Kernel performance in meeting hard real-time system characteristics (the ARTOS real-time kernel)

Describes the design and implementation of an operating system kernel specifically designed to support real-time applications. It emphasises portability and aims to support state-of-the-art concepts in real-time programming. Discusses architectural aspects of the ARTOS kernel, and introduces new concepts on the areas of interrupt processing, scheduling, mutual exclusion and inter-task communication. Also explains the programming environment of ARTOS kernal and its task model, defines the real-time task states and system data structures and discusses exception handling mechanisms which are used to detect missed deadlines and take corrective action.

Prototyping real-time systems using Petri net and object-oriented models

The objective of the research for this thesis is to develop techniques in order to build an executable model of a real-time system. This model is to be used early in the development of the system not only to detect errors in the specification of the system but also to validate expectations of the developer as to the operation of the system. A graphical specification of a real-time system called the transformation schema was chosen to be used to build the model. Two executable models of a real-time system are described.

RFID-based real-time smart waste management system

In an environmental context, the use of RFID (radio frequency identification) and load cell sensor technology can be employed for not only bringing down waste management costs, but also to facilitate automating and streamlining waste (e.g., garbage, recycling, and green) identification and weight measurement processes for designing smart waste management systems. In this paper, we outline a RFID and sensor model for designing a system in real-time waste management. An application of the architecture is described in the area of RFID and sensor based automatic waste identity, weight, and stolen bins identification system (WIWSBIS).

Key success factors in real-time industrial system assurance

The thesis analyses the issues of implementing real-time software systems in industrial applications. The benefit is the development of the Integrated Mega Project Development Model, to improve the effectiveness of planning and timely delivery of software, the quality of the delivered software and reducing problems associated with integrating software systems.

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.

A real-time abnormality detection system for intensive care management

Detecting abnormalities from multiple correlated time series is valuable to those applications where a credible realtime event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). For example, in an intensive care scenario, anesthetists perform a vital role in monitoring the patient and adjusting the flow and type of anesthetics to the patient during an operation. An early awareness of possible complications is vital for an anesthetist to correctly react to a given situation. In this demonstration, we provide a comprehensive medical surveillance system to effectively detect abnormalities from multiple physiological data streams for assisting online intensive care management. Particularly, a novel online support vector regression (OSVR) algorithm is developed to approach the problem of discovering the abnormalities from multiple correlated time series for accuracy and real-time efficiency. We also utilize historical data streams to optimize the precision of the OSVR algorithm. Moreover, this system comprises a friendly user interface by integrating multiple physiological data streams and visualizing alarms of abnormalities. © 2013 IEEE.

Effect of creatine supplementation on housekeeping genes in human skeletal muscle using real-time RT-PCR

The present study examined the validity and reliability of measuring the expression of various genes in human skeletal muscle using quantitative real-time RT-PCR on a GeneAmp 5700 sequence detection system with SYBR Green 1 chemistry. In addition, the validity of using some of these genes as endogenous controls (i.e., housekeeping genes) when human skeletal muscle was exposed to elevated total creatine levels and exercise was also examined. For all except 28S, linear relationships between the logarithm of the starting RNA concentrations and the cycle threshold (C_T) values were established for ß-actin, ß2-microglobulin (ß₂M), cyclophilin (CYC), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). We found a linear response between C_T values and the logarithm of a given amount of starting cDNA for all the genes tested. The overall intra-assay coefficient of variance for these genes was 1.3% and 21% for raw C_T values and the linear value of 2^-C_T, respectively. Interassay variability was 2.3% for raw C_T values and 34% for the linear value of 2^-C_T. We also examined the expression of various housekeeping genes in human skeletal muscle at days 0, 1, and 5 following oral supplementation with either creatine or a placebo employing a double-blind crossover study design. Treatments were separated by a 5-wk washout period. Immediately following each muscle sampling, subjects performed two 30-s all-out bouts on a cycle ergometer. Creatine supplementation increased (P < 0.05) muscle total creatine content above placebo levels; however, there were no changes (P > 0.05) in C_T values across the supplementation periods for any of the genes. Nevertheless, 95% confidence intervals showed that GAPDH was variable, whereas ß-actin, ß₂M, and CYC were the least varying genes. Normalization of the data to these housekeeping genes revealed variable behavior for ß₂M with more stable expressions for both ß-actin and CYC. We conclude that, using real-time RT-PCR, ß-actin or CYC may be used as housekeeping genes to study gene expression in human muscle in experiments employing short-term creatine supplementation combined with high-intensity exercise.

Providing real-time information services for public bus commuters

This paper investigates RTIS for public bus commuters; identifying their needs and proposing the architecture and design for this service. Considering that the service needs to be extensible, adaptable and interoperative, the new system is based on the use of XML Web Services. This gives the structure flexibility for further development and integration with other like services.

Real-time early infectious outbreak detection systems using emerging technologies

The use of emerging technologies ( such as RFID - Radio Frequency Identification and remote sensing) can be employed to reduce health care costs and also to facilitate the automatic streamlining of infectious disease outbreak detection and monitoring processes in local health departments. It can assist medical practitioners with fast and accurate diagnosis and treatments. In this paper we outline the design and application of a real-time RFID and sensor-base Early Infectious (e.g., cholera) Outbreak Detection and Monitoring (IODM) system for health care.

Super-realistic rendering using real-time tweening

The realism of contemporary computer graphics (and especially Virtual Reality {VR}) is limited by the great computational cost of rendering objects of appropriate complexity with convincing lighting and surface effects. We introduce a framework that allows rendering of objects in true photographic quality using tweening. The simple but effective design of our system allows us not only to perform the necessary operations in real-time on standard hardware, but also achieve other effects like morphing. Furthermore, it is shown how our system can be gainfully employed in non-VR contexts like extreme low-bandwidth video-conferencing and others.

Real-time observation of fiber network formation in molecular organogel : supersaturation-dependent microstructure and its related rheological property

Low-molecular mass organic gelators self-organizing into three-dimensional fiber networks within organic solvents have attracted much attention in recent years. However, to date, how the microstructure of fiber network is formed in a gelation process and the key factors that govern the topological structure of a gel network remain to be determined. In this work, we address these issues by investigating the in situ formation of the gel networks in the N-lauroyl-l-glutamic acid di-n-butylamide (GP-1)/propylene glycol (PG) system. By using optical microscopy, the time evolution of the gel network microstructure was investigated under various supersaturation conditions. It is found that supersaturation is one of the key factors that govern the topological structure of a gel network. In particular, the creation of the junctions turns out to be supersaturation-dependent. The rheological experiments further revealed the correlation between topological structure and mechanical properties. It suggests that the rheological properties can be effectively modified by tuning the microstructure topology of the gel network. Our results reported here provide new physical insight into the formation kinetics of a molecular gel. Furthermore, this work could be important in constructing and engineering a supramolecular structure for the purpose of applications.

Real-time detection of parked vehicles from multiple image streams

We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.