The global demand for aquaculture has surged over the past decade, driving the expansion of offshore fish farming systems such as net pens [1, 2]. These structures, while effective for large-scale fish production, are continuously exposed to harsh marine environments that can degrade structural integrity, compromise biosecurity, and increase the risk of fish escape or environmental contamination [3]. As a result, regular and reliable inspection of aquaculture net pens is critical to ensuring operational safety, productivity, and regulatory compliance [4]. Recent advances in underwater robotics, control systems, and computer vision have enabled significant progress in autonomous inspection [5, 6]. Remotely Operated Vehicles (ROVs), in particular, offer a practical platform for deploying sensing payloads such as cameras, sonars and performing close-range inspection in confined underwater environments [7]. However, most existing ROV-based systems operate in isolation, with limited autonomy and minimal adaptability to dynamic conditions such as power constraints, actuator degradation, and evolving mission demands [8, 9]. Moreover, mission planning and coordination typically require expert operators, limiting the scalability and responsiveness of these systems in real-world aquaculture operations [10, 11, 12]. To address these challenges, we propose AquaChat++, a novel framework that combines the reasoning capabilities of Large Language Models (LLMs) with multi-ROV coordination, battery-aware mission planning, and fault-tolerant control [13, 14]. Unlike traditional inspection pipelines that rely on fixed scripts or manual supervision, AquaChat++ enables natural language-driven task planning and dynamic allocation across multiple ROVs.

One of the key safety considerations of battery manufacturing is thermal runaway, the uncontrolled increase in temperature which can lead to fires, explosions, and emissions of toxic gasses. As such, development of automated systems capable of detecting such events is of considerable importance in both academic and industrial contexts. In this work, we investigate the use of deep learning for detecting thermal runaway in the battery production line of VDL Nedcar, a Dutch automobile manufacturer. Specifically, we collect data from the production line to represent both baseline (non thermal runaway) and thermal runaway conditions. Thermal runaway was simulated through the use of external heat and smoke sources. The data consisted of both optical and thermal images which were then preprocessed and fused before serving as input to our models. In this regard, we evaluated three deep-learning models widely used in computer vision including shallow convolutional neural networks, residual neural networks, and vision transformers on two performance metrics. Furthermore, we evaluated these models using explainability methods to gain insight into their ability to capture the relevant feature information from their inputs. The obtained results indicate that the use of deep learning is a viable approach to thermal runaway detection in battery production lines.

Octilium, Lugano, Switzerland Originally presented at: 2024 32nd Telecommunication Forum (TELFOR), Belgrade, Serbia Please cite this manuscript as: M. Kolakowski and S. B. Bader, "ML-based Short Physical Performance Battery future score prediction based on questionnaire data," 2024 32nd Telecommunications Forum (TELFOR), Belgrade, Serbia, 2024, pp. Additional information: Continuation of the study (prediction of performance in Intrinsic Capacity domains) is presented in: Kolakowski, M.; Lupica, A.; Ben Bader, S.; Djaja-Josko, V .; Personal use of this material is permitted. Abstract --Effective slowing down of older adults' physical capacity deterioration requires intervention as soon as the first symptoms surface. In this paper, we analyze the possibility of predicting the Short Physical Performance Battery (SPPB) score at a four-year horizon based on questionnaire data.

This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalent Circuit Models (ECMs) and machine-learning approaches. In particular, a recent Virtual Sensor (VS) synthesis technique is considered, which operates as follows: (i) learn an Affine Parameter-Varying (APV) model of the cell directly from data, (ii) derive a bank of linear observers from the APV model, (iii) train a machine-learning technique from features extracted from the observers together with input and output data to predict the SOC. The SOC predictions returned by the VS are supplied to an Extended KF (EKF) as output measurements along with the cell terminal voltage, combining the two paradigms. A data-driven calibration strategy for the noise covariance matrices of the EKF is proposed. Experimental results show that the designed approach is beneficial w.r.t. SOC estimation accuracy and smoothness.

All-day smart glasses are likely to emerge as platforms capable of continuous contextual sensing, uniquely positioning them for unprecedented assistance in our daily lives. Integrating the multi-modal AI agents required for human memory enhancement while performing continuous sensing, however, presents a major energy efficiency challenge for all-day usage. Achieving this balance requires intelligent, context-aware sensor management. Our approach, EgoTrigger, leverages audio cues from the microphone to selectively activate power-intensive cameras, enabling efficient sensing while preserving substantial utility for human memory enhancement. EgoTrigger uses a lightweight audio model (YAMNet) and a custom classification head to trigger image capture from hand-object interaction (HOI) audio cues, such as the sound of a drawer opening or a medication bottle being opened. In addition to evaluating on the QA-Ego4D dataset, we introduce and evaluate on the Human Memory Enhancement Question-Answer (HME-QA) dataset. Our dataset contains 340 human-annotated first-person QA pairs from full-length Ego4D videos that were curated to ensure that they contained audio, focusing on HOI moments critical for contextual understanding and memory. Our results show EgoTrigger can use 54% fewer frames on average, significantly saving energy in both power-hungry sensing components (e.g., cameras) and downstream operations (e.g., wireless transmission), while achieving comparable performance on datasets for an episodic memory task. We believe this context-aware triggering strategy represents a promising direction for enabling energy-efficient, functional smart glasses capable of all-day use -- supporting applications like helping users recall where they placed their keys or information about their routine activities (e.g., taking medications).

FOX Business' Kelly Saberi joins'America's Newsroom' to discuss President Donald Trump and the Environmental Protection Agency's push to rescind an emissions law enacted by former President Barack Obama. As demand for clean energy grows, so does the need for smarter storage solutions. Lithium-ion batteries are leading the charge, but they don't last forever. That creates a big problem: what do we do with all the dead batteries? Thanks to a new method developed by researchers at Worcester Polytechnic Institute (WPI), we may finally have an answer.

This project presents the development of a gait recognition system using Tiny Machine Learning (Tiny ML) and Inertial Measurement Unit (IMU) sensors. The system leverages the XIAO-nRF52840 Sense microcontroller and the LSM6DS3 IMU sensor to capture motion data, including acceleration and angular velocity, from four distinct activities: walking, stationary, going upstairs, and going downstairs. The data collected is processed through Edge Impulse, an edge AI platform, which enables the training of machine learning models that can be deployed directly onto the microcontroller for real-time activity classification.The data preprocessing step involves extracting relevant features from the raw sensor data using techniques such as sliding windows and data normalization, followed by training a Deep Neural Network (DNN) classifier for activity recognition. The model achieves over 80% accuracy on a test dataset, demonstrating its ability to classify the four activities effectively. Additionally, the platform enables anomaly detection, further enhancing the robustness of the system. The integration of Tiny ML ensures low-power operation, making it suitable for battery-powered or energy-harvesting devices.

Inspection of aquaculture net pens is essential for maintaining the structural integrity, biosecurity, and operational efficiency of fish farming systems. Traditional inspection approaches rely on pre-programmed missions or manual control, offering limited adaptability to dynamic underwater conditions and user-specific demands. In this study, we propose AquaChat, a novel Remotely Operated Vehicle (ROV) framework that integrates Large Language Models (LLMs) for intelligent and adaptive net pen inspection. The system features a multi-layered architecture: (1) a high-level planning layer that interprets natural language user commands using an LLM to generate symbolic task plans; (2) a mid-level task manager that translates plans into ROV control sequences; and (3) a low-level motion control layer that executes navigation and inspection tasks with precision. Real-time feedback and event-triggered replanning enhance robustness in challenging aquaculture environments. The framework is validated through experiments in both simulated and controlled aquatic environments representative of aquaculture net pens. Results demonstrate improved task flexibility, inspection accuracy, and operational efficiency. AquaChat illustrates the potential of integrating language-based AI with marine robotics to enable intelligent, user-interactive inspection systems for sustainable aquaculture operations.

Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative method based on the analysis of a discharge pulse at the end of the charge phase. The parameters of the equivalent electrical model describing the voltage evolution across the battery terminals during this current pulse are then used to estimate the SoH. Based on the experimental data acquired so far, the initial results demonstrate the relevance of the proposed approach. After training using the parameters of two batteries with a capacity degradation of around 85%, we successfully predicted the degradation of two other batteries, cycled down to approximately 90% SoH, with a mean absolute error of around 1% in the worst case, and an explainability score of the estimator close to 0.9. If these performances are confirmed, this method can be easily integrated into battery management systems (BMS) and paves the way for optimized battery management under continuous operation.

There's a certain peace of mind that comes from being able to see who's outside your door before you even get up from your seat. If you haven't yet made the jump, or you want an upgrade, the Ring Battery Doorbell Pro is on sale for its very best price of 150 ahead of Prime Day. The Ring Battery Doorbell Pro is usually available for 230 and, unlike other similar products, it's not constantly on sale, making this 35% discount even more enticing. The video doorbell captures head-to-toe images in 1536p, which means you'll get a full view of who's outside your door, what's on the porch, and well beyond that. There are many useful features you'll end up loving.