In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session. Due to the distinct nature of the data, current methods typically design individual architectures and train separate models for each task. This fragmentation results in redundant training processes, repetitive technological innovations, and limited cross-modal knowledge sharing. In contrast, SUTrack demonstrates that a single model with a unified input representation can effectively handle various SOT tasks, eliminating the need for task-specific designs and separate training sessions. Additionally, we introduce a task-recognition training strategy and a soft token type embedding to further enhance SUTrack's performance with minimal overhead. Experiments show that SUTrack outperforms previous task-specific counterparts across 11 datasets spanning five SOT tasks. Moreover, we provide a range of models catering edge devices as well as high-performance GPUs, striking a good trade-off between speed and accuracy. We hope SUTrack could serve as a strong foundation for further compelling research into unified tracking models.

Physical layer encryption is an emerging security paradigm which supplements the higher-layer encryption solutions. To realize physical layer encryption, channel measurement is typically utilized as a randomness source to generate secret keys. Due to non-perfect reciprocity of the channels and the inevitable channel estimation errors resulting from noises and interferences, the channel measurements at the legitimate transceivers are not the same, yielding a mismatch in the generated keys. To address this issue, a distributed physical layer key generation scheme based on deep learning is proposed in this paper. A channel state information (CSI) learning neural network (CLNet) based on the autoencoder is deployed at both the transmitter and the receiver side to refine the initial channel estimation results. The CLNet takes the least square (LS) channel estimations as input and outputs the channel estimations more similar to the real channel. To train this CLNet, a channel equalization module together with a pre-trained maximum-likelihood (ML) detection network is deployed, which is used to make a decision on the transmitted pilot signal. Since the pilot is known as a priori, the cross entropy loss between the known pilot and the recovered one can be calculated, which implicitly indicates the accuracy of the refined CSI. Further, the weighted sum of the aforementioned cross entropy loss and the mean square loss between the original and the refined CSI estimates is utilized for training, which not only improves the CSI estimation quality but also avoids the over-fitting effect. Extensive simulation results show that compared with benchmark schemes, the proposed scheme improves the key agreement rate and the key generation rate at the legitimate users. Moreover, our method ensures a higher key disagreement rate between the legitimate user and the eavesdropper as well, especially in the low signal-to-noise ratio (SNR) regime.