Design of a collaborative system for real time haptic feedback in distributed virtual environments over IP networks

This paper presents an investigation into system architectures for real time haptic feedback in distributed virtual environments over IP switched network. Network impairments such as time delay, jitter and packet loss have a different impact on remote haptic collaborations than the traditional master-slave tele-operation. A hybrid architecture has been proposed and developed to address the challenges in the new use scenario. Experiments have been conducted to show the performance of this architecture in comparison with the currently available time delay compensation algorithms, i.e. dead reckoning. A set of network Quality of Service (QoS) parameters for these types of haptic collaborative systems is obtained. Findings of the study are presented in the paper with recommendations for developing systems that support haptic collaboration

UMA ARQUITETURA DE SOFTWARE PARA O MORFEU: APOIANDO A REALIZAÇÃO DE ARQUITETURAS PEDAGÓGICAS EM ESPAÇOS VIRTUAIS COLABORATIVOS.

As lacunas tecnológicas no apoio as atividades colaborativas possibilitam à criação de novas propostas para atender à demanda por suporte tecnológico nas atividades a distância. Este trabalho apresenta uma arquitetura de software, baseado da proposta do MOrFEu, que favorece à criação e a organização flexível de espaços virtuais colaborativos. Entre as principais características desta arquitetura destacam-se a flexibilidade do apoio a colaboração pelas formas diferenciadas de coordenar as interações e organizar as produções, individuais e coletivas, tendo como referência espaços de autoria reorganizáveis e flexíveis. Por fim, foi realizado um estudo de caso, utilizando um protótipo de software, na avaliação do suporte tecnológico no atendimento aos requisitos das atividades de comunicação, cooperação e principalmente de coordenação da Arquitetura Pedagógica Debate de Teses

Addressing training data sparsity and interpretability challenges in AI based cellular networks

To meet the diverse and stringent communication requirements for emerging networks use cases, zero-touch arti cial intelligence (AI) based deep automation in cellular networks is envisioned. However, the full potential of AI in cellular networks remains hindered by two key challenges: (i) training data is not as freely available in cellular networks as in other fields where AI has made a profound impact and (ii) current AI models tend to have black box behavior making operators reluctant to entrust the operation of multibillion mission critical networks to a black box AI engine, which allow little insights and discovery of relationships between the configuration and optimization parameters and key performance indicators. This dissertation systematically addresses and proposes solutions to these two key problems faced by emerging networks. A framework towards addressing the training data sparsity challenge in cellular networks is developed, that can assist network operators and researchers in choosing the optimal data enrichment technique for different network scenarios, based on the available information. The framework encompasses classical interpolation techniques, like inverse distance weighted and kriging to more advanced ML-based methods, like transfer learning and generative adversarial networks, several new techniques, such as matrix completion theory and leveraging different types of network geometries, and simulators and testbeds, among others. The proposed framework will lead to more accurate ML models, that rely on sufficient amount of representative training data. Moreover, solutions are proposed to address the data sparsity challenge specifically in Minimization of drive test (MDT) based automation approaches. MDT allows coverage to be estimated at the base station by exploiting measurement reports gathered by the user equipment without the need for drive tests. Thus, MDT is a key enabling feature for data and artificial intelligence driven autonomous operation and optimization in current and emerging cellular networks. However, to date, the utility of MDT feature remains thwarted by issues such as sparsity of user reports and user positioning inaccuracy. For the first time, this dissertation reveals the existence of an optimal bin width for coverage estimation in the presence of inaccurate user positioning, scarcity of user reports and quantization error. The presented framework can enable network operators to configure the bin size for given positioning accuracy and user density that results in the most accurate MDT based coverage estimation. The lack of interpretability in AI-enabled networks is addressed by proposing a first of its kind novel neural network architecture leveraging analytical modeling, domain knowledge, big data and machine learning to turn black box machine learning models into more interpretable models. The proposed approach combines analytical modeling and domain knowledge to custom design machine learning models with the aim of moving towards interpretable machine learning models, that not only require a lesser training time, but can also deal with issues such as sparsity of training data and determination of model hyperparameters. The approach is tested using both simulated data and real data and results show that the proposed approach outperforms existing mathematical models, while also remaining interpretable when compared with black-box ML models. Thus, the proposed approach can be used to derive better mathematical models of complex systems. The findings from this dissertation can help solve the challenges in emerging AI-based cellular networks and thus aid in their design, operation and optimization

Low Latency Rendering with Dataflow Architectures

The research presented in this thesis concerns latency in VR and synthetic environments. Latency is the end-to-end delay experienced by the user of an interactive computer system, between their physical actions and the perceived response to these actions. Latency is a product of the various processing, transport and buffering delays present in any current computer system. For many computer mediated applications, latency can be distracting, but it is not critical to the utility of the application. Synthetic environments on the other hand attempt to facilitate direct interaction with a digitised world. Direct interaction here implies the formation of a sensorimotor loop between the user and the digitised world - that is, the user makes predictions about how their actions affect the world, and see these predictions realised. By facilitating the formation of the this loop, the synthetic environment allows users to directly sense the digitised world, rather than the interface, and induce perceptions, such as that of the digital world existing as a distinct physical place. This has many applications for knowledge transfer and efficient interaction through the use of enhanced communication cues. The complication is, the formation of the sensorimotor loop that underpins this is highly dependent on the fidelity of the virtual stimuli, including latency. The main research questions we ask are how can the characteristics of dataflow computing be leveraged to improve the temporal fidelity of the visual stimuli, and what implications does this have on other aspects of the fidelity. Secondarily, we ask what effects latency itself has on user interaction. We test the effects of latency on physical interaction at levels previously hypothesized but unexplored. We also test for a previously unconsidered effect of latency on higher level cognitive functions. To do this, we create prototype image generators for interactive systems and virtual reality, using dataflow computing platforms. We integrate these into real interactive systems to gain practical experience of how the real perceptible benefits of alternative rendering approaches, but also what implications are when they are subject to the constraints of real systems. We quantify the differences of our systems compared with traditional systems using latency and objective image fidelity measures. We use our novel systems to perform user studies into the effects of latency. Our high performance apparatuses allow experimentation at latencies lower than previously tested in comparable studies. The low latency apparatuses are designed to minimise what is currently the largest delay in traditional rendering pipelines and we find that the approach is successful in this respect. Our 3D low latency apparatus achieves lower latencies and higher fidelities than traditional systems. The conditions under which it can do this are highly constrained however. We do not foresee dataflow computing shouldering the bulk of the rendering workload in the future but rather facilitating the augmentation of the traditional pipeline with a very high speed local loop. This may be an image distortion stage or otherwise. Our latency experiments revealed that many predictions about the effects of low latency should be re-evaluated and experimenting in this range requires great care

Experimental Study of Haptic Interaction in Distributed Virtual Environments

International audienceThis paper reports an on-going work in which technologies are being developed to support multisensory communication for distributed virtual environments. A series of experiments for investigating the requirements of distant haptic interaction on IP-based networks have been conducted by using the custom-built experimental platforms. Existing research has identified the challenges of using haptics in distributed virtual environments and proposed several approaches to overcome these problems. However, there is still a need to investigate the network issues systematically in order to determine the allowable latency, jitter and packet loss for haptic data communication. This is one of our research objectives. In this paper, we present the details of the experimental studies that have been conducted so far

An Asynchronous Architecture to Manage Communication, Display, and User Interaction in Distributed Virtual Environments

In Distributed Virtual Environments, each machine equally handles the communications over the network, provides the user with a view of the world, and processes her requests. A major issue is to ensure that the network communication does not hinder the interactivity between the machine and the user. In this paper, we present a program designed to achieve this goal, based on tools rarely used in this area

Scalable Data Management Using User-Based Caching and Prefetching in Distributed Virtual Environments

For supporting real-time interaction in distributed virtual environments (DVEs), it is common to replicate virtual world data at clients from the server. For efficient replication, two schemes are used together in general-prioritized transfer of objects and a caching and prefetching technique. Existing caching and prefetching approaches for DVEs exploit spatial relationship based on distances between a user and objects. However, spatial relationship fails to determine which types of objects are more important to an individual user, not reflecting user's interests. We propose a scalable data management scheme using user-based caching and prefetching exploiting the object's access priority generated from spatial distance and individual user's interest in objects in DVEs. We also further improve the cache hit rate by incorporating user's navigation behavior into the spatial relationship between a user and the objects in the cache. By combining the interest score and popularity score of an object with the spatial relationship, we improve the performance of caching and prefetching since the interaction locality between the user and objects are reflected in addition to spatial locality. The simulation results show that the proposed scheme outperforms the hit rate of existing caching and prefetching by 10% on average when the cache size is set to basic cache size, the size of expected number of objects included in the user's viewing range