This paper presents and discusses two methods for collecting data from decentralised control applications designed in IEC 61499 architecture. The topic is justified by the growing use of Cloud-based storage and presen...
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Room-temperature magnetization dynamics of multicore magnetic nanoparticles often account for intrinsic dipolar magnetism to behave as a single macrospin at low-frequency regime. Either magnetic particle imaging or hy...
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Room-temperature magnetization dynamics of multicore magnetic nanoparticles often account for intrinsic dipolar magnetism to behave as a single macrospin at low-frequency regime. Either magnetic particle imaging or hyperthermia benefits from the resulting superparamagnetism in terms of nonlinear magnetization response and relaxation losses at frequencies where the rotation of magnetic moments dominates over Brownian motion for a given sinusoidal field. For this situation, spontaneous thermal relaxation (in the absence of external fields) of each composing particle moment within the cluster is critical to define effective Néel time constant and may intersect with ferromagnetic resonance (FMR) at GHz range. Here, we performed broadband AC susceptometry on both immobilized single-core and multicore iron-oxide nanoparticles up to 26.5GHz under DC bias fields. For each solid sample, we confirmed FMR frequency, where large single-core nanoparticle systems demonstrated typical resonance blueshift as DC field increased. Interestingly, high DC field induced the secondary satellite peak in the imaginary part of AC susceptibility spectra for the case of multicore nanoparticle systems. We further highlighted that a synchronous precession of the polarized macrospins under nonuniform effective fields was responsible for splitting FMR peaks at nearby microwave frequencies. Upon curve fitting of the field-dependent FMR frequency spectra, the Landau-Lifshitz-Gilbert-Kittel model later elaborates on the complex moment dynamics of multicore nanoparticle systems in correlation with distribution functions.
Adaptive game design is a dynamic gamification approach that changes game elements such as challenges, feedback mechanisms, and rewards based on players’ preferences, behaviors, and needs. It is an emerging research ...
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Now Unmanned Aerial Vehicle (UAV) with Mobile Edge Computing (MEC) severs and Device-to-Device (D2D) communications provide offload computing services for User Devices (UDs). However, the UAV has relatively high trans...
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The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,D...
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The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv
Optimizing the charging protocol for large-scale electric vehicles is complex and computationally costly. Therefore, this paper proposes an advanced approach using machine learning-assisted mean field game theory to h...
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Diffusion models have recently been shown to be relevant for high-quality speech generation. Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the s...
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As multi-core systems continue to grow in complexity, Network-on-Chip (NoC) architectures have emerged as a scalable and efficient solution for managing on-chip communication. However, ensuring reliable communication ...
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One of the major issues of secure communication in resource-constrained contexts is the overhead of integrity protection. A message authentication code (MAC) is a few-byte block used to authenticate a message. The rec...
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Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinald...
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Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.
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