ETRI Journal最新文献

Traditional navigation methods for mobile robots face significant challenges in dynamic environments, including local minima avoidance and efficient path planning. This paper introduces the semantic potential field (SPF) method, which synergizes geometric and semantic data using a semantic grid map to improve navigation efficiency and adaptability. The key features of the SPF method include (i) a semantic grid map combining light detection and ranging (LiDAR) and camera data to distinguish static and dynamic obstacles and (ii) a dynamically modulated potential field incorporating semantic weights for adaptive path planning and obstacle avoidance. The experimental results demonstrate that the SPF method significantly reduces the travel distance and computation time compared with those of traditional methods, ensuring robust navigation in diverse environments. By addressing the limitations in real-time navigation systems, the SPF represents a significant advancement in mobile robot path planning, with promising applications in disaster response, autonomous logistics, and defense.

As 5G technology expands globally, the demand for secure and reliable military communication is growing significantly. This study examines 5G military network architectures that integrate commercial 5G elements, ranging from isolated to shared configurations, to balance security, cost, and coverage. We propose a network leveraging commercial next-generation Node B (gNodeB or gNB) with a military Public Land Mobile Network (PLMN) ID, ensuring extensive coverage, robust security, and cost efficiency. To enhance secure access, a zero trust (ZT) architecture using a software-defined perimeter (SDP) is employed. The connection process for user equipment accessing private networks is detailed through 5G authentication and ZT access management. Simulated 5G analysis compares scenarios before and after applying SDP under denial-of-service (DoS) and IP scanning attacks. Results demonstrate that SDP mitigates IP scanning threats, improves throughput during DoS attacks, and maintains performance. Validation on a real-world 5G testbed confirms feasibility, robust security, and stability. These findings underscore the potential of ZT-based 5G military networks for modern defense communication.

Traditional camouflage focuses on visual concealment, but the growing use of infrared (IR) detection systems has created a need for materials that can manipulate IR wavelengths. Electrochromic devices—commonly used for light and heat control—offer a promising solution for dynamic IR control. These systems rely on transparent conducting electrodes, with indium tin oxide (ITO) being the most common. However, ITO presents a challenge, as it generally blocks IR light transmissions. In this study, we optimize for IR transmissions by adjusting the ITO thickness, demonstrating excellent modulation in electrochromic devices. Devices with ITO thicknesses of 40 nm, 75 nm, and 302 nm are tested, with the 75-nm electrode achieving 67.73% transmittance modulation in the visible range and 51.41% in the near-infrared range. Response times for bleaching and coloration are 4.0 s and 2.8 s, respectively.

Traditional radio frequency (RF)-based anti-drone technologies focus on jamming the command and control (C2) RF signal or global navigation satellite system (GNSS) signal of a target drone. Although these methods efficiently neutralize target drones, they may generate unintended RF collisions with other equipment and cause the drone to crash. We introduce a method for hijacking a frequency hopping spread spectrum (FHSS)-based drone through C2 signal emission for takeover and a precision spoofing signal to invalidate the owner's transmitter signal using a multi-modem in this work. The proposed method simultaneously transmits C2 signals to the target drone for safe takeover and precision spoofing signals to neutralize the owner's transmitter, thereby minimizing RF collisions with other equipment.

Modern warfare, involving both conventional combat and terrorism, often occurs in urban environments. As opposed to traditional outdoor operations, urban operations rely on indoor concealment, creating many hazardous situations. Operations inside buildings result in numerous hiding spots and challenges, such as stairs and varying ceiling heights, which can endanger friendly forces. Effectively responding to enemy concealment and anticipating threats in such environments are critical. The development and research of small equipment for situational awareness have increased rapidly in recent years. This study proposes a framework that uses edge devices, which are widely used in current robotics, in clusters to support effective indoor military operations. The framework enables real-time object location estimation and risk zone identification, even in environments in which the resources required for robot movement are limited. The proposed framework provides auxiliary and intuitive information for situational assessment during indoor military operations, such as building clearing.

This paper introduces a compact microstrip antenna specially designed for ultra-wideband (UWB) technology with dual notch band (DNB) properties. The antenna consists of a semi-annular ring-shaped patch and a partial ground plane. An arc-shaped slot and a rectangular complementary split-ring resonator are used to create the DNBs. The UWB antenna operates within a frequency spectrum spanning from 3.15 GHz to 13.57 GHz. DNBs are generated for the frequency ranges of 3.43 GHz to 4.25 GHz and 6.57 GHz to 7.81 GHz to reduce interference from the WiMAX band and X-band downlink satellite channel, respectively. The DNBs can be tuned separately by tuning the structural parameters of the antenna. Within the UWB frequency range, the simulated and measured data demonstrate that the antenna exhibits good radiation efficiency with nearly bidirectional radiation characteristics in the E-plane with a peak gain of 3.5 dBi. The proposed antenna has potential applications in UWB communication, mobile communication, and wireless body area networks due to its compact size and low manufacturing cost.

Time series predictions are commonly used in the fields of energy, meteorology, and finance, among others. The accurate prediction of time series data is critical for making decisions and planning. In the real world, non-stationary time series data with statistical properties shift over time, making prediction more challenging. Although, conventional time series processing methods—such as multi-scale feature extraction or Transformer-based algorithms—produce superior prediction results, when dealing with data that contain more noise and outliers, the prediction ability of such methods can suffer. To address this problem, we proposed the WPFormer model, which incorporated time-frequency analysis into the Transformer architecture to increase the long-term series prediction accuracy. The model employed wavelet packet decomposition to identify and eliminate noise efficiently, increasing its immunity to interference. We evaluated WPFormer on four publicly available datasets and compared its performance against the Informer, LogTrans, Reformer, LSTMa, LSTNet, and DeepAR models using MSE and MAE metrics. On average, the WPFormer model surpassed the benchmark models by 16%.

At the forefront of digital image processing, image super-resolution has emerged as a flourishing research area. However, despite remarkable progress, current methods still encounter significant hurdles, particularly when enhancing noisy images. To overcome this limitation, this study introduces a state-of-the-art super-resolution reconstruction technique called DenoSR, which leverages a pretrained diffusion model and is categorized as a zero-shot super-resolution reconstruction methodology. DenoSR, in its engagement with noisy images, progressively refines high-frequency image features through an inverse diffusion mechanism, thereby ensuring the accurate reconstruction of fine details. An exhaustive quantitative analysis conducted on publicly available benchmark datasets demonstrated that DenoSR outperformed existing methodologies in terms of image reconstruction quality. Furthermore, qualitative assessments corroborate the superiority of DenoSR in terms of reconstruction fidelity, highlighting significant advancements in enhancing the realism and naturalness of visual perception.

We analyze the secrecy attributes of an energy-harvesting-enabled decode-and-forward relay-based cooperative nonorthogonal multiple access (NOMA) system in the presence of an eavesdropper. Information flow through the wireless channel exposes legitimate users to eavesdropping by unintended users on confidential information. We study the secrecy performance of two-user NOMA systems by calculating the secrecy outage probability (SOP) and ergodic secrecy capacity (ESC). An eavesdropper overhears the signal from a source and relays it from a relay node. We evaluate the secrecy performance of both nodes (i.e., relay node ($�{�U�}_{�1�}$) and distant node ($�{�U�}_{�2�}$)) by developing a novel closed-form expression for node $�{�U�}_{�1�}$. For node $�{�U�}_{�2�}$, we evaluate an analytical but intractable expression of SOP and tractable analytical expression of ESC. We examine the effects of network parameters on secrecy and link outages. We also observe the reliability of the link outage probability of node $�{�U�}_{�2�}$ and system outage probability. Monte Carlo simulations are conducted to verify the analytical results and evaluate the effect of SOP on imperfect channel state information and perfect channel state information.

The development of multimodal machine translation (MMT) systems has attracted significant interest due to their potential to enhance translation accuracy with visual information. However, there are two limitations: (i) scarce large-scale corpus data in the form of (text, image, text) triplets and (ii) the semantic information learned by pre-training cannot transfer to multilingual translation tasks. To address these challenges, we propose a novel multimodal retrieval-augmented generation framework for machine translation, abbreviated as MRF-MT. Specifically, using the source text as a query, we retrieve relevant (image, text) pairs to guide image generation and feed the generated images into the image encoder of Multilingual Contrastive Language-Image Pre-training (M-CLIP) for learning visual information. Subsequently, we employ a projection network to transfer visual information learned by M-CLIP as a decoder prefix to Multilingual Bidirectional and Auto-Regressive Transformers (mBART) and train the mBART decoder using a two-stage pre-training pipeline. Initially, the mBART decoder is trained for image captioning with a visual–textual decoder prefix from M-CLIP's image encoder projection network. Subsequently, it undergoes training for caption translation, using prefixes from M-CLIP's text encoder. Extensive experiments show that MFR-MT achieves promising performance compared with baselines.