Real-time Conditions Research Articles

QoE-Based Performance Comparison of AVC, HEVC, and VP9 on Mobile Devices with Additional Influencing Factors

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users.

AI-IOT-Based Adaptive Control Techniques for Electric Vehicles

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed the field of sustainable transportation planning, notably in the context of Electric Vehicles (EVs). The Problems in all the Existing models are not having proper coordination of optimizing the elements of EV functioning. This research proposes a novel strategy to improving sustainable transportation planning by utilizing AI and IoT. AI-powered algorithms analyze real-time data from IoT sensors installed in EVs and the surrounding environment. These adaptive control algorithms are designed to solve issues including range anxiety, charging infrastructure optimization and energy efficiency. Predictive analytics, route optimization, energy management, and grid interface are all part of the proposed system. Energy management algorithms alter EV settings dynamically to maximize efficiency while taking into account real-time traffic conditions thereby increasing the range extension is upto 2.5% and the total energy efficiency is improved upto 92%. Furthermore, bidirectional connection allowed by IoT devices facilitates the integration of EVs into the energy grid. EVs may engage intelligently in Vehicle-to-Grid (V2G) interactions, providing grid services such as energy delivery during peak demand periods and grid stability.

Investigating Driver Preferences for Traffic Information Using Digital Signage and Road Surface Holograms

This study investigates road users' information preferences to enhance traffic information provision strategies. Traditional road signs primarily convey static information like speed limits and restricted areas. However, smart street poles now offer the capability to collect real-time road conditions and deliver dynamic traffic updates promptly. Leveraging these smart poles allows for more effective and tailored information delivery strategies. The research examines three types of media for information dissemination: digital signage, road surface hologram, and a combination of both. Additionally, it surveys road users' preferences for information delivery methods, including text, graphics, or a mixture, based on the type of information. Conjoint analysis of 220 responses reveals essential attributes influencing users when they receive information. The findings indicate that a majority of road users prefer a blend of digital signage and road surface holograms, with a notable preference among younger individuals for digital signage. For road surface holograms, the favored approach involves a mix of text and graphics. This study lays the foundation for user-centric information strategies, offering insights into tailoring approaches based on the type of information and user preferences.

Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study.

Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC). Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance. Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data. Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.

Moving-Principal-Component-Analysis-Based Structural Damage Detection for Highway Bridges in Operational Environments.

With the deterioration of bridge performance and ever-increasing amounts of traffic, bridge safety is becoming a concern for the engineering community. A method that can assess a bridge's condition in real time is urgently needed. The main factors that hinder bridge condition assessment are the uncertain operational environments. A new moving principal component analysis (MPCA)-based method is developed for structural damage detection in bridges in operational environments in this paper. Two main operational environmental factors, the environmental temperature and traffic loads, are studied in the assessment process to verify the robustness and practicality of the proposed method. The numerical and experimental results show that the proposed method is effective and accurate in assessing the bridge's condition in operational environments.

Investigating tooth surface reconstruction principle of a gear rack and pinion system with variable transmission ratio pinion

Rack-and-pinion gear sets with variable transmission ratios have gradually become a standard core component in steering systems, as the harmony between portability and sensitivity in vehicle steering can be achieved with such applications. When the conventional meshing theory is adopted to calculate point clouds of variable-ratio tooth surfaces, drawbacks will occur with respect to unevenness in point cloud density, inaccessible tooth surface boundaries, and point cloud fusion at the interface between the working tooth surface and fillet. Thus, the generated point cloud of variable ratio tooth surfaces is inapplicable for computer-aided engineering (CAE) and plastic forming. In this study, the point cloud reconstruction of pinion tooth surfaces with variable transmission ratio was investigated. Meanwhile, based on the analysis of transient length in contact lines that occur during gear meshing, various criteria were proposed to determine the validity of the variable-ratio rack-and-pinion design in terms of whether continuous and reverse driving can be achieved. By adopting the proposed methods, the designed variable-ratio pinion tooth surfaces demonstrate point clouds with equidistant distributions and clear boundaries. CAE analysis results also indicate a good quality in gear meshing. The real-time gear meshing status of variable-ratio rack-and-pinion sets is characterized by the transient length of contact lines. The measured variation curves for the transmission ratios of a prototype demonstrate significant consistency with the theoretical curves, indicated by attaining transmission ratio errors within a reasonable range. The validity of the design methods proposed in this study has been verified by the aforementioned results.

Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index “SI” (i.e., the ratio of percent severe crashes to percent exposure of a specific weather state during the crash period) was introduced to evaluate the impact of different real-time weather states on fatal and severe injury crashes. Furthermore, the standard mixed logit (MXL), correlated mixed logit (CMXL), and correlated mixed logit with heterogeneity in means (CMXLHM) models were fitted and compared to identify the risk factors contributing to crash injury severity while accounting for unobserved heterogeneity. The results showed that the CMXLHM model was statistically superior to the CMXL and MXL models based on various goodness-of-fit measures (e.g., Akaike information criterion “AIC” and McFadden pseudo R-squared). Results from the SI analysis and CMXLHM model showed that real-time weather-related factors (e.g., air temperature ≥ 70 0F and relative humidity ≥ 90 %) were significantly associated with higher severe injury likelihood. Further, driving under the influence (DUI), young drivers, and vehicle travel speed were associated with greater injury severities. On the other hand, presence of horizontal curve, passenger cars, and hourly traffic volume were associated with lower injury severity likelihood. The study outcomes can help in incident management by suggesting specific real-time weather-related states to feed to dynamic message signs (DMS) to enhance travelers’ safety along the interstates.

Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone

This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.

A Patient Management System Using an Edge Computing-Based IoT Pulse Oximeter

Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real time, improving the efficiency of short- and long-term patient management. Medical equipment measures a large amount of biosignal data for analyzing diseases and patient health conditions. However, analyzing and monitoring biosignal data using a centralized server or cloud limit the medical institutions’ ability to analyze patients’ conditions in real time, preventing prompt treatment. Therefore, edge computing can enhance the efficiency of patient biosignal data collection and analysis for patient management systems. Analyzing biosignals using edge computing can eliminate the wait time present in cloud computing. Hence, this study aims to develop an IoT pulse oximeter using edge computing for medical institutions and proposes an architecture for providing a real-time monitoring service. The proposed system utilizes five types of raw (IR AC, IR DC, red AC, red DC, AMB), pulse, and SpO2 data measured using IoT pulse oximeters. Edge nodes are installed in every hospital ward to collect, analyze, and monitor patient biosignal data through a wireless network. The collected biosignal data are transmitted to the cloud for managing and monitoring the data of all patients. This system enables medical institutions to collect and analyze raw biosignal data in real time, by which an integrated management system can be established by connecting various types of IoT-based medical equipment.

Prediction of Dexterous Finger Forces with Forearm Rotation using Motoneuron Discharges.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination (R2) and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

SSIOE: Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management

This paper aims to estimate indoor occupancy given the real-time observed signals from the existing sensors; next, as a practical application, we build a dynamic control schedule for energy saving based on the estimated indoor occupancy. However, several issues need to be addressed. First, it is impossible to train the model with rich labels due to the expensive labeling cost. Second, manual annotation of the continuous occupancy rate is complex. Third, the mapping relationship between sensor data and occupancy will change in the long run. In this paper, we proposed a new algorithm named Self-Supervised Indoor Occupancy Estimation (SSIOE) to overcome the challenges. Specifically, our training scheme aims to (I) generate a set of pseudo labels in a simple way to mark the time periods believed to be either in a high or low occupancy state and (II) utilize these sparse labels for training a network to infer the continuous occupancy ratio. By reformulating the problem as a Wasserstein-distance-like estimation, SSIOE is a novel learning-based method that can rely only on the weak/sparse labels of either “high-occupancy” or “low-occupancy” and learn to estimate the continuous occupancy ratio. Furthermore, to deal with the scarce annotation problem, we proposed a novel physical constraint loss to model the physical prior. (III) Last but not least, to strengthen the adaptability, we integrated Model-Agnostic Meta-Learning (MAML) to train the model for dynamic model updating. Experimental results show that SSIOE can provide reliable occupancy estimation and flexibly adapt to various control modes without retraining the model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the real-time occupancy estimation problem, which is crucial for building management systems to arrange lighting and air conditioning. A dynamic schedule adapted to the occupancy would help balance the user’s comfort and energy saving; however, installing the occupancy sensors raises additional costs. Therefore, this paper proposes SSIOE, a label-free machine learning algorithm that can learn to estimate the continuous occupancy ratio given the sensor status in real time. Prior works trained upon supervised machine learning methods suffer from heavy manual annotation workload, while the unsupervised machine learning approaches fail to provide continuous occupancy estimation values. This paper addresses the above challenges by proposing a novel self-supervised training scheme. Preliminary experiments suggest that this approach is feasible to work in real-world buildings. However, it should be noted that the occupancy estimated by SSIOE is a ratio (0.0-1.0), which is relative to the maximum and minimum occupancy of the training data (or fine-tuning data). For example, if the maximum number of occupants in the training data is 200 and the minimum number of occupants is 50, then the estimated occupancy value of 1.0 is about 200 occupants, and the occupancy value of 0.0 is about 50 occupants. When the maximum and minimum occupants of the space change, the model needs to be updated to capture the new mapping of the estimated occupancy ratio to the number of occupants. In future research, we plan to further improve and simplify the adaptation ability of SSIOE by continuously and automatically updating the system with the latest collected as the system runs.

Research on the Innovation of Teaching Mode of Orienteering Sports Courses in Colleges and Universities Based on Mobile Intelligent Terminals

Abstract This paper studied motion state recognition algorithms using acceleration signals (ACC). At the same time, research is also being conducted on ECG motion artifact suppression algorithms and respiration rate detection algorithms. Based on this, we designed a mobile intelligent terminal sports app that allows users to view physiological signals and sports status in real time at any time. In college and university orienteering classes, the mobile intelligent terminal movement app is used to teach the sport. The results of comparing the experimental group and the control group in the orienteering course show that the mobile intelligent terminal orienteering teaching makes a significant difference in the men’s physical quality, except for the seated forward body flexion (P&lt;0.05). There were significant differences in the women’s group in terms of 50 m running (P = 0.013) and 800 m (P = 0.042). The experiment improved the dimensions of the Orienteering Campus Short Distance Test, Learning Motivation Indicator, and Emotional Management.

THE MECHANICAL PROPERTIES AND FAILURE MODE OF SIMULATED LUNAR ROCK BY IN SITU TEMPERATURE REAL-TIME ACTION OF LUNAR-BASED

To achieve in situ condition-preserved coring of the lunar surface and deep lunar rocks and a return mission, it is necessary to explore the mechanical properties and failure modes of simulated lunar rocks that have physical and mechanical properties approximately equivalent to those of mare basalt under simulated lunar temperature environments ([Formula: see text][Formula: see text]C to 200[Formula: see text]C). To this end, real-time uniaxial compression tests were conducted on simulated lunar rocks under corresponding in situ temperature conditions, and the mechanical properties, deformation characteristics, micromorphology, and failure modes were analyzed. Based on the macroscopic analysis, as the environmental temperature decreases, the uniaxial compressive strength, peak strain, and peak strain duration of simulated lunar rocks exhibit a nonlinear increasing trend, with maximum increases of 33.00[Formula: see text], 36.16[Formula: see text], and 49.25[Formula: see text] from those at room temperature, respectively. Based on microscopic analysis, the intergranular fractures run through the entire samples under the environmental temperature. As the environmental temperature increases, intergranular and transgranular fractures coexist, and layered fractures appear at high temperatures. At the same time, some samples exhibit undulating and stepped morphologies caused by shear stress. For the fractal dimension of a simulated lunar rock main fracture surface, the fractal dimension of the actual angle and the corrected angle increase first and then decrease with increasing environmental temperature, and the maximum error of the two is only 1.84[Formula: see text]. The overall fractal dimension ranges from 2.02 to 2.28, and the fractal dimension under real-time high-temperature conditions is higher than that under real-time low-temperature conditions. In addition, the failure mode of the simulated lunar rocks under real-time in situ temperature changes is a combined tensile–shear failure mode with shear failure (primary) and tensile failure (secondary). The above research results are expected to be applied to in situ condition-preserved coring in extreme lunar environments and provide a scientific basis for the design and development of in situ condition-preserved coring robot systems for the extreme environment of deep space.

Dynamic coordinated strategy for parking guidance in a mixed driving parking lot involving human-driven and autonomous vehicles

&lt;abstract&gt; &lt;p&gt;The advent of autonomous vehicles (AVs) poses challenges to parking guidance in mixed driving scenarios involving human-driven vehicles (HVs) and AVs. This study introduced a dynamic and coordinated strategy (DCS) to optimize parking space allocation and path guidance within a mixed driving parking lot, aiming to enhance parking-cruising efficiency. DCS considers the distinctive characteristics of HVs and AVs and dynamically formulates parking guiding schemes based on real-time conditions. The strategy encompasses four main steps: Triggering scheme formulation, identifying preoccupied parking spaces, updating the parking lot traffic network and optimizing the vehicle-path-space matching scheme. A programming model was established to minimize the total remaining cruising time, and iterative optimization was conducted with vehicle loading test based on timing. To elevate computational efficiency, the concept of parking-cruising path tree (PCPT) and its updating method were introduced based on the dynamic shortest path tree algorithm. Comparative analysis of cases and simulations demonstrated the efficacy of DCS in mitigating parking-cruising duration of different types of vehicles and minimizing forced delays arising from lane blocking. Notably, the optimization effect is particularly significant for vehicles with extended cruising durations or in parking lots with low AV penetration rates and high saturation, with an achievable optimization rate reaching up to 18%. This study addressed challenges related to drivers' noncompliance with guidance and lane blocking, thereby improving overall operational efficiency in mixed driving parking lots.&lt;/p&gt; &lt;/abstract&gt;

Digital design trends of train and shunting operations at railway stations

This article explores in detail the problems associated with the digitization of train and shunting work at railway stations. Based on the analysis of the indicators of the use of the “Ch” station under the JSC “Uzbekistan Railways”, the factors that negatively affect the development of the digitization stages have been studied. A daily executed work-plan graph of the sorting station was also studied in real-time conditions. It found that the finished contents would remain standing at the station departure park for an average of 4 hours or more. One of the main reasons for this was the insufficient capacity of the adjacent station to handle trains. The station staff were therefore shown to be making possible the formation of “abandoned trains”. Recommendations were made to introduce a digitized complex system to improve train and shunting operations at railway stations. The functional capabilities of the proposed system were analyzed. Key words: railway station, loading of station, “abandoned train”, station daily executed work-plan graph, sorting hump capacity, digitized system, functional capacity, software.

Deep reinforcement learning task scheduling method based on server real-time performance.

Server load levels affect the performance of cloud task execution, which is rooted in the impact of server performance on cloud task execution. Traditional cloud task scheduling methods usually only consider server load without fully considering the server's real-time load-performance mapping relationship, resulting in the inability to evaluate the server's real-time processing capability accurately. This deficiency directly affects the efficiency, performance, and user experience of cloud task scheduling. Firstly, we construct a performance platform model to monitor server real-time load and performance status information in response to the above problems. In addition, we propose a new deep reinforcement learning task scheduling method based on server real-time performance (SRP-DRL). This method introduces a real-time performance-aware strategy and adds status information about the real-time impact of task load on server performance on top of considering server load. It enhances the perception capability of the deep reinforcement learning (DRL) model in cloud scheduling environments and improves the server's load-balancing ability under latency constraints. Experimental results indicate that the SRP-DRL method has better overall performance regarding task average response time, success rate, and server average load variance compared to Random, Round-Robin, Earliest Idle Time First (EITF), and Best Fit (BEST-FIT) task scheduling methods. In particular, the SRP-DRL is highly effective in reducing server average load variance when numerous tasks arrive within a unit of time, ultimately optimizing the performance of the cloud system.

Hybrid short-term traffic flow prediction based on the effect of non-linear sequence noise

&lt;abstract&gt; &lt;p&gt;Short-term traffic flow prediction is crucial for intelligent transport systems and mitigating traffic congestion. Therefore, precise prediction of real-time traffic conditions is becoming more important. Currently, the existing prediction models lack the ability to effectively extract spatio-temporal characteristics and fail to adequately account for the impact of non-linear noise. To address these issues, the study proposes a hybrid short-term traffic flow prediction model based on spatio-temporal characteristics. First, the method decomposes the initial spatio-temporal traffic sequence data into multiple modal components using the complementary ensemble empirical modal decomposition method. Then, spatio-temporal characteristics are extracted from the decomposed spatio-temporal components using a deep residual network. The predicted values of each factor are combined to obtain the final predicted values. To validate the model, traffic flow data that is collected at point 4909A on the M25 motorway in London is used. The results indicate that the proposed model outperforms other models in terms of accuracy metrics such as root mean square error, mean absolute percentage error, mean absolute error, mean squared error, and coefficient of determination. Therefore, the model has high accuracy and practicality and exhibits great potential for short-term traffic flow prediction.&lt;/p&gt; &lt;/abstract&gt;

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Design of an expert system based on fuzzy logic for real-time oil condition monitoring and fault diagnosis in heavy-duty diesel engines

Power Optimization of Unmanned Aerial Vehicle-Assisted Future Wireless Communication Using Hybrid Beamforming Technique in Disaster Management

Several established procedures are required to be followed when responding to disasters. Although most of them have so far been successful, they pose their own set of difficulties. The primary factor is response time, which is crucial for disaster management. The use of unmanned aerial vehicles (UAVs) for wireless communication not only increases response time but also is energy efficient. However, UAVs have limited power capacities, which makes their use in wireless communication challenging. In wireless communication, hybrid beamforming is a technique for optimizing the battery consumption of unmanned aerial vehicles (UAVs). Massive MIMO technology facilitates hybrid beamforming by deploying antenna arrays, efficiently directing transmitted energy to intended receivers, and minimizing power waste. Beamforming relies heavily on accurate Channel State Information (CSI). Advanced channel estimation and tracking techniques optimize power allocation based on real-time channel conditions, thereby minimizing energy consumption and ensuring connection reliability. Hybrid beamforming combines the advantages of digital and analog beamforming to reduce power consumption while maintaining communication performance. This paper proposes a power optimization technique for UAV-based wireless communication using hybrid beamforming to maximize spectral efficiency and increase the battery life of UAVs.

A study on the relationship between vehicle operating conditions and NOx emission characteristics

To address the challenges faced by vehicles during actual operation when passing through traffic lights or intersections, where rapid changes in operating conditions can lead to a decrease in SCR efficiency and an increase in NOx emissions, this study focuses on defining characteristic position scenarios and analyzing the NOx emission characteristics of vehicles when passing through these scenarios based on real-time vehicle conditions. Through the analysis of several specific events, several road condition factors and driver behavior factors that may contribute to increased NOx emissions at characteristic position scenarios are identified. The findings indicate that when vehicles pass through traffic lights and intersections, the main causes of increased NOx emissions are low SCR temperature resulting from prolonged idling or engine shutdown, and high exhaust NOx emissions resulting from sudden excessive fuel injection. The driver behaviors associated with these situations primarily include coasting when approaching intersections, frequent stop-start behaviour due to traffic congestion or road crowding, choosing to shut off the engine for fuel efficiency during prolonged traffic congestion, and aggressive acceleration by stepping on the accelerator when the green light is about to turn red.