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Signal scattering in the troposphere is a phenomenon referred to as “troposcatter”, and it is promising method for wireless communication beyond the line of sight (b-LOS). For a troposcatter communication system, it is important to develop a channel model for the tropospheric scatter. Recently, b-LOS communication using troposcatter received much attention for its application to military communication. Thus, a comprehensive review and analysis of conventional ITU-R troposcatter propagation models and recent results would be a timely study. Accordingly, in this study, we analyze and simulate the tropospheric scatter propagation model proposed by the ITU-R. Troposcatter propagation models in ITU-R recommendations consist of three parts: basic transmission loss, gaseous absorption, and a precipitation fading model. The ITU-R P.2001 simulation model shows basic transmission losses of 210 dB and 240 dB at 0.2 GHz and 2 GHz in 300 km distances, respectively. The ITU-R P.452 model results in 20 dB less loss, but this is dependent on measurement conditions. Gaseous absorption and precipitation fading have less than 1 dB loss for frequencies less than 2 GHz, but can increase to be over 10 dB in loss for frequencies of 2 GHz and above. We also review recent studies on tropospheric scatter channel models and communication systems. In addition to these reviews, we examine recent studies on ray tracing methods for tropospheric scatter channel models.

Herein, dual‐gate field‐effect transistors (DG FETs) fabricated on Si substrate and a corresponding NOR‐type array designed for low‐power on‐chip trainable hardware neural networks (HNNs) are presented. The fabricated DG FET exhibits notable endurance characteristics, with the subthreshold swing remaining consistently within a 2.45% range of change and ΔV th per cycle maintaining stability at 4.5% over repetitive program and erase operations, up to 104 cycles. Furthermore, a multilevel characteristic is achieved through low‐power program/erase operations based on Fowler–Nordheim (FN) tunneling, which exhibit 0.09 and 0.99 fJ per spike, respectively. These characteristics provide the HNN stability, along with high performance and power efficiency. The NOR‐type array in this work demonstrates selective update and bidirectional vector‐by‐matrix multiplication capabilities. This enables on‐chip training based on a gradient descent algorithm, without the need for an additional array for backpropagation. Subsequently, a simulation of the Modified National Institute of Standards and Technology classification is conducted to evaluate the accuracy and training power consumption of the proposed device in comparison to other two‐terminal memristor devices. The results show that the DG FET array achieves superior accuracy while maintaining over 180.4 times higher energy efficiency, demonstrating the potential of the DG FET as a promising candidate for low‐power HNN applications.

Multilayer systems, mainly used for cabin noise control, etc., usually include porous materials. When performing noise analysis, in addition to classical properties such as Young's modulus and density, 5 additional non-acoustic parameters are required. However, these parameters cannot be easily defined because they are difficult to be measured. Many studies have been conducted to effectively define them by introducing engineering optimization techniques. In this paper, an estimation method by genetic algorithm among evolutionary algorithms is presented. The engineering reliability of the estimated results was verified with NOVA, a commercial software.

Background/Aims: Patients with cardiac implantable electronic devices (CIEDs) undergoing endoscopic electrosurgery (EE) are at a risk of electromagnetic interference (EMI). We aimed to analyze the effects of EE in CIED patients. Methods: Patients with CIED who underwent EE procedures such as snare polypectomy, endoscopic submucosal dissection (ESD), and endoscopic retrograde cholangiopancreatography (ERCP) with endoscopic sphincterotomy (EST) were retrospectively analyzed. Postprocedural symptoms as well as demographic and outpatient follow-up data were reviewed through medical records. Electrical data, including preprocedural and postprocedural arrhythmia records, were reviewed through pacemaker interrogation, 24-hour Holter monitoring, or electrocardiogram. Results: Fifty-nine procedures in 49 patients were analyzed. Fifty procedures were performed in 43 patients with a pacemaker, and nine were performed in six patients with an implantable cardioverter-defibrillator. There were one gastric and 44 colon snare polypectomies, five gastric and one colon ESDs, and eight ERCPs with EST. Fifty-five cases of electrical follow-up were noted, with two postprocedural changes not caused by EE. Thirty-one pacemaker interrogations had procedure recordings, with two cases of asymptomatic tachycardia. All patients were asymptomatic with no adverse events. Conclusions: Our study reports no adverse events from EE in patients with CIED, suggesting that this procedure is safe. However, because of the possibility of EMI, recommendations on EE should be followed.

Fault detection and isolation are important tasks in statistical process control. A real-time contrasts (RTC) control chart converts the statistical process-monitoring problem to the real-time classification problem, thus outperforming traditional monitoring techniques. An RTC assigns a class to reference data and the other class to a window of real-time contrasts. However, RTC control charts often fail to detect abnormal states when both normal and abnormal data exist together in the window. To enable more rapid detection of an improved RTC control chart, this paper proposes a multivariate process monitoring system with an improved RTC control chart. Although previous RTC control charts proposed by other studies outperform the original RTC chart, it is still difficult to detect an abnormal state when normal and abnormal data exist together. To overcome this problem, this paper proposes an RTC control chart using novelty detection and variable importance with random forests. Novelty detection and variable importance were used so that fault can be detected when the control limit could not be exceeded despite the abnormal state. The proposed method extracts representative data in the sliding window and adds the extracted data to the window to quickly detect the abnormal state. Experiments demonstrate the proposed method to outperform the original RTC chart.