This paper presents the development of self-powered breath monitoring sensors using triboelectric nanogenerators (TENGs) technique fabricated from electro spun polycaprolactone (PCL) and cellulose acetate (CA) fibers. The CA was chemically modified with 0.2M NaOH, achieving a maximum open-circuit voltage (Voc) of 14.3 V, short-circuit current (Isc) of$1.67 \mu \mathrm{A}$and charge (Qsc) of 6.61 nC. Incorporating 3 % chitosan into PCL further enhanced the sensor's performance, yielding a Voc of 20.8 V, Isc of 3.178 µA, and Qsc of 9.9 nC. The sensor demonstrated excellent sensitivity, with a linear response and a sensitivity of 2.209 V/kPa across a pressure range of 0.625-12.5 kPa. It also exhibited rapid response times of 34–36 ms and recovery times of 24–26 ms. The optimized materials and sensor configuration show promise for continuous real-time breath monitoring. This innovation provides a significant advancement in wearable health monitoring systems, enabling early detection of respiratory illnesses and chronic disease management.

Recommendation systems have become an indispensable part of daily life. Social recommendation systems, which utilize social relationships and past behaviors to infer users’ preferences, have gained popularity in recent years. Exploring the inherent characteristics implied by higher-order relationships offers a new approach to social recommendation. However, it is challenging due to sparse social networks, influence heterogeneity, and noisy feedback. In this article, we propose a Heterogeneous Meta-path Graph Learning model for Higher-order Social Recommendation (HEAL). Within HEAL, we introduce a heterogeneous graph in social recommendation and utilize a meta-path-guided random walk to generate higher-order relationships. By encoding higher-order structures and semantics along different meta-graphs, HEAL can mitigate the limitation of data sparsity. Moreover, HEAL exploits aspect-aware and semantic-aware attentions to adaptively propagate and aggregate useful features from different meta-neighbors and higher-order relations. These attention-based aggregation layers allow HEAL to suppress the heterogeneity of social influences. Furthermore, HEAL adopts contrastive learning as a supplemental task to the recommendation task by maximizing the consistency between the self-discriminating objectives. This auxiliary task enables the model to learn more differentiated representations, further reducing its sensitivity to noisy feedback. We evaluate the performance of HEAL through extensive experiments on public datasets. The results demonstrate that leveraging higher-order relations can enhance the quality of social recommendations by better capturing the complexity and diversity of users’ preferences and interactions.

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and graph structure, especially on label-scarce or attribute-missing data. In this article, we propose a novel framework, called graph coneighbor neural network (GCoNN), for node classification. It is composed of two modules: GCoNN$_{\Gamma}$and GCoNN$_{\mathop{\Gamma}\limits^{\circ}}$. GCoNN$_{\Gamma}$is trained to establish the fundamental prototype for attribute learning on labeled data, while GCoNN$_{\mathring{\Gamma}}$learns neighbor dependence on transductive data through pseudolabels generated by GCoNN$_{\Gamma}$. Next, GCoNN$_{\Gamma}$is retrained to improve integration of node attribute and neighbor structure through feedback from GCoNN$_{\mathring{\Gamma}}$. GCoNN tends to convergence iteratively using such an approach. From a theoretical perspective, we analyze this iteration process from a generalized expectation–maximization (GEM) framework perspective which optimizes an evidence lower bound (ELBO) by amortized variational inference. Empirical evidence demonstrates that the state-of-the-art performance of the proposed approach outperforms other methods. We also apply GCoNN to brain functional networks, the results of which reveal response features across the brain which are physiologically plausible with respect to known language and visual functions.

Attention-deficit hyperactivity disorder (ADHD) is a prevalent psychological disorder characterized by attention deficits and high impulsivity, impacting both adults and children. This study aims to assess the effectiveness of task-related electroencephalography (EEG) and resting-state EEG in distinguishing adult patients with ADHD from healthy controls. Machine learning techniques are employed to classify the patients’ status based on EEG features. The primary objective of this investigation is to determine whether the classification performance of task-based EEG data recorded during a stop-signal task recruiting inhibitory processes outperforms that of resting-state EEG data. We hypothesize that task-based EEG data contains valuable biomarkers related to inhibitory control that can be utilized to detect ADHD, whereas resting-state EEG data does not possess such useful biomarkers.

The P300 speller is a widely used application in brain-computer interface research. It has been demonstrated that the P300 speller can serve as a neurofeedback training tool for attention enhancement by gradually increasing the difficulty of the spelling task. This adaptive approach makes it harder for users to spell words correctly, encouraging them to improve their attention to counteract the increasing difficulty. Therefore, the adaptive P300 speller has the potential to serve as a treatment option for children with ADHD, elderly patients with dementia, and as a cognitive enhancement tool for healthy adults. However, the training length, including setup time, needs to be quick to ensure user acceptability. This study investigates the effect of different electrode subsets on P300 speller performance, with and without the use of the xDAWN spatial filter. Results indicate that the xDAWN spatial filter can improve performance with many electrodes but can decrease results with fewer than eight electrodes. For scenarios where near-perfect performance is crucial and many electrodes are available, a set of 16 electrodes with the xDAWN spatial filter is recommended. For situations where cost and setup time are a concern and lower performances are acceptable, using six electrodes without the spatial filter were found to be sufficient.

Temporal link prediction (TLP) is among the most important graph learning tasks, capable of predicting dynamic, time-varying links within networks. The key problem of TLP is how to explore potential link-evolving tendency from the increasing number of links over time. There exist three major challenges toward solving this problem: temporal nonlinear sparsity, weak serial correlation, and discontinuous structural dynamics. In this article, we propose a novel transfer learning model, called DNformer, to predict temporal link sequence in dynamic networks. The structural dynamic evolution is sequenced into consecutive links one by one over time to inhibit temporal nonlinear sparsity. The self-attention of the model is used to capture the serial correlation between the input and output link sequences. Moreover, our structural encoding is designed to obtain changing structures from the consecutive links and to learn the mapping between link sequences. This structural encoding consists of two parts: the node clustering encoding of each link and the link similarity encoding between links. These encodings enable the model to perceive the importance and correlation of links. Furthermore, we introduce a measurement of structural similarity in the loss function for the structural differences of link sequences. The experimental results demonstrate that our model outperforms other state-of-the-art TLP methods such as Transformer, TGAT, and EvolveGCN. It achieves the three highest AUC and four highest precision scores in five different representative dynamic networks problems.

Due to an aging population and a greater prevalence of diseases like dementia and stroke, there is a growing need for non-invasive and easy to use cognitive training and rehabilitation. Brain-computer interfaces are emerging as a means for such non-invasive cognitive training. This paper proposes a phenomenological model of performance in a P300-speller task, which takes task difficulty into account. The model can be used for simulation purposes and to inform how to adapt the task difficulty in cognitive training or rehabilitation. Inspired by the phenomena of slacking and neural plasticity, a nonlinear autoregressive model with exogenous inputs was trained on, and validated against, the Akimpech dataset, which contains EEG data from healthy subjects who completed several runs of a P300-speller task. The model can simulate or predict the performance in each run of the task. An average $\mathrm{R}^{2}$ score of 94.15% and 94.11% was achieved on validation data for simulation and 1-step ahead prediction, respectively. The ability of the model to generalize to other experimental setups will be evaluated in the future.

Detecting self-organized coalitions from functional networks is one of the most important ways to uncover functional mechanisms in the brain. Determining these raises well-known technical challenges in terms of scale imbalance, outliers and hard-examples. In this article, we propose a novel self-adaptive skeleton approach to detect coalitions through an approximation method based on probabilistic mixture models. The nodes in the networks are characterized in terms of robust k -order complete subgraphs ( k -clique ) as essential substructures. The k -clique enumeration algorithm quickly enumerates all k -cliques in a parallel manner for a given network. Then, the cliques, from max -clique down to min -clique, of each order k , are hierarchically embedded into a probabilistic mixture model. They are self-adapted to the corresponding structure density of coalitions in the brain functional networks through different order k . All the cliques are merged and evolved into robust skeletons to sustain each unbalanced coalition by eliminating outliers and separating overlaps. We call this the k -CLIque Merging Evolution (CLIME) algorithm. The experimental results illustrate that the proposed approaches are robust to density variation and coalition mixture and can enable the effective detection of coalitions from real brain functional networks. There exist potential cognitive functional relations between the regions of interest in the coalitions revealed by our methods, which suggests the approach can be usefully applied in neuroscientific studies.

This is a guide on how to obtain and deploy the artifact and the expected results.

Motor imagery-based brain–computer interface (MI-BCI) has been proposed as a rehabilitation tool to facilitate motor recovery in stroke. However, the calibration of a BCI system is a time-consuming and fatiguing process for stroke patients, which leaves reduced time for actual therapeutic interaction. Studies have shown that passive movement (PM) (i.e., the execution of a movement by an external agency without any voluntary motions) and motor imagery (MI) (i.e., the mental rehearsal of a movement without any activation of the muscles) induce similar EEG patterns over the motor cortex. Since performing PM is less fatiguing for the patients, this paper investigates the effectiveness of calibrating MI-BCIs from PM for stroke subjects in terms of classification accuracy. For this purpose, a new adaptive algorithm called filter bank data space adaptation (FB-DSA) is proposed. The FB-DSA algorithm linearly transforms the band-pass-filtered MI data such that the distribution difference between the MI and PM data is minimized. The effectiveness of the proposed algorithm is evaluated by an offline study on data collected from 16 healthy subjects and 6 stroke patients. The results show that the proposed FB-DSA algorithm significantly improved the classification accuracies of the PM and MI calibrated models ( p < 0.05). According to the obtained classification accuracies, the PM calibrated models that were adapted using the proposed FB-DSA algorithm outperformed the MI calibrated models by an average of 2.3 and 4.5 % for the healthy and stroke subjects respectively. In addition, our results suggest that the disparity between MI and PM could be stronger in the stroke patients compared to the healthy subjects, and there would be thus an increased need to use the proposed FB-DSA algorithm in BCI-based stroke rehabilitation calibrated from PM.

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of$2.6 - 7.4\times$and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.

Inertial measurement units (IMUs) may be used during exercise performance to assess form and technique. To maximise practicality and minimise cost a single-sensor system is most desirable. This study sought to investigate whether a single lumbar-worn IMU is capable of identifying seven commonly observed squatting deviations. Twenty-two volunteers (18 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2 kg) performed the squat exercise correctly and with 7 induced deviations. IMU signal features were extracted for each condition. Statistical analysis and leave one subject out classifier evaluation were used to assess the ability of a single sensor to evaluate performance. Binary level classification was able to distinguish between correct and incorrect squatting performance with a sensitivity of 64.41%, specificity of 88.01% and accuracy of 80.45%. Multi-label classification was able to distinguish between specific squat deviations with a sensitivity of 59.65%, specificity of 94.84% and accuracy of 56.55%. These results indicate that a single IMU can successfully discriminate between squatting deviations. A larger data set must be collected and more complex classification techniques developed in order to create a more robust exercise analysis IMU-based system.

Outer joins are ubiquitous in databases and big data systems. The question of how best to execute outer joins in large parallel systems is particularly challenging as real world datasets are characterized by data skew leading to performance issues. Although skew handling techniques have been extensively studied for inner joins, there is little published work solving the corresponding problem for parallel outer joins. Conventional approaches to this problem such as ones based on hash redistribution often lead to load balancing problems while duplication-based approaches incurs significant overhead in terms of network communication. In this paper, we propose a new algorithm, query with counters (QC), for directly handling skew in outer joins on distributed architectures. We present an efficient implementation of our approach based on the asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate the performance of our approach on a cluster of 192 cores (16 nodes) and datasets of 1 billion tuples with different skew. Experimental results show that our method is scalable and, in cases of high skew, faster than the state-of-the-art.

The performance of joins in parallel database management systems is critical for data intensive operations such as querying. Since data skew is common in many applications, poorly engineered join operations result in load imbalance and performance bottlenecks. State-of-the-art methods designed to handle this problem offer significant improvements over naive implementations. However, performance could be further improved by removing the dependency on global skew knowledge and broadcasting. In this paper, we propose PRPQ (partial redistribution & partial query), an efficient and robust join algorithm for processing large-scale joins over distributed systems. We present the detailed implementation and a quantitative evaluation of our method. The experimental results demonstrate that the proposed PRPQ algorithm is indeed robust and scalable under a wide range of skew conditions. Specifically, compared to the state-of-art PRPD method, we achieve 16% - 167% performance improvement and 24% - 54% less network communication under different join workloads.

High-performance analytical data processing systems often run on servers with large amounts of memory. A common data structure used in such environment is the hash tables. This paper focuses on investigating efficient parallel hash algorithms for processing large-scale data. Currently, hash tables on distributed architectures are accessed one key at a time by local or remote threads while shared-memory approaches focus on accessing a single table with multiple threads. A relatively straightforward “bulk-operation” approach seems to have been neglected by researchers. In this work, using such a method, we propose a high-level parallel hashing framework, Structured Parallel Hashing, targeting efficiently processing massive data on distributed memory. We present a theoretical analysis of the proposed method and describe the design of our hashing implementations. The evaluation reveals a very interesting result - the proposed straightforward method can vastly outperform distributed hashing methods and can even offer performance comparable with approaches based on shared memory supercomputers which use specialized hardware predicates. Moreover, we characterize the performance of our hash implementations through extensive experiments, thereby allowing system developers to make a more informed choice for their high-performance applications.

Academia and industry are investigating novel approaches for processing vast amounts of data coming from enterprises, the Web, social media and sensor readings in an area that has come to be known as Big Data. Logic programming has traditionally focused on complex knowledge structures/programs. The question arises whether and how it can be applied in the context of Big Data. In this paper, we study how the well-founded semantics can be computed over huge amounts of data using mass parallelization. Specifically, we propose and evaluate a parallel approach based on the X10 programming language. Our experiments demonstrate that our approach has the ability to process up to 1 billion facts within minutes.

Consistency is a key requirement of networked multiplayer computer games. Several methods exist that strive to reduce network traffic in an attempt to maintain an acceptable level of consistency. Currently, these methods update an entity's states based on measures of its spatial and temporal inconsistencies. However, these measures do not, in general, consider inconsistencies associated with the entity's interactions with other environmental objects, which can potentially lead to significance differences in what users see and experience. This is particularly evident in physics-aware, peer-to-peer distributed interactive applications such as networked multiplayer computer games. Thus, this paper proposes a novel entity state update technique for such applications. In doing so, the concept of a physics-consistency-cost is introduced. The proposed technique consists of a dynamic authority scheme for shared objects and an enhanced physics-aware dead reckoning model with an adaptive error threshold. The former places a bound on the overall inconsistency present in shared objects, while the latter minimises the instantaneous inconsistency during users' interactions with shared objects. The performance of the proposed entity state update mechanism is validated through simulation, the results of which are presented and discussed within.

This paper presents a dynamic, rate-based method that regulates consistency, in response to changes in the underlying network, for client-server-based Multiplayer Computer Games. It operates on the premise that, as the network conditions between client and server changes, so too does the inconsistency from the client's viewpoint. Hence, adapting the rate of updates between the server and the client can help maintain an acceptable level of consistency.

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a "ground truth" signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this "ground truth," together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare "time bomb" has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

Inconsistency arises across a Distributed Virtual Environment due to network latency induced by state changes communications. Predictive Contract Mechanisms (PCMs) combat this problem through reducing the amount of messages transmitted in return for perceptually tolerable inconsistency. To date there are no methods to quantify the efficiency of PCMs in communicating this reduced state information. This article presents an approach derived from concepts in information theory for a deeper understanding of PCMs. Through a comparison of representative PCMs, the worked analysis illustrates interesting aspects of PCMs operation and demonstrates how they can be interpreted as a form of lossy information compression.

Various Information Management techniques have been developed to help maintain a consistent shared virtual world in a Networked Virtual Environment. However, such techniques have to be carefully adapted to the application state dynamics and the underlying network. This work presents a novel framework that minimizes inconsistency by optimizing bandwidth usage to deliver useful information. This framework measures the state evolution using an information model and dynamically switches extrapolation models and the packet rate to make the most information-efficient usage of the available bandwidth. The results shown demonstrate that this approach can help optimize consistency under constrained and time-varying network conditions.

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user's overall performance over time can be assessed by a clinician.

A key factor determining the success of a Distributed Interactive Application (DIA) is the maintenance of a consistent shared virtual world. To help maintain consistency, a number of Information Management techniques have been developed. However, unless carefully tuned to the underlying network, they can negatively impact on consistency. This work presents a novel adaptive algorithm for optimizing consistency by maximizing available bandwidth usage in DIAs. This algorithm operates by estimating bandwidth from trends in network latency, and modifying data transmission rates to match the estimated value. Results presented within demonstrate that this approach can help optimise consistency levels in a DIA.

Consistency maintenance in distributed interactive applications (DIAs) is subjected to network characteristics such as limited bandwidth and latency. Predictive contract mechanisms are techniques that compensate for the effect of network latency by extrapolating future entity states from historical records. These approaches trade inconsistency within human perceptual limits for reduced network traffic and latency. This paper explores the use of an information metric to analyse the effect of network latency on remote consistency and thus establishes a novel framework to model predictive contract mechanisms as a lossy information sharing process. Such a perspective facilitates a novel explicit analysis of the trade-off between network traffic and inconsistency.

One of the ongoing challenges for Distributed Interactive Applications (DIAs) is balancing the quality of service delivered to the end user with the operational costs involved. In particular the resultant network traffic should be minimized without affecting the end user experience where possible. This article proposes the use of remote feedback as a method of maintaining a desired consistency level within a peer-to-peer DIA. Though many existing techniques attempt to maintain consistency within a DIA, they operate in an open-loop manner and do not take error introduced into the system due to transmission delay into consideration. The goal of the work presented in this article is to transform this open-loop scheme into a closed-loop control system utilizing feedback from the remote users. By incorporating remote error into the systems update paradigm, the Protocol Data Unit (PDU) transmission rate can be dynamically altered to reflect changing network conditions. The performance of the resultant closed-loop control system is presented within.

Collaboration within a distributed interactive application (DIA) requires that a high level of consistency be maintained between remote hosts. However, this can require large amounts of network resources, which can negatively affect the scalability of the application, and also increase network latency. Predictive models, such as dead reckoning, provide a sufficient level of consistency, whilst reducing network requirements. Dead reckoning traditionally uses a spatial error threshold metric to operate. In previous work, it was shown how the use of the spatial threshold could result in potentially unbounded local absolute inconsistency. To remedy this, a novel time-space threshold was proposed, that placed bounds on local absolute inconsistency. However, use of the time-space threshold could result in unacceptably large spatial inconsistency. A hybrid approach that combined both error threshold measures was then shown to place bounds on both levels of inconsistency. However, choosing suitable threshold values for use within the hybrid scheme has been problematic, as no direct comparisons can be made between the two threshold metrics. In this paper, a novel comparison scheme is proposed. Under this approach, an error threshold look-up table is generated, based on entity speed and equivalent inconsistency measures. Using this look-up table, it is shown how the performance of comparable thresholds is equal on average, from the point of view of network packet generation. These error thresholds are then employed in a hybrid threshold scheme, which is shown to improve overall consistency in comparison to the previous solution of simply using numerically equal threshold values

Dead reckoning is widely employed as an entity update packet reduction technique in distributed interactive applications (DIAs). Such techniques reduce network bandwidth consumption and thus limit the effects of network latency on the consistency of networked simulations. A key component of the dead reckoning method is the underlying error threshold metric, as this directly determines when an entity update packet is to be sent between local and remote users. The most common metric is the spatial threshold, which is simply based on the distance between a local user's actual position and their predicted position. Other, recently proposed, metrics include the time-space threshold and the hybrid threshold, both of which are summarised within. This paper investigates the issue of user movement in relation to dead reckoning and each of the threshold metrics. In particular the relationship between the curvature of movement, the various threshold metrics and absolute consistency is studied. Experimental live trials across the Internet allow a comparative analysis of how users behave when different threshold metrics are used with varying degrees of curvature. The presented results provide justification for the use of a hybrid threshold approach when dead reckoning is employed in DIAs

A distributed Monte Carlo simulation which models the propagation of light through tissue has been developed. It allows for improved calibration of medical imaging devices for investigating tissue oxygenation in the white matter of the cerebral cortex. The application can distribute the simulation over an unbounded number of processors in parallel. We have found that this application is highly parallelisable resulting in up to 91% efficiency at 60 processors running on a homogeneous Java distributed system. A distributed system with 150 heterogeneous processors was used to simulate the paths of photons in a brain tissue model. We found that the source illumination footprint has an effect on the distribution of photons in the head and that lasers do produce a small beam in a highly scattering medium. This application will help researchers to improve the accuracy of their experiments

Collaboration and competition are important factors of Networked Virtual Environments (NVE). Both require a certain level of consistency in order for the interaction to be fruitful and compelling. However, finite network bandwidth and communication delay are key factors affecting this aspect of interactivity. A popular method in their mitigation for dynamic entities is the IEEE DIS standard dead reckoning mechanism [1].

Human-to-human interaction across distributed applications requires that sufficient consistency be maintained among participants in the face of network characteristics such as latency and limited bandwidth. Techniques and approaches for reducing bandwidth usage can minimize network delays by reducing the network traffic and therefore better exploiting available bandwidth. However, these approaches induce inconsistencies within the level of human perception. Dead reckoning is a well-known technique for reducing the number of update packets transmitted between participating nodes. It employs a distance threshold for deciding when to generate update packets. This paper questions the use of such a distance threshold in the context of absolute consistency and it highlights a major drawback with such a technique. An alternative threshold criterion based on time and distance is examined and it is compared to the distance only threshold. A drawback with this proposed technique is also identified and a hybrid threshold criterion is then proposed. However, the trade-off between spatial and temporal inconsistency remains.

The hybrid strategy-based modeling approach is a method for reducing the number of network packets that need to be transmitted to maintain global consistency in Distributed Interactive Applications. It combines a short-term model such as dead reckoning with a long-term strategy model. A key aspect of this approach is to determine strategies that users adopt in navigating the simulated environment to satisfy some objective or goal. Computer-generated artificial entities called BOTS, navigate by employing an Artificial Intelligence technique called path finding. This paper proposes using the A* path finding algorithm to automatically compute strategies that human users might take through the simulated environment. Since the A* algorithm operates on a graph representation of the environment and because of the real-time constraints imposed on Distributed Interactive Applications, the paper also carries out a comparative analysis of two extreme graph representations of the environment — a standard regular grid and a minimal grid representation. The comparison shows that the minimal grid leads to an order of magnitude reduction in real-time computation compared to the regular grid. In addition the paths computed using the minimal grid and the A* algorithm are used to determine strategy models as part of the hybrid strategy-based modeling approach. It is shown that this reduces the network traffic required to maintain global consistency of entity dynamics in two simulated environments.

As Distributed Interactive Applications (DIAs) become increasingly more prominent in the video game industry they must scale to accommodate progressively more users and maintain a globally consistent worldview. However, network constraints, such as bandwidth, limit the amount of communication allowed between users. Several methods of reducing network communication packets, while maintaining consistency, exist. These include dead reckoning and the hybrid strategy-based modelling approach. This latter method combines a short-term model such as dead reckoning with a long-term strategy model of user behaviour. By employing the strategy that most closely represents user behaviour, a reduction in the number of network packets that must be transmitted to maintain consistency has been shown. In this paper a novel method for constructing multiple long-term strategies using dead reckoning and polygons is described. Furthermore the algorithms are implemented in an industry-proven game engine known as Torque. A series of experiments are executed to investigate the effects of varying the spatial density of strategy models on the number of packets that need to be transmitted to maintain the global consistency of the DIA. The results show that increasing the spatial density of strategy models allows a higher consistency to be achieved with fewer packets using the hybrid strategy-based model than with pure dead reckoning. In some cases, the hybrid strategy-based model completely replaces dead reckoning as a means of communicating updates.

Distributed Interactive Applications (DIAs) have been gaining commercial success in recent years due to the widespread appeal of networked multiplayer computer games. Within these games, participants interact with each other and their environment, producing complex behavioural patterns that evolve over time. These patterns are non-linear, and often appear to exhibit dependencies under certain conditions. In this paper, we analyse the behavioural patterns of two users interacting in a DIA. Our motivation behind this analysis is to construct models of user behaviour that can be used within Entity-State-Update (ESU) mechanisms. By representing their behaviour as time-series datasets, we investigate the use of simple statistical dependence measures to help partition the datasets and identify three different types of behavioural states exhibited by the two users. It is our intention that future research on ESU mechanisms can utilize this behavioural partitioning to reduce the network traffic in a DIA based on a hybrid-model approach.