Wave optics analysis by phase-shifting real-time holographic interferometry

The purpose of this work is to study the potentialities in the phase-shifting real-time holographic interferometry using photorefractive crystals as the recording medium for wave-optics analysis in optical elements and non-linear optical materials. This technique was used for obtaining quantitative measurements from the phase distributions of the wave front of lens and lens systems along the propagation direction with in situ visualization, monitoring and analysis in real time. (C) 2008 Elsevier GmbH. All rights reserved.

Light-induced lens analysis in photorefractive crystals employing phase-shifting real-time holographic interferometry

The purpose of this work is to study the potentialities of phase-shifting real-time holographic interferometry for the analysis of light-induced lens in photoreffactive and nonlinear optical materials. We show that this technique can be used for quantitative evaluation of the phase distribution of a wavefront changed by a light-induced lens and, consequently, the refractive index changes in these materials. The basic principle of this technique combines real-time holographic interferometry with phase-shifting technique for interferogram analysis. This method is demonstrated with in situ visualization, monitoring and analysis in real-time and uses a Bi(12)SiO(20) crystal as the holographic medium and a Bi(12)TiO(20) as the test sample. (C) 2008 Elsevier B.V. All rights reserved.

Experimental characterization of nonlinear systems: a real-time evaluation of the analogous Chua`s circuit behavior

This paper presents an experimental characterization of the behavior of an analogous version of the Chua`s circuit. The electronic circuit signals are captured using a data acquisition board (DAQ) and processed using LabVIEW environment. The following aspects of the time series analysis are analyzed: time waveforms, phase portraits, frequency spectra, Poincar, sections, and bifurcation diagram. The circuit behavior is experimentally mapped with the parameter variations, where are identified equilibrium points, periodic and chaotic attractors, and bifurcations. These analysis techniques are performed in real-time and can be applied to characterize, with precision, several nonlinear systems.

EFFECTIVE NETWORKS FOR REAL-TIME DISTRIBUTED PROCESSING

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.

Simple telemedicine for developing regions: Camera phones and paper-based microfluidic devices for real-time, off-site diagnosis

This article describes a prototype system for quantifying bioassays and for exchanging the results of the assays digitally with physicians located off-site. The system uses paper-based microfluidic devices for running multiple assays simultaneously, camera phones or portable scanners for digitizing the intensity of color associated with each colorimetric assay, and established communications infrastructure for transferring the digital information from the assay site to an off-site laboratory for analysis by a trained medical professional; the diagnosis then can be returned directly to the healthcare provider in the field. The microfluidic devices were fabricated in paper using photolithography and were functionalized with reagents for colorimetric assays. The results of the assays were quantified by comparing the intensities of the color developed in each assay with those of calibration curves. An example of this system quantified clinically relevant concentrations of glucose and protein in artificial urine. The combination of patterned paper, a portable method for obtaining digital images, and a method for exchanging results of the assays with off-site diagnosticians offers new opportunities for inexpensive monitoring of health, especially in situations that require physicians to travel to patients (e.g., in the developing world, in emergency management, and during field operations by the military) to obtain diagnostic information that might be obtained more effectively by less valuable personnel.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Development of a Real-time Formative Feedback Student Response System

This paper is focusing IT-supported real-time formative feedback in a classroom context. The development of a Student and Teacher Response System (STRS) is described. Since there are a number of obstacles for effective interaction in large classes, IT can be used to support the teachers aim to find out if students understand the lecture and accordingly adjust the content and design of the lecture. The system can be used for formative assessment before, during, and after a lecture. It is also possible for students to initiate interaction during lectures by posing questions anonymously. The main contributions of the paper are a) the description of the interactive real-time system and b) the development process behind it.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

An Agent-Based Approach to Real-Time Patient Identification for Clinical Trials

Patient recruitment for clinical trials is expensive and has been a significant challenge, with many trials not achieving their recruitment goals. One method that shows promise for improving recruitment is the use of interactive prompts that inform practitioners of patient eligibility for clinical trials during consultation. This paper presents the ePCRN-IDEA recruitment system, which utilises an agent-based infrastructure to enable real-time recruitment of patients. In essence, whenever patients enter a clinic, the system compares their details against eligibility criteria, which define the requirements of active clinical trials. If a patient is found to be eligible, a prompt is raised to notify the user. In this way, it becomes possible for recruitment to take place quickly in a cost effective manner, whilst maintaining patient trust through the involvement of their own health care practitioner.

On Real-Time Monitoring And Control For Efficient Management Of Drinking Water Networks: Barcelona Case Study

Drinking water utilities in urban areas are focused on finding smart solutions facing new challenges in their real-time operation because of limited water resources, intensive energy requirements, a growing population, a costly and ageing infrastructure, increasingly stringent regulations, and increased attention towards the environmental impact of water use. Such challenges force water managers to monitor and control not only water supply and distribution, but also consumer demand. This paper presents and discusses novel methodologies and procedures towards an integrated water resource management system based on advanced ICT technologies of automation and telecommunications for largely improving the efficiency of drinking water networks (DWN) in terms of water use, energy consumption, water loss minimization, and water quality guarantees. In particular, the paper addresses the first results of the European project EFFINET (FP7-ICT2011-8-318556) devoted to the monitoring and control of the DWN in Barcelona (Spain). Results are split in two levels according to different management objectives: (i) the monitoring level is concerned with all the aspects involved in the observation of the current state of a system and the detection/diagnosis of abnormal situations. It is achieved through sensors and communications technology, together with mathematical models; (ii) the control level is concerned with computing the best suitable and admissible control strategies for network actuators as to optimize a given set of operational goals related to the performance of the overall system. This level covers the network control (optimal management of water and energy) and the demand management (smart metering, efficient supply). The consideration of the Barcelona DWN as the case study will allow to prove the general applicability of the proposed integrated ICT solutions and their effectiveness in the management of DWN, with considerable savings of electricity costs and reduced water loss while ensuring the high European standards of water quality to citizens.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Application Of Real-Time Control Strategy To Improve Nitrogen Removal In Wastewater Treatment

Biological nitrogen removal is an important task in the wastewater treatment. However, the actual removal of total nitrogen (TN) in the wastewater treatment plant (WWTP) is often unsatisfactory due to several causes, one of which is the insufficient availability of carbon source. One possible approach to improve the nitrogen removal therefore is addition of external carbon source, while the amount of which is directly related to operation cost of a WWTP. It is obviously necessary to determine the accurate amount of addition of external carbon source according to the demand depending on the influent wastewater quality. This study focused on the real-time control of external carbon source addition based on the on-line monitoring of influent wastewater quality. The relationship between the influent wastewater quality (specifically the concentration of COD and ammonia) and the demand of carbon source was investigated through experiments on a pilot-scale A/O reactor (1m3) at the Nanjing WWTP, China. The minimum doses of carbon source addition at different situations of influent wastewater quality were determined to ensure the effluent wastewater quality meets the discharge standard. The obtained relationship is expected to be applied in the full-scale WWTPs. .

A Gradient-Type Method For Real-Time State Estimation Of Water Distribution Networks

Drinking water distribution networks risk exposure to malicious or accidental contamination. Several levels of responses are conceivable. One of them consists to install a sensor network to monitor the system on real time. Once a contamination has been detected, this is also important to take appropriate counter-measures. In the SMaRT-OnlineWDN project, this relies on modeling to predict both hydraulics and water quality. An online model use makes identification of the contaminant source and simulation of the contaminated area possible. The objective of this paper is to present SMaRT-OnlineWDN experience and research results for hydraulic state estimation with sampling frequency of few minutes. A least squares problem with bound constraints is formulated to adjust demand class coefficient to best fit the observed values at a given time. The criterion is a Huber function to limit the influence of outliers. A Tikhonov regularization is introduced for consideration of prior information on the parameter vector. Then the Levenberg-Marquardt algorithm is applied that use derivative information for limiting the number of iterations. Confidence intervals for the state prediction are also given. The results are presented and discussed on real networks in France and Germany.

Adaptive, Decentralized, And Real-Time Sampling Strategies For Resource Constrained Hydraulic And Hydrologic Sensor Networks

We discuss the development and performance of a low-power sensor node (hardware, software and algorithms) that autonomously controls the sampling interval of a suite of sensors based on local state estimates and future predictions of water flow. The problem is motivated by the need to accurately reconstruct abrupt state changes in urban watersheds and stormwater systems. Presently, the detection of these events is limited by the temporal resolution of sensor data. It is often infeasible, however, to increase measurement frequency due to energy and sampling constraints. This is particularly true for real-time water quality measurements, where sampling frequency is limited by reagent availability, sensor power consumption, and, in the case of automated samplers, the number of available sample containers. These constraints pose a significant barrier to the ubiquitous and cost effective instrumentation of large hydraulic and hydrologic systems. Each of our sensor nodes is equipped with a low-power microcontroller and a wireless module to take advantage of urban cellular coverage. The node persistently updates a local, embedded model of flow conditions while IP-connectivity permits each node to continually query public weather servers for hourly precipitation forecasts. The sampling frequency is then adjusted to increase the likelihood of capturing abrupt changes in a sensor signal, such as the rise in the hydrograph – an event that is often difficult to capture through traditional sampling techniques. Our architecture forms an embedded processing chain, leveraging local computational resources to assess uncertainty by analyzing data as it is collected. A network is presently being deployed in an urban watershed in Michigan and initial results indicate that the system accurately reconstructs signals of interest while significantly reducing energy consumption and the use of sampling resources. We also expand our analysis by discussing the role of this approach for the efficient real-time measurement of stormwater systems.