ICT Express最新文献

With the recent expansion of 5th generation mobile communication services, data demand is increasing. Correspondingly, the use of high-frequency millimeter waves and unmanned aerial vehicles (UAVs), which have features of high-altitude operation and mobility, and ease of deployment, is growing. However, for millimeter waves, narrow beams are used to compensate for large path losses, and appropriate beam allocation algorithms are essential. Therefore, in this paper, we propose an algorithm that utilizes a Gaussian process to predict the position of UAV-user equipment (UAV-UE). We then calculate the codebook based on the predicted position information and track the optimal beam in UAV-enabled communication systems. The results of a performance analysis confirm that the proposed algorithm can predict the UAV-UE position with high accuracy, and it has a high spectral efficiency. In addition, in the process of tracking the optimal beam, the complexity of codebook calculation is reduced by using the previously predicted UAV-UE position information.

The Maritime Tactical Data System is a software system that collects track data from maritime sensors to compile and show on a sea map to provide maritime patrol vessels or maritime surveillance stations with situational awareness. Speed and precision in tracking multiple targets are crucial in achieving situational awareness. A multi-target tracking problem is NP-hard if it involves more than two sensors and a large amount of data since it generates many potential solutions that must be evaluated. Previous research has demonstrated that the Density-based Spatial Clustering of Applications with Noise (DBSCAN) method can perform track-to-track association with pretty good results; nevertheless, the density-reachable concept of DBSCAN poses a problem when two targets are within a distance less than the threshold. Another limitation is the inability of DBSCAN to associate tracks as soon as sensor track data is received. DBSCAN must run after all data has been collected in a database. In this paper, a novel track-to-track association method called Neighborhood Clustering Track Association and Fusion (NCTAF) is proposed to address the limitations of DBSCAN. According to the experiment results, NCTAF overcame the inaccurate cluster form generated by DBSCAN. The most remarkable result is that NCTAF performs track associations in an average of one second after receiving sensor track data involving three sensors, 4000 track data per sensor, and an update rate of 5-12 s per sensor. In contrast, DBSCAN required more than 10 min for the same scenario.

This investigation explores the use of mixed-reality in collaborative diagnosis by sharing medical data in real-time between multiple physicians using Head-Mounted Display (HMD) devices. Object detection and alignment of the digitized data with the object are the backbone in any mixed-reality application. In this paper, deep-learning networks are used in detecting the patient’s face in the physical world and the medical data is aligned to the patient via the Region-Enhanced-Weight-and-Perturb Iterative-Closest-Point (RE-WAPICP) algorithm. Experiments were performed by sharing a 3D digital model of intracerebral vascular with multi-viewers in a mix-reality environment and the results show that this approach is feasible.

Ultraviolet communication (UVC) is a promising technology due to its ability to operate in non-line-of-sight (NLOS) mode thereby eliminating the pointing acquisition and tracking (PAT) requirement as needed by infrared and visible light communications. However, NLOS UVC suffers from very high attenuation and turbulence-induced fading when operated over a long distance. Due to these limitations, the existing literature on the NLOS UVC is mostly restricted to short-distance communications only. Therefore, this paper addresses these challenges by proposing an outdoor subcarrier-intensity-modulation (SIM) based multi-relay cooperative communication system employing the best relay selection and decode-and-forward (DF) relaying protocol. The turbulence-induced fading is modelled using lognormal distribution under weak atmospheric turbulence conditions. We derive novel closed-form analytical expressions of outage probability and ergodic capacity. Correctness of the derived analytical expressions is validated through numerical simulations.

The main causes for the risks of used-car trading lie in the information asymmetry between buyers and sellers and the absence of a trust mechanism. This study proposed applying the Ethereum blockchain and InterPlanetary File System (IPFS) to construct a used-car trading and management information system that supports decentralized data storage services. This mechanism could support the permanent storage, immutability, and traceability of car maintenance data through the operation of smart contracts. Furthermore, car information is stored and managed by IPFS. Slither monitors the security of this system by detecting security bugs in the operation of smart contracts.

There is a growing demand for emotion recognition systems (ERS) to be adopted in everyday life from various fields, particularly automotive, education, and social security. Recently, the use of cardio-based physiological signals, electrocardiogram (ECG), and photoplethysmogram (PPG) in ERS has yielded promising results. Furthermore, the development of wearable devices equipped with cardio-based physiological sensors has significantly aided towards the adoption of ERS in daily life. This paper systematically reviews emotion recognition using cardio-based physiological signals, encompassing emotion models, emotion elicitation methods, and ERS development methods, emphasizing feature extraction, feature selection methods, feature dimension reduction methods, and classifiers. A summary and comparison of recent studies are presented to highlight existing studies’ gaps and suggest future research for better ERS especially using cardio-based signals.

5G-based MANET has received a lot of attention recently. Its fundamental feature is that nodes are constantly subjected to high traffic loads, while QoS requirements are extremely stringent. When applied to 5G-based MANETs, existing routing protocols have shown drawbacks. In this paper, we propose an enhanced AODV protocol solution for 5G-based MANETs. Using reinforcement learning, each node updates a state information database of intermediate nodes along routes to destinations. This database is used by the routing algorithm to find guaranteed QoS routes. Our solution is highly efficient in terms of throughput, end-to-end delay, and SNR, according to the simulation results.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) system is a state-of-the-art emerging technology targeted towards beyond fifth-generation (B5G) and sixth-generation (6G) communication networks. This network pertains to a dense deployment of access points (APs) dispersed over a large geographical area to serve a small number of users at the same frequency and time resources. The CF-mMIMO architecture offers resilient connectivity, interference management, power efficiency, high throughput, and macrodiversity. Moreover, this communication technique eliminates cell boundaries and facilitates the users by introducing overlapping regions, thus providing consistent quality of service (QoS) throughout the region. However, the complexity of CF-mMIMO systems increases considerably when numerous APs are dispersed over a large geographical area. Therefore, several studies have been carried out to determine the optimal solution with minimum complexity of the CF-mMIMO system. Herein, a thorough investigation of the literature on the CF-mMIMO system is presented, considering all aspects from architecture to applications. The study provides a detailed survey of CF-mMIMO architecture, fronthaul, and backhaul, as well as the challenges associated with them; deployment methodologies and challenges for practical implementation of CF-mMIMO systems are also discussed. Furthermore, we reviewed the impact of transmitter and receiver antennae on the capacity of CF-mMIMO enabled with millimeter wave (mmWave). The numerical findings indicate that the higher degree of freedom required for spatial multiplexing allows multiantenna users to surpass single-antenna users in terms of capacity. This study holds significance owing to the thorough examination of the CF-mMIMO system model, channel estimation, scalability problems, working algorithms, communication protocol, deep learning-based solutions, linkage to B5G and 6G, and key challenges. Moreover, this study presents a detailed discussion and research survey on the system model, deployment issues, deep learning, and potential applications of the CF-mMIMO system.

Deep convolution neural networks are the recent algorithms used for robotic vision. However, the complex crop–weed vegetation and the background interferences required a robust feature representation. Therefore, we proposed a Dual-branch Deep neural network for the semantic segmentation of crops and weeds. The branches utilized distinct feature extraction algorithms that extract essential semantic cues, and a decoder combined these features to improve the global contextual information. Finally, the hybrid feature selection module(HSFM) utilized the decoder features to complement one another. Experimental results show the proposed method obtained mean intersection of union scores of 0.8613 and 0.9099 on CWFID and BoniRob datasets, respectively.

We herein propose a low-approximated least-squares method for underwater drone localization that does not require depth information. On the basis of error analysis, we propose a rule to deploy a set of surface drones that minimizes the impact of measurement errors such as GPS or distance measurement errors. The Evaluation results indicate that the proposed method outperforms the least-squares method when depth information is not provided.