This work deals with model based fault diagnosis of discrete event systems. The model of the system, expressed as an interpreted Petri net (IPN) describes partially observed events and states, and includes all possible faulty states. Based on a modular modelling methodology, the input-output diagnosability property is introduced and structurally characterized. Then a diagnoser scheme is proposed allowing fault detection and location in polynomial time.

Time-Sensitive Networking (TSN) is an in-development technology that enables predictability over Ethernet or wireless networks. Network interfaces compliant with the IEEE 802.1Qbv standard provide different queues/gates on each bridge egress port. In this way, a global network schedule can be set by defining the opening and closing times (Gate Control List, GCL) for each gate. In this paper, we propose a new method to schedule GCLs by dividing the problem into several subproblems. We use Weighted Fair Queuing (WFQ) to set the ordering of frames, and then generate an Integer Linear Programming (ILP) model to optimize the TSN scenario. Next, we assign gates to the scheduled windows, trying to ensure frame isolation whenever possible. Our results show that we can schedule GCLs around 2 times faster than previous studies and up to 5.5 orders of magnitude faster if we choose to obtain any valid solution instead of the optimal one. In addition, we are able to schedule systems with utilization up to 85%, whereas previous papers reach 65%. Moreover, our approach does not need to predefine the number of windows or gates, as required by other methods.

A critical aspect of virtual reality is the extent to which the user forgets their real surroundings and becomes completely engaged within the virtual environment. Diverse factors affect this user perception, which are grouped into two main concepts: immersion and presence. Although the study of presence is extensive, researchers have not reached a consensus on a protocol with specific instruments and stages to evaluate it. This leads to a wide variety of results with different assessment methods, experimental setups, stimuli implemented, and applications. Therefore, this article aims to provide an analysis of the state-of-the-art methods for assessing presence in VR systems during the last few years. This study seeks to determine and improve the understanding of current techniques used for presence assessment, human data collected, data analysis methods, and the technologies and virtual environments implemented. In addition, four opportunities are discussed to provide researchers guidelines that can lead to enhanced presence assessments and personalized VR experiences.

This study focuses on generating reliable signals from measured noisy signals through an enhanced sensor fusion method. The main contribution of this research is the development of a novel sensor fusion architecture that creates virtual sensors, improving the system’s redundancy. This architecture utilizes an input observer to estimate the system input, then it is introduced to the system model, the output of which is the virtual sensor. Then, this virtual sensor includes two filtering stages, both derived from the system’s dynamics—the input observer and the system model—which effectively diminish noise in the virtual sensors. Afterwards, the same architecture includes a classical sensor fusion scheme and a voter to merge the virtual sensors with the real measured signals, enhancing the signal reliability. The effectiveness of this method is shown by applying merged signals to two distinct diagnosers: one utilizes a high-order sliding mode observer, while the other employs an innovative extension of a predefined-time observer. The findings indicate that the proposed architecture improves diagnostic results. Moreover, a three-wheeled omnidirectional mobile robot equipped with noisy sensors serves as a case study, confirming the approach’s efficacy in an actual noisy setting and highlighting its principal characteristics. Importantly, the diagnostic systems can manage several simultaneous actuator faults.