Real-time Interactive Applications Research Articles

Analysis of landing gear simplified modeling and simulation based on amesim

Aiming at the theoretical research of aircraft take-off and landing taxiing control characteristics and real-time interactive application scenarios, the simplified model modeling method of landing gear is studied. Through the reasonable simplification hypothesis of the landing gear pillar, a benchmarked, general, and pillar-type simplified model of the landing gear is formed. In the process of ground motion, based on the force analysis of aircraft taxiing, a six-degree-of-freedom mathematical model of ground taxiing is established. Based on the Amesim six-degree-of-freedom model, the aircraft is built, and the simulation test of aircraft taxiing turning and landing brake is carried out in combination with the simplified take-off and landing model. The results show that the general benchmarked pillar landing gear model formed in this paper is reasonable and feasible, and can quickly construct the aircraft landing gear model, which is convenient for subsequent optimization and design.

NVM-enhanced Machine Learning Inference in 6G Edge Computing

With the increasing popularization of smart terminals and real-time interactive applications, fast growing technical requirements push both academia and industry to look beyond 5G and conceptualize the sixth generation (6G) mobile network. Artificial intelligence (AI) with machine learning capacities at the edge is one crucial component of a 6G mobile network which makes various time-sensitive and high-stake services possible, e.g., smart security, virtual reality, self-driving vehicles. However, resource constraints, especially the memory limitation of edge servers, become major obstacles to deploying machine learning services at the edge. Fortunately, the new generation of non-volatile memory (NVM) provides new affordable memory resources that can be easily attached to existing edge servers. In this paper, we propose a novel machine learning application placement scheme using the NVM technology at the edge to reduce the end-to-end latency. Specifically, the proposed NVM-enhanced placement scheme takes into consideration the latency of various machine learning applications over NVM devices and the network. The corresponding optimization problem is exceedingly challenging, i.e., NP-hard. Therefore, we developed a novel approximation algorithm with both low computational complexity and theoretical guarantees. Experiments and extensive simulations using real-world applications highlight that our scheme provides significantly lower end-to-end latency compared with existing baselines.

An overview of interactive immersive services

Immersive services are the typical emerging services in current IMT-2020 network. With the development of network evolution, real-time interactive applications emerge one after another. This article provides an overview on immersive services which focus on real-time interaction. The scenarios, framework, requirements, key technologies, and issues of interactive immersive service are presented.

Passenger Alert Application

Abstract: This paper describes the creation of an intelligent real-time interactive public transport application. The application focuses on enhancing the user experience and improving communication among passengers. The main features of the application include a chat module that enables users to have conversations, facilitating the exchange of information and providing a platform for passengers to inform others about any issues or disruptions in the public transportation system. Additionally, the application incorporates an alarm module designed to alert or awaken passengers from their sleep or quick naps when the bus enters a buffer zone, indicating that their destination is approaching.By leveraging real-time data and intelligent algorithms, the application enhances the overall quality of public transportation services. The chat module promotes community engagement and empowers passengers to actively contribute to the improvement of the system's reliability and efficiency. Furthermore, the alarm module serves as a valuable tool for passengers, ensuring they are informed and prepared as their destination approaches, thus minimizing the risk of missing their stop.The development of this intelligent application addresses the growing need for efficient and user-centric public transportation solutions. It harnesses the power of real-time data and interactive communication to create a seamless and reliable travel experience for passengers. The findings and insights from this project contribute to the advancement of intelligent transportation systems, offering practical solutions to the challenges faced by public transportation networks worldwide.

Comparison of Client-Server Solutions in the Development of Massively Multiplayer Online Games on Unity

This paper presents a critique of the traditional approach used to create a multiplayer game in the Unity real-time interactive application development system, especially in the case of a large number of concurrent users. As a hypothesis, an alternative option, which is not common, but which solves many of the problems of the previous approach, is proposed. Two client-server solutions have been compared for developing multiplayer online games in Unity, and the advantages of both approaches have been described for different cases. A game development architecture using a more up-to-date method is proposed: instead of the Mirror library, a standard toolkit for Unity development, microservices written in Golang are used. We present solid proofs of the preference of the alternative approach, the main advantage of which is the support of modern architecture providing high-speed communication between microservices, supported tests on messaging on different platforms.
 The test results confirm the hypothesis put forth, and we can conclude that the Unity bundle with Golang is more effective for multiplayer video games.
 The article also contains basic methods for debugging multi-threaded application in Golang bundled with Unity game development system and suggests a technological method that allows to get a fast way of data transfer between the client and the server.

Online eye-movement classification with temporal convolutional networks.

The simultaneous classification of the three most basic eye-movement patterns is known as the ternary eye-movement classification problem (3EMCP). Dynamic, interactive real-time applications that must instantly adjust or respond to certain eye behaviors would highly benefit from accurate, robust, fast, and low-latency classification methods. Recent developments based on 1D-CNN-BiLSTM and TCN architectures have demonstrated to be more accurate and robust than previous solutions, but solely considering offline applications. In this paper, we propose a TCN classifier for the 3EMCP, adapted to online applications, that does not require look-ahead buffers. We introduce a new lightweight preprocessing technique that allows the TCN to make real-time predictions at about 500 Hz with low latency using commodity hardware. We evaluate the TCN performance against other two deep neural models: a CNN-LSTM and a CNN-BiLSTM, also adapted to online classification. Furthermore, we compare the performance of the deep neural models against a lightweight real-time Bayesian classifier (I-BDT). Our results, considering two publicly available datasets, show that the proposed TCN model consistently outperforms other methods for all classes. The results also show that, though it is possible to achieve reasonable accuracy levels with zero-length look ahead, the performance of all methods improve with the use of look-ahead information. The codebase, pre-trained models, and datasets are available at https://github.com/elmadjian/OEMC.

Educational 5G Edge Computing: Framework and Experimental Study

Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency.

Delay-Based Shaper With Dynamic Token Bucket Algorithm for Deterministic Networks

To improve the experience of real-time interactive applications based on video and audio, there is a growing demand to realize deterministic networks that can transmit these media with low latency. In recent years, various deterministic network technologies have been studied such as Time Sensitive Networking(TSN). Time slot allocation type schemes such as Time-Aware Shaper (TAS) offer guaranteed delayed determinism and zero-jitter, but suffer from low network accommodation efficiency. Shaper-based techniques such as Asynchronous Traffic Shaping (ATS) provide high network accommodation efficiency and delay determinism inside the network. However, if the input traffic from the sender is bursty, End-to-End delay increases due to the shaping delay caused by the shapers at the edges of the network. In this paper, we propose Delay-Based Shaper (DBS), which dynamically controls the bandwidth while shaping the bursts so that the upper bound of the shaping delay is protected. In addition, we also propose a Dynamic Token Bucket Algorithm (DTBA) that extends the conventional token bucket algorithm to implement DBS. We show that DBS can both shape bursts and comply with the upper bound of shaping delay by comparing the behavior when bursts are input via a conventional shaper. We also demonstrate good End-to-End delay determinism and high network accommodation efficiency by applying DBS to the edge of the network.

On Streaming Codes With Unequal Error Protection

Error control codes for real-time interactive applications such as audio and video streaming must operate under strict delay constraints and be resilient to burst losses. Previous works have characterized optimal streaming codes that guarantee perfect and timely recovery of all source packets when the burst loss is below a certain maximum threshold. In this work, we generalize the notion of streaming codes to the unequal error protection (UEP) setting. Toward this end, we define two natural notions of streaming codes; symbol-level UEP and packet-level UEP. In the symbol-level UEP, the symbols within each source packet have varying recoverability requirements. In the packet-level UEP scenario, packets at even time slots and odd time slots have different recovery guarantees. We discuss practical motivations for both settings and develop coding schemes. We establish optimality or near-optimality guarantees through information-theoretic converse bounds. Simulations over Gilbert and Fritchman channels show that our coding schemes outperform baseline schemes over a wide range of channel parameters.

Computational Modeling: Human Dynamic Model

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

Approximate diffraction modeling for real-time sound propagation simulation.

Convincing simulation of diffraction around obstacles is critical in modeling sound propagation in virtual environments. Due to the computational complexity of large-scale wavefield simulations, ray-based models of diffraction are used in real-time interactive multimedia applications. Among popular diffraction models, the Biot-Tolstoy-Medwin (BTM) edge diffraction model is the most accurate, but it suffers from high computational complexity and hence is difficult to apply in real time. This paper introduces an alternative ray-based approach to approximating diffraction, called Volumetric Diffraction and Transmission (VDaT). VDaT is a volumetric diffraction model, meaning it performs spatial sampling of paths along which sound can traverse the scene around obstacles. VDaT uses the spatial sampling results to estimate the BTM edge-diffraction amplitude response and path length, with a much lower computational cost than computing BTM directly. On average, VDaT matches BTM results within 1-3 dB over a wide range of size scales and frequencies in basic cases, and VDaT can handle small objects and gaps better than comparable state-of-the-art real-time diffraction implementations. A GPU-parallelized implementation of VDaT is shown to be capable of simulating diffraction on thousands of direct and specular reflection path segments in small-to-medium-size scenes, within strict real-time constraints and without any precomputed scene information.

A Methodology for Visualising Growth and Development of the Human Temporal Bone.

This chapter presents a methodological framework which could be used to produce accurate anatomical 3D models and animations of the developing skull, with a focus on the temporal bone. Initial modelling is based on information from core texts and visual references, before optimising these models for use in interactive real-time applications. A series of 3D modelling and animation workflows typically used in computer games and animation industry were tested and compared. Workflows most suitable for the production of a 3D visualisation of the developing temporal bone were documented in detail and used to produce the final 3D models. 3D models of the developing temporal bone were then implemented in an interactive mobile application, which allowed users to explore the 3D models on their Android mobile device and use augmented reality to enhance real-world information. Results of tests conducted in this research suggest that 3D modelling workflows which mimic the processes occurring during development of the temporal bone are most suitable for producing realistic 3D models. Animation workflows tested in this research have all shown potential to produce morphing animations of the developing temporal bone. The significant time required to create deformation setups and animations themselves however suggests that using scripting to automate these workflows would increase their usability in projects with a limited timeframe.

AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks

Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today's vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver's safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks.

Congestion Control using Fuzzy based Adaptive TCP Window Adjustment

Objectives: To design an adaptive window adjustment mechanism for Transmission Control Protocol (TCP) in Mobile Ad hoc NETworks (MANET). Methods/Statistical Analysis: A congestion control using Fuzzy based adaptive TCP window adjustment technique is proposed. In this technique, the bottleneck bandwidth and total round-trip delay metrics are considered as input for the fuzzy logic decision model. The corresponding congestion window adjustment size is returned as the output. The proposed Fuzzy TCP-WA technique is simulated using NS2 and compared with FL-TCP. The proposed TCP-WA technique can be applied in interactive real-time applications like live chat session and File Transfer Protocol (FTP). Simulation results using NS2 suggest that the proposed congestion control technique Findings: educes the packet drop and delay and increases the throughput. Keywords: Bandwidth and Delay Product, Bottleneck Bandwidth, Congestion Control, Congestion Window, Fuzzy Logic, Roundtrip Delay, Transmission Control Protocol (TCP)

A New Dynamic Load Balancing Algorithm for Multi-ROIA

Real-time Online Interactive Application (ROIA) is an emerging class of large-scale distributed application which can support millions of concurrent users around the world. Due to the dynamic changes in the number of concurrent users as well as the uncertainty of user operations, the dynamic load balancing is a key issue for ROIA. However, most of previous works are dedicated to the load balancing in a single ROIA without considering the variety of different type ROIAs. We take the advantage of differences between ROIAs and propose a new load balancing algorithm for multi-ROIA to improve the scalability of ROIA and increase the resource utilization of system. This paper firstly describes the motivation of the new load balancing algorithm, then presents the dynamic load balancing algorithm for multi-ROIA. Finally, the simulation results are also presented to show the efficiency and feasibility of the new algorithm.

Energy-Latency Tradeoff in Ultra-Reliable Low-Latency Communication With Retransmissions

High-fidelity, real-time interactive applications are envisioned with the emergence of the Internet of Things and tactile Internet by means of ultra-reliable low-latency communications (URLLC). Exploiting time diversity for fulfilling the URLLC requirements in an energy efficient manner is a challenging task due to the nontrivial interplay among packet size, retransmission rounds and delay, and transmit power. In this paper, we study the fundamental energy-latency tradeoff in URLLC systems employing incremental redundancy (IR) hybrid automatic repeat request (HARQ). We cast the average energy minimization problem with a finite blocklength (latency) constraint and feedback delay, which is non-convex. We propose a dynamic programming algorithm for energy efficient IR-HARQ optimization in terms of number of retransmissions, blocklength, and power per round. Numerical results show that our IR-HARQ approach could provide around 25% energy saving compared with one-shot transmission (no HARQ).

Enhancing and Measuring the Performance in Software Defined Networking

Software Defined Networking (SDN) is a challenging chapter in today’s networking era. It is a network design approach that engages the framework to be controlled or 'altered' adroitly and halfway using programming applications. SDN is a serious advancement that assures to provide a better strategy than displaying the Quality of Service (QoS) approach in the present correspondence frameworks. SDN etymologically changes the lead and convenience of system instruments using the single high state program. It separates the system control and sending functions, empowering the network control to end up specifically. It provides more functionality and more flexibility than the traditional networks. A network administrator can easily shape the traffic without touching any individual switches and services which are needed in a network. The main technology for implementing SDN is a separation of data plane and control plane, network virtualization through programmability. The total amount of time in which user can respond is called response time. Throughput is known as how fast a network can send data. In this paper, we have design a network through which we have measured the Response Time and Throughput comparing with the Real-time Online Interactive Applications (ROIA), Multiple Packet Scheduler, and NOX.

Emotion Recognition from Facial Expressions using Images with Pose, Illumination and Age Variation for Human-Computer/Robot Interaction

A technique for emotion recognition from facial expressions in images with simultaneous pose, illumination and age variation in real time is proposed in this paper. The basic emotions considered are anger, disgust, happy, surprise, and neutral. Feature vectors that were formed from images from the CMU-MultiPIE database for pose and illumination were used for training the classifier. For real-time implementation, Raspberry Pi II was used, which can be placed on a robot to recognize emotions in interactive real-time applications. The proposed method includes face detection using Viola Jones Haar cascade, Active Shape Model (ASM) for feature extraction, and AdaBoost for classification in real- time. Performance of the proposed method was validated in real time by testing with subjects from different age groups expressing basic emotions with varying pose and illumination. 96% recognition accuracy at an average time of 120 ms was obtained. The results are encouraging, as the proposed method gives better accuracy with higher speed compared to existing methods from the literature. The major contribution and strength of the proposed method lie in marking suitable feature points on the face, its speed and invariance to pose, illumination and age in real time.

Modeling the Scalability of Real-Time Online Interactive Applications on Clouds

We address the scalability of Real-Time Online Interactive Applications (ROIA) on Clouds. Popular examples of ROIA include, e.g., multi-player online computer games, simulation-based e-learning, and training in real-time virtual environments. Cloud computing allows to combine ROIA’s high demands on QoE (Quality of Experience) with the requirement of efficient and economic utilization of computation and network resources. We propose a generic scalability model for ROIA on Clouds that monitors the application performance at runtime and predicts the benefit–cost ratio of load-balancing decisions: by weighting the potential benefits of particular load-balancing actions against their time and resources overhead, our model recommends whether and how often to redistribute workload or add/remove Cloud resources when the number of users changes. We describe how the scalability is modeled w.r.t. two kinds of resources – computation (CPU) and communication (network) – and how we combine these models together. We experimentally evaluate the quality of our combined model using a challenging multi-player shooter game as a use case.

Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application.