This paper studies the removal of salt-and-pepper noise from images using median filter (MF) and simple three-layer autoencoder (AE) within recursive threshold algorithm. The performance of denoising is assessed with two metrics: the standard Structural Similarity Index SSIMImg of restored and clean images and a newly applied metric SSIMMap - the SSIM of entropy maps of these images computed via 2D Sample Entropy in sliding windows. We shown that SSIMMap is more sensitive to blur and local intensity transitions and complements SSIMImg. Experiments on low- and high-resolution grayscales images demonstrate that recursive threshold MF robustly restores images even under strong noise (50-60 %), whereas simple AE is only capable of restoring images with low levels of noise (<30 %). We propose two scalable schemes: (i) 2MF, which uses two MFs with different window sizes and a final thresholding step, effective for highlighting sharp local details at low resolution; and (ii) MFs-AE, which aggregates features from multiple MFs via an AE and is beneficial for restoring the overall scene structure at higher resolution. Owing to its simplicity and computational efficiency, MF remains preferable for deployment on resource-constrained platforms (edge/IoT), whereas AE underperforms without prior denoising. The results also validate the practical value of SSIMMap for objective blur assessment and denoising parameter tuning.

Navigating and localizing in partially observable, stochastic environments with magnetic anomalies presents significant challenges, especially when balancing the accuracy of state estimation and the stability of localization. Traditional approaches often struggle to maintain performance due to limited localization updates and dynamic conditions. This paper introduces a multi-objective global path planner for magnetic anomaly navigation (MagNav), which leverages entropy maps to assess spatial frequency variations in magnetic fields and identify high-information areas. The system generates paths toward these regions by employing a potential field planner, enhancing active localization. Hardware experiments demonstrate that the proposed method significantly improves localization stability and accuracy compared to existing active localization techniques. The results underscore the effectiveness of this method in reducing localization uncertainty and highlight its adaptability to various gradient-based navigation maps, including topographical and underwater depth-based environments.

This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions of a pedestrian agent situated in a specific point of the simulated environment with an heatmap approach. Experimental results highlighting the relevance of this tool supporting modelers are provided and discussed. Keywords: Data Visualization · Modelling and Simulation · Stochastic Models. 1 Introduction & Related Works Computer simulation of complex systems often employs stochastic models: implied randomness is a way to account for aspects that are potentially relevant to the overall phenomenon but cannot be explicitly considered to keep the model and the modelling phase manageable [3]. Pedestrian and crowd behaviour simulation, for instance, requires considering different kinds of decisions, taken at distinct levels of abstraction, employing heterogeneous information and knowledge about the environment, from path planning [7] to the regulation of distance from other pedestrians and obstacles present in the environment[2,8]. Exploring implications of randomness and situations of indecision, irresolution in case of choice among alternative lines of behaviour such as the exits from an environment in an emergency situation [10], can be a very significant step, with important implications of overall simulation results. This paper presents an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. As in the framework of probability theory [12], we use the concept of entropy to provide a measure of uncertainty over the simulated space The paper, first of all, describes a general decision making model for supporting wayfinding, which comes from previous work by the authors [8,7].

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i.e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.

We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit. In order to select maximally-informative queries, the intelligent agent needs to be able to compute the relevance of a question. This is accomplished by employing the inquiry calculus, which is dual to the probability calculus, and extends information theory by explicitly requiring context. Here, we consider the interaction between two question-asking intelligent agents, and note that there is a potential information redundancy with respect to the two questions that the agents may choose to pose. We show that the information redundancy is minimized by maximizing the joint entropy of the questions, which simultaneously maximizes the relevance of each question while minimizing the mutual information between them. Maximum joint entropy is therefore an important principle of information-based collaboration, which enables intelligent agents to efficiently learn together.