Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, being the most frequently diagnosed cancer among women and the second leading cause of cancer-related mortality. Precise tumor segmentation is essential for breast cancer assessment, as it enables accurate estimation of tumor size, monitoring of disease progression, and evaluation of treatment effectiveness. Despite the importance of this task, the development of reliable automatic methods is hindered by the scarcity of fully annotated datasets, which makes manual labeling both time-consuming and subject to inter-observer variability. In this study, we propose an unsupervised 3D tumor segmentation method based on Fuzzy C-Means (FCM) clustering, specifically designed for volumetric Dynamic Contrast-Enhanced MRI (DCE-MRI) of the breast. Unlike supervised deep learning approaches, our method does not require manual annotations for training, making it especially valuable in scenarios with limited labeled data. The proposed pipeline combines preprocessing, region-of-interest extraction, and FCM-based clustering to generate accurate segmentation masks with minimal human intervention. We evaluated our approach using clinical data from the ACRIN-6698 dataset, comparing the automatic segmentations against expert manual annotations. The method achieved high performance across multiple metrics, including accuracy, precision, recall, specificity, Dice-Sørensen coefficient (DSC), and Jaccard index (IoU). These results demonstrate the feasibility of unsupervised clustering techniques for volumetric breast tumor segmentation, offering a promising alternative to supervised methods in clinical contexts where annotated data is limited.

Abstract Photosensitivity, in relation to epilepsy, is a genetically determined condition in which patients have epileptic seizures of different severity provoked by visual stimuli. It can be diagnosed by detecting epileptiform discharges in their electroencephalogram (EEG), known as photoparoxysmal responses (PPR). The most accepted PPR detection method—a manual method—considered as the standard one, consists in submitting the subject to intermittent photic stimulation (IPS), i.e. a flashing light stimulation at increasing and decreasing flickering frequencies in a hospital room under controlled ambient conditions, while at the same time recording her/his brain response by means of EEG signals. This research focuses on introducing virtual reality (VR) in this context, adding, to the conventional infrastructure a more flexible one that can be programmed and that will allow developing a much wider and richer set of experiments in order to detect neurological illnesses, and to study subjects’ behaviours automatically. The loop includes the subject, the VR device, the EEG infrastructure and a computer to analyse and monitor the EEG signal and, in some cases, provide feedback to the VR. As will be shown, AI modelling will be needed in the automatic detection of PPR, but it would also be used in extending the functionality of this system with more advanced features. This system is currently in study with subjects at Burgos University Hospital, Spain.

One of the biggest problems in EEG recordings is the contamination of the signal caused by artifacts because these interferences hinder the analysis of real neural information. Thus, their elimination while preserving as much brain data as possible is a key procedure before the study of the EEG. To address the automatic removal of craniofacial artifacts, this paper proposes a two-stages procedure: the former one is the detection stage - where both a MLP neural network and a dynamic threshold method are applied to detect the contaminated areas of the EEG-, while the latter is the removal stage - combining CCA and EEMD algorithms to remove the artifact data only. Experimental results show that both detection methods are comparable, but with the dynamic threshold detection slightly outperforming the MLP. Also, the combined technique can completely remove those artifacts scattered in all the EEG channels. This study will be extended to ocular artifacts, where more complex models would be required.

Abstract Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite‐state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dictionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimentation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed similarly for all the individuals tested. Furthermore, the positive effects of TL in this context are clearly remarked.

The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.

Programmable logic controllers (PLCs) are embedded computers widely used in industrial control systems. Ensuring that a PLC software complies with its specification is a challenging task. Formal verification has become a recommended practice to ensure the correctness of safety-critical software, but is still underused in industry due to the complexity of building and managing formal models of real applications. In this paper, we propose a general methodology to perform automated model checking of complex properties expressed in temporal logics [e.g., computation tree logic (CTL) and linear temporal logic (LTL)] on PLC programs. This methodology is based on an intermediate model (IM) meant to transform PLC programs written in various standard languages [structured text (ST), sequential function chart (SFC), etc.] to different modeling languages of verification tools. We present the syntax and semantics of the IM, and the transformation rules of the ST and SFC languages to the nuXmv model checker passing through the IM. Finally, two real cases studies of the European Organization for Nuclear Research (CERN) PLC programs, written mainly in the ST language, are presented to illustrate and validate the proposed approach.

Testing of critical PLC (Programmable Logic Controller) programs remains a challenging task for control system engineers as it can rarely be automated. This paper proposes a model based approach which uses the BIP (Behavior, Interactions and Priorities) framework to perform automated testing of PLC programs developed with the UNICOS (UNified Industrial COntrol System) framework. This paper defines the translation procedure and rules from UNICOS to BIP which can be fully automated in order to hide the complexity of the underlying model from the control engineers. The approach is illustrated and validated through the study of a water treatment process.

This paper presents MIOOP, a new extension of the IEC 61131-3 norm which allows for an object oriented programming paradigm (OOPP) approach for the development of PLC control software. MIOOP supports the basis of OOPP, i.e. abstraction, encapsulation, modularity, hierarchy and polymorphism. In addition, the five programming languages of the IEC 61131-3 norm are supported by MIOOP. Generated code is compliant with the standard specification, allowing for real implementation in those PLCs vendors supporting the new standard.

The development of control programs is a complex task that needs to be systematised and automated in order to reduce costs. It is demonstrated within the software engineering discipline that the use of analysis and modelling methodologies helps to reduce the software costs by systematising its development. Several methodologies have been designed to analyse and model batch and real-time systems, but not discrete event sequential systems (DESS). This paper demonstrates that the DESS control programs development process can benefit, as batch and real-time systems do, From the improvements in the software engineering, by designing a new object-oriented methodology adapted to the DESS peculiarities.