Search Results (4,876)

The problem of finding a maximum acyclic matching in a simple undirected graph is known to be NP-complete. In this paper, we present new results; we show that a maximum acyclic matching in a given undirected graph (with n vertices and m edges) can be computed recursively with a recursion depth $\mathcal{O}(lnm)$ in expectation. Consequently, employing a recursive computation of a maximum acyclic matching in a given graph, if the recursion depth meets the expectation $\mathcal{O}(lnm)$, then a maximum acyclic matching can be computed in time $\mathcal{O}\left(n^{3.4}\right)$ and space $\mathcal{O}(mlnm)$. However, for the general case, the complexity of the recursive computation of a maximum acyclic matching is in $\mathcal{O}\left(n^2{2}^m\right)$ time and in $\mathcal{O}\left(m^2\right)$ space. Full article

DeepFake detection models play a crucial role in ambient intelligence and smart environments, where systems rely on authentic information for accurate decisions. These environments, integrating interconnected IoT devices and AI-driven systems, face significant threats from DeepFakes, potentially leading to compromised trust, erroneous decisions, and security breaches. To mitigate these risks, neural-network-based DeepFake detection models have been developed. However, their substantial computational requirements and long training times hinder deployment on resource-constrained edge devices. This paper investigates compression and transfer learning techniques to reduce the computational demands of training and deploying DeepFake detection models, while preserving performance. Pruning, knowledge distillation, quantization, and adapter modules are explored to enable efficient real-time DeepFake detection. An evaluation was conducted on four benchmark datasets: “SynthBuster”, “140k Real and Fake Faces”, “DeepFake and Real Images”, and “ForenSynths”. It compared compressed models with uncompressed baselines using widely recognized metrics such as accuracy, precision, recall, F1-score, model size, and training time. The results showed that a compressed model at 10% of the original size retained only 56% of the baseline accuracy, but fine-tuning in similar scenarios increased this to nearly 98%. In some cases, the accuracy even surpassed the original’s performance by up to 12%. These findings highlight the feasibility of deploying DeepFake detection models in edge computing scenarios. Full article

The evolution of smart cities is intrinsically linked to advancements in computing paradigms that support real-time data processing, intelligent decision-making, and efficient resource utilization. Edge and cloud computing have emerged as fundamental pillars that enable scalable, distributed, and latency-aware services in urban environments. Cloud computing provides extensive computational capabilities and centralized data storage, whereas edge computing ensures localized processing to mitigate network congestion and latency. This survey presents an in-depth analysis of the integration of edge and cloud computing in smart cities, highlighting architectural frameworks, enabling technologies, application domains, and key research challenges. The study examines resource allocation strategies, real-time analytics, and security considerations, emphasizing the synergies and trade-offs between cloud and edge computing paradigms. The present survey also notes future directions that address critical challenges, paving the way for sustainable and intelligent urban development. Full article

The temperature-based crop water stress index (CWSI) is the most robust metric among precise techniques that assess the severity of crop water stress, particularly in susceptible crops like maize. This study used a unmanned aerial vehicle (UAV) to remotely collect data, to use in combination with the random forest regression algorithm to detect the maize CWSI in smallholder croplands. This study sought to predict a foliar temperature-derived maize CWSI as a proxy for crop water stress using UAV-acquired spectral variables together with random forest regression throughout the vegetative and reproductive growth stages. The CWSI was derived after computing the non-water-stress baseline (NWSB) and non-transpiration baseline (NTB) using the field-measured canopy temperature, air temperature, and humidity data during the vegetative growth stages (V5, V10, and V14) and the reproductive growth stage (R1 stage). The results showed that the CWSI (CWSI < 0.3) could be estimated to an R² of 0.86, RMSE of 0.12, and MAE of 0.10 for the 5th vegetative stage; an R² of 0.85, RMSE of 0.03, and MAE of 0.02 for the 10th vegetative stage; an R² of 0.85, RMSE of 0.05, and MAE of 0.04 for the 14th vegetative stage; and an R² of 0.82, RMSE of 0.09, and MAE of 0.08 for the 1st reproductive stage. The Red, RedEdge, NIR, and TIR UAV-bands and their associated indices (CCCI, MTCI, GNDVI, NDRE, Red, TIR) were the most influential variables across all the growth stages. The vegetative V10 stage exhibited the most optimal prediction accuracies (RMSE = 0.03, MAE = 0.02), with the Red band being the most influential predictor variable. Unmanned aerial vehicles are essential for collecting data on the small and fragmented croplands predominant in southern Africa. The procedure facilitates determining crop water stress at different phenological stages to develop timeous response interventions, acting as an early warning system for crops. Full article

Generative AI (GenAI) models are designed to produce realistic and natural data, such as images, audio, or written text. Due to their high computational and memory demands, these models traditionally run on powerful remote compute servers. However, there is growing interest in deploying GenAI models at the edge, on resource-constrained embedded devices. Since 2018, the TinyML community has proved that running fixed topology AI models on edge devices offers several benefits, including independence from internet connectivity, low-latency processing, and enhanced privacy. Nevertheless, deploying resource-consuming GenAI models on embedded devices is challenging since the latter have limited computational, memory, and energy resources. This review paper aims to evaluate the progresses made to date in the field of Edge GenAI, an emerging area of research within the broader domain of EdgeAI which focuses on bringing GenAI on edge devices. Papers released between 2022 and 2024 that address the design and deployment of GenAI models on embedded devices are identified and described. Additionally, their approaches and results are compared. This manuscript contributes to understand the ongoing transition from TinyML to Edge GenAI and provides valuable insights to the AI research community on this emerging, impactful, and quite under-explored field. Full article

In intralogistics, autonomous mobile robots (AMRs) operate without predefined paths, leading to complex traffic patterns and potential conflicts that impact system efficiency. This paper proposes a bio-inspired optimization method for autonomously generating spatial movement constraints for autonomous mobile robots (AMRs). Unlike traditional multi-agent pathfinding (MAPF) approaches, which focus on temporal coordination, our approach proactively reduces conflicts by adapting a weighted directed grid graph to improve traffic flow. This is achieved through four mechanisms inspired by ant colony systems: (1) a movement reward that decreases the weight of traversed edges, similar to pheromone deposition, (2) a delay penalty that increases edge weights along delayed paths, (3) a collision penalty that increases weights at conflict locations, and (4) an evaporation mechanism that prevents premature convergence to suboptimal solutions. Compared to the existing approaches, the proposed approach addresses the entire intralogistic problem, including plant layout, task distribution, release and dispatching algorithms, and fleet size. Its autonomous movement rule generation and low computational complexity make it well suited for dynamic intralogistic environments. Validated through physics-based simulations in Gazebo across three scenarios, a standard MAPF benchmark, and two industrial environments, the movement constraints generated using the proposed method improved the system throughput by up to 10% compared to unconstrained navigation and up to 4% compared to expert-designed solutions while reducing the need for conflict-resolution interventions. Full article

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation. Full article

Multi-access edge computing (MEC) has emerged as a cornerstone technology for deploying 6G network services, offering efficient computation and ultra-low-latency communication. The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) further enhances wireless propagation, capacity, and coverage, presenting a transformative paradigm for next-generation networks. This paper addresses the critical challenge of task offloading and resource allocation in an MEC-based system, where a massive MIMO base station, serving multiple macro-cells, hosts the MEC server with support from a UAV-equipped RIS. We propose an optimization framework to minimize task execution latency for user equipment (UE) by jointly optimizing task offloading and communication resource allocation within this UAV-assisted, RIS-aided network. By modeling this problem as a Markov decision process (MDP) with a discrete-continuous hybrid action space, we develop a deep reinforcement learning (DRL) algorithm leveraging a hybrid space representation to solve it effectively. Extensive simulations validate the superiority of the proposed method, demonstrating significant latency reductions compared to state-of-the-art approaches, thereby advancing the feasibility of MEC in 6G networks. Full article

The Greiner–Hormann algorithm is a commonly used polygon overlay analysis algorithm. It uses a double-linked list structure to store vertex data, and its intersection calculation step has a significant effect on the overall operating efficiency of the algorithm. To address the time-consuming intersection calculation process in the Greiner–Hormann algorithm, this paper presents two kernel functions that implement a GPU parallel improvement algorithm based on CUDA multi-threading. This method allocates a thread to each edge of the subject polygon, determines in parallel whether it intersects with each edge of the clipping polygon, transfers the number of intersection points back to the CPU for calculation, and opens up corresponding storage space on the GPU side on the basis of the total number of intersection points; then, information such as intersection coordinates is calculated in parallel. In addition, experiments are conducted on the data of eight polygons with different complexities, and the optimal thread mode, running time, and speedup ratio of the parallel algorithm are statistically analyzed. The experimental results show that when a single CUDA thread block contains 64 threads or 128 threads, the parallel transformation step of the Greiner–Hormann algorithm has the highest computational efficiency. When the complexity of the subject polygon exceeds 53,000, the parallel improvement algorithm can obtain a speedup ratio of approximately three times that of the serial algorithm. This shows that the design method in this paper can effectively improve the operating efficiency of the polygon overlay analysis algorithm in the current large-scale data context. Full article

Reducing distortion of spectral simulation signals in infrared detection systems is essential to improve the precision of detecting fine spectra in space-based carbon monitoring satellites. The rigid-flex printed circuit board (PCB), a vital interconnection structure between detectors and signal conditioning circuits, exhibits signal quality variations due to impedance fluctuations and parasitic capacitance changes induced by its meshed return plane geometry. This periodically varying structure necessitates full-wave field solutions to include longitudinal discontinuity. Although full-wave simulations provide accurate characterization, they demand substantial computational resources and time. To address these challenges, we propose an innovative approach to effectively determine optimal meshed return plane designs across various transmission rates. The method integrates a convolutional neural network (CNN) with improved particle swarm optimization (IPSO). First, a CNN model is employed efficiently to predict scattering parameters (S-parameters) for different design configurations, thereby overcoming the inefficiencies associated with iterative full-wave simulation optimization. Then, an IPSO algorithm has been implemented to address the optimization challenge of crosstalk and inter-symbol interference (ISI) in signal transmission. Furthermore, to increase the optimization speed and evaluate the system performance under extreme conditions, we propose a fitness function construction method based on double-edge responses (DER) to rapidly generate a worst-case peak distortion analysis (PDA) eye diagram within the IPSO algorithm. The proposed methodology reduces computational complexity by two orders of magnitude relative to the full-wave simulation. Quantitative analysis conducted at a transmission rate of 5 Gbps demonstrates substantial signal quality improvements compared to empirical PCB design: the eye height increased by 49.7%, and the eye width expanded by 35.7%. The effectiveness of these improvements has been verified through commercial simulation software, proving that the method can provide design support for infrared detection systems. Full article

Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. Finally, to prevent the detection accuracy impact due to excessive lightweighting, the lightweight shared head (LSH) is constructed. The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB. Full article

Advanced generations of mobile network technologies (XG), last-mile connectivity and edge computing solutions can offer invaluable support for farmers and agribusinesses, fostering sustainable development, though unequal access to these digital technologies may lead to a digital divide. It remains, however, unclear to what extent and why farmers are (not) ready to adopt digital technology solutions in agricultural production systems. Hence, this study identifies and assesses the constraints on and drivers for the adoption and diffusion of smart XG, last-mile connectivity and edge computing solutions in agricultural production systems, using the Adoption and Diffusion Outcome Prediction Tool (ADOPT) in a stakeholder workshop setting. Results for the case of the ‘digital shepherd’ in Flanders (Belgium) show that there is substantial potential for its adoption (~40% of the target population) and diffusion (~15 years to peak adoption). To motivate farmers to adopt the ‘digital shepherd’, its profitability, environmental benefits and management convenience are pivotal; to accelerate adoption of the ‘digital shepherd’, its trialability and evaluability, as well as farmers’ skills and knowledge, are pivotal. Addressing these factors can significantly reduce the risk of a digital divide and, hence, allow policy makers to define corresponding strategies. Full article

This article provides a comprehensive review of quantum chemical computational studies on the thermal and photochemical reactions of organosilicon compounds, based on fundamental concepts such as initial complex formation, HOMO-LUMO interactions, and subjacent orbital interactions. Despite silicon’s position in group 14 of the periodic table, alongside carbon, its reactivity patterns exhibit significant deviations from those of carbon. This review delves into the reactivity behaviors of organosilicon compounds, particularly focusing on the highly coordinated nature of silicon. It is poised to serve as a valuable resource for chemists, offering insights into cutting-edge research and fostering further innovations in synthetic chemistry and also theoretical chemistry. Full article

In subway tunnel construction, current hand–eye integrated drilling robots use a camera mounted on the drilling arm for image acquisition. However, dust interference and long-distance operation cause a decline in image quality, affecting the stability and accuracy of the visual recognition system. Additionally, the computational complexity of high-precision detection models limits deployment on resource-constrained edge devices, such as industrial controllers. To address these challenges, this paper proposes a dual-arm tunnel drilling robot system with hand–eye separation, utilizing the first-frame localization and follower tracking method. The vision arm (“eye”) provides real-time position data to the drilling arm (“hand”), ensuring accurate and efficient operation. The study employs an RFBNet model for initial frame localization, replacing the original VGG16 backbone with ShuffleNet V2. This reduces model parameters by 30% (135.5 MB vs. 146.3 MB) through channel splitting and depthwise separable convolutions to reduce computational complexity. Additionally, the GIoU loss function is introduced to replace the traditional IoU, further optimizing bounding box regression through the calculation of the minimum enclosing box. This resolves the gradient vanishing problem in traditional IoU and improves average precision (AP) by 3.3% (from 0.91 to 0.94). For continuous tracking, a SiamRPN-based algorithm combined with Kalman filtering and PID control ensures robustness against occlusions and nonlinear disturbances, increasing the success rate by 1.6% (0.639 vs. 0.629). Experimental results show that this approach significantly improves tracking accuracy and operational stability, achieving 31 FPS inference speed on edge devices and providing a deployable solution for tunnel construction’s safety and efficiency needs. Full article

Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations. Full article