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.

Interface defect response time is a key parameter in MOS devices interface defect density measurements such as the hi-lo CV measurement as well as the conductance measurement. It is widely accepted that interface defects at energies close to the band edge have too short a response time to be measured properly by these techniques, unless very high measurement frequencies are employed, which is challenging experimentally. Consequently, near band edge interface defects are difficult to measure. Here we show that the time constant of band edge defects are not as short as commonly believed. Specifically, we show that as the capacitor is biased into accumulation to measure defect levels near the band edge, the defect response time levels off instead of continue to decrease. As a result, the commonly available 1MHz measurement frequency is more than adequate even for defects near the band edge.

Cross-topology generalization is essential for improving the practicality of deep learning models in power system analysis (PSA) tasks. While large pre-trained models hold substantial promise for PSA, existing research lacks insight into which neurons are pivotal for topology generalization, leading to inefficient fine-tuning processes. This letter reveals the adaptation mechanism of deep PSA models to topological changes by introducing a causally-guided framework to analyze functional differences among neurons. A subset of neurons, termed topology-sensitive neurons, is identified as selectively activated by specific topological inputs and shown to causally influence model performance. Using integrated gradient methods, these neurons are localized and targeted with a masked low-rank adaptation (LoRA) strategy, which prioritizes parameter updates based on causal impact rather than indiscriminate fine-tuning. Experimental results demonstrate that focusing on topologysensitive neurons improves fine-tuning efficiency, reducing trainable parameters by 89% while maintaining high accuracy.

We present BALDUR, a novel Bayesian algorithm designed to deal with multi-modal datasets and small sample sizes in high-dimensional settings while providing explainable solutions. To do so, the proposed model combines within a common latent space the different data views to extract the relevant information to solve the classification task and prune out the irrelevant/redundant features/data views. Furthermore, to provide generalizable solutions in small sample size scenarios, BALDUR efficiently integrates dual kernels over the views with a small sample-to-feature ratio. Finally, its linear nature ensures the explainability of the model outcomes, allowing its use for biomarker identification. This model was tested over two different neurodegeneration datasets, outperforming the state-of-the-art models and detecting features aligned with markers already described in the scientific literature.

This article explores hardware backdoors, highlighting examples like Rakshasa and rosenbridge. Discusses prevention and detection methodologies, vulnerability analysis and research tools. It also addresses future research directions and recommendations for industry and academia, emphasizing the importance of a robust approach to hardware security.

The clothing industry demands unending novelty in attire designs to meet developing client advantages. However, established forms of founding attire patterns are labor-exhaustive, high-priced, and frequently impassable to tiny designers and arising fashion brands. FashionFusion presents an AI-compelled answer that automates the production of singular and superior attire patterns, leveraging the capacity of deep education and fruitful models. By streamlining the design process, this method allows designers to speedily original creative patterns, lowering two together opportunity and cost. The manifesto democratizes approach to progressive design finishes, enabling consumers to forge embodied designs outside the need for thorough mechanics knowledge. With this resolution, the impediments to fashion artistry are considerably reduced, promoting a more all-embracing and vital design environment.

In this paper, we propose a novel Privacy-Preserving clearance mechanism for Local Energy Markets (PP-LEM), designed for computational efficiency and social welfare. PP-LEM incorporates a novel competitive game-theoretical clearance mechanism, modelled as a Stackelberg Game. Based on this mechanism, a privacy-preserving market model is developed using a partially homomorphic cryptosystem, allowing buyers’ reaction function calculations to be executed over encrypted data without exposing sensitive information of both buyers and sellers. The comprehensive performance evaluation demonstrates that PP-LEM is highly effective in delivering an incentive clearance mechanism with computational efficiency, enabling it to clear the market for 200 users within the order of seconds while concurrently protecting user privacy. Compared to the state of the art, PP-LEM achieves improved computational efficiency without compromising social welfare while still providing user privacy protection.

Evaluating privacy in data-driven software is hindered by its subjective nature and the multitude of privacyenhancing technologies. To address this, we propose a framework for categorizing data based on its privacy implications. This proposal aims to establish a standardized approach for quantifying privacy across various data sources, aligned with DevPrivOps.

Achieving interpretive coherence across diverse linguistic structures remains a primary challenge in generative model design, particularly as dependency alignment degrades in multi-layered architectures with extended text sequences. Contextual Dependency Harmonization (CDH) introduces a novel solution to this problem, employing synthetic gradient dynamics to reinforce dependency relationships directly within the latent layers of the model. Through the application of CDH, the model demonstrates enhanced stability in maintaining dependency consistency, achieving significant improvements in context consistency rates, harmonization scores, and interpretive accuracy over baseline configurations. This study details the implementation and efficacy of CDH in expanding latent semantic coverage, dynamically harmonizing dependency alignment across varied syntactic structures, and optimizing interpretive fidelity across multiple contexts. The findings illustrate that synthetic gradients not only reduce cumulative interpretive error during backpropagation but also enable a broader semantic range by promoting stable dependency alignment across latent clusters, thereby supporting more diverse and accurate language generation. The research demonstrates the viability of CDH as an advanced dependency modeling approach, with synthetic gradient integration proving essential for scalable, contextually adaptive interpretive processes in complex language environments.

Imaging spectroscopy and lightweight unmanned aerial vehicles (UAVs) have revolutionized remote sensing of vegetation, by providing high spectral and spatial resolution data. Comprehensive wetland monitoring should be based on reliable mapping of biophysical aquatic vegetation parameters, which in turn requires low bias in measured spectra, e.g. minimization of reflectance anisotropy. This study aims to quantitatively investigate how sensor scan direction and solar zenith angle (SZA) affect vegetation spectra over two dominant aquatic plants, Phragmites australis and Nuphar lutea, representing different functional types (i.e. emergent and floating) with divergent canopy structure and affinity with water, using data collected with a hyperspectral (400-1000 nm range) push-broom sensor mounted on a UAV. Anisotropy factor (ANIF), mean reflectance scatterplots and regression analysis were used for assessing the magnitude of spectral anisotropy at both pseudo-leaf and canopy scales. Our findings showed more accentuated anisotropic behaviour in the visible than near-infrared domain at SZA<60° as the scan direction approaches that of solar principal plane. The increase in reflectance in near-forward direction affects the spectra of floating-leaved N. lutea at both leaf and canopy scales, suggesting the presence of water (as a film over leaves or as canopy background) as a likely anisotropy driver. Backward hotspot is evident in canopy spectra of emergent P. australis, with driving mechanisms similar to terrestrial species (i.e. shadow-hiding). At SZA>60°, reflectance anisotropy appears to be negligible, independent of functional type and scan direction. This research underscores the importance of considering sun-target-sensor geometry in remote sensing studies on aquatic vegetation.

In this brief we prove that both forms of Middlebrook's formula for the transfer impedance T of a two-port can be seen as particular instances of the more general expression T = T* (p_nq_3 + q_np_3)/(p_d q_3 + q_d p_3) , which involves a generic reference immittance, not necessarily an open-circuit or a short-circuit. The p-and q-parameters arising above owe to the use of a homogeneous description of the extra element and of the double null and the single injection immittances. The formula holds under minimal regularity assumptions.

This paper presents a method for localization of a time-harmonic source in a cavity with magnitude-only measurements using the principles of electromagnetic time reversal. A time-harmonic point source that radiates an electromagnetic field at a specific frequency is considered. The source is assumed to be at an unknown position within the cavity. Scatterers of known geometry and dielectric constants present in the cavity alter the source field. The magnitude of the electric field is measured at certain locations within the cavity. Using these magnitude measurements, the position of the source is determined. Linear constraints based on electromagnetic time reversal are derived. These constraints depend only on the magnitude of electric fields. A convex optimization problem minimising the L1 norm subject to the derived linear constraints is formulated. The Matching pursuit algorithm is used to solve the optimization problem. The solution to this optimization problem reveals the source location. The proposed method is validated by numerical experiments in both 2D and 3D cavities. Numerical results suggest that it is possible to localize a time-harmonic point source with three magnitude-only measurements (despite having many scatterers).

Software engineers integrate third-party components into their applications. The resulting software supply chain is vulnerable. To reduce the attack surface, we can verify the origin of components (provenance) before adding them. Cryptographic signatures enable this. This article describes traditional signing, its challenges, and changes introduced by next generation signing platforms.

Federated learning has emerged as a promising approach for distributed machine learning, particularly in the edge-cloud infrastructures, where clients (i.e., edge devices) collaboratively train models while maintaining data privacy. However, the dynamic heterogeneity induced by the FEderated Edge Learning (FEEL) environment poses many performance challenges. Indeed, real-world edge devices are considered heterogeneous, where both system resources and data distributions fluctuate significantly over time across edge clients. Thus, selecting the most contributing and resource-efficient clients is crucial in determining the computational efficiency and performance of the overall FEEL system. We propose FedCDRP, an efficient and adaptive Class Diversity and Resource Performance-aware client selection strategy, founded upon our FedCD-CS and FedRP-CS methodologies. Our approach prioritizes clients based on two factors: their resource capabilities and the diversity of their local data. Specifically, FedCD-CS tackles the imbalanced non-I.I.D. data by considering the richness of the local data of each edge device, to enhance the overall learning system by selecting the most contributing clients. Additionally, based on a real-time cross-dimensional system performance profiling, FedRP-CS efficiently assesses the system resources of each edge device, to guarantee the selection of the most resource-efficient clients to effectively manage the computational needs. By combining these factors, our FedCDRP strategy identifies clients that can contribute significantly to the global model update while maintaining computational efficiency in dynamic heterogeneous environments. Through real-world circumstances, the conducted experiments demonstrated the computational efficiency of our FedCDRP strategy, compared to the-state-of-the-art protocols, achieving a 2.9-fold speedup in reaching the target accuracy of 99%.  

This paper explores the design and implementation of a cloud-based burst buffer system aimed at accelerating I/O performance for data-intensive applications in cloud environments. By leveraging the high throughput between compute nodes, the system buffers application data, effectively mitigating the I/O bottlenecks typical in shared cloud storage. A prototype implementation on Amazon EC2/S3 demonstrated significant performance enhancements, achieving up to 2.9 times improvement in execution time for real-world applications and reducing monetary costs by 50%. The proposed system not only addresses the critical issue of low I/O throughput but also offers scalable and cost-effective solutions for high-performance computing in the cloud.

With the popularization of Bluetooth technology, its security issues become especially critical. As a typical security threat, Bluetooth man-in-the-middle attack can cause serious damage to the integrity and privacy of data transmission. To address this challenge, this paper proposes a real-time Bluetooth signal anomaly detection model based on graph neural network (GNN). The model focuses on detecting and identifying potential man-in-the-middle attacks from complex Bluetooth signals. We first construct a Bluetooth signal dataset containing rich scenarios that model diverse communication environments and potential attack patterns. Using a graph representation learning approach, we convert the signals into graph structures, enabling the use of graph topology to analyze signal properties. Then, we design a novel graph convolution operation that captures spatio-temporal features in the signals and efficiently extracts patterns associated with attack behaviors. In the experimental section, we demonstrate the significant advantages of the proposed model in terms of detection accuracy, robustness and real-time performance by comparing it with existing methods. The model not only exhibits high accuracy in anomaly detection on the dataset, but also meets the demand of real-time monitoring in terms of processing speed. In addition, we also evaluate the generalization ability of the model and demonstrate its stability and reliability on different environments and devices.

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment during the power-up process, they are subject to low challenge-response repeatability. The dependability of SRAM-PUFs is usually accomplished by combining complex error correcting codes (ECCs) with fuzzy extractor structures resulting in an increase in power consumption, area, cost, and design complexity. In this study, we established effective metrics on the basis of the separatrix concept and cell mismatch to estimate the percentage of cells that, due to the effect of variability, will tend to the same initial state during power-up. The effects of noise and temperature in cell start-up processes were used to validate the proposed metrics. The presented metrics may be applied at the SRAM-PUF design phases to investigate the impact of different design parameters on the percentage of reliable cells for PUF applications.

The rapid evolution of language models has significantly enhanced their ability to generate human-like text; however, challenges persist in achieving complex syntactic and contextual understanding. Introducing Dynamic Syntax Transfer (DST), a novel methodology that dynamically adjusts syntactic structures in response to contextual variations, thereby enabling large language models to produce text with improved grammatical accuracy and contextual relevance. The implementation of DST within an open-source language model architecture has demonstrated notable advancements in syntactic precision and contextual coherence, as evidenced by comprehensive quantitative and qualitative analyses. These findings demonstrate DST's potential to advance context-aware syntax modeling, offering a promising avenue for future research and applications in sophisticated language generation systems.