Video Quality Assessments on Digital TV and Video Streaming Services Using Objective Metrics

There is a wide range of telecommunications services that transmit voice, video and data through complex transmission networks and in some cases, the service has not an acceptable quality level for the end user. In this sense the study of methods for assessing video quality and voice have a very important role. This paper presents a classification scheme, based on different criteria, of the methods and metrics that are being studied in recent years. This paper presents how the video quality is affected by degradation in the transmission channel in two kinds of services: Digital TV (ISDB-TB) due the fading in the air interface and video streaming service on an IP network due packet loss. For Digital TV tests was set up a scenario where the digital TV transmitter is connected to an RF channel emulator, where are inserted different fading models and at the end, the videos are saved in a mobile device. The tests of streaming video were performed in an isolated scenario of IP network, which are scheduled several network conditions, resulting in different qualities of video reception. The video quality assessment is performed using objective assessment methods: PSNR, SSIM and VQM. The results show how the losses in the transmission channel affects the quality of end-user experience on both services studied.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Suitability of distance metrics as indexes of home-range size in tropical rodent species

Estimators of home-range size require a large number of observations for estimation and sparse data typical of tropical studies often prohibit the use of such estimators. An alternative may be use of distance metrics as indexes of home range. However, tests of correlation between distance metrics and home-range estimators only exist for North American rodents. We evaluated the suitability of 3 distance metrics (mean distance between successive captures [SD], observed range length [ORL], and mean distance between all capture points [AD]) as indexes for home range for 2 Brazilian Atlantic forest rodents, Akodon montensis (montane grass mouse) and Delomys sublineatus (pallid Atlantic forest rat). Further, we investigated the robustness of distance metrics to low numbers of individuals and captures per individual. We observed a strong correlation between distance metrics and the home-range estimator. None of the metrics was influenced by the number of individuals. ORL presented a strong dependence on the number of captures per individual. Accuracy of SD and AD was not dependent on number of captures per individual, but precision of both metrics was low with numbers of captures below 10. We recommend the use of SD and AD instead of ORL and use of caution in interpretation of results based on trapping data with low captures per individual.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Complex sochastic boolean systems : new properties of the intrinsic order graph

[EN]A complex stochastic Boolean system (CSBS) is a system depending on an arbitrary number n of stochastic Boolean variables. The analysis of CSBSs is mainly based on the intrinsic order: a partial order relation defined on the set f0; 1gn of binary n-tuples. The usual graphical representation for a CSBS is the intrinsic order graph: the Hasse diagram of the intrinsic order. In this paper, some new properties of the intrinsic order graph are studied. Particularly, the set and the number of its edges, the degree and neighbors of each vertex, as well as typical properties, such as the symmetry and fractal structure of this graph, are analyzed…

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Searching and retrieving in content-based repositories of formal mathematical knowledge

In this thesis, the author presents a query language for an RDF (Resource Description Framework) database and discusses its applications in the context of the HELM project (the Hypertextual Electronic Library of Mathematics). This language aims at meeting the main requirements coming from the RDF community. in particular it includes: a human readable textual syntax and a machine-processable XML (Extensible Markup Language) syntax both for queries and for query results, a rigorously exposed formal semantics, a graph-oriented RDF data access model capable of exploring an entire RDF graph (including both RDF Models and RDF Schemata), a full set of Boolean operators to compose the query constraints, fully customizable and highly structured query results having a 4-dimensional geometry, some constructions taken from ordinary programming languages that simplify the formulation of complex queries. The HELM project aims at integrating the modern tools for the automation of formal reasoning with the most recent electronic publishing technologies, in order create and maintain a hypertextual, distributed virtual library of formal mathematical knowledge. In the spirit of the Semantic Web, the documents of this library include RDF metadata describing their structure and content in a machine-understandable form. Using the author's query engine, HELM exploits this information to implement some functionalities allowing the interactive and automatic retrieval of documents on the basis of content-aware requests that take into account the mathematical nature of these documents.

Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain

Investigation uses simulation to explore the inherent tradeoffs ofcontrolling high-speed and highly robust walking robots while minimizing energy consumption. Using a novel controller which optimizes robustness, energy economy, and speed of a simulated robot on rough terrain, the user can adjust their priorities between these three outcome measures and systematically generate a performance curveassessing the tradeoffs associated with these metrics.

Dynamics of Shifting Cultivation Landscapes in Northern Lao PDR Between 2000 and 2009 Based on an Analysis of MODIS Time Series and Landsat Images

The rotational nature of shifting cultivation poses several challenges to its detection by remote sensing. Consequently, there is a lack of spatial data on the dynamics of shifting cultivation landscapes on a regional, i.e. sub-national, or national level. We present an approach based on a time series of Landsat and MODIS data and landscape metrics to delineate the dynamics of shifting cultivation landscapes. Our results reveal that shifting cultivation is a land use system still widely and dynamically utilized in northern Laos. While there is an overall reduction in the areas dominated by shifting cultivation, some regions also show an expansion. A review of relevant reports and articles indicates that policies tend to lead to a reduction while market forces can result in both expansion and reduction. For a better understanding of the different factors affecting shifting cultivation landscapes in Laos, further research should focus on spatially explicit analyses.

Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

Accelerometer based measurements of combustion in an automotive turbocharged diesel engine

The capability to detect combustion in a diesel engine has the potential of being an important control feature to meet increasingly stringent emission regulations, develop alternative combustion strategies, and use of biofuels. In this dissertation, block mounted accelerometers were investigated as potential feedback sensors for detecting combustion characteristics in a high-speed, high pressure common rail (HPCR), 1.9L diesel engine. Accelerometers were positioned in multiple placements and orientations on the engine, and engine testing was conducted under motored, single and pilot-main injection conditions. Engine tests were conducted at varying injection timings, engine loads, and engine speeds to observe the resulting time and frequency domain changes of the cylinder pressure and accelerometer signals. The frequency content of the cylinder pressure based signals and the accelerometer signals between 0.5 kHz and 6 kHz indicated a strong correlation with coherence values of nearly 1. The accelerometers were used to produce estimated combustion signals using the Frequency Response Functions (FRF) measured from the frequency domain characteristics of the cylinder pressure signals and the response of the accelerometers attached to the engine block. When compared to the actual combustion signals, the estimated combustion signals produced from the accelerometer response had Root Mean Square Errors (RMSE) between 7% and 25% of the actual signals peak value. Weighting the FRF’s from multiple test conditions along their frequency axis with the coherent output power reduced the median RMSE of the estimated combustion signals and the 95th percentile of RMSE produced from each test condition. The RMSE’s of the magnitude based combustion metrics including peak cylinder pressure, MPG, peak ROHR, and work estimated from the combustion signals produced by the accelerometer responses were between 15% and 50% of their actual value. The MPG measured from the estimated pressure gradient shared a direct relationship to the actual MPG. The location based combustion metrics such as the location of peak values and burn durations were capable of RMSE measurements as low as 0.9°. Overall, accelerometer based combustion sensing system was capable of detecting combustion and providing feedback regarding the in cylinder combustion process

Augmenting Static Source Views in IDEs with Dynamic Metrics

Mainstream IDEs such as Eclipse support developers in managing software projects mainly by offering static views of the source code. Such a static perspective neglects any information about runtime behavior. However, object-oriented programs heavily rely on polymorphism and late-binding, which makes them difficult to understand just based on their static structure. Developers thus resort to debuggers or profilers to study the system's dynamics. However, the information provided by these tools is volatile and hence cannot be exploited to ease the navigation of the source space. In this paper we present an approach to augment the static source perspective with dynamic metrics such as precise runtime type information, or memory and object allocation statistics. Dynamic metrics can leverage the understanding for the behavior and structure of a system. We rely on dynamic data gathering based on aspects to analyze running Java systems. By solving concrete use cases we illustrate how dynamic metrics directly available in the IDE are useful. We also comprehensively report on the efficiency of our approach to gather dynamic metrics.

Dynamic Optimization of SLA-Based Services Scaling Rules

Current advanced cloud infrastructure management solutions allow scheduling actions for dynamically changing the number of running virtual machines (VMs). This approach, however, does not guarantee that the scheduled number of VMs will properly handle the actual user generated workload, especially if the user utilization patterns will change. We propose using a dynamically generated scaling model for the VMs containing the services of the distributed applications, which is able to react to the variations in the number of application users. We answer the following question: How to dynamically decide how many services of each type are needed in order to handle a larger workload within the same time constraints? We describe a mechanism for dynamically composing the SLAs for controlling the scaling of distributed services by combining data analysis mechanisms with application benchmarking using multiple VM configurations. Based on processing of multiple application benchmarks generated data sets we discover a set of service monitoring metrics able to predict critical Service Level Agreement (SLA) parameters. By combining this set of predictor metrics with a heuristic for selecting the appropriate scaling-out paths for the services of distributed applications, we show how SLA scaling rules can be inferred and then used for controlling the runtime scale-in and scale-out of distributed services. We validate our architecture and models by performing scaling experiments with a distributed application representative for the enterprise class of information systems. We show how dynamically generated SLAs can be successfully used for controlling the management of distributed services scaling.

Optimal Workflow and Process-Based Performance Measures for Endovascular Therapy in Acute Ischemic Stroke: Analysis of the Solitaire FR Thrombectomy for Acute Revascularization Study.

BACKGROUND AND PURPOSE We report on workflow and process-based performance measures and their effect on clinical outcome in Solitaire FR Thrombectomy for Acute Revascularization (STAR), a multicenter, prospective, single-arm study of Solitaire FR thrombectomy in large vessel anterior circulation stroke patients. METHODS Two hundred two patients were enrolled across 14 centers in Europe, Canada, and Australia. The following time intervals were measured: stroke onset to hospital arrival, hospital arrival to baseline imaging, baseline imaging to groin puncture, groin puncture to first stent deployment, and first stent deployment to reperfusion. Effects of time of day, general anesthesia use, and multimodal imaging on workflow were evaluated. Patient characteristics and workflow processes associated with prolonged interval times and good clinical outcome (90-day modified Rankin score, 0-2) were analyzed. RESULTS Median times were onset of stroke to hospital arrival, 123 minutes (interquartile range, 163 minutes); hospital arrival to thrombolysis in cerebral infarction (TICI) 2b/3 or final digital subtraction angiography, 133 minutes (interquartile range, 99 minutes); and baseline imaging to groin puncture, 86 minutes (interquartile range, 24 minutes). Time from baseline imaging to puncture was prolonged in patients receiving intravenous tissue-type plasminogen activator (32-minute mean delay) and when magnetic resonance-based imaging at baseline was used (18-minute mean delay). Extracranial carotid disease delayed puncture to first stent deployment time on average by 25 minutes. For each 1-hour increase in stroke onset to final digital subtraction angiography (or TICI 2b/3) time, odds of good clinical outcome decreased by 38%. CONCLUSIONS Interval times in the STAR study reflect current intra-arterial therapy for patients with acute ischemic stroke. Improving workflow metrics can further improve clinical outcome. CLINICAL TRIAL REGISTRATION: URL http://www.clinicaltrials.gov. Unique identifier: NCT01327989.