Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression;however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficient...
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Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. The core concept of GNNs is to find a ...
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The term 'smart grid' describes the future generation of electrical power networks, which are very complicated systems. It must take into account all aspects of the electrical system, increasing its intelligen...
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The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing...
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The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa
Group-IV color centers in diamond (SiV–, GeV–, and SnV–) have emerged as leading solid-state spin-photon interfaces for quantum information processing applications. However, these qubits require cryogenic temperatu...
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Group-IV color centers in diamond (SiV–, GeV–, and SnV–) have emerged as leading solid-state spin-photon interfaces for quantum information processing applications. However, these qubits require cryogenic temperatures to achieve high fidelity operation due to interactions with the thermal phonon environment. In this paper, we (i) derive a detailed model of the decoherence from first-order acoustic phonon processes acting on the spin-orbit fine structure of these color centers, (ii) demonstrate agreement of the model's predicted coherence times with previous measurements, and (iii) identify regimes to suppress phonon-mediated decoherence by changing magnetic field and strain bias to allow higher temperature operation. This methodology enables prediction of decoherence processes in other color centers and solid-state qubit systems coupled to a thermal bath via a parasitic two-level system. By experiment-anchored decoherence models, we facilitate optimizing qubit coherence for specific applications and devices.
Epileptic seizures are a common neurological disorder characterized by abnormal brain activity. Early and accurate detection of seizures plays a crucial role in effective treatment and improving the quality of life fo...
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In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems...
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In the field of access control locks, conventional methods often require users to download applications with Bluetooth connectivity, and biometric measures such as fingerprints or face recognition that introduce chall...
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Amid the escalating global threat of severe cyberattacks, integrating Machine Learning (ML) into cybersecurity has become a critical research priority. This study addresses this imperative by training several distinct...
Amid the escalating global threat of severe cyberattacks, integrating Machine Learning (ML) into cybersecurity has become a critical research priority. This study addresses this imperative by training several distinct ML models using a refined dataset that underwent a meticulous double-feature reduction process. The objective is to enable the accurate detection of different malicious network traffic, mainly DoS and DDoS, within real-time operational environments. To validate the efficacy of ML algorithms in controlled settings, network packets are captured and analyzed in real-time using a synergistic combination of PyShark and CICFlowMeter tools. The results of this investigation are highly promising, revealing the successful development of robust intrusion detection models through a novel dual-feature selection approach. Notably, these models achieved exceptional accuracy rates in detecting cyberattacks, demonstrating a remarkable 99% success.
Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwi...
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Millimeter-Wave(mmWave)Massive MIMO is one of the most effective technology for the fifth-generation(5G)wireless *** improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base ***,increasing the number of antennas requires a large number of radio frequency(RF)chains which results in high power *** order to reduce the RF chain’s energy,cost and provide desirable quality-ofservice(QoS)to the subscribers,this paper proposes an energy-efficient hybrid precoding algorithm formm Wave massive MIMO networks based on the idea of RF chains *** sparse digital precoding problem is generated by utilizing the analog precoding ***,it is jointly solved through iterative fractional programming and successive convex optimization(SCA)*** results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.
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