ETRI Journal最新文献

Image dehazing is aimed to reconstruct a clear latent image from a degraded image affected by haze. Although vision transformers have achieved impressive success in various computer vision tasks, the limitations in scale and quality of available datasets have hindered the transformer effectiveness for image dehazing. Thus, convolutional neural networks (CNNs) remain the mainstream approach for image dehazing, offering robust performance and adaptability. We further explore the potential of CNNs in image dehazing by proposing a multiscale implicit frequency selection network (MIFSN). The proposed MIFSN enhances multiscale representation learning based on U-shaped networks. As hazy and clear images considerably differ in high-frequency components, we introduce an implicit frequency selection module to amplify high-frequency components of features and generate candidate feature maps. Implicit frequency selection attention is then used to emphasize and merge beneficial frequency components. Results from extensive experiments on synthetic and real-world datasets demonstrate the superior performance of MIFSN for image dehazing.

This paper proposes a new companding scheme called three $�\mu$-law companding to reduce the peak-to-average power ratio (PAPR) in a universal filtered multicarrier system. The proposed scheme is more flexible and provides better PAPR reduction and bit error rate (BER) through the selection of the three companding parameters. However, the proposed scheme suffers from high side lobes and increases the power of the companded signal. Hence, an enhanced three $�\mu$-law, which overcomes these limitations, is proposed. Unlike the first scheme, the enhanced scheme does not change the mean power of the companding signal because it incorporates power-level normalization. Simulation results show that, compared with other companding transforms, the proposed enhanced three $�\mu$-law scheme offers better BER characteristics and side-lobe level suppression. It also provides better PAPR reduction of 7.4 dB and net gain of 6.3 dB. The proposed enhanced three $�\mu$-law was evaluated over a multipath fading channel and was more robust than other companding techniques. Finally, the proposed enhanced algorithm was verified in real time using the Wireless Open-Access Research Platform.

Routing in underwater sensor networks (UWSNs) is highly challenging because of harsh underwater conditions, such as deep water, high pressure, and rapid ocean currents. Furthermore, UWSNs are vulnerable to jamming attacks because of their limited bandwidth and battery capacity. Advancements in machine learning enable numerous routing methods to address these problems. Accordingly, we propose a novel max or minimax Q-learning (M-Qubed)-based opportunistic routing method for UWSNs. The method uses an opportunistic routing protocol, in which nodes dynamically select the next relay node by considering the status of their neighbors. Moreover, M-Qubed can maximize the benefits for both players in a two-player repeated game through reinforcement learning. Hence, it can reduce the energy loss caused by jamming attacks during routing, thereby increasing the routing efficiency in UWSNs. Simulation results reveal that the proposed routing scheme is less affected by jamming attacks than existing state-of-the-art routing methods. In addition, it can balance energy consumption across the nodes in a UWSN.

In recent years, the growing demand for massive connectivity has focused researchers' attention on next-generation radio access techniques. Nonorthogonal multiple access (NOMA) is a candidate for supporting massive connectivity. However, NOMA systems generate severe intracluster interference, which affects user groups (clusters) with low channel gain differences because of user clustering and the low power disparity between users. Therefore, multiuser NOMA clustering and power management policies must be adopted. We propose schemes for multiuser clustering and joint power allocation. We analytically formulate power allocation for a given signal-to-interference-plus-noise ratio considering users in the cluster with different and similar locations (channel gains) in the cell. The formulation is then applied to NOMA clusters. Finally, we evaluate the optimal cluster size according to the number of users. The proposed schemes outperform other approaches in terms of total cluster throughput and user throughput with the highest channel gain.

We propose a compact, wideband, U-shaped, circularly polarized textile antenna (UCPTA) resonating at 2.45 GHz for wireless body area network (W-BAN) applications. The radiator of the antenna is printed on the top of the substrate (size of $�53�.�3�\times �59�.�8�\times �1$ mm³; $�0�.�43�\lambda �\times �0�.�48�\lambda �\times �0�.�0081�\lambda$) and has a full ground plane on the other side. The proposed antenna's substrate consists of a single layer of denim ($�{�ϵ�}_{�r�}�=�1�.�7$), and conductive copper tape was used to fabricate the patch. The antenna has a low-profile design that is suitable for wearable applications. A small slot was introduced at the center of the U-shaped patch to enhance the 3 dB axial ratio bandwidth (ARBW) up to 55.17%. The proposed antenna radiates with a 10 dB return loss (RL), impedance bandwidth (IBW) of 64.40%, and a simulated gain of 5.1 dBi. The wearable textile antenna with broadside radiation response is appropriate for off-body communications. The prototype of UCPTA is fabricated for experimental validation. The measured results for the fabricated antenna were in good agreement with the simulated results. Further, it has been demonstrated that the antenna's performance is stable during structural deformation and human body loading. This textile antenna covers an ARBW in the frequency band of 2.1 GHz–3.72 GHz and has a broad IBW (2 GHz–3.9 GHz) for LTE (2.17 GHz), MBAN (2.360 GHz–2.4 GHz), WPAN (2.395 GHz–2.4 GHz), and WLAN (2.4 GHz–2.482 GHz) applications.

The rapid evolution of wireless networks is leading to the emergence of 6G, promising unprecedented performance. We propose a nonorthogonal multiple access (NOMA)-based multiuser generalized spatial modulation (GSM) system that allows various transmitting antennas. Integrating unmanned aerial vehicles (UAVs) with NOMA and GSM enhances overall network performance. This paper proposes optimizing UAV-GSM-NOMA systems in 6G using the butterfly optimization algorithm (BOA) in the two-wave with diffuse power (TWDP) channel. Results are compared with those obtained from Nakagami and Rayleigh channels. Objectives include enhancing the sumrate, outage probability (OP), and energy efficiency (EE) in perfect and imperfect channel state information (CSI). Simulation results yield significant improvements in the TWDP channel compared with the Nakagami and Rayleigh channels. The OP is reduced, the sumrate is enhanced, and the EE is notably improved. These findings underscore the BOA's potential for 6G UAV-GSM-NOMA networks in challenging channel environments, ensuring reliable and efficient communication.

Access control is vital in interconnected environments like the Internet of Things, Industry 4.0, and smart connectivity, ensuring authorized access for security. Biometric-based access, particularly speaker verification (SV), enhances security with unique vocal features, offering nonintrusive authentication with continuous monitoring. Single-domain features prove insufficient in distinguishing similar traits, prompting latest SV advancements to adopt multidomain-based speech features. This paradigm addresses the limitations of single-domain features by amalgamating the merits of individual domains, establishing a cutting-edge approach. It utilizes cepstral–frequency–time domain feature fusion, achieved via cepstral mean-variance normalization for generalizability. The weighted city block Minkowski distance is proposed to compare reference and test speech templates. Parameters are computed based on the confusion matrix, template matching distance functions, dynamic acoustic conditions, and additive white Gaussian noise. A deep convolutional neural network classifier is assessed on open-source LibriSpeech and Speaker in the Wild corpora, surpassing the current methodologies.

Next-generation wireless systems constitute a promising technology, and intelligent reflecting surface (IRS)-aided mobile communications can be used to design and simulate a smart and flexible radio environment. We examine the average symbol error probability (ASEP) on an IRS-aided wireless network over a κ–μ fading channel with imperfect phase error. In addition, the ASEP for non-coherent M-ary frequency shift keying, M-ary phase shift keying, and M-ary differential phase shift keying on an IRS-aided wireless network over a κ–μ fading channel are analyzed. Furthermore, exact closed-form mathematical expressions for ASEP across different modulation schemes including ASEP with imperfect phase error are derived. The accuracy of the derived expressions is validated through simulations. The system effectiveness is comprehensively examined, uncovering valuable insights into channel fading values and the number of reflecting components.

In real-world video super-resolution, the complexity and diversity of degradations pose substantial challenges during both training and inference. Videos captured in real-world settings often depict activities at varying resolutions. Typically, these activities are filmed from a distance that reduces the resolution of imagery, which thus lacks discriminative features. To address this problem, we introduce an activity recognition solution. First, a unique integration of data transformation and attention-based average discriminator are employed for super-resolution feature augmentation. This approach mitigates the lack of discriminative cues in low-resolution videos. Subsequently, high-resolution features extracted from the recovered data are directly fed into a model ensemble for activity recognition. We evaluate the resulting method on the TinyVIRAT-v2 and HMDB51 datasets, achieving improved visual quality by leveraging the super-resolution and model ensemble strategy. The proposed method enhances the quality of textures and boosts activity recognition in low-resolution videos.

This paper presents a testbed for unshielded facilities utilizing planar electromagnetic absorbers. These slabs demonstrate comparable shielding effectiveness to that of reinforced concrete in the frequency range from 1 GHz to 6 GHz. The testbed serves as a platform for developing a measurement method to assess unshielded facilities. It can be configured with different materials that have various levels of shielding effectiveness. We evaluate the shielding effectiveness of the absorber slab and extract its electromagnetic parameters. The testbed is implemented in a semi-anechoic chamber with inner dimensions of 3.00 × 3.06 × 2.50 m³. The electromagnetic wave attenuation for the unshielded facilities is defined as the ratio of received power within the assessment space relative to the incident power. The measurement results provide quantitative information on the attenuation of electromagnetic waves within unshielded facilities. The testbed configured with two types of incident planes is evaluated and compared with the mean attenuation and distribution characteristics.