Academic literature on the topic 'Real-time city generation'
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Journal articles on the topic "Real-time city generation"
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Delma, K. Johnson, Sunny K. Delsha, Krishna P. R. Devi, and K.S Salkala. "Smart Cities in Real Time Entities." Journal of Research and Review: Future Internet and Hyperconnectivity 1, no. 1 (2025): 24–30. https://doi.org/10.5281/zenodo.15095219.
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<em>The rapid expansion of urban populations, coupled with technological advancements, has necessitated the evolution of smart cities as a transformative approach to modern urban governance. This paper presents a comprehensive framework that integrates three key components: Digital Twin Cities, In- ternet of Things (IoT) infrastructure, and Smart City Hubs to enhance data-driven decision-making. By leveraging real-time digital models, sensor networks, and centralized data platforms, smart cities can optimize infrastructure, improve sustainability, and enhance the quality of urban life. This study explores core technologies, key applications, challenges, and future research directions to establish a robust model for the next generation of smart cities. The findings highlight the potential of AI-powered analytics, blockchain for secure urban transactions, and IoT- enabled urban monitoring in fostering resilient, adaptive, and citizen-centric urban environments.</em>
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Ko, Hyung-Hwa, GeunTae Kim, and Hyunmin Kim. "Specific Area Style Transfer on Real-Time Video." International Journal of Innovative Technology and Exploring Engineering 10, no. 5 (2021): 50–56. http://dx.doi.org/10.35940/ijitee.e8689.0310521.
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Since deep learning applications in object recognition, object detection, segmentation, and image generation are needed increasingly, related research has been actively conducted. In this paper, using segmentation and style transfer together, a method of producing desired images in the desired area in real-time video is proposed. Two deep neural networks were used to enable as possible as in real-time with the trade-off relationship between speed and accuracy. Modified BiSeNet for segmentation and CycleGAN for style transfer were processed on a desktop PC equipped with two RTX-2080-Ti GPU boards. This enables real-time processing over SD video in decent level. We obtained good results in subjective quality to segment Road area in city street video and change into the Grass style at no less than 6(fps).
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*Hyung-Hwa, Ko, Kim GeunTae, and Kim Hyunmin. "Specific Area Style Transfer on Real-Time Video." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 5 (2021): 50–56. https://doi.org/10.35940/ijitee.E8689.0310521.
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Since deep learning applications in object recogni-tion, object detection, segmentation, and image generation are needed increasingly, related research has been actively conducted. In this paper, using segmentation and style transfer together, a method of producing desired images in the desired area in real-time video is proposed. Two deep neural networks were used to enable as possible as in real-time with the trade-off relationship between speed and accuracy. Modified BiSeNet for segmentation and CycleGAN for style transfer were processed on a desktop PC equipped with two RTX-2080-Ti GPU boards. This enables real-time processing over SD video in decent level. We obtained good results in subjective quality to segment Road area in city street video and change into the Grass style at no less than 6(fps).
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d’Angelo, P., and F. Kurz. "AIRCRAFT BASED REAL TIME BUNDLE ADJUSTMENT AND DIGITAL SURFACE MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1643–47. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1643-2019.
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<p><strong>Abstract.</strong> This paper introduces a system for real-time generation of digital surface models (DSM) based on an optical multi-camera system flown on board of a manned airplane or helicopter. The system consists of high end consumer cameras, GNSS/IMU system, and on-board computers for real-time data processing. Usually, generation of digital surface models from aerial imagery is done in an off-line process, leading to delayed availability of height data. The proposed system processes data in real time on board of the aircraft and downlinks the generated DSM to a ground station. This paper evaluates the GNSS/IMU on-line solution quality and its impact on dense stereo matching. The proposed real time sliding window based bundle adjustment significantly improves image orientations and DSM quality, allowing generation of detailed digital surface models with a resolution of 2*GSD. Experiments using two flight patterns are conducted over the city of Landsberg and the resulting DSMs are evaluated against a LiDAR generated reference point cloud. The online bundle adjustment is shown to minimize the effect of systematic GNSS/IMU offsets while adding only a limited delay.</p>
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S, Manjunatha, and Annappa B. "Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications." Scalable Computing: Practice and Experience 21, no. 4 (2020): 611–23. http://dx.doi.org/10.12694/scpe.v21i4.1759.
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Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts.
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Nikolskiy, Serhiy, and Iryna Klymenko. "EMBEDDED IOT PLATFORM FOR REMOTE TRAFFIC CONTROL IN SMART CITY IOT INFRASTRUCTURE." Measuring Equipment and Metrology 84, no. 3 (2023): 31–38. http://dx.doi.org/10.23939/istcmtm2023.03.031.
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A justified hybrid multilevel approach to IoT infrastructure implementation is set to facilitate the achievement of a scalable IoT (Internet of Things) infrastructure, incorporating integration into cloud technologies and services. The localization of hardware-software traffic management means at the lower level of the IoT infrastructure, close to data collection devices, ensures the generation of control influences in real-time, and relieves communication channels at the higher levels of the IoT infrastructure architecture. A technology for generating control influences for remote traffic management is proposed, which is based on the developed AT command system for deploying a web server and generating web pages using the capabilities of the embedded IoT platform on modern microcontrollers. The proposed technology allows for the formation of control influences in real-time, in an easily comprehensible textual format, using a web interface in the local domain of the IoT infrastructure. It also enables the visualization of information on remote displays and information boards, as well as on displays integrated into automotive equipment. The proposed technology can be used to inform road traffic participants about critical situations and can be embedded in smart traffic lights within remote traffic management systems or used to implement virtual traffic lights.
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Hong, Tao, Qiye Yang, Peng Wang, et al. "Multitarget Real-Time Tracking Algorithm for UAV IoT." Wireless Communications and Mobile Computing 2021 (August 24, 2021): 1–15. http://dx.doi.org/10.1155/2021/9999596.
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Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.
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Abolhassani Khajeh, Sima, Morteza Saberikamarposhti, and Amir Masoud Rahmani. "Real-Time Scheduling in IoT Applications: A Systematic Review." Sensors 23, no. 1 (2022): 232. http://dx.doi.org/10.3390/s23010232.
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The Internet of Things (IoT) is a telecommunication network in the next generation of applications with the rapid progress of wireless sensor network techniques that have touched many spheres of life today. Hardware, telephony, communications, storage, secure platforms, software and services, and data processing platforms are all part of the IoT environment. IoT sensors collect data from their environment and share it by connecting to the Internet gateway. These sensors often perform tasks without human intervention. This article aims to review real-time scheduling in the IoT to fully understand the issues raised in this area published from 2018 to 2022. A classification for IoT applications based on practical application is provided for selected studies. Selected studies include healthcare, infrastructure, industrial applications, smart city, commercial applications, environmental protection, and general IoT applications. Studies are sorted into groups based on related applications and compared based on indicators such as performance time, energy consumption, makespan, and assessment environments depending on the provided classification. Finally, this paper discusses all reviewed studies’ main concepts, disadvantages, advantages, and future work.
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Shankar, H., M. Sharma, K. Oberai, and S. Saran. "DEVELOPMENT OF WEBGIS BASED REAL TIME ROAD TRAFFIC INFORMATION SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (November 15, 2018): 37–45. http://dx.doi.org/10.5194/isprs-annals-iv-5-37-2018.
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<p><strong>Abstract.</strong> Rapid increase in road traffic density results into a serious problem of Traffic Congestion (TC) in cities. During peaks hours TC is very high and hence public search least congested path for their journeys in order to minimize ravel time and hence transportation cost. In this study, a new empirical model was developed to estimate congestion levels using real time road Traffic Parameters (TPs) such as vehicle density, speed, class and vehicle-to-vehicle (V2V) gap. These real time road TPs were collected using latest generation Inductive Loop Detector (ILD) technology. Further, a WebGIS based Road Traffic Information System (RTIS) for Dehradun city was developed for real time TD analyses and visualisation. This RTIS is very useful for public and user departments for planning and decision making processes. No other such system is available in India, which handles multiple traffic parameters simultaneously to provide solution of day-to-day problems.</p>
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Khoshkhah, Kaveh, Mozhgan Pourmoradnasseri, Amnir Hadachi, et al. "Real-Time System for Daily Modal Split Estimation and OD Matrices Generation Using IoT Data: A Case Study of Tartu City." Sensors 22, no. 8 (2022): 3030. http://dx.doi.org/10.3390/s22083030.
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In recent years, we have witnessed the emergence of the implementation and integration of significant working solutions in transportation, especially within the smart city concept. A lot of cities in Europe and around the world support this initiative of making their cities smarter for enhanced mobility and a sustainable environment. In this paper, we present a case study of Tartu city, where we developed and designed a daily real-time system for extracting and performing a modal split analysis. Our web-based platform relied on an optimization approach for calibrating our simulation in order to perform the analysis with the use of real data streams from IoT devices installed around the city. The results obtained from our system demonstrated acceptable performance versus the quality of the available data source. In addition, our platform provides downloadable OD matrices for each mode of mobility for the community.
Book chapters on the topic "Real-time city generation"
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Ortiz, Juan P., Juan D. Valladolid, and Soledad Gutiérrez. "Performance Analysis of the LiDAR-Based 3D LIO-SAM Algorithm in Urban Areas: Case Study Cuenca City in the Ecuadorian Andes." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_20.
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Abstract Autonomous Vehicles (AVs) technology has increased dramatically over the last years. Accurate 3D point cloud maps are needed to perform this autonomous driving task. Mapping allows AVs to adapt to the surrounding environment and maneuver in complex situations. In this paper, a methodology for performance analysis of the LiDAR Inertia Odometry via Smoothing and Mapping (LIO-SAM) framework for the generation of 3D point cloud maps is proposed. The test scenario is intended for outdoor autonomous vehicles making use of an inertial measurement unit (IMU) sensor, a LiDAR sensor and a Global Navigation Satellite System (GNSS) module. The route selected for the collection of the data set is located in the city of Cuenca-Ecuador in the Ecuadorian Andes at an altitude of 2581 m a.s.l. The LIO-SAM framework was meticulously evaluated for accurate real-time vehicle trajectory estimation and mapping. The results obtained were highly precise with a Root Mean Squared Error (RMSE) of 0.04 m, 0.09 m and 0.08 m for the elevation, northing and easting, respectively. This results show a few centimeters of difference between the control points evaluated with cartographic precision equipment and Real-Time Kinematic (RTK) services versus LIO-SAM framework.
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Tan, Chuheng, and Ximing Zhong. "A Rapid Wind Velocity Prediction Method in Built Environment Based on CycleGAN Model." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_22.
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AbstractAlthough the wind microclimate and wind environment play important roles in urban prediction, the time-consuming and complicated setup and process of wind simulation are widely regarded as challenges. There are several methods to use deep learning (DL) models for wind speed prediction by labeling pairs of wind simulation dataset samples. However, many wind simulation experiments are needed to obtain paired datasets, which is still time-consuming and cumbersome. Compared with previous studies, we propose a method to train a DL model without labelling paired data, which is based on Cycle Generative Adversarial Network (cycleGAN). To verify our hypothesis, we evaluate the results and process of the pix2pix model (requires paired datasets) and cycleGAN (does not requires paired datasets), and explore the difference of results between these two DL models and professional CFD software. The result shows that cycleGAN can perform as well as pix2pix in accuracy, indicating that some random city plans image samples and random wind simulation samples can train surrogate models as accurate as labelled DL methods. Although the DL method has similar results to the professional CFD method, the details of the wind flow results still need improvement. This study can help designers and policymakers to make informed decisions to choose Dl methods for real-time wind speed prediction for early-stage design exploration.
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Pasta, Francesco. "Fikirtepe in limbo: urban transformation, cross-border migration, and re-peripheralization in Istanbul." In Embodying Peripheries. Firenze University Press, 2022. http://dx.doi.org/10.36253/978-88-5518-661-2.08.
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The historical development of Istanbul’s gecekondu areas (informally-originated neighborhoods) can be broadly interpreted as a progression toward the center and subsequent re-peripheralization, both in sociopolitical terms and in actual urban geography. While Istanbul emerged in recent decades as a magnet for transnational migrants and for capitals pouring into the debt-fueled real estate sector, many such neighborhoods have been targeted by speculative socio-spatial restructuring projects, while also absorbing much of the migratory influx. The recent economic crisis plunged these urban redevelopment sites into a deadlock, generating a fragmented urbanscape in which multiple layers of uncertainty, suspension, and informalization overlap and interact. This chapter explores the unfolding transformation in Fikirtepe, the largest ongoing redevelopment project in the city, which has seen its social and urban fabric torn apart by the redevelopment and is currently stuck in an unstable but protracted limbo. As Fikirtepe becomes “unlivable” for many of its long-time dwellers, a number of migrants are moving in, etching out a living: a collateral effect of redevelopment failure, creating a space of opportunity for new disenfranchised populations with varied backgrounds, legal statuses, and life trajectories. Within this setting, this chapter analyzes the periphery as a condition that is articulated, reproduced, and transformed through embodied practices. With their practices, narratives, and trajectories, those who inhabit such botched urban transformation embody different layers of the periphery, contributing to shape an understanding of it as a perspectival condition with a polyvalent spatiality and temporality.
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Fortino, Giancarlo, Carlos Calafate, and Pietro Manzoni. "Robust Broadcasting of Media Content in Urban Environments." In Next Generation Content Delivery Infrastructures. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1794-0.ch005.
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In this work, the authors apply raptor codes to obtain a reliable broadcast system of non-time critical contents, such as multimedia advertisement and entertainment files, in urban environments. Vehicles in urban environments are characterized by a variable speed and by the fact that the propagation of the radio signal is constrained by the configuration of the city structure. Through real experiments, the authors demonstrate that raptor codes are the best option among the available Forward Error Correction techniques to achieve their purpose. Moreover, the system proposed uses traffic control techniques for classification and filtering of information. These techniques allow assigning different priorities to contents in order to receive firstly the most important ones from broadcasting antennas. In particular, as vehicle speed and/or distance from the broadcasting antenna increase, performance results highlight that these techniques are the only choice for a reliable data content delivery.
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Hughes, Daniel, Klaas Thoelen, Wouter Horré, et al. "Building Wireless Sensor Network Applications with LooCI." In Advancing the Next-Generation of Mobile Computing. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0119-2.ch005.
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Considerable research has been performed in applying run-time reconfigurable component models to the domain of wireless sensor networks. The ability to dynamically deploy and reconfigure software components has clear advantages in sensor networks, which are typically large in scale and expected to operate for long periods in the face of node mobility, dynamic environmental conditions, and changing application requirements. LooCI is a component and binding model that is optimized for use in resource-constrained environments such as Wireless Sensor Networks. LooCI components use a novel event-based binding model that allows developers to model rich component interactions, while providing support for run-time reconfiguration, reflection, and policy-based management. This paper reports on the design of LooCI and describes a prototype implementation for the Sun SPOT. This platform is then evaluated in context of a real-world river monitoring and warning scenario in the city of São Carlos, Brazil.
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Voore, Dr Subrahmanyam, N. Santosh Ramchander, and Mahesh Kumar Thota. "A FEASIBILITY SOLUTION FOR INTELLIGENT TRAFFIC MANAGEMENT AND ROAD SAFETY SYSTEM BY INTERNET OF THINGS (IOT) ENABLED 5G TECHNOLOGY." In Futuristic Trends in IOT Volume 3 Book 4. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bdio4p1ch1.
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As per the growing of population and introducing of modern vehicles in the automobile industry and also utility of vehicles making high traffic, pollution, in-convenience to traffic control the city people that cause vehicle accidents as well as health problems. It is an essential and responsibility to control and management heavy traffic. In our proposal an intelligent system to control the traffic and provide facility for the city to control heavy traffic causing heavy pollution. In this proposed paper we are introducing a real-time an architectural design of a framework that supposed to control traffic in the city by applying Internet of Things (IoT), 5G and Artificial Intelligence technologies. IoT is a next generation of Internet to sense, connect and communicate real world objects with sensors devices, actuators, network, protocols to generate real-time data via Internet. The Application layer protocols of IoT MQTT protocol managing vehicle data over the Cloud. 5G is next generation of mobile communications by e MBB (Enhanced Mobile Broadband), Latency Communication), m MTC (stands for massive Machine Type Communication) u RLLC(Ultra Reliability and Low Latency Communication) for better communication and automation of vehicle control in the City. The Artificial Intelligence (AI) used for better prediction and data analysis. AI with Robotics for patrolling the city for tracking the information in a city. Nature Inspired Computing Algorithms like Ant Colony Optimization (ACO) are to optimize shortest distance to route for better connectivity of road paths for vehicles moving around the City. Our aim is to build an Architecture Design Framework in an efficient way to control traffic, accidents and health system. Our Intelligent Transport System is introduced a technology features of Vehicle-To-Vehicle(V2V) communication, Autonomous Driving Vehicles, Road Safety Measure, IoT Protocols with Cloud, Navigation System and AI Drones(UAV) Robots.
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Cibin, Alessia. "Forms of Night-Time Economy Governance." In Advances in Civil and Industrial Engineering. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7004-3.ch003.
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This chapter introduces a framework for understanding forms of night-time economy governance. First, the chapter identifies, defines, and classifies six ideal types of night-time economy governance modes drawing upon key literatures and real-world night-time economy governance practices. The six ideal types of night-time economy governance modes are: public-private partnership, night-time commission, night mayors, night city managers, night lobby groups, and night advocacy groups. Second, the chapter discusses the ideal type of night-time economy governance modes, and their potential role for night-time economy resilience and recovery during and post-COVID-19 crisis. Local governments, policy practitioners, night-time economy advocates have the opportunity to rethink night-time economy governance through entrepreneurialism, flexibility, adaptation, leadership, resource mobilization, coordination, and knowledge generation. Alternatively, we may assist to a weak night-time economy policy implementation and the decadence of forms of night-time economy governance. Recently, public discourse praises a night mayor or a night city manager with little knowledge around governance models.
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Shanmugasundaram, Singaravelan, Arun Ramaiah, Arun Shunmugam Dhiraviyam, et al. "Next Generation Smart Street Light Monitoring and Controlling System Using IoT." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4326-5.ch018.
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Multi-functional and unique IoT cloud-based smart street lighting control system with central management system software. It is a perfect solution to manage large-scale outdoor lighting networks in order to control and monitor street lighting infrastructure while saving energy and reducing maintenance costs. Smart street lighting control system allows a city to schedule lights on or off easily and set dimming levels of lights so a city can provide the right level of lighting intelligently. Smart street lighting control systems accurately detect light failure and other maintenance problems in real time so malfunctions can be fixed quickly. This intelligent system provides the operator with web access for automatic or manual monitoring and control over illumination performance. Smart Street Lighting solution gives cities and municipalities the capability to remotely monitor and control the street lights in a much effective way. Our Smart Street Lighting Solution. Our smart street lighting solution can be used for highways, urban/rural buildings, parks, and industries.
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Divakar, M. H., Nandaraja H. Shadlageri, and Arjunkumar M. "Smart Energy Grid Architectures with AI Driven Demand Forecasting and Load Balancing for Efficient Power Management." In IoT Architectures and AI-Enabled Smart City Solutions for Sustainable Urban Development. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552241-08.
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The integration of Artificial Intelligence (AI) into smart grid systems has revolutionized load forecasting, offering advanced solutions for managing the complexity and uncertainty inherent in modern energy systems. This chapter explores the role of AI-based hybrid systems in dynamic load forecasting and their potential for mitigating uncertainty in energy demand predictions. As renewable energy sources increasingly dominate power grids, the unpredictability of their generation requires adaptive forecasting models capable of adjusting to real-time data. Hybrid AI systems, which combine machine learning techniques such as neural networks, support vector machines, and probabilistic models, offer a robust framework for enhancing forecasting accuracy and reliability. By addressing challenges associated with uncertainty, such as fluctuating demand patterns and the variability of renewable energy, these systems enable more precise load predictions and improve grid stability. The chapter also highlights the real-time integration of renewable energy data, emphasizing its role in adapting to fluctuating supply conditions and optimizing energy management. Furthermore, the integration of uncertainty mitigation strategies, including Bayesian approaches and Monte Carlo simulations, is discussed in the context of enhancing decision-making processes. The insights presented underscore the critical need for innovative AI-driven models to support the next generation of smart grids, providing reliable and efficient power management solutions. This chapter contributes to advancing the understanding of AI's application in energy systems, offering practical approaches for addressing current and future challenges in load forecasting.
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Maurya, Jay Prakash, Vijay Kumar Trivedi, Vinesh Kumar, and Dheresh Soni. "Experiment and Simulation of Proposed V2I System." In Networking, Transport, and Quality of Service in Vehicular Networks. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6422-2.ch005.
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Research and development in Next generation networks is a growing field, including subfields like Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to everything (V2X) that helps application development and participating towards advanced smart city projects in India. Smart City projects in India demands fast communication channel, secure channel as well as data exchange policies to control infrastructure components. This article proposed a real-time simulation of a next generation network improving existing infrastructure of cities through the involvement of Quantum Computing. Quantum key based protocol BB84 has been simulated and tested in this article work for comparing Traditional and modern V2I based software defined network. Comparative results of proposed simulation in PoliQI and NS3 found effective in terms of security, performance, and scalability for different numbers of nodes. This article delivers a computational technique that may be used creates secure channels for key exchange between cloud service providers and customers, ensuring encryption keys
Conference papers on the topic "Real-time city generation"
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Kang, Kyung-Kyu, and Chang-Joon Park. "Real-Time Snow Cover Map Generation for Virtual City Tours." In 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2020. http://dx.doi.org/10.1109/ictc49870.2020.9289542.
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Park, Jung Woon, and Seok Hee Oh. "A Study on Creation and Usability of Real Time City Generator via Procedural Content Generation: – Focus on virtual reality contents for senior." In 2019 International Symposium on Multimedia and Communication Technology (ISMAC). IEEE, 2019. http://dx.doi.org/10.1109/ismac.2019.8836162.
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Sharma, Manish, Uma Yadav, Namrata Bansal, and Javalkar Dinesh Kumar. "Next-Generation Street Lighting Infrastructure: IoT-powered Smart City Lighting." In International Conference on Cutting-Edge Developments in Engineering Technology and Science. ICCDETS, 2024. http://dx.doi.org/10.62919/uere2173.
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The evolution of street lighting infrastructure toward smarter, more integrated systems is increasingly driven by the development of Internet of Things (IoT) technologies. The transition to next-generation street lighting in smart cities encompasses not only the deployment of energy-efficient LEDs but also the integration of IoT for optimized control and functionality. This research paper delves into the innovative framework of IoT-powered smart city lighting, examining how IoT enhances street lighting systems to become more adaptive, energy-efficient, and responsive to environmental changes. Key aspects include remote management, real-time data analytics, and enhanced public safety features, facilitating significant energy savings and improved urban living. Through a comprehensive analysis, this study explores the technical advancements, potential benefits, and challenges in implementing such systems, providing insights into their scalability and sustainability in urban environments.
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Alam, Mahmudul, Md Khairul Alam, MD Shahrukh Adnan Khan, Muhammad Shahjalal, Md Humaun Kabir, and Shikder Shafiul Bashar. "A Real Time Industrial Level Atmospheric CO, CO2 and Sound Level Monitoring in Bangladesh- A Part of Smart City Planning for Next Generation Advancements." In 2019 IEEE International Conference on Power, Electrical, and Electronics and Industrial Applications (PEEIACON). IEEE, 2019. http://dx.doi.org/10.1109/peeiacon48840.2019.9071953.
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Xu, Liangfei, Jianqiu Li, Minggao Ouyang, Jianfeng Hua, and Xiangjun Li. "A New Generation of Fuel Cell Hybrid Powertrain for Public Traffic." In ASME 2010 8th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2010. http://dx.doi.org/10.1115/fuelcell2010-33008.
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Fuel cell hybrid city buses are set to play a prominent role in public traffic applications by offering a more energy efficient and less polluting drive-train alternative to conventional internal combustion engine (ICE) vehicles. During the past ten years the Chinese university - industry - collaboration organization developed several fuel cell hybrid city buses. Three of them were demonstrated in the Beijing Olympic Games of 2008, and caused great attention. However, the hybrid powertrain needs to be improved, so as to fulfill the vehicle power requirement, prolong the working life time of the fuel cell stacks, and keep the battery charge sustaining. Similar to the old one, the new generation consists of two fuel cell stacks, a traction battery and an electric motor. The differences reflect in three aspects. Firstly, the two stacks are regulated independent with each other. Secondly, a real-time applicable average power prediction algorithm of the electric motor is exploited. Thirdly, the output power of each stack is determined in accordance with the states, which are estimated on-line. A novel energy management strategy considering average electric motor power prediction, SOC balancing, battery temperature feedback control, fuel cell resistance estimating is exploited. Experimental results show the effectiveness of the proposed system.
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Rodríguez Romero, Eva Juana, Carlota Sáenz de Tejada Granados, and Rocío Santo-Tomás Muro. "The role of historical green spaces in the identity and image of today’s cities: The case of Madrid." In 24th ISUF 2017 - City and Territory in the Globalization Age. Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/isuf2017.2017.5340.
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The role of historical green spaces in the identity and image of today’s cities: The case of Madrid Eva J. Rodríguez Romero¹, Carlota Sáenz de Tejada Granados², Rocío Santo-Tomás Muro3 1, 2, 3 Departamento de Arquitectura y Diseño. Universidad CEU San Pablo. Escuela Politécnica Superior, Campus de Montepríncipe. 28668 Boadilla del Monte, Madrid. E-mail: rodrom@ceu.es, carlota.saenztejada@ceu.es, rocio.santotomasmuro@beca.ceu.es Keywords: landscape history, proximity landscape, city iconography, sense of place, Madrid Conference topics and scale: Urban green space The image that a city offers when approaching it, depending on its topographical situation, the drawing of its borders or its urban form, generates a perceptive construction, for both locals and tourists, with the potential to become an iconic image and therefore play a part in the collective imagery. The character and value of those landscapes is largely determined by their green spaces, preserved in most European cities for their ecological or historical significance. Being able to recognize the worthiness of these proximity visions, in the context of today’s growing cities, is of fundamental relevance in order to enhance the sense of place, amongst other community values. In this communication we study the above-mentioned aspects in the image of the city of Madrid, within the framework of the project ‘Proximity landscapes of the city of Madrid. From the 19thC to the present’ currently in process. Through a landscape analysis of a selection of iconographic representations of the surroundings of the city, we draw special attention to the presence of historical green spaces throughout time, and its relation with architectural landmarks in the progressive construction of an iconic image of the city. From here, we can deduce the relevance that these elements have in the generation of a recognizable character and the decisive role of protection mechanisms in order to preserve it. References Lasso de la Vega, M. (2007) Quintas de recreo. Las casas de campo de la aristocracia alrededor de Madrid, 2Vol. (Madrid City Council, Madrid) Martínez, A. (2008). El entorno urbano del Palacio Real de Madrid entre 1735 y 1885 (Madrid City Council, Madrid). Ortega, J., Martínez, A. &amp; Martín, F.J. (2008) Entre los Puentes del Rey y de Segovia. Secuencias gráficas del río Manzanares (Madrid City Council, Madrid). Ramón-Laca, L., Tardío, F.J. (2005) ‘Vegetal products used in Madrid between the 14th and 19thC, Asclepio (LVII-2, 25-44. Wester-Heber, M. (2004) ‘Underlying concerns in land-use conflicts-the role of place identity in risk perception’, Environmental Science &amp; Policy, 7, 109-116.
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Tenemaza, Maritzol, Roger Laza, Jonathan Vargas, and Sergio Luján-Mora. "Tourist Recommendation Systems: solving mobility in a private vehicle with support for parking." In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003600.
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At present, tourists have all the facilities to travel, but they face the difficulty of establishing the itineraries of visits and the routes to follow, the reason is the numerous attractions and the complexity of the Metropolitan cities, Gavalas [1] .In previous works by this author, algorithms for the generation of intelligent itineraries have been published. Likewise, surveys carried out with end users have clarified some additional requirements, which were observed as future work. One of them is the requirement to search for parking spaces when the tourist moves by vehicle from the place where he is located. and when you need it.The tourist can move in a city on foot, or can hire a private vehicle and a third option, move through the Metropoli-tano transit network. But, when a user hires a vehicle, they have the question of whether it will be easy to find parking spaces and how to get to each one of them in the city, which is unknown to tourists in itself, in addition to the fact that the search of parking affects the en-vironment, and therefore affects the tourist due to the additional use of fuel and wasted time.In this document, the solution to the problem of finding parking lots from any place where the user is, the routes to get to each one of them, identifying infor-mation about the parking lot, if it is open and if there are spaces available, will be presented. The solution to this problem is of public interest and we differ from other researchers in that we include the option of identifying empty spaces in nearby parking lots, this information is presented in real time, the study is carried out in a specific city.To solve the problem, a tourist recommendation system will be presented that will include a preliminary study of the interests of that user from comments and reactions on tourist images published on the Facebook network, which will be subjected to sentiment analysis, to characterize the interest or not.Once the user's interests are identified, and in order to enrich the itinerary, the best POIs with tourist value in the city, evaluated by other users, are offered in the geographical area where the user is located.The presentation of the routes and itineraries will be done on a two-dimensional map, you can choose routes on foot or by vehicle. In addition, if the user de-cides to stop anywhere, they will have the option to identify parking spaces and how to get to them.Finally, an experiment will be carried out with end users, to measure both the ease of use and the utility of the proposal. As future work, the option of park-ing reservations in real time will be included. Another job that is related is mo-bility through the Metropolitan transportation system in a specific city. [1]Gavalas, D., Kasapakis, V., Konstantopoulos, C., Pantziou, G., Vathis, N., & Zaroliagis, C. (2015). The eCOMPASS multi-modal tourist tour planner. Expert systems with Applica-tions, 42(21), 7303-7316.
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Satish, Arvind, Abhijith Sabu, Johnson Xavier Saldanha, and Nagesh A P. "Development of Drive Cycle using Fleet Data for Two-Wheelers in Indian Market." In Small Engine Technology Conference & Exposition. Society of Automotive Engineers of Japan, 2020. http://dx.doi.org/10.4271/2019-32-0545.
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<div class="section abstract"><div class="htmlview paragraph">Generally, to produce reliable two-wheelers, manufacturers resort to intense engineering efforts to make sure the two-wheeler can withstand the most harsh testing conditions and requirements. This sometimes leads to a higher cost in realizing such outlier requirements. Thus, a drive cycle matching the actual riding characteristics will enable better understanding of the requirements and an optimized engineering effort. There have been several attempts by governmental and non-governmental organizations to realize a real drive cycle for various cities and countries, trying to capture the typical riding style in those regions. But the drive patterns observed in most representative cycles do not match with the scenario in India with frequently dense traffic, constrained roads and slow driving speeds.</div><div class="htmlview paragraph">To understand the driving pattern in India, a drive cycle generation algorithm is developed which uses real time on-road data captured from a fleet of vehicles in India and creating a database of micro-trips. These micro-trips are first categorized based on their average speeds. The algorithm concatenates these micro-trips to make a drive cycle, such that the average speed of the resulting drive cycle matches closely to the average speed of the captured on-road data. The algorithm then iterates different sequencing of these micro-trips in the drive cycle to minimize the error in various parameters like average acceleration, time percentage of acceleration, &amp; deceleration, time percentage of idle, between the resulting drive cycle and the captured on-road data. Representative cycles of different cities and regions have been developed and described in this paper.</div><div class="htmlview paragraph">This paper aims in explaining the approach of extracting a drive cycle from the data collected from a fleet of two-wheelers on-road in the Indian market and comparing the different riding patterns found in different regions. The algorithm developed can be extended to any level of data, ranging from a particular city to even combining different countries together.</div></div>
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Mask, Gene Michael, and Xingru Wu. "Generating New Production Type Curves through Machine Learning Utilizing Dimensional Analysis." In SPE Oklahoma City Oil and Gas Symposium. SPE, 2023. http://dx.doi.org/10.2118/213080-ms.
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Abstract Generating production-type curves for new horizontal wells in unconventional reservoirs is an evolving process that requires continuous calibration to maintain the most accurate forecast over time. History matching production alone is no longer sufficient to maintain such models. Obstacles to creating production type curves are attributed to the complexities in heterogeneous reservoir properties, improved drilling and completion techniques, and evolving production and operation procedures. This paper will highlight improvements to a proposed machine-learning algorithm to generate production type curves for new wells in oil and gas unconventional reservoirs. The algorithm utilizes dimensionless groups created from the raw data in different categories and scales, thus reducing the dimension of the problem, decreasing the processing time, and improving the efficiency of the machine-learning model. The dimensionless groups are developed using inspectional and dimensional analysis depending on the data available for feature inputs. Many of the dimensionless groups have physical meanings and can be upscaled. We advanced the ability of the previously developed algorithm utilizing production, completion, and petrophysical data from both oil and gas reservoirs to generate new type curves by using the "engineering" code that was laid out in our previous case study. The algorithm incorporates physics into the machine learning (ML) process supporting the outputs with math and science. When using multiple reservoirs from different formations in the data, the algorithm utilizes logic in the code to determine between oil and gas wells. The quality of the results is impacted when using data from reservoirs with phase envelopes that are not similar, for example, a heavy oil and a dry gas reservoir. The algorithm is updated to include logic that can determine the major phase to predict oil and gas production more accurately. The quantity of oil and gas production is more accurately predicted using cumulative production rates rather than over time. The machine learning model maintains an R2 &gt;= 0.8 when cross-validating both cumulative oil and gas production. The algorithm consistently predicts cumulative production over time on test data with R2 &gt;=0.8. The predicted rates for new type curves are compared to conventional production type curves, thus validating the quality and goodness of fit for production rates, decline profile, and ultimate recovery. The results demonstrate how late-time production can be either extrapolated using the machine learning algorithm or combining traditional methods by utilizing hyperbolic and exponential declines where training data is unavailable for the machine learning model to perform late-time forecasting. The algorithm of the ML model is proving to be a supplementary tool when generating new production type curves. The speed and efficiency provide support to the DCA generated type curves. It is versatile in its ability to combine data from multiple formations and discern between the major phase, thus providing production type curves we have confidence. The scalability of the dimensionless input parameters can account for changes in completions and reservoir properties within minutes of updating the database hence providing insight in near real-time for engineers.
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Marko Šidlovský, Marko Šidlovský, Filip Ravas Filip Ravas, and Václav Jirovský Václav Jirovský. "Uniqway - students' carsharing project transforms mobility." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2021-dgt-040.
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"When we try to have a clear look at the evolution of transportation, we could see bright beginnings with shady todays. Many governmental organizations, institutions, technological companies, and even NGOs are trying to deal with current challenges in transport, mostly resulting in costly congestions. Moreover, almost everybody is expecting the change will be instant. A scientific view of the topic cannot be that simple. The current world of transport offers many data-based so-called solutions dealing with congestions. Furthermore, we have new transport devices, smaller, efficient, single purpose. Last but not least, old technological tools are being transformed by a new approach to the relation between the transportation provider and its user. We can see navigation apps enhanced with real-time traffic data promoting selfish behavior leading to the devastation of calm neighborhoods and local roads by directing high traffic through such places. History of narrow city streets is thwarted by oversized shared bicycles waiting for their next rider or literally blocked by piles of e-scooters. Hence, the environment is adapting to these novelties. Often it is performed in the most convenient way, not in an optimal manner. One of the new approaches to old types of transport is carsharing – user still drives a car by himself, but he does not own the vehicle. It is expected that the current global vehicle fleet of private cars could be reduced almost by 30% if proper carsharing systems are widely adopted. However, proper adoption and time to achieve it are the most significant challenges. The average lifecycle of a single vehicle ends after ca. twenty years of service. Technologies, which allowed the rise of carsharing exist and have widely penetrated into the market in the last few years, even though the carsharing itself is known since ca. 1950s. Therefore, if we would like to observe the real benefit of carsharing, we need to wait until the lifecycle of at least 30% of vehicles will end, while the carsharing is highly available to anybody anywhere. It could be a very long time. On the other hand, current trends in ownership show a different approach to the new generations. Some of the studies doubt the longevity of their alternative attitude, as the needs change with age, family life, employment, etc. Nevertheless, dealing with the young generation, such as students, means dealing with people without a car and relatively low income. Thus, on the one hand, it is an excellent opportunity to reduce the number of stationary vehicles on the streets in the future, but it is a tough challenge in promoting relatively expensive service. Project Uniqway is a carsharing service entirely developed and operated by students with the financial support of ŠKODA AUTO. The student team's limited capacity and focus on development resulted in heavy deployment automation and migration from custom high maintenance and virtual machine-based solutions to cloud-based infrastructure. Challenges of this transformation and data-driven approach pointing out specifics of immensely successful carsharing service in the Czech Republic are unveiled in the proposed article."