ETRI Journal最新文献

We experimentally demonstrate 100 Gb/s bidirectional transmission over 40 km using a multi-wavelength bidirectional optical sub-assembly (BOSA) based on a single bidirectional multi-wavelength Mux/Demux. The Mux/Demux consists of an optical zig-zag glass block and thin film filters. Four wavelengths with 800 GHz spacing (that is, two wavelengths for each direction) within a 20-nm band in the O-band are utilized, and there is no four-wave mixing penalty. The multi-wavelength BOSA enables the bidirectional transmission of 2 × 50 Gb/s PAM4 signals over a 40-km single-fiber link. The multi-wavelength BOSA with multi-level modulation presents a suitable approach for future speed and transmission distance increases in 6G networks and data-center interconnections.

The advent of deep-learning technology has enhanced the performance of biometric systems, including facial and fingerprint recognition systems. Although facial recognition is now commonly used for authenticating mobile device users, it can be easily falsified as it relies on external traits. In this study, we design a user-independent model for surface electromyogram (sEMG) biometric authentication using the convolutional Siamese network with an N-pair loss function. We then implement the shift-equivariant model by exploiting the convolution and padding operations to deal with small shifts in multichannel sEMG sensors for various users. Additionally, the augmentation methods for time-series data and spectrograms are used to further improve the performances of the model. We employ the public Gesture Recognition and Biometrics electroMyogram (GRABMyo) dataset, comprising 43 subjects and 16 gestures collected over 3 days, to train and evaluate the model. The proposed model achieves equal error rates of 5.62% and 8.94% for unknown subjects while preserving and leaking the gesture code, respectively. In cross-day experiments, the model achieves rates of 4.92% and 7.56%, respectively, demonstrating robustness to intersession variations.

To meet the growing demand for higher data speeds and denser network coverage, intelligent reflecting surfaces (IRS) have gained considerable attention owing to their ability to control electromagnetic waves with accurate beam orientation. However, an optimal IRS design for maximum wireless power transfer (WPT) is essential and requires the integration of tuning features into reflect arrays. Earlier studies employed varactor and PIN diodes for the tuning of metallic reflectors; by contrast, graphene offers inherent tuning without extra components owing to its unique material properties. We propose the design of optimal graphene-enabled IRS (GIRS) to maximize WPT in Internet of Things (IoT) communications. Specifically, we formulate an optimization problem to maximize the power received by an IoT user, thereby obtaining a global optimal solution in terms of the Fermi level of GIRS. Furthermore, the closed-form expression of the Fermi level was derived as a function of the reflection amplitude controlled by GIRS, incident frequency, and other GIRS parameters. Finally, we numerically validated all investigations and provide several insights into the necessary GIRS design parameters.

Multilayer inverters (MLIs) play an important role in their efficiency and effectiveness. This study proposes a new MLI that is optimally adapted using DQZ control and a vague neurological approach for tracking the single maximum power point of a hybrid renewable energy source. This MLI has a bidirectional fixed switch, the purpose of which is to reduce harmonics and increase the voltage level. The maximum power point tracking (MPPT) method proposed here is the only MPPT method that uses neuro-fuzzy control algorithms, making it superior to other methods. The proposed inverter consists of 12 power semiconductor switches (IGBTs) connected to three DC power sources—that is, photovoltaic, wind, and tidal energy power sources. The switching angle for pulse-width modulation can be calculated using the DQZ principle in the proposed MLI. Evaluation of the effectiveness of the proposed method uses MATLAB/Simulink simulations, the results being compared to those of the prototype mechanism. We also compare the performance of the MPPT algorithm and prototype mechanism, which is connected to a single-phase microgrid. The proposed method achieves total harmonic distortion (THD) efficiency with a satisfactory performance increase.

Low-light image enhancement has made significant advancements in recent years. However, enhancing high-contrast images that exhibit both under- and overexposure remains a major challenge. To address this issue, we propose an exposure-correction method called AGCSNet. Two gamma corrections, $�{�\gamma �}_{�1�}$ and $�{�\gamma �}_{�2�}$, were applied separately to correct for underexposure and overexposure, producing two gamma-corrected images. An illumination map was used to differentiate between the underexposed and overexposed regions in the gamma-corrected images. To mitigate the saturation anomalies caused by the gamma corrections, we introduced a new saturation-correction method with a factor $�s$. Moreover, optimal values for $�{�\gamma �}_{�1�}�,��{�\gamma �}_{�2�}$, and $�s$ can be predicted using our factor-estimation deep-learning model. We evaluated our method on eight datasets. In comparison with over 20 prior methods, our method demonstrates competitive performance with and, in some cases, surpasses state-of-the-art methods that are closely aligned with human visual perception.

Ternary content-addressable memory (TCAM) is widely used in the design of high-speed search engines such as network routers and artificial-intelligence-based applications. However, traditional TCAM designs suffer from two major drawbacks. Static random access memory (RAM)-based TCAMs do one operation at a time, causing the search operation to be suspended while the update operation is in progress, rendering them unsuitable for applications with high-frequency updates. Moreover, during the implementation of wider TCAMs, when the match results are transferred from one slice to another, the last look-up table (LUT) in the slice is always set to logic one, which results in resource wastages. This research aims to overcome the problems associated with traditional TCAM design. The proposed work used six-input (RAM64X1S) LUTs in field-programmable gate arrays by allowing both search and update operations to be performed simultaneously during the data update in a particular LUT. To overcome resource wastage, the proposed design used four RAM64X1S blocks instead of RAM64M blocks. Moreover, the proposed TCAM architecture was considerably simpler, comprising LUTs with AND slicing, thus reducing FPGA resources such as slice registers and slice logic. For TCAM sizes of 512 × 36 and 1024 × 144, the slice utilization was reduced by 17% and 29%, respectively, with their speed being increased by 17% and 26%, respectively. Moreover, the lookup rate and the update rate of the designed TCAMs also improved considerably. The proposed architecture employed high-speed single-cycle searches, making it ideal for fast search applications.

We investigate the electro-optical properties of indium tin oxide (ITO) transparent electrodes on polyimide (PI) and glass substrates and their impact on organic light-emitting diode (OLED) performance. At a given thickness, the ITO layer exhibits a lower sheet resistance on glass substrates compared to PI substrates. The optical transmission spectra of ITO films are also analyzed, revealing substantial variations owing to interference phenomena. The optimal ITO layer and electron transporting layer (ETL) thicknesses for maximizing the luminance are identified, with glass substrates achieving higher luminance. The luminance on PI substrates shows smaller changes owing to their refractive indices being similar to those of the organic layers. Device fabrication confirms the simulation results, showing that luminance on PI substrates is more sensitive to ETL thickness. Incorporating SiN_x and Al₂O₃ as thin-film encapsulation shifts the optimal ITO layer thickness and slightly reduces luminance. Replacing SiN_x with SiO₂ optimizes luminance, yielding better outcomes. These results emphasize the importance of optimizing encapsulation materials and structures to enhance OLED performance.

In recent decades, the rapid growth of the Internet of Things (IoT) has highlighted several network security problems. In this study, an efficient intrusion detection (ID) system is implemented by using both machine learning and data mining concepts for detecting intrusion patterns. During the initial phase, the intrusion data are collected from NSL-KDD and University of New South Wales-Network Based 15 (UNSW-NB15) datasets. The collected intrusion data are then normalized/scaled by employing a standard scaler technique. Next, the informative feature values are selected by employing the proposed optimization algorithm—that is, the Niche-Strategy-based Gorilla Troops Optimization (NSGTO) algorithm. Finally, these selected informative feature values are transferred to the Long Short-Term Memory (LSTM) model to classify the types of intrusion attacks on both datasets. In comparison to the existing ID systems, the proposed ID system based on the NSGTO-LSTM model obtains a classification accuracy of 99.98% and 99.90% on both datasets.

The information interaction and positioning technology based on commercial Wi-Fi plays a crucial role in expanding the applications for the Internet of Things and smart homes. Achieving high-precision indoor positioning through the optimal deployment of Wi-Fi access points remains a significant challenge. This study proposes two indoor positioning optimization algorithms based on the received signal strength indicator (RSSI) of commercial Wi-Fi signals. The enhanced weighted centroid positioning (EWCP) algorithm introduces a novel weighting method that effectively leverages RSSI ranging errors to improve positioning accuracy and further minimize positioning errors. Meanwhile, the adaptive iterative weighted centroid positioning (AIWCP) algorithm incorporates a detailed weighting approach during the movement of the signal transmitter, providing specific movement indications and enhancing positioning performance. Both simulation and experimental results confirm the effectiveness of these two positioning methods.

In recent years, flying ad hoc networks (FANETs) have been extensively applied owing to rapid advancements in unmanned aerial vehicles (UAVs). The continuous mobility of UAVs, coupled with their operational environment in three-dimensional space, presents significant challenges for routing in FANETs. Additionally, factors such as link stability, link quality, link bitrate, and the ability to forward packets of the next-hop node directly impact routing efficiency. Combining machine learning and location-based routing approaches helps address these challenges. This paper proposes a location-based routing protocol that integrates the Q-learning (QL) reinforcement learning algorithm with dynamic link bitrate adjustment and a penalty mechanism for QL based on retransmission signals from the link layer. Performance evaluations conducted using OMNeT++ demonstrate that the proposed algorithm has markedly enhanced network performance in terms of packet delivery ratio, network throughput, end-to-end delay, link broken count, and packet error rate compared with other well-known routing algorithms.