Non-Intrusive Load Monitoring (NILM) identifies the operating status and energy consumption of each electrical device in the circuit by analyzing the electrical signals at the bus, which is of great significance for smart power management. However, the complex and changeable load combinations and application environments lead to the challenges of poor feature robustness and insufficient model generalization of traditional NILM methods. To this end, this paper proposes a new non-intrusive load monitoring method that integrates "image load signature" and continual learning. This method converts multi-dimensional power signals such as current, voltage, and power factor into visual image load feature signatures, and combines deep convolutional neural networks to realize the identification and classification of multiple devices; at the same time, self-supervised pre-training is introduced to improve feature generalization, and continual online learning strategies are used to overcome model forgetting to adapt to the emergence of new loads. This paper conducts a large number of experiments on high-sampling rate load datasets, and compares a variety of existing methods and model variants. The results show that the proposed method has achieved significant improvements in recognition accuracy.

Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) demand robust spatial perception capabilities, including Simultaneous Localization and Mapping (SLAM), to support both remote and autonomous tasks. Vision-based systems have been integral to these advancements, capturing rich color and texture at low cost while enabling semantic scene understanding. However, underwater conditions -- such as light attenuation, backscatter, and low contrast -- often degrade image quality to the point where traditional vision-based SLAM pipelines fail. Moreover, these pipelines typically rely on monocular or stereo inputs, limiting their scalability to the multi-camera configurations common on many vehicles. To address these issues, we propose to leverage multi-modal sensing that fuses data from multiple sensors-including cameras, inertial measurement units (IMUs), and acoustic devices-to enhance situational awareness and enable robust, real-time SLAM. We explore both geometric and learning-based techniques along with semantic analysis, and conduct experiments on the data collected from a work-class ROV during several field deployments in the Trondheim Fjord. Through our experimental results, we demonstrate the feasibility of real-time reliable state estimation and high-quality 3D reconstructions in visually challenging underwater conditions. We also discuss system constraints and identify open research questions, such as sensor calibration, limitations with learning-based methods, that merit further exploration to advance large-scale underwater operations.

Accurate localization is essential for a wide range of applications, including asset tracking, smart agriculture, and animal monitoring. While traditional localization methods, such as Global Navigation Satellite System (GNSS), Wi-Fi, and Bluetooth Low Energy (BLE), offer varying levels of accuracy and coverage, they have drawbacks regarding power consumption, infrastructure requirements, and deployment flexibility. Ultra-Wideband (UWB) is emerging as an alternative, offering centimeter-level accuracy and energy efficiency, especially suitable for medium to large field monitoring with capabilities to work indoors and outdoors. However, existing UWB localization systems require infrastructure with mains power to supply the anchors, which impedes their scalability and ease of deployment. This underscores the need for a fully battery-powered and energy-efficient localization system. This paper presents an energy-optimized, battery-operated UWB localization system that leverages Long Range Wide Area Network (LoRaWAN) for data transmission to a server backend. By employing single-sided two-way ranging (SS-TWR) in a time-slotted localization approach, the power consumption both on the anchor and the tag is reduced, while maintaining high accuracy. With a low average power consumption of 20.44 mW per anchor and 7.19 mW per tag, the system allows fully battery-powered operation for up to 25 days, achieving average accuracy of 13.96 cm with self-localizing anchors on a 600 m2 testing ground. To validate its effectiveness and ease of installation in a challenging application scenario, ten anchors and two tags were successfully deployed in a tropical zoological biome where they could be used to track Aldabra Giant Tortoises (Aldabrachelys gigantea).

We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user preference. Given the lack of accurate and generalizable battery degradation models, we leverage input convex neural networks (ICNNs) to develop a data-driven degradation model trained on extensive experimental datasets. This approach enables our model to capture nonconvex dependencies on battery temperature and time while maintaining convexity with respect to the charging rate. Such a partial convexity property ensures that the second stage of our methodology remains computationally efficient. In the second stage, we integrate our data-driven degradation model into a multi-objective optimization framework to generate an optimal smart charging profile for each EV. This profile effectively balances the trade-off between financial benefits from V2G participation and battery degradation, controlled by a hyperparameter reflecting the user prioritization of battery health. Numerical simulations show the high accuracy of the ICNN model in predicting battery degradation for unseen data. Finally, we present a trade-off curve illustrating financial benefits from V2G versus losses from battery health degradation based on user preferences and showcase smart charging strategies under realistic scenarios.

Accurate prediction of the Remaining Useful Life (RUL) is essential for enabling timely maintenance of lithium-ion batteries, impacting the operational efficiency of electric applications that rely on them. This paper proposes a RUL prediction approach that leverages data from recent charge-discharge cycles to estimate the number of remaining usable cycles. The approach introduces both a novel signal processing pipeline and a deep learning prediction model. In the signal preprocessing pipeline, a derived capacity feature $\dot{Q}(I, Q)$ is computed based on current and capacity signals. Alongside original capacity, voltage and current, these features are denoised and enhanced using statistical metrics and a delta-based method to capture differences between the current and previous cycles. In the prediction model, the processed features are then fed into a hybrid deep learning architecture composed of 1D Convolutional Neural Networks (CNN), Attentional Long Short-Term Memory (A-LSTM), and Ordinary Differential Equation-based LSTM (ODE-LSTM) blocks. This architecture is designed to capture both local signal characteristics and long-range temporal dependencies while modeling the continuous-time dynamics of battery degradation. The model is further evaluated using transfer learning across different learning strategies and target data partitioning scenarios. Results indicate that the model maintains robust performance, even when fine-tuned on limited target data. Experimental results on two publicly available large-scale datasets demonstrate that the proposed method outperforms a baseline deep learning approach and machine learning techniques, achieving an RMSE of 101.59, highlighting its strong potential for real-world RUL prediction applications.

The performance of battery materials is determined by their composition and the processing conditions employed during commercial-scale fabrication, where raw materials undergo complex processing steps with various additives to yield final products. As the complexity of these parameters expands with the development of industry, conventional one-factor-at-a-time (OFAT) experiment becomes old fashioned. While domain expertise aids in parameter optimization, this traditional approach becomes increasingly vulnerable to cognitive limitations and anthropogenic biases as the complexity of factors grows. Herein, we introduce an iterative machine learning (ML) framework that integrates active learning to guide targeted experimentation and facilitate incremental model refinement. This method systematically leverages comprehensive experimental observations, including both successful and unsuccessful results, effectively mitigating human-induced biases and alleviating data scarcity. Consequently, it significantly accelerates exploration within the high-dimensional design space. Our results demonstrate that active-learning-driven experimentation markedly reduces the total number of experimental cycles necessary, underscoring the transformative potential of ML-based strategies in expediting battery material optimization.

Personal use of this material is permitted. Abstract --Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies. Virtual Platforms (VPs) enable comprehensive system modeling and simulation before physical production [1] and are thus a crucial resource in the design of modern embedded systems, characterized by heterogeneity and tight integration with the physical environment.

Recently, Wireless Rechargeable Sensor Networks (WRSNs) that leveraged the advantage of wireless energy transfer technology have opened a promising opportunity in solving the limited energy issue. However, an ineffective charging strategy may reduce the charging performance. Although many practical charging algorithms have been introduced, these studies mainly focus on optimizing the charging path with a fully charging approach. This approach may lead to the death of a series of sensors due to their extended charging latency. This paper introduces a novel partial charging approach that follows a bi-level optimized scheme to minimize energy depletion in WRSNs. We aim at optimizing simultaneously two factors: the charging path and time. To accomplish this, we first formulate a mathematical model of the investigated problem. We then propose two approximate algorithms in which the optimization of the charging path and the charging time are considered as the upper and lower level, respectively. The first algorithm combines a Multi-start Local Search method and a Genetic Algorithm to find a solution. The second algorithm adopts a nested approach that utilizes the advantages of the Multitasking and Covariance Matrix Adaptation Evolutionary Strategies. Experimental validations on various network scenarios demonstrate that our proposed algorithms outperform the existing works. Introduction A Wireless Sensor Network (WSN) consists of a collection of battery-powered sensor nodes deployed in a region of interest to monitor the physical environment and transfer the sensing information to the Base Station (BS) via multi-hop communication. However, limited energy issues remain as a major bottleneck phenomenon in WSNs. When a sensor's battery is exhausted, the sensor becomes a dead node and loses its monitoring and communicating ability causing a series of negative impacts on the whole network performance [1, 7]. Therefore, one of the most critical conditions in continuously maintaining the network's operation is to avoid the energy depletion of the sensor nodes. Energy-saving methods have been applied to prolong the sensor lifetime during the past decade [2, 8].

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.

Microsoft has begun testing a new power-saving technology within Windows, as well as assigning AI actions to a right-click menu within File Explorer. Microsoft is also tweaking the way in which widgets are laid out, letting Copilot handle the decisions itself. Microsoft published the changes as part of the Windows 11 Insider Preview Build 26120.4151 By testing these features, Microsoft doesn't necessarily have to commit to eventually rolling them out, although many appear to be under consideration for a more general release. Under the hood, Microsoft said that it's testing out what it calls User Interaction-Aware CPU Power Management, "an OS-level enhancement that helps reduce power consumption and extend your battery life."