Research on the Decision Making Support Function Using the Markov Property

Abstract. Global navigation satellite systems, e.g. the current GPS and the future European Galileo system, are frequently used in car navigation systems or smart phones to determine the position of a user. The calculation of the mobile position is based on the signal propagation times between the satellites and the mobile terminal. At least four time of arrival (TOA) measurements from four different satellites are required to resolve the position uniquely. Further, the satellites need to be line-of-sight to the receiver for exact position calculation. However, in an urban area, the direct path may be blocked and the resulting multipath propagation causes errors in the order of tens of meters for each measurement. and in the case of non-line-of-sight (NLOS), positive errors in the order of hundreds of meters. In this paper an advanced algorithm for multipath mitigation known as CRMM is presented. CRMM features reduced algorithmic complexity and superior performance in comparison with other state of the art multipath mitigation algorithms. Simulation results demonstrate the significant improvements in position calculation in environments with severe multipath propagation. Nevertheless, in relation to traditional algorithms an increased effort is required for real-time signal processing due to the large amount of data, which has to be processed in parallel. Based on CRMM, we performed a comprehensive design study including a design space exploration for the tracking unit hardware part, and prototype implementation for hardware complexity estimation.

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5–19.7%, weekday; 19.4–31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8–34.0%, weekend), and from July to September 2021 (3.2–13.1%, weekday; 38.3–44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

The most of very large traffic system by growing the variety of services, the relationships between the vehicle network and the infrastructure are more complex. Moreover, intelligent transportation systems are getting more and more to develop a better combination of travel safety and efficiency since long time ago. Vehicle is being evolved and traffic environment is especially also organized well-defined schedules priorities, which is real time based wireless network traffic condition, variable traffic condition, and traffic pattern from the vehicle navigation system. Accordingly, we propose to Knowledge-based Self-adaptive Vehicle Trajectory Service using genetic algorithm in this paper.KeywordsVehicle networkIntelligent transportation system (ITS)Knowledge-based trajectory data (KTD)Self-adaptive trajectory service (STS)

The objective of this work is to develop a global navigation system for autonomous agricultural vehicles can perform missions in crop fields through a system of path planning. Missions can be understood as tasks (eg monitoring, sampling, etc.). Through small routes that vehicles must follow throughout their daily jobs, traveling the shortest possible distance between the points of origin and destination. The path planning was divided into steps to make it easy to understand each one. The mapping presented in this work was done in coffee-growing regions in the state of So Paulo and Minas Gerais. The map points have been sampled using a GPS receiver module along the path where it is possible to move the vehicle within the plantation. An important step for the success of this system is the data pre-processing step. In this step are inserted the relations between the points of the mapping. The missions are predefined in order to test if the calculation of the minimum cost path made by Dijkstra algorithm is correct. At each point of the route is given the vehicle heading angle (vehicle position towards the geographic North). According to the intended change of the heading angle is proposed a smoothing method to smooth this change by changing the route to an arc. In this case, the turning radius is reported. The last step is to provide the maximum speed of the vehicle due to the change of direction and maximum angular speed of the center of mass. The system proposed in this paper was able to determine the path with the shortest distance between two points of the mapping (geographic coordinates) and calculate the distance between these points. Although the formula used to calculate the distance between two geographical coordinates consider the shape of the Earth as a sphere, this did not generate significant errors for the proposed application. The proposed smoothing allowed, in some cases, to increase the vehicle speed by making the change of heading angle less abrupt.

It is possible for the visually impaired to walk their known area independently by using a white cane. However, in their unknown area, they cannot walk without help of others even if they can use a white cane very well. In such cases, not only a visually impaired person but he/she who assists them receives many stress in dairy activity. Therefore, many types of independent walking assist system for the visually impaired are developing. Our objective of this study is the development of an auto navigation system for the visually impaired which assists independent walking of them. This system measures the position of a visually impaired person (a user) by using GPS (Global Positioning System) and navigates him/her to the destination like as a car navigation system. In our system, a location data is stored in the map database and a position of user are analyzed and optimal route for a user to the destination is calculated. Our system guides a user to the destination along this route, and notifies the user of this route and some information for the safety walking by artificial voice. Three normal subjects were tested with our navigation system. All subjects were blind folded by an eye mask and equipped a navigation system. As a result, our system worked well and all subjects were able to walk to the destination following guidance voice. So, we are developing a new navigation system which is installed on a cellular phone. In Japan, there are some cellular phones that include GPS. And most of application programs for cellular phone are written by JAVA. Therefore, GPS included in a cellular phone will be used and every functions of the navigation system will be written by JAVA. In this paper, we also reports about this new navigation system.

Although reinforcement learning (RL) shows advantages in cellular network load balancing, it suffers from a low generalization ability, preventing it from real-world applications. Specifically, if network traffic pattern changes, the learned RL policy cannot adapt accordingly, resulting in system performance degradation. To address this issue, we propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to learn a generalized load balancing policy for adapting the real-world traffic pattern changes. The key is that different teachers represent different traffic patterns, and can learn various system models. By distilling and transferring the teacher knowledge, the student network is able to learn a generalized system model that covers different traffic patterns and unseen situations. Moreover, to improve the robustness of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. Results show that, compared with state-of-the-art RL methods, MOBA improves the minimal throughput and total throughput of a cellular network by up to 28.6% and 23.2%. Results also show that MOBA improves the training efficiency by up to 64%.

The Autofocus (AF) technology has become well-known over the past four decades. When attached to a camera, it eliminates the need to manually focus by giving the viewer a perfectly focused image in a matter of seconds. Modern AF systems are needed to achieve high-resolution images with optimal focus, and AF has become very important for many fields, possessing advantages such as high efficiency and autonomously interacting with Fenvironmental conditions. The proposed AF vision system for Unmanned Aerial Vehicle (UAV) navigation uses an autoencoder technique to extract important features from images. The system's function is to monitor and control the focus of a camera mounted to a drone. On an AF dataset, the proposed autoencoder model exhibited an amazing 95% F-measure and 90% accuracy, so it can be considered a robust option for achieving precision and clarity in varying conditions since it can effectively identify features.

Navigation and orbit computation aspects of the ESA NAVSAT system concept

Development of network cybersecurity technology needs IDS on network technology because modern networks face against dynamic attacks which display various attack methods. To address this problem, we present a different combination of a hybrid ideal solution for an adaptive intrusion detection framework that is incorporated with various analytical techniques approach to achieve accuracy and reliability in detecting threats. This proposed ideal design combines Random Forest based noise-handling classifier and recognizing structured and also static traffic behaviour, while the LSTM neural model helps to analyze traffic behavior by understanding temporal patterns. Security professionals can track network-based attack behavior through their ability to monitor traffic pattern changes which occur over time. The forecast detector system detects unusual network traffic patterns which the fusion model layer uses to create precise threat responses. The framework receives validation through testing with the CICIDS2017 is the dataset which contains various type of traffic patterns from both normal and attack activities. It performs comprehensive data preprocessing, feature selection and normalization to get better input data quality. The model becomes more adaptable through pseudo-labeling which enables it to analyze and learn from unclassified network traffic while utilizing this each set of information for subsequent training cycles to detect new traffic patterns. The system performs continuous network behavior monitoring through its clustering with retraining process. The method demonstrates that uniting statistical analysis with temporal data and adaptive learning into one system framework produces better intrusion detection results which also enable detecting intrusion for network at large scales.

Wireless devices operating under IEEE 802.11 x standards have the ability to transmit at various data-rates. However, there is no specification in the standard for dynamically selecting a suitable value of data-rate. Numerous solutions are available, mainly from device manufacturers and research community; focusing on a particular algorithm and its suitability for the overall system. However, none of the previous designs were flexible to change their behavior with changes in traffic nature and pattern. Not all traffic types have the same requirements and so should be the behavior of underlying algorithms for data-rate control. This paper highlights various data-rate control algorithms; classifying them on the basis of several parameters and discusses their suitability for different traffic patterns. It also proposes an algorithm, specifically designed for real time services. The cross-layer framework monitors traffic nature and patterns at the transport layer and activates one of three available rate control algorithms which suits the requirements of a particular type of traffic. According to the specific design of a selected algorithm, it then selects data-rate values at runtime. The results verify the tremendous difference among the three algorithms in terms of throughput and delay and signify their usability for specific traffic types.

5G resources should be properly planned for users to have a good quality of service. This planning includes defining the most suitable numerology indexes and best spectrum allocation considering the requirements of current traffic, which may change over time. Furthermore, when accounting for changes in traffic pattern, any necessary reconfigurations should be minimized. Here in this article, an optimization model is developed for the planning of spectrum allocation to the best mix numerology. The model considers adapting the operating numerology mix according to the current presence (or not) of traffic requirements. The model also works under any wireless communication scenario in 5G, and under any traffic pattern.

BackgroundThe Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns.MethodsTo capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department.ResultsThere were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM2.5 or further from major highways.ConclusionWe conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments.

The Sum-and-Lattice-Points Method Based on an Evidential-Reasoning System Applied to the Real-Time Vehicle Guidance Problem

Operating room (OR) human traffic has been implicated as a cause of surgical site infection. We first observed the normal human traffic pattern in our Pediatric Orthopedic ORs, and then examined the effect of surveillance on that traffic pattern. This study consisted of 2 phases: phase I sought to observe the OR traffic pattern (number of door swings, maximum and minimum number of OR personnel, number of OR personnel at 30-minute intervals, or changes in nursing, anesthesia, or surgeon staff) during surgical cases without OR personnel being notified, and for phase II, the same traffic pattern was monitored with their knowledge. Two thousand four hundred forty-two minutes of surgical time were observed in phase I, and 1908 minutes were observed in phase II. There was no difference (P=0.06) in the time between door swings between phase I (1.39 min) and phase II (1.70 min), no difference (P=1.000) in the maximum number of people in the OR between phase I (11.5 people, range: 7-15 people) and phase II (11.5 people, range: 8-20 people), and no difference (P=1.000) in the minimum number of people in the OR between phase I (4.67 people, range: 4-6 people) and phase II (4.71 people, range: 3-6 people). There was a difference in the time between door swings (P=0.03) and maximum number of people in the OR (P=0.005) based on the length of the surgery (less or more than 120 min). There was no difference in the time between door swings (P=0.11), but there was a difference in the maximum number of people in the OR (P=0.002) based on type of surgery (spine vs. others). There was no role of surveillance of human traffic in the OR. To achieve any change in the OR traffic pattern, monitoring alone may not be sufficient; other novel techniques or incentives may need to be considered.

Neural networks may be useful alternatives for statistical classification techniques when the available data is incomplete. This paper discusses the results obtained from a statistical technique called hierarchical grouping and the Kohonen neural network for classifying traffic patterns. The Kohonen neural network is shown to be a reasonable approximation of the hierarchical-grouping technique. It is suggested that hierarchical grouping of a small sample of typical traffic patterns may be a useful first step in setting up a Kohonen neural network for traffic-pattern classification. The Kohonen neural network can be used for classifying a large number of complete as well as incomplete traffic patterns as they become available. The neural network can also adapt the classification process to the change in the typical traffic patterns over time.