Hasil untuk "Electronics"

K. Suganuma

Z. Suo, E. Ma, H. Gleskova et al.

Zeyu Zhang, Long Wan, Yong Yang et al.

Overcoming the strength-ductility trade-off dilemma is paramount for advanced materials engineering. Herein, we prepared 7075 aluminium alloys with superior strength and ductility via additive friction stir deposition (AFSD) and subsequent heat treatment. Compared with the commercial base material, the heat-treated 7075 aluminium alloy maintained a high ultimate tensile strength of 556 MPa, while the uniform elongation increased from 12.2% to 26.7%, exhibiting the highest strength-ductility synergy reported among commercial Al-Zn-Mg-Cu alloy systems. Grain boundary sliding was activated via the equiaxed grains to accommodate substantial plastic strain. This method provides a promising and cost-effective pathway for developing strength-ductility on Al-Zn-Mg-Cu alloys.

S. Banerjee, G. Verghese

E. Gobbetti, R. Scateni

A. Tylka, J. Adams, P. Boberg et al.

Abdulrahman M. Abdulghani, Azizol Abdullah, A. R. Rahiman et al.

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

C. E. Ngene, Prabhat Thakur, Ghanshyam Singh

ABSTRACT This paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and 6G communication networks. The light‐emitting diode (LED) is used for carrier signal generation to transmit signals to the two users (photodiodes, PDs) due to its advantages, such as its programmable nature and flexibility. The potential challenge is how the signals could be controlled with an IRS approach, which prompted this research. We have used IRS, which is a cutting‐edge enabling technology that modifies the signal's reflection by utilizing numerous inexpensive passive reflecting elements to improve the signal's performance. Furthermore, deep reinforcement learning (DRL) is deployed to control the reflected signals, simulate, make decisions, and link LED‐IRS‐PDs, redirecting the signals. The entire system is successfully synchronized, and then the bit error rate (BER), line of sight (LOS), and non‐line of sight (NLOS) performances are investigated. Furthermore, we place a blocker at the center of the model as a NLOS to check how the transmitted signals will perform. We observed that the propagated signal improved the BER as per LOS, hence, the NLOS blocker reduced the signal's performance. Furthermore, we optimized the signals to investigate BER, LOS, and NLOS signal performance. We observed that LOS signals performed better than NLOS signals.

Taofeek Olaidey Salaudeen, Nur Idora Abdul Razak, Syahrul Afzal Bin Che Abdullah et al.

Efficient edge caching is vital for meeting the stringent latency and reliability demands of Ultra-Reliable Low Latency Communication (URLLC) in 5G and beyond. This paper proposes CacheHitPredictor, a federated learning-based framework for predictive edge caching that preserves data privacy while handling client heterogeneity and dynamic user behavior. A URLLC scenario was simulated using CloudSimPlus to generate realistic user request traces. The resulting dataset was used to train a federated neural network model via the Flower framework, with varying Dirichlet factors to evaluate data heterogeneity. The trained model was deployed via a Flask REST API and integrated with the CloudSimPlus simulation to enable online cache decision-making. Experimental results demonstrate that the proposed model performs on par with LFU under skewed workloads, underscoring the practical potential of federated learning to enable adaptive and efficient caching in URLLC edge environments.

L. Carroll, C. Gorman

D. Mitzi, K. Chondroudis, C. Kagan

A. Schenning, E. Meijer

Meng Wu, Feng Xu, Geetha Subramaniam et al.

Hematopoietic stem cell transplantation (HSCT) is a crucial treatment option for hematological disorders. This study aims to design and develop a blockchain-based system tailored explicitly for HSCT typing and evaluate its performance and capabilities. The system, built on Hyperledger Fabric 2.4.6 and integrated with LevelDB and MySQL databases, implements features such as data on-chaining, access control, information retrieval, and matching through chaincode. It ensures accurate and efficient human leukocyte antigen (HLA) typing while protecting patient and hospital data security. Tests demonstrate the system’s ability to safeguard data security and reliability effectively. Performance metrics, including block size, CPU utilization, network throughput, and response latency, were evaluated on Ubuntu 20.04.2 operating systems using VMware Workstation 16 Pro and Docker containerization. By leveraging blockchain’s traceability and immutability, the system achieves data security, reliability, and verifiability, enabling faster and more accurate typing and improved healthcare efficiency. This innovative approach optimizes user experience, maintains data integrity, and ensures privacy. Future integration with existing databases could enable secure data sharing among healthcare institutions, allowing rapid verification of data ownership and unified authentication. This study contributes a significant advancement in hematologic disease treatment and research, offering practical implications for improving HSCT processes.

Hongming Peng, Siyu Xiang, Mingju Chen et al.

Road segmentation is an important task in the field of semantic segmentation, and the Deeplabv3+ algorithm, which is commonly used for road segmentation, has shortcomings, such as numerous parameters and a tendency to lose detailed information. Therefore, this paper proposes DCN-Deeplabv3+, an improved road segmentation algorithm with dual attention modules based on the Deeplabv3+ network, aiming to reduce the model parameters and computation while improving the segmentation accuracy. <xref ref-type="disp-formula" rid="deqn1">(1)</xref> MobileNetV2 is used as the backbone network to reduce model parameters and memory consumption. <xref ref-type="disp-formula" rid="deqn2">(2)</xref> DenseASPP+SP is used for multi-scale information fusion to obtain a larger sensory field for improved model performance. <xref ref-type="disp-formula" rid="deqn3">(3)</xref> The deep learning model&#x2019;s understanding of the spatial structure of the input data is enhanced by using CA (coordinate attention) to improve the model&#x2019;s performance in dealing with spatial structure-related tasks. <xref ref-type="disp-formula" rid="deqn4">(4)</xref> The neural attention mechanism (NAM) is applied to better focus on key regions in the image, thereby improving the accuracy of target detection. The experimental results show that mIoU and mPA are improved by 1.20% and 2.30% on the PASCAL VOC 2012 dataset, mIoU and mPA are improved by 3.15% and 3.90% on the Cityscapes dataset, respectively. It can be concluded that the method proposed in this paper outperforms the baseline method and has excellent segmentation accuracy on roads.

Toni Utech, Henning Kruppa, Anya Vollpracht et al.

Biological materials found in nacre or glass sponges reveal specific layered organic and inorganic structures known for their fracture toughness caused by crack deflection and bridging. This work aims to bio-mimic the brick-and-mortar (BnM) structure from nacre and the layer-by-layer (LbL) structure from the glass sponge filaments to incorporate these effects into the contact zone of carbon fiber-reinforced concrete (CFRC) in order to prevent brittle composite failure. To build up BnM- and LbL-structure materials such as nanoclays, sodium water glass, and polymer dispersions were selected since they are well-established low-cost materials in building. Nanoclays were analyzed regarding their size, dispersibility and exfoliation. Montmorillonite (MMT) was used to be mixed with polymers to produce self-assembled BnM-structured films and coatings. Also, LbL-structures were formed by alternating layers of sodium water glass and polymer. Scanning electron microscopy and energy-dispersive X-ray spectroscopy were used to verify the morphology. The MMT-containing coatings demonstrated enhanced nucleation potential when exposed to cementitious eluate. Micromechanical pull-out tests on single carbon fibers with BnM- and LbL-coatings embedded in concrete demonstrate the potential to increase composite toughness. The successful implementation of the bio-inspired structures using affordable materials lays the groundwork for their scalability and integration into composite structures for building.

Agha Dena N. Qasim, Yahya Zahraa

This article evaluates SiGe/Si heterojunction vertical tunnel field-effect transistor (VTFET-hetero) biosensors, using SiGe in the source region to enhance sensitivity. It detects smaller analyte concentrations for biomedical applications. Non-ideal sensor behavior is explained by steric hindrance and irregular probe/receptor positions. Based on the simulation results, sensitivity is determined for four different cases in which partially filled nanogaps have decreasing, increasing, concave, and convex profiles. Simulation shows concave step profiles having the highest sensitivity. The VTFET-hetero structure exhibits higher sensitivity than horizontal biosensors, achieving a sensitivity of 8.64 × 107 for immobilized charged biomolecules.

H. Akkerman, P. Blom, D. Leeuw et al.

Jun LUO, Chi LIU, Binglei WANG

The communication network based on quantum key distribution (QKD) has the ability to realize “perfect secrecy”.At present, it cannot meet the needs of large-scale applications and needs to combine classical cryptography in practical applications.Firstly, the functional architecture model of the telecom-operators cryptography application system integrating quantum key distribution was proposed, and then the hierarchy, the core network elements, the functional modules, and the interface relationships of the model were described.Furthermore, the framework of the application system was given, and the main components of the framework were introduced.Finally, the typical application scenarios and workflow were illustrated.

Jiaxing Hao, Xuetian Wang, Sen Yang et al.

The target simulation of airplanes is an important research topic. It is particularly important to find the right balance between high performance and low cost. In order to balance the contradictions between realistic target simulations and controllable costs, the scientific formulation of the performance parameters of target simulation is the key to achieving high performance. This paper proposes an intelligent simulation technology based on RCS imaging simulation through the combination of 60° variation corner reflector and a Luneberg lens reflector. It is designed to simulate several important RCS characteristics of the aircraft. At the same time, the different RCS images are automatically shifted to the corresponding gear position to achieve the purpose of simulation, and the price is low and the performance is good. It can be used for the training of radar target searching.

Robert Mroczyński, Grzegorz Głuszko, Romuald B. Beck et al.

This work reports on changes in the properties of ultra-thin PECVD silicon oxynitride layers after high- temperature treatment. Possible changes in the structure, composition and electrophysical properties were investigated by means of spectroscopic ellipsometry, XPS, SIMS and electrical characterization methods (C-V, I-V and charge- pumping). The XPS measurements show that SiOxNy is the dominant phase in the ultra-thin layer and high-temperature annealing results in further increase of the oxynitride phase up to 70% of the whole layer. Despite comparable thickness, SIMS measurement indicates a densification of the annealed layer, because sputtering time is increased. It suggests complex changes of physical and chemical properties of the investigated layers taking place during high-temperature annealing. The C-V curves of annealed layers exhibit less frequency dispersion, their leakage and charge-pumping currents are lower when compared to those of as-deposited layers, proving improvement in the gate structure trapping properties due to the annealing process.