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The beamforming technique has attracted considerable attention in wireless communication due to its various advantages such as interference reduction and improved wireless resource efficiency. However, the beam alignment between transmitting and receiving devices, which is fundamentally required for the beamforming, poses a significant challenge due to the continuous variability of the wireless channel. Recently, a deep learning-based technique has been proposed to predict narrow beam indices by measuring wide beams. However, there is room for improvement in the performance of the neural network architecture employed in this technique. Therefore, we suggest a novel deep learning model architecture that incorporates a channel attention module for beam training. The simulation results show a significant enhancement in performance with our scheme compared to both a state-of-the-art approach and other existing methods across all scenarios. Particularly, we confirm that even when reducing the number of wide beams used for measurement by approximately 50 % , our proposed approach achieves a performance close to that of the state-of-the-art scheme. [ABSTRACT FROM AUTHOR]

In this work, we fabricated an ITO/WOX/TaN memristor device by reactive sputtering to investigate resistive switching and conduct analog resistive switching to implement artificial synaptic devices. The device showed good pulse endurance (104 cycles), a high on/off ratio (>10), and long retention (>104 s) at room temperature. The conduction mechanism could be explained by Schottky emission conduction. Further, the resistive switching characteristics were performed by additional pulse-signal-based experiments for more practical operation. Lastly, the potentiation/depression characteristics were examined for 10 cycles. The results thus indicate that the WOX-based devices are appropriate candidates for synaptic devices as well as next-generation nonvolatile memory. [ABSTRACT FROM AUTHOR]

This study proposed a virtual reality (VR) course that addresses the overall understanding and application of VR technology. After investigating previous studies, we found that two technologies must be applied to design a VR course that fits the latest trends. One is hardware technology dealing with the technical background, while the other is software utilization and development using the merits of VR technology. To accommodate these needs, we designed a VR course consisting of three steps: VR-related theory, TA-led content creation training, and team projects. Through this course, students will improve their ability to develop applications that apply to their research fields after studying the technical background and courses of VR. We conducted a semester-long study with nine students to verify the proposed method and then evaluated them through an in-depth interview and a questionnaire with a five-point Likert scale consisting of nine items. Considering this feedback, we have added several steps to improve the educational effect among students. [ABSTRACT FROM AUTHOR]

In this work, we evaluate the resistive switching (RS) and synaptic characteristics of a fully transparent resistive random-access memory (T-RRAM) device based on indium-tin-oxide (ITO) electrodes. Here, we fabricated ITO/WOX/ITO capacitor structure and incorporated DC-sputtered WOX as the switching layer between the two ITO electrodes. The device shows approximately 77% (including the glass substrate) of optical transmittance in visible light and exhibits reliable bipolar switching behavior. The current-voltage (I–V) curve is divided into two types: partial and full curves affected by the magnitude of the positive voltage during the reset process. In the partial curve, we confirmed that the retention could be maintained for more than 104 s and the endurance for more than 300 cycles could be stably secured. The switching mechanism based on the formation/rupture of the filament is further explained through the extra oxygen vacancies provided by the ITO electrodes. Finally, we examined the responsive potentiation and depression to check the synaptic characteristics of the device. We believe that the transparent WOX-based RRAM could be a milestone for neuromorphic devices as well as future non-volatile transparent memory. [ABSTRACT FROM AUTHOR]

Resistive random–access memory (RRAM) for neuromorphic systems has received significant attention because of its advantages, such as low power consumption, high–density structure, and high–speed switching. However, variability occurs because of the stochastic nature of conductive filaments (CFs), producing inaccurate results in neuromorphic systems. In this article, we fabricated nitrogen–doped tantalum oxide (TaOx:N)–based resistive switching (RS) memory. The TaOx:N–based device significantly enhanced the RS characteristics compared with a TaOx–based device in terms of resistance variability. It achieved lower device–to–device variability in both low-resistance state (LRS) and high–resistance state (HRS), 8.7% and 48.3% rather than undoped device of 35% and 60.7%. Furthermore, the N–doped device showed a centralized set distribution with a 9.4% variability, while the undoped device exhibited a wider distribution with a 17.2% variability. Concerning pulse endurance, nitrogen doping prevented durability from being degraded. Finally, for synaptic properties, the potentiation and depression of the TaOx:N–based device exhibited a more stable cycle–to–cycle variability of 4.9%, compared with only 13.7% for the TaOx–based device. The proposed nitrogen–doped device is more suitable for neuromorphic systems because, unlike the undoped device, uniformity of conductance can be obtained. [ABSTRACT FROM AUTHOR]

Concrete cracks can threaten the usability of structures and degrade the aesthetics of buildings. Furthermore, minor cracks can develop into large-scale cracks that may lead to structural failure when exposed to excessive external loads. In addition, the concrete crack width and depth should be precisely measured to investigate the effects of concrete cracks on the stability of structures. Thus, a nondestructive and noncontact testing method was introduced for detecting concrete crack depth using thermal images and machine learning. The thermal images of the cracked specimens were obtained using a constant test setup for several months under daylight conditions, which provided sufficient heat for measuring the temperature distributions of the specimens, with recording parameters such as air temperature, humidity, and illuminance. From the thermal images, the crack and surface temperatures were obtained depending on the crack widths and depths using the parameters. Four machine-learning algorithms (decision tree, extremely randomized tree, gradient boosting, and AdaBoost) were selected, and the results of crack depth prediction were compared to identify the best algorithm. In addition, data bias analysis using principal component analysis, singular value decomposition, and independent component analysis were conducted to evaluate the efficiency of machine learning. [ABSTRACT FROM AUTHOR]

This paper discusses how to optimize the weighting of individual subarrays to derive the low sidelobe level (SLL) based on quadratic programming (QP) and how to derive QP parameters to ensure that the objective function is composed of the quadratic function form, with the actual number identical to the standard objective function of QP. Next, in order to analyze the SLL, a 24 × 24 phased array antenna was compared with 96 transmit–receive modules (TRMs) attached only to the subarray stage and a phased array antenna with 576 TRMs attached to all radiating elements without a subarray. Optimized weighting was applied to the array antennas with a subarray, and Taylor weighting was applied to the array antennas without a subarray. The number of TRMs used in the phased array antenna with the optimized weighting was reduced by 83.3% compared to the phased array antenna in which TRMs were attached to all radiating elements. The SLL and the half-power beamwidths (HPBWs) of the two antennas were practically identical in a narrow beam-scanning environment. Finally, an array pattern (AP) in which mutual coupling between the radiating elements was considered was calculated to verify the optimized weighting. Moreover, the optimized weighting was applied to CST Microwave Studio (an EM full-wave simulation) to compare the results from the AP calculation and a simulation. It was confirmed that the two results above are largely indistinguishable. The analysis found that the HPBW is 3.6 ∘ × 3.6 ∘ and the SLL is −26.18 dB from AP calculations in the boresight direction. When each 5 ∘ beam was scanned at the azimuth and elevation, the corresponding HPBW values were 3.7 ∘ × 3.7 ∘ and 3.7 ∘ × 3.7 ∘ and the SLLs were −22.70 dB and −24.44 dB according to the AP calculations. [ABSTRACT FROM AUTHOR]

Radiation from a cavity-backed circular aperture array antenna enclosed by a frequency selective surface (FSS) radome is studied using the hybrid analysis method, by combining the mode matching method, the ray tracing technique, and Huygens's principle. The equivalent magnetic surface currents on the apertures are derived from the aperture electromagnetic fields, which are calculated based on the mode matching method. The rays are generated from the equivalent magnetic surface currents and used to analyze the FSS radome based on the ray tracing technique. After being obtained from both the mode matching method and the ray tracing technique, electromagnetic fields on an outermost radome are transformed into the equivalent electric and magnetic surface currents using Huygens's principle. The radiated fields are computed from the equivalent surface currents and compared with the measured data. [ABSTRACT FROM AUTHOR]