Leakage temperature dependency aware real-time scheduling for power and thermal optimization

Catering to society's demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. ^ In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research. ^

Localized surface plasmon resonance biosensors for real-time biomolecular binding study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ∼7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Real-time scheduling of embedded applications on multi-core platforms

For the past several decades, we have experienced the tremendous growth, in both scale and scope, of real-time embedded systems, thanks largely to the advances in IC technology. However, the traditional approach to get performance boost by increasing CPU frequency has been a way of past. Researchers from both industry and academia are turning their focus to multi-core architectures for continuous improvement of computing performance. In our research, we seek to develop efficient scheduling algorithms and analysis methods in the design of real-time embedded systems on multi-core platforms. Real-time systems are the ones with the response time as critical as the logical correctness of computational results. In addition, a variety of stringent constraints such as power/energy consumption, peak temperature and reliability are also imposed to these systems. Therefore, real-time scheduling plays a critical role in design of such computing systems at the system level. We started our research by addressing timing constraints for real-time applications on multi-core platforms, and developed both partitioned and semi-partitioned scheduling algorithms to schedule fixed priority, periodic, and hard real-time tasks on multi-core platforms. Then we extended our research by taking temperature constraints into consideration. We developed a closed-form solution to capture temperature dynamics for a given periodic voltage schedule on multi-core platforms, and also developed three methods to check the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research by incorporating the power/energy constraint with thermal awareness into our research problem. We investigated the energy estimation problem on multi-core platforms, and developed a computation efficient method to calculate the energy consumption for a given voltage schedule on a multi-core platform. In this dissertation, we present our research in details and demonstrate the effectiveness and efficiency of our approaches with extensive experimental results.

Optimal kernel development for real-time communications

The purpose of this research is to develop an optimal kernel which would be used in a real-time engineering and communications system. Since the application is a real-time system, relevant real-time issues are studied in conjunction with kernel related issues. The emphasis of the research is the development of a kernel which would not only adhere to the criteria of a real-time environment, namely determinism and performance, but also provide the flexibility and portability associated with non-real-time environments. The essence of the research is to study how the features found in non-real-time systems could be applied to the real-time system in order to generate an optimal kernel which would provide flexibility and architecture independence while maintaining the performance needed by most of the engineering applications. Traditionally, development of real-time kernels has been done using assembly language. By utilizing the powerful constructs of the C language, a real-time kernel was developed which addressed the goals of flexibility and portability while still meeting the real-time criteria. The implementation of the kernel is carried out using the powerful 68010/20/30/40 microprocessor based systems.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

Real-time tracking system using C80 DSPs and a binocular robotic head

[EN]An active vision system to perform tracking of moving objects in real time is described. The main goal is to obtain a system integrating off-the-self components. These components includes a stereoscopic robotic-head, as active perception hardware; a DSP based board SDB C80, as massive data processor and image acquisition board; and finally, a Pentium PC running Windows NT that interconnects and manages the whole system. Real-time is achieved taking advantage of the special architecture of DSP. An evaluation of the performance is included.

Real-time audio interaction in serious games for music learning

In this LBD, we present several Apps for playing while learning music or for learning music while playing. The core of all the games is based on the good performance of the real-time audio interaction algorithms developed by the ATIC group at Universidad de Ma ́laga (SPAIN).

Maximizing real-time streaming services based on a multi-servers networking framework

In recent years, we have witnessed substantial exploitation of real-time streaming applications, such as video surveillance system on road crosses of a city. So far, real world applications mainly rely on the traditional well-known client-server and peer-to-peer schemes as the fundamental mechanism for communication. However, due to the limited resources on each terminal device in the applications, these two schemes cannot well leverage the processing capability between the source and destination of the video traffic, which leads to limited streaming services. For this reason, many QoS sensitive application cannot be supported in the real world. In this paper, we are motivated to address this problem by proposing a novel multi-server based framework. In this framework, multiple servers collaborate with each other to form a virtual server (also called cloud-server), and provide high-quality services such as real-time streams delivery and storage. Based on this framework, we further introduce a (1-?) approximation algorithm to solve the NP-complete "maximum services"(MS) problem with the intention of handling large number of streaming flows originated by networks and maximizing the total number of services. Moreover, in order to backup the streaming data for later retrieval, based on the framework, an algorithm is proposed to implement backups and maximize streaming flows simultaneously. We conduct a series of experiments based on simulations to evaluate the performance of the newly proposed framework. We also compare our scheme to several traditional solutions. The results suggest that our proposed scheme significantly outperforms the traditional solutions.

Robust real-time bio-kinematic movement tracking using multiple kinects for tele-rehabilitation

This paper investigates the robust and accurate capture of human joint poses and bio-kinematic movements for exercise monitoring in real-time tele-rehabilitation applications. Recently developed model-based estimation ideas are used to improve the accuracy, robustness, and real-time characteristics considered vital for applications, where affordability and domestic use are the primary focus. We use the spatial diversity of the arbitrarily positioned Microsoft Kinect receivers to improve the reliability and promote the uptake of the concept. The skeleton-based information is fused to enhance accuracy and robustness, critical for biomedical applications. A specific version of a robust Kalman filter (KF) in a linear framework is employed to ensure superior estimator convergence and real-time use, compared to other commonly used filters. The algorithmic development was conducted in a generic form and computer simulations were conducted to verify our assertions. Hardware implementations were carried out to test the viability of the proposed state estimator in terms of the core requirements of reliability, accuracy, and real-time use. Performance of the overall system implemented in an information fusion context was evaluated against the commercially available and industry standard Vicon system for different exercise routines, producing comparable results with much less infrastructure and financial investment.

Real-time traffic flow statistical analysis based on network-constrained moving object trajectories

In this paper, we propose a novel traffic flow analysis method, Network-constrained Moving Objects Database based Traffic Flow Statistical Analysis (NMOD-TFSA) model. By sampling and analyzing the spatial-temporal trajectories of network constrained moving objects, NMOD-TFSA can get the real-time traffic conditions of the transportation network. The experimental results show that, compared with the floating-car methods which are widely used in current traffic flow analyzing systems, NMOD-TFSA provides an improved performance in terms of communication costs and statistical accuracy.

Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms

Big Data technologies are exciting cutting-edge technologies that generate, collect, store and analyse tremendous amount of data. Like any other IT revolution, Big Data technologies also have big challenges that are obstructing it to be adopted by wider community or perhaps impeding to extract value from Big Data with pace and accuracy it is promising. In this paper we first offer an alternative view of «Big Data Cloud» with the main aim to make this complex technology easy to understand for new researchers and identify gaps efficiently. In our lab experiment, we have successfully implemented cyber-attacks on Apache Hadoop's management interface «Ambari». On our thought about «attackers only need one way in», we have attacked the Apache Hadoop's management interface, successfully turned down all communication between Ambari and Hadoop's ecosystem and collected performance data from Ambari Virtual Machine (VM) and Big Data Cloud hypervisor. We have also detected these cyber-attacks with 94.0187% accurateness using modern machine learning algorithms. From the existing researchs, no one has ever attempted similar experimentation in detection of cyber-attacks on Hadoop using performance data.

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.

Localized Surface Plasmon Resonance Biosensors for Real-Time Biomolecular Binding Study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ~7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

A real time electromagnetic analysis of electric machines for educational purposes and laboratory implementation

This thesis presents a system for visually analyzing the electromagnetic fields of the electrical machines in the energy conversion laboratory. The system basically utilizes the finite element method to achieve a real-time effect in the analysis of electrical machines during hands-on experimentation. The system developed is a tool to support the student's understanding of the electromagnetic field by calculating performance measures and operational concepts pertaining to the practical study of electrical machines. Energy conversion courses are fundamental in electrical engineering. The laboratory is conducted oriented to facilitate the practical application of the theory presented in class, enabling the student to use electromagnetic field solutions obtained numerically to calculate performance measures and operating characteristics. Laboratory experiments are utilized to help the students understand the electromagnetic concepts by the use of this visual and interactive analysis system. In this system, this understanding is accomplished while hands-on experimentation takes place in real-time.