Hasil untuk "Telecommunication"

Majdi Owda, Ahmad Abumihsan, Amani Yousef Owda et al.

<b>Background/Objectives</b>: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. <b>Methods</b>: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. <b>Results</b>: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). <b>Conclusions</b>: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design.

Rahat Ullah, Sibghat Ullah, Jianxin Ren et al.

The rapid advancement of terahertz (THz) communication systems has positioned this technology as a key enabler for next-generation telecommunication networks, including 6G, secure communications, and hybrid wireless-optical systems. This review comprehensively analyzes THz communication, emphasizing its integration with free-space optical (FSO) systems to overcome conventional bandwidth limitations. While THz-FSO technology promises ultra-high data rates, it is significantly affected by atmospheric absorption, particularly absorption beyond 500 GHz, where the attenuation exceeds 100 dB/km, which severely limits its transmission range. However, the presence of a lower-loss transmission window at 680 GHz provides an opportunity for optimized THz-FSO communication. This paper explores recent developments in high-power THz sources, such as quantum cascade lasers, photonic mixers, and free-electron lasers, which facilitate the attainment of ultra-high data rates. Additionally, adaptive optics, machine learning-based beam alignment, and low-loss materials are examined as potential solutions to mitigating signal degradation due to atmospheric absorption. The integration of THz-FSO systems with optical and radio frequency (RF) technologies is assessed within the framework of software-defined networking (SDN) and multi-band adaptive communication, enhancing their reliability and range. Furthermore, this review discusses emerging applications such as self-driving systems in 6G networks, ultra-low latency communication, holographic telepresence, and inter-satellite links. Future research directions include the use of artificial intelligence for network optimization, creating energy-efficient system designs, and quantum encryption to obtain secure THz communications. Despite the severe constraints imposed by atmospheric attenuation, the technology’s power efficiency, and the materials that are used, THz-FSO technology is promising for the field of ultra-fast and secure next-generation networks. Addressing these limitations through hybrid optical-THz architectures, AI-driven adaptation, and advanced waveguides will be critical for the full realization of THz-FSO communication in modern telecommunication infrastructures.

Pairoj Waiwanijchakij, Thanapat Chotsiri, Pisit Janpangngern et al.

The integration of autonomous vehicles in industrial settings necessitates advanced positioning and navigation systems to ensure operational safety and efficiency. This study rigorously evaluates the application of Ultra-Wideband (UWB) technology in autonomous industrial trucks and compares its effectiveness with conventional systems such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), and cameras. Through comprehensive experiments conducted in a real factory environment, this study meticulously assesses the accuracy and reliability of UWB technology across various reference distances and under diverse environmental conditions. The findings reveal that UWB technology consistently achieves positioning accuracy within 0.2 cm 99% of the time, significantly surpassing the 10 cm and 5 cm accuracies of GPS and LiDAR, respectively. The exceptional performance of UWB, especially in environments afflicted by high metallic interference and non-line-of-sight conditions—where GPS and LiDAR’s efficacy decreased by 40% and 25%, respectively—highlights its potential to revolutionize the operational capabilities of autonomous trucks in industrial applications. This study underscores the robustness of UWB in maintaining high accuracy even in adverse conditions and illustrates its low power consumption and efficiency in multi-user scenarios without signal interference. This study not only confirms the superior capabilities of UWB technology but also contributes to the broader field of autonomous vehicle technology by highlighting the practical benefits and integration potential of UWB systems in complex and dynamic environments.

Budi Setiawan, Nico Surantha

In the fishery industry, Indonesia’s large water area has the potential for developing and cultivating fisheries such as vannamei shrimp. For this reason, aquaculture, particularly vannamei shrimp farming, can play a crucial role in Indonesia’s economy and food supply. However, challenges such as fluctuating water quality, disease outbreaks, turbidity levels, and irregular shrimp feeding schedules in ponds can affect the productivity and sustainability of shrimp farming. The smart aquaculture system integrates technologies, such as IoT-based sensors, automated feeding mechanisms, and real-time water quality monitoring to optimize the farming process. The research proposes a smart aquaculture design for vannamei shrimp farming based on the Quality Function Development (QFD) method. It starts by creating questionnaires to identify stakeholders’ level of interest. The questionnaire results are used as a reference for system redesign using the QFD method to improve the quality and quantity of shrimp harvest, cultivating effectively and efficiently and helping and facilitating the supervision of pond managers on pond water quality, feeding, and feed availability. The result highlights the application of QFD in creating a tailored, technology-driven solution that supports better decision-making, resource optimization, and improved shrimp health. The system reduces human error, enhances farm management, and promotes higher yields by providing real-time data and automation. The evaluation results show that the proposed design can achieve high stakeholder satisfaction. It also achieves better scores compared to the other two competitor’s designs.

Dimitrios Chatzoulis, Costas Chaikalis, Dimitrios Kosmanos et al.

Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.

T. W. Cenggoro, Raditya Ayu Wirastari, Edy Rudianto et al.

Abstract To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

Jinzhi Du, Weijia Cui, Bin Ba et al.

Abstract In order to solve the problems of high complexity and absence of adaptability with the joint estimation for time delay (TD) and angle of arrival (AOA) in coherent multipath environments, a fast estimation algorithm based on orthogonal frequency division multiplexing (OFDM) technique is proposed. First, we combine OFDM signal characteristics with array features to obtain extended arrays. Then, we obtain the channel frequency domain response covariance matrices for TD and AOA estimation separately by smoothing preprocessing in the spatial and frequency domains, respectively. Finally, we estimate the TD values by a one‐dimensional (1‐D) spectral peak search, as well as determine the closed‐form solution for AOA by Unitary‐ESPRIT. In comparison with previous work, the proposed algorithm not only improves the adaptability in correlated or mixed multipath environments but also significantly reduces the complexity. The simulation results show the effectiveness and robustness of the proposed joint estimation algorithm.

Yifeng WANG, Yuanbo GUO, Qingli CHEN et al.

In order to protect against unknown threats and evasion attacks, a new method based on contrastive learning for fine-grained unknown malicious traffic classification was proposed.Specifically, based on variational auto-encoder (CVAE), it included two classification stages, and cross entropy and reconstruction errors were used for known and unknown traffic classification respectively.Different form other methods, contrastive learning was adopted in different classification stages, which significantly improved the classification performance of the few-shot and unknown (zero-shot) classes.Moreover, some techniques (e.g., re-training and re-sample) combined with contrastive learning further improved the classification performance of the few-shot classes and the generalization ability of model.Experimental results indicate that the proposed method has increased the macro recall of few-shot classes by 20.3% and the recall of unknown attacks by 9.1% respectively, and it also has protected against evasion attacks on partial classes to some extent.

Lei Lv, W. Dang, Xiaoxi Wu et al.

High-performance short-wave infrared (SWIR) photodetectors (PDs) possess great potential in health monitoring, remote control, telecommunication, and medical diagnostics. In comparison with inorgan...

Xiang Liu

Fiber Optics; Information and Communication Technologies; Telecommunication Engineering

B. Cappers, J. V. Wijk

Tayyaba Akram, Shen Lei, Muhammad Jamal Haider et al.

This study is an attempt to find out the impact of knowledge sharing on the innovative work behavior of employees working in telecommunication sector of China. Particularly, the focus of this study is on the two important dimensions of knowledge sharing namely knowledge donating and knowledge collecting. For this purpose, data of 200 employees from telecommunication sector of China was collected and analyzed through correlation and multiple regression techniques. The results suggest that both knowledge donating and knowledge collecting are positively and significantly affect the innovative work behavior of the employees working in telecommunication industry. However, knowledge collecting was found as a better contributor in facilitating the employee innovative work behavior.

M. García-Valls, T. Cucinotta, Chenyang Lu

Hojoong Jung, C. Xiong, K. Fong et al.

Aluminum nitride (AlN) is an appealing nonlinear optical material for on-chip wavelength conversion. Here we report optical frequency comb generation from high-quality-factor AlN microring resonators integrated on silicon substrates. By engineering the waveguide structure to achieve near-zero dispersion at telecommunication wavelengths and optimizing the phase matching for four-wave mixing, frequency combs are generated with a single-wavelength continuous-wave pump laser. Further, the Kerr coefficient (n₂) of AlN is extracted from our experimental results.

A. Capretti, Yu Wang, N. Engheta et al.

Stefan Bauernschuster, Stefan Bauernschuster, Oliver Falck et al.

Does the Internet undermine social capital or facilitate inter-personal and civic engagement in the real world? Merging unique telecommunication data with geo-coded German individual-level data, we investigate how broadband Internet affects several dimensions of social capital. One identification strategy uses panel information to estimate value-added models. A second exploits a quasi-experiment in East Germany created by a mistaken technology choice of the state-owned telecommunication provider in the 1990s that still hinders broadband Internet access for many households. We find no evidence that the Internet reduces social capital. For some measures including children's social activities, we even find significant positive effects.

M. Marcus

D. Lee, Ji Eun Kim, T. Han et al.

Xuanli WU, Ziyi XIE, Wei WU

An interference graph based adaptive interference coordination method was proposed for indoor scenario of ultra dense network (UDN).The algorithm aimed at maximizing system throughput.Firstly,the interference relationship in the system was modeled as an interference graph,and the iterative coloring algorithm was used to determine the available resources of each small cell base station (SBS).Thereafter,the SBS allocated resources to each user by using a throughput optimizing resource allocation algorithm.The method could adaptively select a resource allocation strategy according to the network topology and channel conditions,thereby mitigating interference in the system.The simulation results show that compared with the existing methods,the proposed method effectively reduces the system outage probability while significantly improving the throughput performance through a small additional signaling overhead.

M. Pun