P2P audio/video protocol with global positioning data in real time for mobile devices

In this paper, we propose an original method to geoposition an audio/video stream with multiple emitters that are at the same time receivers of the mixed signal. The obtained method is suitable when a list of positions within a known area is encoded with precision tailored to the visualization capabilities of the target device. Nevertheless, it is easily adaptable to new precision requirements, as well as parameterized data precision. This method extends a previously proposed protocol, without incurring in any performance penalty.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.

Practical methods of tracking of nonstationary time series applied to real-world data

In this paper, we discuss some practical implications for implementing adaptable network algorithms applied to non-stationary time series problems. Two real world data sets, containing electricity load demands and foreign exchange market prices, are used to test several different methods, ranging from linear models with fixed parameters, to non-linear models which adapt both parameters and model order on-line. Training with the extended Kalman filter, we demonstrate that the dynamic model-order increment procedure of the resource allocating RBF network (RAN) is highly sensitive to the parameters of the novelty criterion. We investigate the use of system noise for increasing the plasticity of the Kalman filter training algorithm, and discuss the consequences for on-line model order selection. The results of our experiments show that there are advantages to be gained in tracking real world non-stationary data through the use of more complex adaptive models.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

A near real-time water surface detection method based on HSV transformation of MODIS multi-Spectral time series data

In the face of global population growth and the uneven distribution of water supply, a better knowledge of the spatial and temporal distribution of surface water resources is critical. Remote sensing provides a synoptic view of ongoing processes, which addresses the intricate nature of water surfaces and allows an assessment of the pressures placed on aquatic ecosystems. However, the main challenge in identifying water surfaces from remotely sensed data is the high variability of spectral signatures, both in space and time. In the last 10 years only a few operational methods have been proposed to map or monitor surface water at continental or global scale, and each of them show limitations. The objective of this study is to develop and demonstrate the adequacy of a generic multi-temporal and multi-spectral image analysis method to detect water surfaces automatically, and to monitor them in near-real-time. The proposed approach, based on a transformation of the RGB color space into HSV, provides dynamic information at the continental scale. The validation of the algorithm showed very few omission errors and no commission errors. It demonstrates the ability of the proposed algorithm to perform as effectively as human interpretation of the images. The validation of the permanent water surface product with an independent dataset derived from high resolution imagery, showed an accuracy of 91.5% and few commission errors. Potential applications of the proposed method have been identified and discussed. The methodology that has been developed 27 is generic: it can be applied to sensors with similar bands with good reliability, and minimal effort. Moreover, this experiment at continental scale showed that the methodology is efficient for a large range of environmental conditions. Additional preliminary tests over other continents indicate that the proposed methodology could also be applied at the global scale without too many difficulties

True real-time change data capture with web service database encapsulation

This research is investigating the claim that Change Data Capture (CDC) technologies capture data changes in real-time. Based on theory, our hypothesis states that real-time CDC is not achievable with traditional approaches (log scanning, triggers and timestamps). Traditional approaches to CDC require a resource to be polled, which prevents true real-time CDC. We propose an approach to CDC that encapsulates the data source with a set of web services. These web services will propagate the changes to the targets and eliminate the need for polling. Additionally we propose a framework for CDC technologies that allow changes to flow from source to target. This paper discusses current CDC technologies and presents the theory about why they are unable to deliver changes in real-time. Following, we discuss our web service approach to CDC and accompanying framework, explaining how they can produce real-time CDC. The paper concludes with a discussion on the research required to investigate the real-time capabilities of CDC technologies. © 2010 IEEE.

Real-time surface current data in the Ibiza Channel from January to October 2016

The data set consists of maps of total velocity of surface currents in the Ibiza Channel, derived from HF radar measurements.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Recommendations for in-situ data Near Real Time Quality Control

With the construction of operational oceanography systems, the need for real-time has become more and more important. A lot of work had been done in the past, within National Data Centres (NODC) and International Oceanographic Data and Information Exchange (IODE) to standardise delayed mode quality control procedures. Concerning such quality control procedures applicable in real-time (within hours to a maximum of a week from acquisition), which means automatically, some recommendations were set up for physical parameters but mainly within projects without consolidation with other initiatives. During the past ten years the EuroGOOS community has been working on such procedures within international programs such as Argo, OceanSites or GOSUD, or within EC projects such as Mersea, MFSTEP, FerryBox, ECOOP, and MyOcean. In collaboration with the FP7 SeaDataNet project that is standardizing the delayed mode quality control procedures in NODCs, and MyOcean GMES FP7 project that is standardizing near real time quality control procedures for operational oceanography purposes, the DATA-MEQ working group decided to put together this document to summarize the recommendations for near real-time QC procedures that they judged mature enough to be advertised and recommended to EuroGOOS.

Measurement and data transmission validity of a multi-biosensor system for real-time remote exercise monitoring among cardiac patients

Background: Remote telemonitoring holds great potential to augment management of patients with coronary heart disease (CHD) and atrial fibrillation (AF) by enabling regular physiological monitoring during physical activity. Remote physiological monitoring may improve home and community exercise-based cardiac rehabilitation (exCR) programs and could improve assessment of the impact and management of pharmacological interventions for heart rate control in individuals with AF.

Objective: Our aim was to evaluate the measurement validity and data transmission reliability of a remote telemonitoring system comprising a wireless multi-parameter physiological sensor, custom mobile app, and middleware platform, among individuals in sinus rhythm and AF.

Methods: Participants in sinus rhythm and with AF undertook simulated daily activities, low, moderate, and/or high intensity exercise. Remote monitoring system heart rate and respiratory rate were compared to reference measures (12-lead ECG and indirect calorimeter). Wireless data transmission loss was calculated between the sensor, mobile app, and remote Internet server.

Results: Median heart rate (-0.30 to 1.10 b∙min-1) and respiratory rate (-1.25 to 0.39 br∙min-1) measurement biases were small, yet statistically significant (all P≤.003) due to the large number of observations. Measurement reliability was generally excellent (rho=.87-.97, all P<.001; intraclass correlation coefficient [ICC]=.94-.98, all P<.001; coefficient of variation [CV]=2.24-7.94%), although respiratory rate measurement reliability was poor among AF participants (rho=.43, P<.001; ICC=.55, P<.001; CV=16.61%). Data loss was minimal (<5%) when all system components were active; however, instability of the network hosting the remote data capture server resulted in data loss at the remote Internet server during some trials.

Conclusions: System validity was sufficient for remote monitoring of heart and respiratory rates across a range of exercise intensities. Remote exercise monitoring has potential to augment current exCR and heart rate control management approaches by enabling the provision of individually tailored care to individuals outside traditional clinical environments.

Forecasting daily patient outflow from a ward having no real-time clinical data

OBJECTIVE: Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data.

METHODS: We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features.

RESULTS: Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014.

CONCLUSIONS: In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments.

Introducing a pilot data collection model for real-time evaluation of data redundancy

In order to reduce serious health incidents, individuals with high risks need to be identified as early as possible so that effective intervention and preventive care can be provided. This requires regular and efficient assessments of risk within communities that are the first point of contacts for individuals. Clinical Decision Support Systems CDSSs have been developed to help with the task of risk assessment, however such systems and their underpinning classification models are tailored towards those with clinical expertise. Communities where regular risk assessments are required lack such expertise. This paper presents the continuation of GRiST research team efforts to disseminate clinical expertise to communities. Based on our earlier published findings, this paper introduces the framework and skeleton for a data collection and risk classification model that evaluates data redundancy in real-time, detects the risk-informative data and guides the risk assessors towards collecting those data. By doing so, it enables non-experts within the communities to conduct reliable Mental Health risk triage.

Communication Architecture Design for Real-Time Networked Control Systems

Networked control over data networks has received increasing attention in recent years. Among many problems in networked control systems (NCSs) is the need to reduce control latency and jitter and to deal with packet dropouts. This paper introduces our recent progress on a queuing communication architecture for real-time NCS applications, and simple strategies for dealing with packet dropouts. Case studies for a middle-scale process or multiple small-scale processes are presented for TCP/IP based real-time NCSs. Variations of network architecture design are modelled, simulated, and analysed for evaluation of control latency and jitter performance. It is shown that a simple bandwidth upgrade or adding hierarchy does not necessarily bring benefits for performance improvement of control latency and jitter. A co-design of network and control is necessary to maximise the real-time control performance of NCSs