Fiber-optic distributed acoustic sensor (DAS) offers great potential for railway event monitoring due to its high sensitivity and robustness in complex environments. However, accurate recognition of acoustic events remains challenging, under real-time constraints where only short-duration signal segments with limited discriminative information are available. To overcome this, an efficient recognition framework was proposed by integrating multi-encoding image fusion and a lightweight transformer-convolutional network (CNN). Specifically, 0.032-second DAS signal segments were converted into complementary image representations, which were then fused into RGB images to enhance feature diversity. These multimodal images were processed by a compact backbone that combined vision transformer (ViT) modules with convolutional components, enabling effective extraction of multi-scale local and global features. The hybrid framework effectively mitigated the lack of temporal information in short segments, achieving 98.19% accuracy with only 1.33 million parameters and 3.3 ms latency per sample, substantially faster than existing methods. Compared to baseline models, the proposed architecture achieved superior accuracy and compactness, making it highly suitable for real-time and edge deployment in DAS-based monitoring systems.

We develop a technique to automatically generate a control policy for a robot moving in an environment that includes elements with partially unknown, changing behavior. The robot is required to achieve an optimal surveillance mission, in which a certain request needs to be serviced repeatedly, while the expected time in between consecutive services is minimized. We define a fragment of Linear Temporal Logic (LTL) to describe such a mission and formulate the problem as a temporal logic game. Our approach is based on two main ideas. First, we extend results in automata learning to detect patterns of the partially unknown behavior of the elements in the environment. Second, we employ an automata-theoretic method to generate the control policy.We show that the obtained control policy converges to an optimal one when the unknown behavior patterns are fully learned. We implemented the proposed computational framework in MATLAB. Illustrative case studies are included.

We present a computational framework for automatic synthesis of control and communication strategies for a robotic team from task specifications that are given as regular expressions about servicing requests in an environment. We assume that the location of the requests in the environment and the robot capacities and cooperation requirements to service the requests are known. Our approach is based on two main ideas. First, we extend recent results from formal synthesis of distributed systems to check for the distributability of the task specification and to generate local specifications, while accounting for the service and communication capabilities of the robots. Second, by using a technique that is inspired by linear temporal logic model checking, we generate individual control and communication strategies. We illustrate the method with experimental results in our robotic urban-like environment.

We consider the following problem: GIVEN (1) a set of service requests occurring at known locations in an environment, (2) a set of temporal and logical constraints on how the requests need to be serviced, (3) a team of robots and their capacities to service the requests individually or through collaboration, FIND robot control and communication strategies guaranteeing the correct servicing of the requests. Our approach is hierarchical. At the top level, we check whether the specification, which is a regular expression over the requests, is distributable among the robots given their service and cooperation capabilities; if the answer is positive, we generate individual specifications in the form of finite state automata, and interaction rules in the form of synchronizations on shared requests. At the bottom level, we check whether the local specifications and the synchronizations can be implemented given the motion and communication constraints of the robots; if the answer is positive, we generate robot motion and service plans, which are then mapped to control and communication strategies. We illustrate the method with experimental and simulation results.