Description:
The latest multiview video coding (MVC) standards such as 3D-HEVC and H.264/MVC normally encodes texture and depth videos separately. Significant amount of rate-distortion performance and computational performance are sacrificed due to separate encoding due to the lack of exploitation of joint information. Obviously, separate encoding also creates synchronization issue for 3D scene formation in the decoder. Moreover, the hierarchical frame referencing architecture in the MVC creates random access frame delay. In this paper we develop an encoder and decoder framework where we can encode texture and depth video jointly by forming and encoding 3D cuboid using high dimensional entropy coding. The results from our experiments show that our proposed framework outperforms the 3D-HEVC in rate-distortion performance and reduces the computational time significantly by reducing random access frame delay.
Description:
The latest multiview video coding (MVC) standards such as 3D-HEVC and H.264/MVC normally encodes texture and depth videos separately. Significant amount of rate-distortion performance and computational performance are sacrificed due to separate encoding due to the lack of exploitation of joint information. Obviously, separate encoding also creates synchronization issue for 3D scene formation in the decoder. Moreover, the hierarchical frame referencing architecture in the MVC creates random access frame delay. In this paper we develop an encoder and decoder framework where we can encode texture and depth video jointly by forming and encoding 3D cuboid using high dimensional entropy coding. The results from our experiments show that our proposed framework outperforms the 3D-HEVC in rate-distortion performance and reduces the computational time significantly by reducing random access frame delay.
Description:
Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rat
Description:
Haptic perception is mediated by tactile (skin-surface stimulation) and kinesthetic (perception of movement indicating limb position) inputs. In a recognition experiment, participants were asked to match stimuli of different curvatures to those in a visual choice set. They used tactile-only information, kinesthesis-only information, and a combination of both. Movements were either self-generated or passively-guided. The experiment showed that judgements based on self-generated kinesthetic information were more accurate than those based on tactile-only information.
Description:
In this paper, we propose a new background subtraction technique based on perceptual mixture-of-Gaussians (PMOG). Unlike numerous variants of the classical MOG based approach [1], which can ensure reliable detection result only in known operating environments through proper parameter tuning, PMOG shows superior detection performance across dynamic unconstrained scenarios without any tuning. This is due to PMOG's intrinsic capability of exploiting several perceptual characteristics of human visual system for better understanding of the operating environment to avoid blind reliance on statistical observations. Furthermore, the proposed technique dynamically varies the model adaptation speed, i.e., learning rate, based on observed scene statistics for faster adaptation of changed background and better persistency of detected foreground entities. Comprehensive experimental evaluation on a number of standard datasets validates the robustness of the technique compared to the state-of-the-art.
Description:
One of the challenging aspects for wireless sensor network (WSN) clustering is to estimate the optimal cluster number. Most WSN clustering techniques have used an a priori value to determine the cluster number. A few techniques, available at the literature, attempted to find the optimal cluster number by energy minimization. However, due to the assumption of lossless communication, these initiatives do not always converge to the minimum energy as wireless channels are error-prone. As wireless communication is always vulnerable to the signal interference, environmental noise, and so on, it suffers from data loss. To capture the characteristics of the communication phenomenon in a real context and have better reliability, we need to embed the link quality into the cluster number determination process. To address this issue, in this paper together with the energy, we incorporate link quality metric into the theoretical underpinning to achieve an optimal cluster number analytically and finally justified the result by simulation.
Description:
On 4 August 1888, Reefton became the first town in New Zealand to have its own public electricity supply powered by hydrogeneration. In Australia, the first supply of electricity to the public at large was in two small country towns in New South Wales. Tamworth, with a population of 3,000, switched on arc and incandescent street lighting on 9 November 1888. In April 1889, the smaller town of Young switched on its incandescent street lighting and shortly thereafter went on to connect shops, offices, and homes within reach of its lines. However, the history of electricity supply in Australia traces back earlier, with Brisbane as one of the first cities in Australia to use electricity commercially, in 1882. Thus, electricity utilization down under coincided with the history of its emergence among the countries of the Northern Hemisphere.
Description:
Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
Description:
An Electrocardiogram (ECG) records the electrical impulses of the heart and indicates rhythm anomalies for diagnostic purposes [1], [2]. A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment to reduce morbidity and mortality.
Description:
In this paper an analysis of human engagement behaviour with video is presented based on real life experiments. An engagement model could be employed in classroom education, enhancing programming skills, reading etc. Two groups of people, independent of one another, watched eighteen video clips separately at different times. The first group's participants' eye gaze locations, right and left pupil sizes, and eye blinking patterns are recorded by a state of the art Tobii eye tracker. The second group of people who are video experts opined about the most significant attention points of the videos. A well-known bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is also utilized to create salient points for the videos. Taking into consideration all the above mentioned descriptors the introduced behaviour analysis demonstrates the level of participants' concentration with the videos.
Description:
High Efficiency Video Coding (HEVC) could not provide real time facilities to the limited processing and battery powered electronic devices as its encoding time complexity increases multiple times compared to its predecessor. Numerous researchers contribute to address this limitation by reducing a number of motion estimation (ME) modes where they analyze homogeneity, residual and statistical correlation among different modes. Although their approaches save some encoding time, however, could not reach the similar rate-distortion (RD) performance with HEVC encoder as they merely depend on existing Lagrangian cost function (LCF) within HEVC framework. To overcome this limitation, in this paper, we capture visual attentive Foreground motion and salient region (FMSR) which are sensitive to human visual system for quality assessment. The FMSR features captured by visual attentive and dynamic background modeling are adaptively synthesized to determine a subset of candidate modes. This preprocessing phase is independent from LCF. Since the proposed technique can avoid exhaustive exploration of all modes with simple criteria, it can reduce 27% encoding time on average. With efficient selection of FMSR-based appropriate block partitioning modes, it can also improve up to 1.0dB peak signal-to-noise ratio (PSNR).
Description:
For cost effective deployment and implementation, mobility is introduced in sensor networks to provide dynamic event coverage. A hybrid network of static and mobile nodes, can yield the same desired accuracy and robustness of a static k-coverage detection model with fewer nodes. Since node movement is a costly operation and the movement strategy has to be decided instantly after event occurrence, it is desirable to have a lightweight distributed node selection and movement scheme. In this work, we propose a game theoretic model to provide dynamic event coverage that achieves the desired detection accuracy with significantly fewer number of nodes while balancing the energy consumption due to mobility and keeping the travelling distance minimum. We address and exploit the spatial clustering nature of events to maximize the overall detection performance over the network lifetime.
Description:
While Grid environment has developed increasingly, unfortunately the importance of fault tolerance has not been remarkable in Grid resource management. On the other hand, the cost of computing by grid is important because grid is an economy-based system. Most organizations intend to spend little on their own computations by grid. Therefore, using a better approach to resource scheduling to avoid fault is necessary. This paper presents a new approach on fault tolerance mechanisms for the resource scheduling on grid by using Case-Based Reasoning technique in a local fashion. This approach applies a specific structure in order to prepare fault tolerance between executer nodes to retain system in a safe state with minimum data transferring. Certainly, this algorithm increases fault tolerant confidence therefore, performance of grid will be high.
Description:
2009 5th IEEE GCC Conference and Exhibition, GCC 2009
Description:
OsteoArthritis is a debilitating disease throughout the developed world, causing significant loss of quality of life to individuals and damaging economies, the imaging system described in this paper has the potential to provide an objective means of measuring and reporting lesion dimensions and providing a machine readable data archive for future study. Existing imaging methodologies have been reviewed and contrasted and despite certain limitations the optical method proposed has the potential to address the issues highlighted to date. The preliminary depth measurement results with an accuracy of 200
Description:
Event detection in unseen scenarios is a challenging problem due to high variability of scene type, viewing direction, nature of scene entities, and environmental conditions. Existing event detection approaches mostly rely on context-specific tuning and training. Consequently, these techniques fail to achieve high scalability in a large surveillance network with hundreds of video feeds where scenario specific tuning/training is impossible. In this paper, we present a generic event detection approach where the extracted low-level features represent the global characteristics of the target scene instead of any context-specific information. From the temporal evolution of these context-invariant features over a timeframe, a fixed number of temporal features are extracted based on the periodicity of significant transition points and associated temporal orders. Finally, top-ranked temporal features are used to train binary classifier-based event models. In this approach, supervised training and exhaustive feature extraction are required only once while building the target event models. During real-time operation in unseen scenarios, event detection is performed based on the trained event models by extracting the required features only. The proposed event detection approach has been demonstrated for abnormal event detection in completely unseen public place scenarios from benchmark datasets without additional training and tuning. Furthermore, the proposed event detection approach has also outperformed recent optical flow based event detection technique.
Description:
In addition to the texture, multiview video employs the utilization of depth coding for the reconstruction of 3D video and Free viewpoint video. Standing on some texture-depth correlations, a number of methods in literature reuses texture motion vector for the corresponding depth coding to reduce encoding time by avoiding costly motion estimation process. However, texture similarity metric is not always equivalent to the corresponding depth similarity metric especially at edge levels. Since their approaches could not explicitly detect and encode acute edge motions of depth objects, eventually, could not reach the similar or improved rate-distortion (RD) performance against the High Efficiency Video Coding (HEVC) reference test model (HM). With a view to more accurate motion detection and modeling, the proposed technique exploits an extra Pattern Mode comprising a group of pattern templates (GPTs) with different rectangular and non-rectangular object shapes and edges compared to the existing HEVC block partitioning modes. Moreover, the proposed Pattern Mode only encodes the motion areas and skips the background areas. The experimental results show that the proposed technique could save 30% encoding time and improve average 0.1dB Bjontegard Delta peak signal-to-noise ratio (BD-PSNR) compared to the HM.
Description:
Abstract: The overall objective of this work is to develop a system composed of master, slave and a control system for special medical applications. A PROMIS (Pprime RObot for Minimally Invasive Surgery) system is designed to be cost-effective with a less complication of use and an application-specific for collaborative operations between the surgeon and the robot. This paper focuses on the introduction of a motion safety control methodology for a haptic device. The haptic device is used as a master with a parallel spherical architecture (SPM). The introduced control methodology is based on the workspace of the medical application. Three areas of the workspace are defined with a consideration of the degree of security. An algorithm that constantly keeps the mobile platform in a safe region, when the surgeon moves it, is presented and explained. The developed algorithm runs in real-time using a programmable logic controller (PLC). The control strategy of the haptic device was successfully tested and validated experimentally.
Description:
Existing in-network query processing techniques are categorized as approximation and aggregation based approaches. The former achieves lower query response delay at the expense of accuracy, while the latter reduces query response inaccuracy by executing queries at the actual sensor nodes resulting in longer delay. In this paper, we propose a query processing framework which is delay as well as accuracy aware and capable of dynamic adjustment to meet user/application requirements. When query response is required within specific delay, it provides approximated sensor data meeting the delay requirement. On the other hand, when query response accuracy is vital, it tolerates longer delay in acquiring response with the desired accuracy. To achieve this, we propose a novel method of constructing a delay aware spanning tree (DAST) based on query load and organizing sensor data with varied accuracy. Experimental results illustrate superiority of the proposed framework against competing approaches.
Description:
Vertical handoff plays an important role to provide seamless connectivity for a mobile user in an overlapped multinetwork environment. On the other hand in order to maintain network stability, efficient management of available radio resource becomes crucial as network operators want high network utilization and maximum profit generation. For vertical handoff management, existing research works considered these user centric vertical handoff decision algorithm and network centric call admission control as two isolated decision mechanisms in heterogeneous wireless environment. In this paper, however, we propose a correlation between vertical handoff decisions and call admission control policies. We have developed a novel vertical handoff decision model using the Markov decision process based vertical handoff decision algorithm by refining the optimality criterion to factor in the probabilistic consequence of the call dropping rates so that mobile-centric vertical handoff decision algorithm and network-centric call admission control can work through a feedback mechanism to maximize respective objectives in synergy.
Description:
This paper proposes a force control system using the State Observer Based Disturbance Observer (SO-DOB) and the State Observer Based Reaction Force Observer (SO-RFOB) based on the novel observer gain tuning methodology. The observer gain tuning methodology is based on the stable margin theory. By using the proposed gain tuning methodology, the system stability and the robustness of the SO-DOB and the SO-RFOB is guaranteed. The validity of the proposed method was tested by both simulation and the experiment works. From the results, the control performance was improved compared with the DOB and the RFOB based on the Low Pass Filter (LPF) based estimation.
Description:
The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for forest structure modelling, tree species identification and classification. The integration of LiDAR data and WorldView-2 satellite imagery produced different combinations of input data layers for image segmentations and a large number of variables derived from these data layers for object-based classifications. Assessment of different features (including the input data layers and subsequently derived variables) for object-based forest classification is important. In this study, five image segmentation schemes were explored to test the effectiveness of the different input data layers, in particular, the new WorldView-2 multispectral bands to object-based forest classification. Object-based variables derived from these data layers were assessed to rank their importance before inputting into decision trees for forest classifications. It demonstrated that, using methods developed in this study, the integration of airborne LiDAR and eight WorldView-2 bands can significantly improve the accuracy of forest classification in our study area. The variable importance was ranked, indicating how important a variable contributes to the classification in a particular decision tree. The results showed that using LiDAR data alone or four conventional bands only, the overall accuracies achieved were 61.39% and 61.42% respectively, but the overall accuracy increased to 82.35% when all eight bands and the LiDAR data were used.