Real-time Distance Research Articles

Optimal Ambulance Positioning for Road Accidents With Deep Embedded Clustering

The number of casualties and fatalities brought on by road accidents is one of the most significant concerns in the modern world. Instead of dispatching ambulances only at the time of demand, pre-positioning them can reduce the response time and provide prompt medical attention. Deep learning techniques hold great potential and have proven to be essential for problem-solving and decision-making in the field of healthcare services. This study introduces a deep-embedded clustering-based approach to predict optimal locations for ambulance positing. Various factors and patterns in a geographical region greatly influence the occurrence of road crashes, hence understanding such relationships while model building is crucial. The present study also emphasizes the need of preserving such patterns during model building to ensure real-time results and implements them with the help of another deep-learning-based model, Cat2Vec. The proposed framework is also compared with traditional clustering algorithms like K-means, GMM, and Agglomerative clustering. Moreover, to calculate response time and distance in real time, a novel scoring function has also been introduced for the performance evaluation of various algorithms. The proposed ambulance-positing system exhibits remarkable performance, achieving an accuracy of 95% with k-fold cross-validation and a novel distance score of 7.581 proving the use of the proposed approach is better than all the other traditional algorithms used.

강화학습을 이용한 자율주행 트랙터의 가변형 전방주시거리 튜닝 알고리즘 개발

Robust path tracking is important for an autonomous tractor to accurately track a path, including straight and curved paths, with headland turning in farmlands. To improve the performance of a path-tracking method developed on the basis of fixed look-ahead distances, this paper presents a look-ahead distance tuning algorithm based on reinforcement learning (RL). The RL architecture is designed to provide variable look-ahead distances in real time based on navigation conditions caused by changes in the lateral deviations and heading errors. The RL agent performed self-learning in the simulation while following C-type turning-based paths. For more effective RL, a simulation model was built by applying the actual responses of a tractor steering system measured using a data acquisition system. An evaluation test was conducted in terms of the lateral deviation in response to the reference path. The proposed method improved the path-tracking accuracy of the lateral deviation by a root mean-squared error of about 62% compared to a path-tracking controller with fixed look-ahead distances.

A New United Proportional Navigation Guidance for Impact Angle Constraint without Measurement Distance between Vehicle and Target

This paper proposed a united proportional navigation guidance (UPNG) method to alleviate the guidance command saltation with an impact angle constraint under the condition of no real-time distance between the vehicle and the target (line-of-sight (LOS) distance). Firstly, based on the biased proportional navigation guidance (BPNG), a smooth-biased proportional navigation guidance (SBPNG) method was proposed, whose bias term was designed as a trigonometric function. In SBPNG method, due to the continuous smooth change of the bias term, the guidance command would not saltus anymore, and the impact angle was controlled by the bias integral component. Secondly, biased on SBPNG method, the united proportional navigation guidance (UPNG) method combining SBPNG and variable coefficient proportional navigation guidance (VCPNG) was established. In UPNG method, because there was no LOS distance, the guidance coefficient was designed as a function of the difference between the expected impact angle and the estimated impact angle, so the closed-loop control of impact angle was realized. Finally, a lot of simulation experiments on different guidance laws were carried out without real-time LOS distance. The results verify that the UPNG method proposed in this paper solves the problem of guidance command saltation effectively and has better robustness in impact angle control.

Underground Metal Pipeline Localization Using Low-Cost Wireless Magnetic Sensors Mounted on an Excavator

To minimize the excavation damage of underground pipelines, in this article, we propose to provide real-time feedback to operators by monitoring the distance information from the underground pipeline. Despite multiple efforts to locate underground ferromagnetic pipelines using the magnetic anomaly detection, it has not been implemented in real-time excavation operations due to low signal-to-noise ratios, unknown external magnetic interference (EMI), and high computational power. To address these limitations, we propose an approach to locating a custom-designed wireless sensor system on the excavator bucket to increase accuracy of real-time distance estimation with efficient EMI cancelation. We present the system using a 14:1 lab-scaled excavator and six sizes of ferromagnetic pipes. To create a distance estimation model of each pipe, we measure the magnetic anomaly caused by ferromagnetic pipes from eight distances (from 5 to 75 mm, spaced 10 mm apart). The wireless system with the localization algorithm successfully estimates the distance between the sensor and the pipe with a less than 4.38 ± 1.62 mm error overall. This pilot study shows that the proposed system with the localization algorithm is able to accurately locate pipes. This will lay the foundation for providing accurate spatial information, and ultimately prevent critical excavation/drilling damage at construction sites.

Clinical validation of a novel smartphone application for measuring visual acuity

The rise of telemedicine in the wake of the COVID-19 pandemic has posed challenges for ophthalmologists and optometrists, as there are hardly any smartphone applications that meet high accuracy requirements for obtaining visual acuity (VA) in clinical use. The application vision.app is a novel smartphone app that adjusts the angular size of the optotype based on the face-device distance in real time. The high spatial resolution of modern smartphone screens facilitates measurements at a handheld distance, which makes it particularly valuable for use in pediatric populations which struggle to focus on distant stimuli.

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.

Visual Detection and Deep Reinforcement Learning-Based Car Following and Energy Management for Hybrid Electric Vehicles

Practical vision-based technology is essential for the autonomous driving of intelligent hybrid electric vehicles. In this article, a hierarchical control structure is proposed, which combines you only look once-based object detection and learning-based intelligent control by deep reinforcement learning. After modeling a typical car-following scene, the leading car is detected in the driving image, and the real-time distance between two cars is evaluated by vision-based distance measurement. Then, a deep Q-network is adopted to learn the car-following control strategy and energy management strategy, which achieves multiobjective control of the hybrid powertrain while maintaining a reasonable distance for the following car. When completing off-line training, the online processor-in-the-loop test by the edge computing device NVIDIA Jetson AGX Xavier is performed. Results show that the hierarchical control strategy gets a fuel economy of 5.76 L/100 km while keeping a safe following distance. Moreover, the time consumed to run a driving cycle of 1797 s in the embedded device is 476.87 s, which means that a control loop, including target recognition, distance measurement, car-following control, and energy management, takes 0.26 s. This study proves that vehicle vision can lay the technical foundation for intelligent driving, and the results illustrate that the hierarchical control structure is capable of achieving considerable computing efficiency on embedded devices and has the potential for real vehicle control.

Real-time distance and velocity measurement based on the dual-comb system.

With the development of laser metrology, the dual-comb system has natural superiority in the measuring fields. Specifically, distance and velocity represent a basic state for the target in space. We propose an application mode of the dual-comb interferometry integrated into the field programmable gate array. A high-speed parallel processor truly gives full play to the benefit of the data processing rate. The algorithm of the peak extraction and the address matching also bring an efficient working mode into the whole scheme. To verify the performance of this system, we devise a series of experiments for distance and velocity, respectively. The data processing rate of the distance is 425 Hz and that of the corresponding average velocity is 0.425 Hz, which is flexible for different measuring conditions. The experimental results show that the difference can be well within 252.8 µm at 5 m range and 284.9 µm/s over 0.5 m/s.

기초학력 지원프로그램으로서 개별화 한글 파닉스 CAI 교육의 원격 수업과 원격-대면 혼합 수업의 효과 비교

This study was designed to investigate whether CAI Hangeul phonics instruction, which was developed based on direct instruction, is also as effective when it was delivered via the real-time distance learning system only, and compared to when it was delivered in a combined setting, in-class learning, and real-time distance learning. Thirty-nine first and second graders were recruited from 20 elementary schools across four school districts. The combination group of 18 students received Hangeul phonics CAI instruction by visiting reading support centers run by the school districts. They also received CAI instruction via the real-time distance learning system. The real-time distance learning only group received the same CAI instruction from their schools or from their own homes. The results revealed that both the combination group and the distance learning only group improved their phonics abilities across all measures. Also, no difference was found regarding the students’ improvements between the two CAI groups. This study revealed an interesting result: When the teachers used a scientifically proven instructional software program, and if there is an adult present who supports the distance learning environments, CAI distance learning can bring sufficient improvements in young students’ phonics skills whether in-class instruction was added or not.

Interpreter-mediated interactions between people using a signed respective spoken language across distances in real time: a scoping review

BackgroundCommunication between people who are deaf and sign and people who use a spoken language is possible by means of an interpreter. Interpreting in real time can be performed at a distance, which differs from interpreting face-to-face. Due to COVID-19, interpretation at a distance has increased.ObjectiveThe objective of this study was to map the existing literature to identify key characteristics by addressing the following question: What is known about interpreted mediated interactions between people using a signed respective spoken language across distances in real time?DesignEight online databases, complemented by a search in one nonindexed journal of relevance to the review, were used to identify original studies published in 2010–2020, and 17 publications met the inclusion criteria. Charting of the data revealed insight from 17 original studies that were extracted, summarized, and reported.ResultsFour key characteristics were identified: (1) advantages and challenges in remote interpreting; (2) the need for training in remote interpreting and video relay service (VRS); (3) regulations and organizational structures of VRS; and (4) the interpreter as an active party in VRS.ConclusionRemote interpreting has several challenges but also advantages. Knowledge of these kinds of interactions is limited, and further research must be initiated and realized, not least due to technological developments and the increased number of interpreting events.

Multirobot Collaborative Pursuit Target Robot by Improved MADDPG.

Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combining the intrinsic reward and the external environment. The state similarity module based on the threshold constraint is as a part of the intrinsic reward signal output by the intrinsic curiosity module, which is used to balance overexploration and insufficient exploration, so that the agent can use the intrinsic reward more effectively to learn better strategies. The simulation experiment results show that the proposed method can improve the reward value of robots and the success rate of the pursuit task significantly. The intuitive change is obviously reflected in the real-time distance between the pursuer and the escapee, the pursuer using the improved algorithm for training can get closer to the escapee more quickly, and the average following distance also decreases.

Commuter Bus Operation Rules under Two Traffic Scenarios and Two Weather Conditions: Naturalistic Driving Study on Vehicle Speed and Clearance

This study provides insights into the building of environmentally and socially sustainable and livable cities by investigating commuter buses’ vehicle speed and clearance, considering two traffic scenarios (working days and weekends) and two weather conditions on urban roads. Thirty participants drove ten different routes during natural driving. The drivers were observed under the following traffic conditions: free flow (Grade I), steady flow (Grades II and III), unsteady flow (Grade IV), working days and weekends, and sunny and heavy snow weather. A method for collecting accurate traffic flow data was developed using a radar sensor to measure the real-time distance between vehicles. The real-time vehicle spacing and speed were detected using a radar sensor and a mobile app, respectively. The results showed that speed decreased obviously from 11.2% to 16.5% on working days compared to a similar situation on weekends, especially in heavy snow weather (from 33.8% to 40.7%). The lowest average speed was obtained in the traffic environment Grade IV. Commuter buses maintained a minimum vehicle clearance during working or heavy snow days in traffic environment Grade IV. Policymakers should consider the insights of this study to develop new, dynamic commuter schedules under diverse conditions.

Minimum distance calculation using skeletal tracking for safe human-robot interaction

One of the main issues about safety in human-robot interaction is the distance calculation between robot and human. The acquisition of real-time distance information between robot and human allows the robot to modify its motion to ensure safety. This paper presents a real-time distance calculation framework that enables safe human-robot interaction. The human is tracked by an RGB-D sensor, and the joint positions of the human are acquired from the skeletal tracking algorithm. The representation of the robot and the human are formed by capsules to simplify and speed up the distance calculation. The distance calculation is implemented by using the GJK (Gilbert-Johnson-Keerthi) algorithm. The framework is validated by simulations and real experiments to show the efficiency of the framework. The results demonstrate the applicability of the framework for safe human-robot interaction applications.

In-Device Feedback in Immersive Head-Mounted Displays for Distance Perception During Teleoperation of Unmanned Ground Vehicles.

In recent years, Virtual Reality (VR) Head-Mounted Displays (HMD) have been used to provide an immersive, first-person view in real-time for the remote-control of Unmanned Ground Vehicles (UGV). One critical issue is that it is challenging to perceive the distance of obstacles surrounding the vehicle from 2D views in the HMD, which deteriorates the control of UGV. Conventional distance indicators used in HMD take up screen space which leads clutter on the display and can further reduce situation awareness of the physical environment. To address the issue, in this paper we propose off-screen in-device feedback using vibro-tactile and/or light-visual cues to provide real-time distance information for the remote control of UGV. Results from a study show a significantly better performance with either feedback type, reduced workload and improved usability in a driving task that requires continuous perception of the distance between the UGV and its environmental objects or obstacles. Our findings show a solid case for in-device vibro-tactile and/or light-visual feedback to support remote operation of UGVs that highly relies on distance perception of objects.

Fiber Optic Network Technology of Communication of Specialists via Mental Neurointerfaces

Resonance communication of specialists can be carried out at a distance in real time in dialogue mode through a mental neurointerface with two-way communication. Mental neurointerface on the principles of magnetic resonance tomography captures a hologram of brain activity of the internal speech of the inductor specialist and transmits it to the mental neurointerface of the recipient's interlocutor through ultra-sensitive multi section nano resonators waveguides. The mental neurointerface of the recipient's interlocutor perceives the transmitted hologram of the brain activity of internal speech and resonates its internal speech to it. An interlocutor of the recipient with equivalent semantic memory in a resonant way makes sense of the internal speech of the inductor specialist. He forms the response with internal speech and transmits it to the interlocutor with his mental neurointerface through nano resonators in the form of holograms of the brain activity of internal speech. Interlocutors, as specialists in one subject area, have a similar semantic memory. Semantic memories are considered similar if they correspond to the principle of gold section according to a professional thesaurus. Specialists and interlocutors must learn a professional thesaurus before starting a dialogue through mental neurointerfaces. Thus, the problem of transmitting and reading thoughts at a distance using high technology is solved, taking into account the psychological aspects of the interlocutors. The development of mental neurointerfaces and ultra-sensitive multi section nano waveguide resonators for transmitting holograms of internal speech brain activity is just beginning. The use of resonant communication by mental neurointerfaces through nano waveguides resonators waveguides is currently very relevant in many areas of life activity.

Traditional sources of the last hour signs in the month of ramadan

One sign of the supernatural mission of the last Messenger of Allah’s (s) is the fact that he informed his Companions about everything that would take place before the Judgment Day, in the veracity of which many Muslims and non-Muslims have been convinced numerous times so far. The Qur’an talks indirectly, and the hadith unambiguously, about the appearance of a comet in the middle of the month of Ramadan, which will be the announcement of great social changes. The Messenger of Allah (s) gave precise instructions on how to act then, which presents another sign of supernaturality, confirmed by contemporary science. The appearance of the comet will be at the same time the announcement of the arrival of Mahdi and Dajjal who are mentioned in numerous hadith traditions. The Messenger of Allah (s) also insinuated the appearance of modern communication technology which will enable seeing at distance in real time. All this must not passivize us, because according to the hadith, even if the Last Hour comes, we are obliged to plant the seedling in our hand, before we even move from the place. Keywords: Last Hour, Ramadan, signs, comet, smoke, Mahdi, Dajjal.

A Case Study of Chinese Characters and Distance Learning Applying an Analysis of Learning Obstacles and Improvement Directions in Real-time Distance Learning

A Case Study of Chinese Characters and Distance Learning Applying an Analysis of Learning Obstacles and Improvement Directions in Real-time Distance Learning

A Case Study of Real Time Distance Learning ‘Korean Lexicology’ Class : Based on the KF Global E-school UPI University Case

본 연구는 인도네시아 UPI대를 대상으로 이루어진 실시간 원격 화상 ‘한국어 어휘론’ 교육에 대한 운영 사례를 소개하고 학습 만족도 및 변인 간 상관관계를 분석함으로써 향후 과제를 제시하고자 하였다. 학습 만족도 조사 결과, 화상 수업에 대한 만족도가 전반적으로 높았으며 강의 전 기대를 충족한 것으로 응답하였다. 특히 강의 내용과 평가, 강의 자료, 강의 언어 등에 대해서도 높은 만족도를 보였으며 불안정한 인터넷 연결 등이 가장 큰 어려움인 것으로 나타났다. 상관관계 분석 결과, 강의 전 기대 충족도는 여러 변인과 가장 많은 관계를 보였으며 강의 자료에 있는 현지어 번역은 통계적으로 매우 중요한것으로 나타났다. 실시간 원격 화상 ‘한국어 어휘론’의 효율적인 수업 운영을 위해 세 가지를 제안하였다. 첫째, 실시간 원격 화상 교육은 강의실 접속형이면서 개인 데스크톱과전체 학습자를 볼 수 있는 카메라가 있는 형태의 교육 환경이 효과적이다. 강의실 전면에강의 영상과 자료가 보이고, 학습자마다 데스크톱으로 동일한 자료를 볼 수 있도록 하고헤드폰으로 좋은 음질을 확보하여 상호작용에 어려움을 없애야 한다. 둘째, 교수실재감을 높이면서 교수 촉진자의 역할을 수행할 수 있도록 LMS의 구축, 온라인 도구의 적극적활용이 이루어져야 한다. 셋째, 실시간 화상 원격 교육용 ‘한국어 어휘론’ 교재가 개발되어야 한다. 이 교재는 일반 강의 교재보다 느슨한 교수요목과 구성이 필요하며 강의 개요와 강의 목표를 명료히 제시하고 연습 문제와 시각자료, 번역, 주요 용어 풀이 목록 등을수록하여 해외 한국어학 전공자의 한국어학에 대한 이해를 높일 수 있도록 해야 한다.

Multi-Functional Microwave Photonic Radar System for Simultaneous Distance and Velocity Measurement and High-Resolution Microwave Imaging

A photonic-assisted multi-functional radar system for simultaneous distance and velocity measurement and high-resolution microwave imaging is proposed and experimentally demonstrated by using a composite transmitted microwave signal of a single-chirped linearly frequency-modulated (LFM) signal and a single-tone microwave signal. In the system, the transmitted signal is generated via photonic frequency up-conversion based on a single integrated dual-polarization dual-parallel Mach-Zehnder modulator (DPol-DPMZM), while the echo signals scattered from the target are de-chirped to two low-frequency signals using a microwave photonic frequency mixer. By using the two low-frequency de-chirped signals, the real-time distance and radial velocity of the moving target can be measured accurately according to the round-trip time of the echo signal and its Doppler frequency shift. Compared with the previously reported distance and velocity measurement methods, where two LFM signals with opposite chirps are used, these parameters can be obtained using only a single-chirped LFM signal and a single-tone microwave signal. Meanwhile, high-resolution inverse synthetic aperture radar (ISAR) imaging can also be realized using ISAR imaging algorithm. An experiment is performed to verify the proposed multi-functional microwave photonic radar system. An up-chirped LFM signal from 8.5 to 12.5 GHz and an 8.0 GHz single-tone microwave signal are used as the transmitted signal. The results show that the absolute measurement errors of distance and radial velocity are less than 5.9 cm and 2.8 cm/s, respectively. ISAR imaging results are also demonstrated, which proves the high-resolution and real-time ISAR imaging ability of the proposed system.

Innovative Shoe-Integrated System Based on Time-of-Flight Range Sensors for Fall Detection on Various Terrains

This letter presents a shoe-integrated fall detection system that is based on seven time-of-flight (ToF) range sensors to identify falls on different terrains. The ToF range sensors, which measure the real-time distance between the sensors and the ground surface, can be used to detect simultaneously the variation of the gait cycle and the geometry of the terrain. Using the statistical features of the ToF range signals and an error-correcting output codes-based model, 17 experimental activities, executed by nine participants, are classified into three classes—activities of daily life, near falls, and falls. The experimental F1-scores for these three classes are 100, 98.17, and 99.07%, respectively. The arrangement of ToF range sensors is simplified to develop a compact shoe-integrated system architecture for fall detection.