Transformers have gained attention in atmospheric time series forecasting (ATSF) for their ability to capture global spatial-temporal correlations. However, their complex architectures lead to excessive parameter counts and extended training times, limiting their scalability to large-scale forecasting. In this paper, we revisit ATSF from a theoretical perspective of atmospheric dynamics and uncover a key insight: spatial-temporal position embedding (STPE) can inherently model spatial-temporal correlations even without attention mechanisms. Its effectiveness arises from integrating geographical coordinates and temporal features, which are intrinsically linked to atmospheric dynamics. Based on this, we propose **STELLA**, a **S**patial-**T**emporal knowledge **E**mbedded **L**ightweight mode**L** for ASTF, utilizing only STPE and an MLP architecture in place of Transformer layers. With 10k parameters and one hour of training, STELLA achieves superior performance on five datasets compared to other advanced methods. The paper emphasizes the effectiveness of spatial-temporal knowledge integration over complex architectures, providing novel insights for ATSF.

Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making. Although transformer-based models have made progress in this field, they usually do not make full use of three features of multivariate time series: global information, local information, and variables correlation. To effectively mine the above three features and establish a high-precision prediction model, we propose a double sampling transformer (DSformer), which consists of the double sampling (DS) block and the temporal variable attention (TVA) block. Firstly, the DS block employs down sampling and piecewise sampling to transform the original series into feature vectors that focus on global information and local information respectively. Then, TVA block uses temporal attention and variable attention to mine these feature vectors from different dimensions and extract key information. Finally, based on a parallel structure, DSformer uses multiple TVA blocks to mine and integrate different features obtained from DS blocks respectively. The integrated feature information is passed to the generative decoder based on a multi-layer perceptron to realize multivariate time series long-term prediction. Experimental results on nine real-world datasets show that DSformer can outperform eight existing baselines.

Hypothesis-oriented Multiple Hypothesis Tracking (HOMHT) recursively generates hypotheses on the origins of measurements and manages them, therefore it is computationally intensive. To speed up HOMHT for tracking hundreds of targets in real time, we propose a parallel implementation of this algorithm which distributes hypotheses into independent worker threads residing in multiple CPU cores. The implementation in this paper is based on object-oriented programming: each hypothesis object manages its target data all by itself and the generation and pruning of a hypothesis is achieved by its copy constructor and destructor functions. We evaluate this method by tracking 150 targets through 3 heterogeneous sensors in real-time with 32-best hypotheses running in 1, 2, 4, 8, 16 and 32 worker threads respectively. The results validate the method's scalability in which measurement fusion latency is approximately inversely proportional to worker thread count. We also make a pressure test by tracking 500 targets through 3 sensors, and HOMHT is able to run concurrently in real-time with 32 worker threads.

The prevalent adoptions of GPS-enabled devices have witnessed an explosion of various location-based services which produces a huge amount of trajectories monitoring the individuals' movements. In this paper, we tackle Trajectory-User Link (TUL) problem, which identifies humans' movement patterns and links trajectories to the users who generated them. Existing solutions on TUL problem employ recurrent neural networks and variational autoencoder methods, which face the bottlenecks in the case of excessively long trajectories and fragmentary users' movements. However, these are common characteristics of trajectory data in reality, leading to performance degradation of the existing models. In this paper, we propose an end-to-end attention recurrent neural learning framework, called TULAR (Trajectory-User Link with Attention Recurrent Networks), which focus on selected parts of the source trajectories when linking. TULAR introduce the Trajectory Semantic Vector (TSV) via unsupervised location representation learning and recurrent neural networks, by which to reckon the weight of parts of source trajectory. Further, we employ three attention scores for the weight measurements. Experiments are conducted on two real world datasets and compared with several existing methods, and the results show that TULAR yields a new state-of-the-art performance. Source code is public available at GitHub: https://github.com/taos123/TULAR.

Trajectory-user linking (TUL), whereby a trajectory is linked to its owner in location-based social networks, is a fundamental and critical task in spatio-temporal data mining. It plays a key role in personalized recommendation, anomaly detection, and semantic trajectory mining. Existing methods for TUL are either rule-based methods, which link trajectories and users based on conventional trajectory similarities, or learning-based methods, which learn a classification model to map trajectories to their owners. However, rule-based methods ignore the semantic information in the trajectory sequence, and learning-based methods require retraining the model each time a new user is added. In this paper, we propose a Siamese network-based model for trajectory-user linking (TULSN), which uses a Siamese network to capture semantic information in the trajectory, and instead of retraining the model, it requires only a few labeled trajectories per user to identify the user category of the trajectory. The experimental results show that the TULSN outperforms existing baselines and state-of-the-art methods on real-world datasets.

Human tracking is one of the most important requirements for service mobile robots. Cameras and Laser Ranger Finders (LRFs) are usually used together for human tracking. But these kinds of solutions are too computationally expensive for most embedded processors on these robots as complex computer vision algorithms are needed to process large number of pixels. In this paper, we describe a method combining kinematic measurements from LRF mounted on the robot and Inertial Measurement Unit (IMU) carried by the target. These two types of sensors can calculate human's velocity and position independently, which are used as information for both indentifying and tracking the target. As pixels observed by LRF and IMU are 1D rather than 2D, our method requires much less computation and memory resources and can be implemented with low-performance embedded processors.

No abstract available.

Some previous work has shown the feasibility of Pedestrian Dead Reckoning (PDR) using a mobile phone, but the estimation of length walked is still a big challenge. In this paper, we propose a formula for estimating walking velocity, which can be integrated to get distance.