ICT Express最新文献

Satellite communication has reached a turning point in transmitting data quickly and receiving signals clearly in faraway places. This paper presents a metamaterial-based antenna for distant and military applications that overcomes restrictions and performs unmatched. This project explores metamaterials to create a unique antenna. Expect to be astounded by its power to bend electromagnetic waves, miniaturize, and change our relationship with the universe. The feeding mechanism of this antenna is a modified co-planar waveguide. At 5.6 GHz, the proposed antenna unit cell measures 0.076${\lambda }_0$$\times 0.2{\lambda }_0$, making it small thanks to its MTM characteristic. CP radiation is made when two orthogonally polarized modes are stimulated at the same time and two composite right- and left-handed transmission line unit cells are placed orthogonally. Reduced size, increased bandwidth, and improved Learn how this discovery opens new doors for high-resolution photography, broadband internet, and space exploration. One nanostructure that makes up a metamaterial is the Split Ring Resonator (SRR). SRR dimensions must be smaller than the resonance wavelength, making them crucial for near-infrared and optical responses. This effort examined nanoscale SRR characteristics in the infrared and visible ranges. SRRs composed of aluminum (Al) and gold (Au) were produced on silicon and silica substrates using electron beam lithography (EBL). The proposed structure consists of a microstrip-line-supplied SRR, a parasitic patch that is perpendicular to the ground plane, and two via holes connected by two split rings that feed the patch indirectly. Choosing the right material parameter for all such antennas depends on the planned structure. Fabrication and testing of the antenna matched simulations. Dimensions: 23.7 mm × 16.2 mm × 1.6 mm; substrate dielectric constant: 4.4. Experimental data are detailed and compared to analytical calculations. Simulation results show that metamaterials have negative permittivity and permeability in a certain frequency range, which can be predicted by an analytical method. Simulations indicated eight BRS, WiMAX, radar, and mobile phone resonance frequencies. Radiation is dipole-like in the omnidirectional in the H-plane and E-plane. 10 dB return loss and 3 dB axial ratio bandwidths are 38.6.7% and 8.1%, respectively.

We analyze a remote sensing system in the Internet of things, where uncoordinated nodes send status updates to a common receiver to achieve information freshness, quantified through age of information. We consider a finite horizon scheduling over a random multiple access channel, where colliding messages are lost. We show that nodes must adopt a further randomization to deviate from identical schedules and escape collision deadlocks. Moreover, we discuss the impact of feedback availability if, due to, e.g., energy expenditure, it decreases the number of transmission opportunities.

In this paper, we propose deep learning-based channel estimation and pilot reduction for mmWave point-to-point multi-input multi-output systems. The proposed scheme consists of a two-step approach where the first step is applying a denoising autoencoder for channel estimation. With the denoising characteristic of autoencoder, sparse channel estimation can be conducted although the orthogonality of pilot sequences is not guaranteed due to shorter pilots. The second step is exploiting the temporal correlation of the channel, using the previous estimate to extract information for the current estimate. Through simulation, the proposed scheme shows superior performance with reduced pilots.

This paper proposes a method for characterizing user mobility levels in multiple unmanned aerial vehicles (UAV)-assisted networks. In a dynamic environment with varying degrees of mobile ground users, optimizing the placement of UAVs is important in improving network throughput. Moreover, for integrated sensing and communication (ISAC) enabled UAVs, the resource allocation for sensing and communication relies on the dynamic nature of the network environment. To keep track of the changes in the environment UAVs are required to continuously update their trajectory, hence, characterizing the users’ mobility level is an important tool to improve the UAV trajectory optimization. In this paper, a mobility-aware resource allocation for joint sensing and communication is proposed. Our results demonstrate that the proposed algorithm can improve the resource allocation between sensing and communication in an ISAC-enabled UAV-assisted network.

This investigation explores Reinforcement Learning (RL) for dynamic portfolio optimization with risk assessment. The challenges include market complexity, uncertain reactions, and regulatory requirements for risk-averse decisions. Our solution leverages Bayesian Neural Network (BNN) to capture uncertainties. We successfully implemented a risk-averse Reinforcement Learning algorithm, achieving 18 percent lower risk. Reinforcement Learning with risk-aversion shows promise for optimizing portfolios for risk-averse investors.

This paper investigates the use of artificial intelligence (AI) image detection and discrimination technology to address issues related to mixed assortments and defects encountered in the fastener manufacturing and packaging processes. The defect detection system primarily utilizes the YOLOv4-tiny model with parameter setting and data augmentation techniques. The mixed assortments detection system is constructed using U-Net-Light and Siamese network. The research results demonstrate that the developed systems can indeed replace or assist on-site personnel in conducting efficient and accurate inspections and screenings.

This paper proposes an intelligent sensing framework for Internet-of-Things platforms, where sensor measurements stem from multiple causes. Sensors are selectively chosen for data collection to identify the cause with partial measurements. We employ variational deep embedding, a generative model capable of clustering and generation, to identify causes, cluster measurements accordingly, and determine causes for estimating complete measurements from partial data. These estimates aid in efficient sensor selection for data collection. Results demonstrate early and reliable cause sensing and complete measurement estimation using the proposed framework.

Smart transportation systems implemented through vehicular ad hoc networks (VANET) offer significant potential to improve safety. However, the network faces critical challenges related to security, as well as inadequate spectrum sensing and management. To address these issues, researchers have utilized cognitive radio and machine learning technologies. Although, previous survey studies have provided a valuable foundation for understanding the use of cognitive radio in VANET, not all have systematically investigated its impact on mitigating spectrum sensing and management issues or the role of machine learning in supporting cognitive radio functionality. Furthermore, the effects of security issues on both VANET and cognitive radio enhanced VANET have not been consistently examined. This survey aims to systematically review the application of cognitive radio and machine learning approaches to address the identified challenges in smart transportation networks, offering valuable research opportunities for future investigations. The paper extensively explores state-of-the-art approaches and focuses on: (1) Assessing the impact of cognitive radio and machine learning on spectrum sensing and management in smart transportation networks and (2) Evaluating the impact of security issues on both VANET and cognitive radio enhanced VANET.

Multipath TCP (MPTCP) is one of the leading transport layer protocols that enables multipath transmission to improve network performance. However, MPTCP can experience throughput degradation due to link congestion, which causes underutilizing of the network capacity. We proposed a new link congestion detection based on Software-Defined Networking (SDN) for MPTCP that detects and detours link congestion to achieve a stable MPTCP performance. Measurement results show that the proposed scheme enables efficient packet transmission by minimizing the effect of link congestion. Moreover, a combination of the proposed scheme and SDN monitoring enables dynamic traffic control based on the proposed link congestion detection.

Two circularly polarized antenna arrays, which reduce two redundant OAM modes, are proposed in this paper. One antenna array is composed of 8 right-handed circularly polarized (RHCP) antennas and radiates OAM mode $l$ in $x$ component, OAM mode $l$ in $y$ component and OAM mode $(l+1)$ in $z$ component. The other antenna array has 8 left-handed circularly polarized (LHCP) antennas and transmits OAM mode $l$ in $x$ component, OAM mode $l$ in $y$ component and OAM mode $(l-1)$ in $z$ component. The RHCP and LHCP antenna arrays produce OAM waves with fewer redundant OAM modes compared to the traditional single-direction antenna array. The feed excitations of the two circularly polarized antenna arrays have the same feeding system, which includes power dividers and phase shifters. The reflection coefficient parameters, radiation patterns and phase distributions are measured to display the radiated characteristics of the two proposed methods. Both the simulation and the experimental results demonstrate the effectiveness of the proposed methods.