Real-time counting of single electron tunneling through a T-shaped double quantum dot system

Real-time detection of single electron tunneling through a T-shaped double quantum dot is simulated, based on a Monte Carlo scheme. The double dot is embedded in a dissipative environment and the presence of electrons on the double dot is detected with a nearby quantum point contact. We demonstrate directly the bunching behavior in electron transport, which leads eventually to a super-Poissonian noise. Particularly, in the context of full counting statistics, we investigate the essential difference between the dephasing mechanisms induced by the quantum point contact detection and the coupling to the external phonon bath. A number of intriguing noise features associated with various transport mechanisms are revealed.

Real-time far distance micro-vibration measurement using an external cavity semiconductor laser interferometer with a feedback control system

An external cavity semiconductor laser interferometer used to measure far distance micro-vibration in real time is proposed. In the interferometer, a single longitudinal mode and excellent coherent characteristic grating external cavity semiconductor laser is constructed and acted as a light source and a phase compensator. Its coherent length exceeds 200 meters. The angle between normal and incidence beam of the far object is allowed to change in definite range during the measurement with this interferometer, and this makes the far distance interference measurement easier and more convenient. Also, it is not required to keep the amplitudes of the first and second harmonic components equal, and then the dynamic range is increased. A feedback control system is used to compensate the phase disturbance between the two interference beams introduced by environmental vibration.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

A tripartite microbial reporter gene system for real-time assays of soil nutrient status

Plant-derived carbon is the substrate which drives the rate of microbial assimilation and turnover of nutrients, in particular N and P, within the rhizosphere. To develop a better understanding of rhizosphere dynamics, a tripartite reporter gene system has been developed. We used three lux-marked Pseudomonas fluorescens strains to report on soil (1) assimilable carbon, (2) N-status, and (3) P-status. In vivo studies using soil water, spiked with C, N and P to simulate rhizosphere conditions, showed that the tripartite reporter system can provide real-time assessment of carbon and nutrient status. Good quantitative agreement for bioluminescence output between reference material and soil water samples was found for the C and P reporters. With regard to soil nitrate, the minimum bioavailable concentration was found to be greater than that analytically detectable in soil water. This is the first time that bioavailable soil C, N and P have been quantified using a tripartite reporter gene system.

Rapid quantification of microRNAs in plasma using a fast real time PCR system

The ability to rapidly detect circulating small RNAs, in particular microRNAs (miRNAs), would further increase their already established potential as biomarkers in a range of conditions. One rate-limiting factor is the time taken to perform quantitative real time PCR amplification. We therefore evaluated the ability of a novel thermal cycler to perform this step in less than 10 minutes. Quantitative PCR was performed on an xxpress® thermal cycler (BJS Biotechnologies, Perivale, UK), which employs a resistive heating system and forced air cooling to achieve thermal ramp rates of 10 °C/s, and a conventional peltier-controlled LightCycler 480 system (Roche, Basel, Switzerland) ramping at 4.8 °C/s. The threshold cycle (Ct) for detection of 18S rDNA from a standard genomic DNA sample was significantly more variable across the block (F-test, p=2.4x10-25) for the xxpress (20.01±0.47SD) than the LightCycler (19.87±0.04SD). RNA was extracted from human plasma, reverse transcribed and a panel of miRNAs amplified and detected using SYBR green (Kapa Biosystems, Wilmington, Ma, USA). The sensitivity of both systems was broadly comparable and both detected a panel of miRNAs reliably and indicated similar relative abundances. The xxpress thermal cycler facilitates rapid qPCR detection of small RNAs and brings point-of care diagnostics based upon circulating miRNAs a step closer to reality.

Design and implementation of a real-time data acquisition system for the identification of dynamic temperature models in a hydroponic greenhouse

This text describes a real data acquisition and identification system implemented in a soilless greenhouse located at the University of Algarve (south of Portugal). Using the Real Time Workshop, Simulink, Matlab and the C programming language a system was developed to perform real-time data acquisition from a set of sensors.

Real-time data acquisition system for the Identification of dynamic temperature models in a hydroponic greenhouse

A real-time data acquisition and identification system implemented in a soil-less greenhouse located in the south of Portugal is described. The system performs real-time data acquisition from a set of sensors connected to a data logger.

EMMON: a WSN system architecture for large scale and dense real-time embedded monitoring

Wireless sensor networks (WSNs) have attracted growing interest in the last decade as an infrastructure to support a diversity of ubiquitous computing and cyber-physical systems. However, most research work has focused on protocols or on specific applications. As a result, there remains a clear lack of effective, feasible and usable system architectures that address both functional and non-functional requirements in an integrated fashion. In this paper, we outline the EMMON system architecture for large-scale, dense, real-time embedded monitoring. EMMON provides a hierarchical communication architecture together with integrated middleware and command and control software. It has been designed to use standard commercially-available technologies, while maintaining as much flexibility as possible to meet specific applications requirements. The EMMON architecture has been validated through extensive simulation and experimental evaluation, including a 300+ node test-bed, which is, to the best of our knowledge, the largest single-site WSN test-bed in Europe to date.

A traffic differentiation add-on to the IEEE 802.15.4 protocol: implementation and experimental validation over a real-time operating system

The IEEE 802.15.4 is the most widespread used protocol for Wireless Sensor Networks (WSNs) and it is being used as a baseline for several higher layer protocols such as ZigBee, 6LoWPAN or WirelessHART. Its MAC (Medium Access Control) supports both contention-free (CFP, based on the reservation of guaranteed time-slots GTS) and contention based (CAP, ruled by CSMA/CA) access, when operating in beacon-enabled mode. Thus, it enables the differentiation between real-time and best-effort traffic. However, some WSN applications and higher layer protocols may strongly benefit from the possibility of supporting more traffic classes. This happens, for instance, for dense WSNs used in time-sensitive industrial applications. In this context, we propose to differentiate traffic classes within the CAP, enabling lower transmission delays and higher success probability to timecritical messages, such as for event detection, GTS reservation and network management. Building upon a previously proposed methodology (TRADIF), in this paper we outline its implementation and experimental validation over a real-time operating system. Importantly, TRADIF is fully backward compatible with the IEEE 802.15.4 standard, enabling to create different traffic classes just by tuning some MAC parameters.

A Real Time Vision System for Autonomous Systems: Characterization during a Middle Size Match

in RoboCup 2007: Robot Soccer World Cup XI

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

Towards a real-time vision-based navigation system for a small-class UUV

This paper deals with the problem of navigation for an unmanned underwater vehicle (UUV) through image mosaicking. It represents a first step towards a real-time vision-based navigation system for a small-class low-cost UUV. We propose a navigation system composed by: (i) an image mosaicking module which provides velocity estimates; and (ii) an extended Kalman filter based on the hydrodynamic equation of motion, previously identified for this particular UUV. The obtained system is able to estimate the position and velocity of the robot. Moreover, it is able to deal with visual occlusions that usually appear when the sea bottom does not have enough visual features to solve the correspondence problem in a certain area of the trajectory