wi-Fi systems often require the knowledge of the starting position or additional information, like angle-of-arrival (AoA) and time-of-flight (ToF), to recover the trajectory of a moving target. This paper proposes the DoDTrack, a novel Difference-of-Doppler (DoD)-based tracking system, to track an active mobile device using a single receiver with distributed antennas. By comparing the signals received at the distributed receive antennas, which share the oscillator, the DoDs among the antennas can be detected robustly. Then, the reconstruction of the trajectory without prior knowledge of the trajectory starting position can be formulated as a minimum mean square error (MMSE) problem, which can be solved via alternating optimization. Particularly, the starting position and the trajectory shape are updated alternately in the proposed algorithm. In performance validation, we implemented the proposed DoDTrack design on a USRP-X310 platform, and assessed its estimation accuracy with various trajectory shapes in an indoor environment. Experimental results demonstrate that DoDTrack achieves a median tracking error of 0.34 m within a 6 m $\times$ 6 m sensing area, offering a high-precision and low-cost solution for active device tracking.

Comprehensive pKa Data Augmentation from Limited Real Data through an Engineered Models-Quantum Framework

Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling. Building on iBonD, the largest experimental pKa database established, we and other researchers have developed several methods including machine-learning-based empirical prediction and high-accuracy energy calculations. Despite this foundation, the rapid augmentation of high-quality pKa data remains fundamentally constrained. As part of this work, we performed large-scale regression-based pKa prediction on unlabeled molecular datasets using a collection of extensively optimized machine-learning models. The results indicate that, since the feature distributions of unlabeled molecular datasets, the pKa data distribution approximates normality, with extreme scarcity of tail-region samples. Although such augmentation is highly valuable for improving overall data availability and predictive modeling, it remains insufficient for efficiently discovering molecules with broad-spectrum pKa properties. To address this, we explore the targeted generation of molecules with sparse pKa properties from the vast chemical space. Given that traditional continuous latent space VAE-RNN methods for molecular generation suffer from insufficient stability and fail to demonstrate clear advantages in complementing sparse data, we design and implement a quantum-assisted sparse-pKa molecular generation. Feasibility is validated on a simulated quantum annealer, and superior extreme-value sampling is further achieved on physical coherent Ising machines (CIMs). (to be continued)