Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

A Modular and Open-Source Framework for Virtual Reality Visualisation and Interaction in Bioimaging

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. The advent of new imaging technologies, such as lightsheet microscopy, has resulted in the users being confronted with an ever-growing amount of data, with even terabytes of imaging data created within a day. With the possibility of gentler and more high-performance imaging, the spatiotemporal complexity of the model systems or processes of interest is increasing as well. Visualisation is often the first step in making sense of this data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualisations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we present scenery, a modular and extensible visualisation framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features, and discuss its use with VR/AR hardware and in distributed rendering. In addition to the visualisation framework, we present a series of case studies, where scenery can provide tangible benefit in developmental and systems biology: With Bionic Tracking, we demonstrate a new technique for tracking cells in 4D volumetric datasets via tracking eye gaze in a virtual reality headset, with the potential to speed up manual tracking tasks by an order of magnitude. We further introduce ideas to move towards virtual reality-based laser ablation and perform a user study in order to gain insight into performance, acceptance and issues when performing ablation tasks with virtual reality hardware in fast developing specimen. To tame the amount of data originating from state-of-the-art volumetric microscopes, we present ideas how to render the highly-efficient Adaptive Particle Representation, and finally, we present sciview, an ImageJ2/Fiji plugin making the features of scenery available to a wider audience.:Abstract Foreword and Acknowledgements Overview and Contributions Part 1 - Introduction 1 Fluorescence Microscopy 2 Introduction to Visual Processing 3 A Short Introduction to Cross Reality 4 Eye Tracking and Gaze-based Interaction Part 2 - VR and AR for System Biology 5 scenery — VR/AR for Systems Biology 6 Rendering 7 Input Handling and Integration of External Hardware 8 Distributed Rendering 9 Miscellaneous Subsystems 10 Future Development Directions Part III - Case Studies C A S E S T U D I E S 11 Bionic Tracking: Using Eye Tracking for Cell Tracking 12 Towards Interactive Virtual Reality Laser Ablation 13 Rendering the Adaptive Particle Representation 14 sciview — Integrating scenery into ImageJ2 & Fiji Part IV - Conclusion 15 Conclusions and Outlook Backmatter & Appendices A Questionnaire for VR Ablation User Study B Full Correlations in VR Ablation Questionnaire C Questionnaire for Bionic Tracking User Study List of Tables List of Figures Bibliography Selbstständigkeitserklärun

Performance evaluation of a WiMAX system with relay-assisted scheduling

Työssä tutkitaan IEEE 802.16j standardin määrittelemän järjestelmän suorituskykyä ja keskitytään ei-raaliaikaisiin palveluihin ja verkkoon, jossa verkon topologiaan kuuluu tukiasemien lisäksi välittimiä. Päätelaitteet ryhmitellään kahteen luokkaan sen mukaan, ovatko ne suoraan yhteydessä tukiasemaan, vai onko yhteys muodostettu välittimen avulla. Tukiasemassa toimiva skedulointialgoritmi jakaa lähetysresursseja eri käyttäjien kesken hetkellisen kanavatiedon perusteella. Työssä on rakennettu simulaattori, jonka avulla voidaan tutkia erilaisten järjestelmäparametrien, skedulointialgoritmien ja välittimien vaikutusta järjestelmän suorituskykyyn.An IEEE 802.16j based system with relay-assisted scheduling performance is evaluated in terms of the requirements set by the standard. We focus on the non-real-time (NRT) services in the downlink of a cellular network with two-hop relay transmission. Mobile stations (MSs) are grouped into the base station (BS) region and the relay station (RS) region according to their mean path losses. MSs in the BS region are connected directly to BS while MSs in the RS region receive packet from BS directly or indirectly via RS based on the CSI (Channel State Information). The RS operates in either the amplify-and-forward (AF) mode or decode-and-forward (DF) mode. We propose two relay-assisted scheduling schemes, in which the RS assists the BS in its scheduling decision and therefore we make it possible for the BS to exploit CSI on the access links without those of the relay links from all the users directly. A large amount of feedback overhead is avoided. Our objective is to explore the performance of DF and AF relays in these two different scheduling schemes. Moreover, we consider a friendly graphical user interface to realize user interaction and facilitate the investigation of the effect of different parameters to the system performance

Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks

Power optimization, network coding and decision fusion in multi-access relay networks

Multi-access relay (MAR) assisted communication appears in various applications such as hierarchical wireless sensor networks (WSN), two-way relay channels (TWRC) etc. since it provides a high speed and reliable communication with considerably large coverage. In this thesis, we develop the optimal power allocation, network coding and information fusion techniques to improve the performance of MAR channel by considering certain criterion (e.g., minimizing the average symbol error rate (SER) or maximizing the average sum-rate. For this purpose, we first derive optimal information fusion rules for hierarchical WSNs with the use of complete channel state information (CSI) and the partial CSI using channel statistics (CS) with the exact phase information. Later, we investigate the optimization of the MAR channel that employs complex field network coding (CFNC), where we have used two different metrics during the optimization: achievable sum rate and SER bound of the network under the assumption of receiver CSI. After that, we formulate the optimal power allocation problem to maximize the achievable sum rate of the MAR with decode and forward relaying while considering fairness among users in terms of their average achievable information rates under the constraints on the total power and network geometry. We show that this problem is non-convex and nonlinear, and obtain an analytical solution by properly dividing parameter space into four regions. Then, we derive an average SER bound for the CFNC coded MAR channel and aim to jointly optimize the CFNC and the relay power by minimizing SER bound under the total power constraint, which we prove as a convex program that cannot be solved analytically since the Karush-Khun-Tucker (KKT) conditions result in highly nonlinearity equations. Following that, we devise an iterative method to obtain SER optimal solutions which uses the information theoretical rate optimal analytical solution during the initialization and we show that this speeds up the convergence of the iterative method as compared to equal power allocation scheme. Next, we integrate CFNC into WSNs that operate over non-orthogonal communication channel, and derive optimal fusion rule accordingly, combine the SER bound minimization and the average rate-fairness ideas to come up with an approximate analytical method to jointly optimize CFNC and the relay power. Simulation results show that the proposed methods outperform the conventional methods in terms of the detection probability, achievable average sum-rate or average SER

Integration of hybrid networks, AI, Ultra Massive-MIMO, THz frequency, and FBMC modulation toward 6g requirements : A Review

The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1-ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1-GHz, and a capacity of 106 up to Mbps/m2. Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output ( μm -MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research