Plasmonics is a research area merging the fields of optics and nanoelectronics by confining light with relatively large free‐space wavelength to the nanometer scale ‐ thereby enabling a family of novel devices. Current plasmonic devices at telecommunication and optical frequencies face significant challenges due to losses encountered in the constituent plasmonic materials. These large losses seriously limit the practicality of these metals for many novel applications. This paper provides an overview of alternative plasmonic materials along with motivation for each material choice and important aspects of fabrication. A comparative study of various materials including metals, metal alloys and heavily doped semiconductors is presented. The performance of each material is evaluated based on quality factors defined for each class of plasmonic devices. Most importantly, this paper outlines an approach for realizing optimal plasmonic material properties for specific frequencies and applications, thereby providing a reference for those searching for better plasmonic materials.
1983 sitasi
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Computer Science, Materials Science
Santiago Iglesías-Pradas, Á. Hernández-García, Julián Chaparro-Peláez
et al.
The COVID-19 pandemic has caused a massive disruption in the way traditional higher education institutions deliver their courses. Unlike transitions from face-to-face teaching to blended, online or flipped classroom in the past, changes in emergency remote teaching –a temporary shift of instructional delivery to an alternate remote delivery mode due to crisis circumstances– happen suddenly and in an unplanned way. This study analyzes the move to emergency remote teaching at the School of Telecommunication Engineering (Universidad Politécnica de Madrid), and the impact of organizational aspects related to unplanned change, instruction-related variables –class size, synchronous/asynchronous delivery– and use of digital supporting technologies, on students' academic performance. Using quantitative data of academic records across all (N = 43) courses of a bachelor's degree programme in Telecommunication Engineering and qualitative data from a questionnaire delivered to all (N = 43) course coordinators, the research also compares the academic results of students during the COVID-19 pandemic with those of previous years. The results of this case study show an increase in students' academic performance in emergency remote teaching, and support the idea that organizational factors may contribute to successful implementation of emergency remote teaching; the analysis does not find differences across courses with different class sizes or delivery modes. The study further explores possible explanations for the results of the analysis, considering organizational, individual and instruction-related aspects.
Patrick Weber, Artur Sterz, Bernd Freisleben
et al.
Modern telecommunication networks face an increasing complexity due to the rapidly growing number of networked devices and rising amounts of data. The literature advocates for self-managing networks as a means to tackle the resulting challenges. While self-managing networks provide potential solutions to these challenges, current research solely focuses on the perspective of network operators. However, modern telecommunication networks involve various stakeholders, such as service providers and end users, and necessitate interactions between them. By transitioning from a single-stakeholder to a multi-stakeholder perspective, we address the preferences of all involved parties, acknowledging potential conflicts of interest and constraints like information asymmetries. This broader perspective facilitates the development of more effective self-managing networks, significantly enhancing their performance metrics compared to approaches that solely prioritize the concerns of network operators.
Learning high-quality representations of users, items, and tags from historical interactive data is crucial for personalized tag recommendation (PTR) systems. Currently, most PTR models are committed to learning representations from first-order interactions without considering the exploitation of high-order interactive relations, which can be beneficial for avoiding sub-optimal learning. Although several PTR models equipped with graph neural networks (GNN) have been proposed to capture higher-order semantic relevance from raw data, they all carry out representation learning in Euclidean space, which can still easily result in sub-optimal learning due to embedding distortion. In order to further improve the quality of representation learning for PTR, the paper proposes a novel PTR model based on a lightweight GNN framework with hyperbolic embedding, namely GHPTR. GHPTR explicitly injects higher-order relevance into entity representation through the message propagation and aggregation mechanism of GNN and leverages hyperbolic embedding to alleviate the embedding distortion problem. Experimental results on real-world datasets have demonstrated the superiority of our model over its Euclidean counterparts and state-of-the-art baselines.
Abstract Dual‐receiver proxy re‐encryption is a cryptographic technique that enables secure data sharing among multiple authorized users or entities. It has gained significant attention for its ability to manage access permissions, data confidentiality, and streamline communication channels. These schemes have been widely used in various applications, including healthcare systems, cloud computing, Internet of Things (IoT) systems, collaborative environments, and secure communication channels. This paper aims to propose two proxy re‐encryption schemes for dual receivers without pairings. The first is dual receiver lightweight proxy re‐encryption without pairings (DR‐LWPRE‐WP), which uses a public key scheme to reduce computational complexity. The second is dual receiver hybrid proxy re‐encryption without pairings (DR‐HPRE‐WP), which incorporates public key and symmetric key schemes. Both schemes offer protection against selected plaintext attacks based on the decisional Diffie‐Hellman principle. The DR‐LWPRE‐WP scheme reduces computation by approximately 53% compared to pairing‐based schemes, making it suitable for lightweight applications like the Internet of Things. The computational efficacy of these schemes offers significant benefits for resource‐constrained environments and practical implementations.
Abstract A dual-port multiple-input multiple-output (MIMO) filtenna with minimal sizes of 80 × 45 mm2 is set up in this study. Each element in this MIMO filtenna is positioned orthogonally to the one next to it to improve isolation. For the MIMO element to achieve high-frequency selectivity and compact size, a frequency-reconfigurable filtenna that was created by fusing a band-pass filter and a monopole radiator was used. The suggested filtenna can switch between its C-band and X-band operating states with ease. On build the filtenna circuit, a band-pass filter based on defective microstrip structure is inserted to a circular monopole radiator. The developed filtenna operates in the C-band frequency range of 6.5–8 GHz and the X-band frequency range of 8–12 GHz. It is possible to use the X-band operating state for communication in a cognitive radio environment. Used as a decoupling structure, metamaterial structures can increase isolation to more than 40 dB across the bandwidth. The suggested MIMO filtenna system has an envelope correlation coefficient of 2.4e−6, a peak gain of 6 dBi, and an impedance bandwidth of 7.4–7.75 GHz. The MIMO filtenna is constructed and measured, and the findings of the measurement and simulation are in good agreement.
To solve the problem of lack of validation for exchanging messages in BGP, a inter-domain routing mechanism, which consisted of a reputation evaluation mechanism and a reputation-based BGP optimal routing algorithm, was proposed.The reputation evaluation mechanism used a distributed autonomous system (AS) alliance architecture, which divided node routing behavior in detail.The service domain and observation weight were used as indicators to quantify the impact of node behavior.By designing a feedback mechanism, the reputation value not only reflected the good and bad of nodes, but also reflected the node’s resistance to malicious attacks.The reputation-based BGP routing selection algorithm adds a “security” policy to the existing routing selection algorithm by filtering routes containing low-reputation nodes and selecting the best route among high reputation routes.The experimental results show that the proposed mechanism outperform most existing reputation mechanisms by avoiding routes with vulnerable nodes and restraining the propagation of illegal routes, thereby providing a more secure inter-domain routing environment.
Abstract The orthogonal time frequency space (OTFS) modulation is a promising multicarrier waveform which can have good performance in high‐mobility scenarios. Among various receivers designed for the OTFS modulation, the message‐passing (MP) receiver has received much attention which can achieve a good bit error rate (BER) performance. However, the BER performance of MP receiver is usually obtained through simulations due to the lack of corresponding BER formula in the literature. This work studies the BER formula of OTFS modulation with MP receiver over doubly selective fading channel. Specifically, equivalent signal models of the MP receiver are first defined and the expression of the corresponding signal‐noise‐ratio (SNR) is derived, then the probability distribution function of the SNR is estimated by assuming the distribution of SNR as a lognormal distribution, then the methods to estimate the parameters of the distribution function are proposed, and finally the formula for calculating the BER of the OTFS modulation with MP receiver is obtained. Simulation results verify that the proposed BER formula is accurate.
In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.
Gaza Strip suffers from a chronic electricity deficit that affects all industries including the telecommunication field, so there is a need to optimize and reduce power consumption of the telecommunication equipment. In this paper we propose a new model that helps GSM radio frequency engineers to choose the optimal value of hysteresis parameter for Ericsson BTS power saving algorithm which aims to switch OFF unused frequency channels, our model is based on unsupervised machine learning clustering K-means algorithm. By using our model with BTS power saving algorithm we reduce number of active TRX by 20.9%.
To manage the COVID-19 epidemic effectively, decision-makers in public health need accurate forecasts of case numbers. A potential near real-time predictor of future case numbers is human mobility; however, research on the predictive power of mobility is lacking. To fill this gap, we introduce a novel model for epidemic forecasting based on mobility data, called mobility marked Hawkes model. The proposed model consists of three components: (1) A Hawkes process captures the transmission dynamics of infectious diseases. (2) A mark modulates the rate of infections, thus accounting for how the reproduction number R varies across space and time. The mark is modeled using a regularized Poisson regression based on mobility covariates. (3) A correction procedure incorporates new cases seeded by people traveling between regions. Our model was evaluated on the COVID-19 epidemic in Switzerland. Specifically, we used mobility data from February through April 2020, amounting to approximately 1.5 billion trips. Trip counts were derived from large-scale telecommunication data, i.e., cell phone pings from the Swisscom network, the largest telecommunication provider in Switzerland. We compared our model against various state-of-the-art baselines in terms of out-of-sample root mean squared error. We found that our model outperformed the baselines by 15.52%. The improvement was consistently achieved across different forecast horizons between 5 and 21 days. In addition, we assessed the predictive power of conventional point of interest data, confirming that telecommunication data is superior. To the best of our knowledge, our work is the first to predict the spread of COVID-19 from telecommunication data. Altogether, our work contributes to previous research by developing a scalable early warning system for decision-makers in public health tasked with controlling the spread of infectious diseases.
The Schottky diode, BN/GaN layered composite contacting to bulk aluminum, is theoretically plausible to harvest wireless energy above X-band. According to our first principle calculation, the insertion of GaN layers dramatically influences the optical properties of the layered composite. The relative dielectric constant of BN/GaN layered composite as a function of layer-to-layer separation is investigated where the optimized dielectric constant is 3.1. Furthermore, we design another Schottky diode via nanostructuring. Our first principle calculation suggests that the relative dielectric constant of boron nitride monolayer can be minimized to 1.5 only if it is deposited on aluminum monolayer. It is rare to find a semiconductor with the dielectric constant close to 1 which may push the cut-off frequency of Al/BN-based rectenna to the high-band 5G network.
Optical frequency combs generated in whispering gallery mode microresonators are in high demand for basic science and a large number of applications including telecommunication systems and quantum optics. Here, we study experimentally and theoretically optical frequency comb generation in a silica microsphere with a zero dispersion wavelength near $1.55 μm pumped by a continuous wave laser widely tunable in the C-band. We considered the optical frequency comb generation for a pump wavelength in a normal dispersion region, in a low anomalous dispersion region, and very close to the zero dispersion wavelength. Kerr-assisted and Raman-assisted (Stokes) combs as well as anti-Stokes combs emerging due to the four-wave mixing between the Kerr and Raman combs are attained in experiments. The mechanisms of producing individual peaks of optical frequency combs are verified in numerical simulations. A 270-nm optical frequency comb covering the telecommunication E-, S-, C-, L-, U-bands and further up to $1.7 μm is also demonstrated.
A chaos based encrypted polar coding scheme,which could be applied to the negative secrecy capacity case,was proposed.Chaotic sequences were employed to encrypt the information bits and fill the frozen bits.And multi-block polar coding structure was also employed in the proposed scheme.The proposed scheme was featured as lower complexity and higher secrecy transmission rate.Corresponding mathematical analysis had been performed in terms of the error probability,security and transmission rate.The result reveals that the proposed scheme can achieve reliability,security in negative secrecy capacity case.What’s more,it has relatively low complexity and high secrecy transmission rate compared with the existing schemes.
We demonstrate the application of recent advances in statistical mechanics to a problem in telecommunication engineering: the assessment of the quality of a communication channel in terms of rare and extreme events. In particular, we discuss non-Markovian models for telecommunication traffic in continuous time and deploy the "cloning" procedure of non-equilibrium statistical mechanics to efficiently compute their effective bandwidths. The cloning method allows us to evaluate the performance of a traffic protocol even in the absence of analytical results, which are often hard to obtain when the dynamics are non-Markovian.
Dmytro Ageyev, Oleg Bondarenko, Tamara Radivilova
et al.
This article studies the existing methods of virtualization of different resources. The positive and negative aspects of each of the methods are analyzed, the perspectivity of the approach is noted. It is also made an attempt to classify virtualization methods according to the application domain, which allows us to discover the method weaknesses which are needed to be optimized.