Non-Intrusive Load Monitoring (NILM), also known as energy disaggregation, is an effective technique for monitoring the energy consumption of individual household appliances. Although NILM offers a promising solution for energy efficiency, its progress is limited by the lack of benchmarks for a fair comparison of the published results. This problem is particularly acute for evaluating the NILM models trained on high-frequency datasets. To address the lack of benchmarks, we developed NILMbench, a comprehensive evaluation framework with a fully labeled high-frequency dataset, FLEP, and an interpretable domain-specific metric, the modified Jaccard score. The FLEP dataset is the extended version of the well-known PLAID dataset and contains 110,000 aggregated signatures labeled with appliances' power shares and achieves 96% similarity to real-world measurements. It also introduces scenarios with up to 10 concurrently operating devices to evaluate model performance under stress conditions and properly reflect the complexity of real-world settings. NILMbench ensures fair comparison by training, and testing energy disaggregation models under identical conditions, including the same dataset, training framework, and evaluation metric.

The early identification of Autism Spectrum Disorder (ASD) is crucial for facilitating prompt therapies that can markedly enhance quality of life. This work presents an innovative EEG-based Brain-Computer Interface (BCI) system aimed at improving the precision and dependability of ASD classification. Utilizing the BCIAUT P300 dataset, comprising EEG recordings from 15 participants across 105 sessions, we established a novel multimodal framework. This system incorporates Vision Transformer (ViT) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal analysis, merging the advantages of both architectures. The ViT-LSTM model attained a validation accuracy of 94.75%, but the EfficientNet-LSTM model exhibited superior performance with a validation accuracy of 95.61%. Statistical investigations, including a P-value of 0.0288 and a T-value of 5.76 for the EfficientNet-based design, validated the reliability and robustness of these findings. ViT adeptly captures global spatial relationships using self-attention processes, whereas EfficientNet improves spatial representation through its pretrained feature extraction. To enhance usability in clinical environments, Explainable AI (XAI) methodologies, particularly Local Interpretable Model-Agnostic Explanations (LIME), were utilized. LIME offers transparent, feature-specific insights into model predictions, enabling physicians to comprehend and have confidence in the decision-making process. This system sets a new standard in AI-driven brain-computer interfaces by merging cutting-edge accuracy with interpretability, providing scalable and significant solutions for the diagnosis and treatment of ASD.

The underground layers of the Earth contain immense resources that require geophysical surveys. This article presents an end-to-end, self-powered wireless sensor network (WSN) for geophysical surveys. The WSN conducts geophysical surveys in an energy-efficient, portable manner. It includes a sensing element, advanced electronics, data processing and digitization, and wireless transmission with networking capabilities between sensing nodes. The system is equipped with a power management module with solar-powered charging capabilities, allowing for at least six days of effective operation on a few hours' worth of charge. The electronic circuitry performing amplification and filtering provides cutoff frequencies of 8.2-108 Hz, and the sensor node exhibits a sampling frequency of 600 SPS. Furthermore, the system implements power modes (active/sleep) to reduce power consumption, with a nominal power usage of only 650 mW at its maximum. The WSN comprises a multi-hop implementation with smart routing to ensure powerefficient and reliable data transmission. Additionally, message encryption is implemented for enhanced wireless security. A field test was conducted to validate the proposed geophysical data acquisition system. Geophysical signals were detected and wirelessly transmitted over a 200 m 2 area employing a network of six nodes to a storage unit, where they were successfully reconstructed and remained stored for later processing and analysis.

Global e-commerce has transformed international trade, enabling businesses and consumers to connect across geographical and cultural divides. However, cultural and economic disparities often hinder its full potential. This paper explores the role of artificial intelligence (AI) in overcoming these barriers. It examines AI-driven personalization, localization, and accessibility strategies that address language differences, economic constraints, and cultural nuances. By analyzing case studies and real-world implementations-including the innovative strategies of FoodKravings.com-this study highlights the transformative potential of AI in creating a more inclusive global e-commerce landscape. The findings underscore the importance of leveraging AI to foster trust, enhance user experiences, and drive sustainable growth in emerging markets.

The transition from idealized descriptions of memristors to physical implementations, such as resistive random access memory (RRAM) devices, is crucial for realizing many neuromorphic computing concepts in hardware. However, the intrinsic variability and fabrication tolerances of real-world RRAM devices present significant challenges for designing reliable circuits. This study focuses on addressing these challenges in the context of a memristive Hindmarsh-Rose circuit, a model for simulating the spiking and bursting behavior of biological neurons. To evaluate the robustness of the circuit in maintaining oscillatory behavior the Variability Intensity Model (VIM) is introduced, which enables a two-stage analysis with respect to the absolute tolerances and the dynamic variabilities. The results show that high conductance states are the most sensitive parameters, while low conductance states are more robust, optimally matching the behavior of real RRAM devices where relative variability decreases with increasing conductance. In addition, the tolerance analysis shows that spiking dynamics are generally robust to parameter variation, while bursting behavior is more sensitive. These results establish parameter constraints for actual RRAM devices as a basis for designing robust RRAMbased implementations of the Hindmarsh-Rose circuit.

This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordings, utilizing advanced image preprocessing techniques, including CLAHE enhancement and adaptive thresholding. The study optimizes spatial accuracy and computational efficiency through systematic investigation of temporal sampling rates (1s, 5s, 10s, 15s, 20s) and coordinate processing methods. Results demonstrate that the 5second sampling rate, utilizing 4.07% of available frames, provides the optimal balance with a point retention rate of 64% and mean speed accuracy within 4.2% of the 1-second baseline while reducing computational overhead by 80.5%. Comparative analysis of coordinate processing methods reveals that while UTM Zone 33N projection and Haversine calculations provide consistently similar results (within 0.1% difference), raw WGS84 coordinates underestimate distances by 15-30% and speeds by 20-35%. Altitude measurements showed unexpected resilience to sampling rate variations, with only 2.1% variation across all intervals. This research is the first of its kind, providing quantitative benchmarks for establishing a robust framework for drone telemetry extraction and analysis using open-source tools and spatial libraries.

This article presents the hybridization of a multiconductor transmission line (MTL) solver with the circuit solver ngspice to treat the terminations and interconnections of transmission lines. The MTL solver is implemented using the finitedifference time-domain technique (FDTD). Transient analysis and crosstalk in multiconductor transmission lines has been widely studied but the line terminations and the connections between lines are mostly restricted to lumped RLC components. However, realistic systems such as power distribution or communication systems have complex and a priori unknown connections. Interfacing with a circuit solver opens the possibility of including in the MTL simulation any component for which a netlist can be written or already exists, i.e dispersive elements, such as ferrites, and electronic components such as diodes, amplifiers or transistors. This flexibility allows to simulate networks of MTLs terminated in and connected by arbitrary circuits. The proposed method is validated comparing against laboratory measurements.

This paper proposes a control system to guide drones in delivering packages autonomously. The objective is to guide the quadrotor to take off from a ground vehicle, go to the delivery point, return to the ground vehicle, and land on it. The drone should disregard the ground vehicle as its leader when flying toward the delivery point and resume its leadership after delivering the parcel. To ensure this, the leader-follower formation control paradigm is adopted, allowing controlling the follower to track the leader's movement as a reference trajectory. To fully accomplish the delivery task, the delivery drone will follow two leaders at two different moments: the delivery point in the delivery step and the ground vehicle when returning to it. The delivery drone should also avoid obstacles, such as buildings and trees, to which the well-known potential field theory is adopted, thus completing the controller design. Results of an experiment on a lab scale are presented and discussed, which validates the proposed control system. Consequently, the correctness of the underlying hypothesis that the leader-follower control paradigm is a feasible solution to control a delivery drone in accomplishing package delivery is confirmed. This is the main contribution of the study. Moreover, a possible generalization of the proposed control system for a set of delivery drones is outlined.

In the aftermath of natural disasters or conflict-ridden areas, urgent decision-making is imperative for effective emergency medical services (EMS). These situations demand timely interventions, situational assessments, and efficient transport to health facilities, underscoring the importance of accurate road maps. However, rural or remote areas often lack proper mapping infrastructure, particularly during calamities that disrupt regular routes. To address this, our research leverages satellite imagery and deep learning, specifically employing U-Net Architecture and YOLOv8. The curated dataset, drawn from the Bhoonidhi portal, prioritizes geographic diversity and features like landing sites and flood-affected regions. Utilizing U-Net for precise road mapping and YOLOv8 for identifying helicopter landing zones, our approach automates and expedites critical tasks, offering timely information for emergency response. The synergy between these models enhances the overall efficiency of the system, ensuring accurate identification of road networks and suitable landing zones. Testing in selected areas of Karnataka, Andhra Pradesh, and Uttarakhand reveals the system's adaptability to various road dynamics, addressing challenges in both natural disasters and conflict scenarios. This paper proposes an innovative model, signifying a transformative leap in disaster and conflict response, facilitating a seamless transition to clarity and potentially saving countless lives.

In practice, many mechanical systems are underactuated, such as naval vessels, cranes, and helicopters, to reduce energy consumption and enhance flexibility. However, compounded by strong nonlinearity arising from state coupling, the underactuated nature and high-order unavailable states pose great challenges to motion control (particularly for unactuated states lacking independent actuators or kinematic constraints). In this paper, an adaptive controller based on fully-actuated system methods is proposed, together with a general and extensible analysis method. Firstly, a group of high-order auxiliary variables, consisting of actuated/unactuated states, their derivatives, and proportional-differential terms, are designed to rearrange the nonlinear underactuated system as a high-order linear fullyactuated system without any linearization operations. The asymptotic convergence of auxiliary variables theoretically eliminates the steady-state errors of actuated/unactuated states together. For high-order unmeasurable variables, they are recovered by the constructed neural network observer to estimate high-order dynamics, which avoids discontinuous robust terms and improves the accuracy of compensation/positioning. Motivated by the inherent features and advantages of fully-actuated systems, this paper proposes the first fully-actuated system-based continuous adaptive controller for a class of underactuated robots. Moreover, it is convenient to extend the proposed controller to handle more practical problems, such as state delay, without the need to re-conduct Lyapunov-based analysis. In addition to complete theoretical frames, this paper also provides several experimental validation.Remark:  This paper was first submitted to an IEEE Transactions journal on Mar. 28, 2024. It has been revised and submitted on Oct. 02, 2024.  Currently, this paper is under review.

In this paper, we introduce the Chaotic Dynamic Cat Swarm Optimization (CHDCSO) algorithm, an enhanced variant of the traditional Cat Swarm Optimization (CSO), to address the Traveling Salesman Problem (TSP). By integrating chaotic dynamics using logistic chaotic maps, the proposed algorithm aims to improve the balance between exploration and exploitation phases, thereby enhancing the global search capability and robustness of the optimization process. Detailed experimental analysis is conducted on various TSP datasets, comparing the performance of CHDCSO against established optimization techniques, including Particle Swarm Optimization (PSO), Differential Evolution (DDE), Transit Search, Random Optimization Algorithm (ROA), and Random Search Algorithm (RSA). The results demonstrate that the CHDCSO algorithm consistently outperforms these traditional methods in terms of convergence speed and solution quality. This study underscores the potential of chaotic dynamics in optimizing complex problems and highlights the CHDCSO algorithm’s efficacy in achieving superior optimization outcomes.

The Federated Unified Secure Exchange (FUSE) algorithm represents a breakthrough in computer science and cybersecurity, building upon three foundational elements: Homomorphic Encryption, Zero-Knowledge Proofs, and Federated Learning. By deeply integrating these three pillars, FUSE not only guarantees data integrity and privacy in decentralized environments but also maintains high performance and remarkable scalability. This paper details the architecture, mathematical principles, and real-world applications of FUSE across Internet of Things (IoT), healthcare, finance, and supply chains. In addition, it compares FUSE with several “classic” algorithms that have dramatically changed the world—such as QuickSort, RSA, Dijkstra, PageRank, FFT, Backpropagation, SHA, Monte Carlo, Gradient Descent, and Blockchain—highlighting how FUSE addresses existing limitations in data protection and distributed computation.

The Web of Things (WoT) attempts to overcome the Internet of Things (IoT) fragmentation and interoperability limitations by using standard web protocols. While WoT brings a huge potential for developing new and custom distributed applications, the number of system’s components and system complexity keeps growing and is ever more difficult to manage, along with an increase in resource consumption, which is a critical issue in constrained environments as the IoT. In order to tackle this issue, a framework named Self-Orchestrated Web of Things (SOrWoT) is proposed that integrates Hierarchical Finite State Machine (HFSM) constructs into WoT, allowing for the modeling of behavior in applications, breaking down complex systems, and further promoting changes in the architecting of components. A use case for applying SOrWoT’s constructs to a smart city energy management system is discussed, to showcase the framework’s practical application. Our proposed framework has the potential to improve resource efficiency, cut operational costs, and enhance overall performance by decomposing complex processes into smaller, manageable components. This modular and hierarchical strategy also has the potential to improve the system’s adaptability, resilience, and scalability.

A small assembly (a few mm in size) with two millimetric magnets is tracked in the presence of a background field, employing an array of magnetoresistance sensors. We describe the data elaboration and evaluate the performance of the system based on the self-consistency of the tracking output. The methodology has the potential to track eye motion, including the detection of the torsional degree of freedom. The retrieved environmental field provides simultaneous information about the sensor (i.e. the patient's head) orientation.

As the global demand for energy transition and transport decarbonization intensifies, the development of advanced magnetizable materials becomes crucial for supporting large-scale applications. This study presents the optimization of MAGMENT composites, which are produced using recycled ferrite aggregates combined with binders such as cement, asphalt, or epoxy. These composites are engineered to achieve high magnetic permeability and low core losses, key characteristics for efficient energy systems. Our results demonstrate that by fine-tuning the aggregate size and volume fraction, permeability can be significantly enhanced, with volume fractions above 65% showing the most promise. Although cement workability imposes a 73% limit, the performance of these composites still surpasses industry benchmarks, notably the KH-HT 60µ from KEDA, by refining the particle size distribution. Adjusting the Nominal Maximum Aggregate Size (NMAS) from 4.5 to 19 mm changes permeability from 40 to 180. The superior magnetic performance of the MC60 ® grade, particularly its minimal core losses, underscores its potential as a leading material in the market. These advancements are vital for applications in wireless charging, both static and dynamic, and in highpower transmission systems, addressing critical needs in sustainable transport and energy infrastructure. The use of recycled materials further aligns with the global push for environmentally responsible technologies.

This paper presents DA-VINCI, a dynamically configurable and precision-scalable activation function core for versatile iterative neuron implementation to address the increasing demand for diverse activation functions in AI workloads. It leverages the CORDIC methodology to support runtime reconfigurability between Swish, SoftMax, SELU, GELU, Sigmoid, Tanh, and ReLU, achieving a quality of results (QoR) of 98.5%. FPGA evaluations demonstrate reductions of up to 4.5× in LUT usage, 3.2× in FF usage, 1.3× in critical delay, and 5.4× in power consumption compared to state-of-the-art designs. Empirical ASIC analysis further emphasizes the core's efficiency, achieving reductions of up to 16.2× area, 7.8× delay, and 14.3× power at CMOS 45nm, and 1.8× area and 10× delay at CMOS 28nm respectively. These improvements make DA-VINCI a fundamental block for resource-efficient AI accelerators focused on DNNs, RNNs/LSTMs, and Transformers.

This paper presents an innovative wind energy harnessing mechanism, grounded in the fundamental principles of electromagnetic rotary generation. The proposed design introduces a significant advancement through the modification of the generator's shaft, which is engineered to be hollow and incorporates a precision-designed fin tunnel to optimize wind energy conversion efficiency. This approach addresses key limitations of conventional wind turbines, including elevated costs, substantial spatial requirements, and suboptimal performance under low wind speed conditions. Empirical case studies conducted in urban, offshore, and microgrid contexts demonstrate the practicality and effectiveness of this design. Results from simulations and theoretical analyses indicate enhanced energy conversion efficiency, lower manufacturing costs, and a compact structural profile adaptable to a variety of environmental applications. This study aims to contribute to the corpus of renewable energy research by offering a scalable, efficient, and cost-effective solution for wind energy generation, supported by rigorous theoretical underpinnings and empirical evidence. The proposed methodology capitalizes on advancements in composite material science, aerodynamic optimization, and magnetic flux dynamics. Comparative analyses with conventional wind energy systems underscore the superior performance and versatility of this novel design. By synthesizing insights from peer-reviewed literature and performing comprehensive evaluations, this research establishes a robust framework for advancing renewable energy technologies and provides a foundation for subsequent investigative and developmental endeavors.

In this paper, our objective is to show our contribution to the development of a new Positron Emission Tomography (PET) system to reduce its impact on patients. This study was conducted as part of the RETIREX project. PET is a diagnostic system based on functional imaging that uses radioactive substances to visualize and measure the changes in different metabolic processes. There is a constant push to reduce both the radioactive dose injected into the patient and duration of the PET scan. Achieving these and providing real-time images require an innovative imaging principle. In this paper, we present our contribution to an architecture based on a new 3-photon gamma imaging PET scanner with a Liquid Xenon camera. In this project, we aim to define a new architecture for a high-throughput hardware event builder and its integration into the final PET scanner named XEMIS3 (Xenon Medical Imaging System).