Application of an online-biomass sensor in an optical multisensory platform prototype for growth monitoring of biotechnical relevant microorganism and cell lines in single-use shake flasks

In the context of this work we evaluated a multisensory, noninvasive prototype platform for shake flask cultivations by monitoring three basic parameters (pH, pO2 and biomass). The focus lies on the evaluation of the biomass sensor based on backward light scattering. The application spectrum was expanded to four new organisms in addition to E. coli K12 and S. cerevisiae [1]. It could be shown that the sensor is appropriate for a wide range of standard microorganisms, e.g., L. zeae, K. pastoris, A. niger and CHO-K1. The biomass sensor signal could successfully be correlated and calibrated with well-known measurement methods like OD600, cell dry weight (CDW) and cell concentration. Logarithmic and Bleasdale-Nelder derived functions were adequate for data fitting. Measurements at low cell concentrations proved to be critical in terms of a high signal to noise ratio, but the integration of a custom made light shade in the shake flask improved these measurements significantly. This sensor based measurement method has a high potential to initiate a new generation of online bioprocess monitoring. Metabolic studies will particularly benefit from the multisensory data acquisition. The sensor is already used in labscale experiments for shake flask cultivations.

Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe

Estimation of pasture productivity is an important step for the farmer in terms of planning animal stocking, organizing animal lots, and determining supplementary feeding needs throughout the year. The main objective of this work was to evaluate technologies which have potential for monitoring aspects related to spatial and temporal variability of pasture green and dry matter yield (respectively, GM and DM, in kg/ha) and support to decision making for the farmer. Two types of sensors were evaluated: an active optical sensor(OptRx®, which measures the NDVI, Normalized Difference Vegetation Index) and a capacitance probe (GrassMaster II which estimates plant mass). The results showed the potential of NDVI for monitoring the evolution of spatial and temporal patterns of vegetative growth of biodiverse pasture. Higher NDVI values were registered as pasture approached its greatest vegetative vigor, with a significant fall in the measured NDVI at the end of Spring, when the pasture began to dry due to the combination of higher temperatures and lower soil moisture content. This index was also effective for identifying different plant species (grasses/legumes) and variability in pasture yield. Furthermore, it was possible to develop calibration equations between the capacitance and the NDVI (R2 = 0.757; p < 0.01), between capacitance and GM (R2 = 0.799; p<0.01), between capacitance and DM (R2 = 0.630; p<0.01), between NDVI and GM (R2=0.745; p < 0.01), and between capacitance and DM (R2=0.524; p<0.01). Finally, a direct relationship was obtained between NDVI and pasture moisture content (PMC, in %) and between capacitance and PMC (respectively, R2 = 0.615; p<0.01 and R2=0.561; p <0.01) in Alentejo dryland farming systems.

An investigation of rail condition monitoring by Fibre Bragg Grating sensors

Condition monitoring on rails and train wheels is vitally important to the railway asset management and the rail-wheel interactions provide the crucial information of the health state of both rails and wheels. Continuous and remote monitoring is always a preference for operators. With a new generation of strain sensing devices in Fibre Bragg Grating (FBG) sensors, this study explores the possibility of continuous monitoring of the health state of the rails; and investigates the required signal processing techniques and their limitations.

A comprehensive condition monitoring of modern railway

The demand for high quality rail services in the twenty-first century has put an ever increasing demand on all rail operators. In order to meet the expectation of their patrons, the maintenance regime of railway systems has to be tightened up, the track conditions have to be well looked after, the rolling stock must be designed to withstand heavy duty. In short, in an ideal world where resources are unlimited, one needs to implement a very rigorous inspection regime in order to take care of the modem needs of a railway system [1]. If cost were not an issue, the maintenance engineers could inspect the train body by the most up-to-date techniques such as ultra-sound examination, x-ray inspection, magnetic particle inspection, etc. on a regular basis. However it is inconceivable to have such a perfect maintenance regime in any commercial railway. Likewise, it is impossible to have a perfect rolling stock which can weather all the heavy duties experienced in a modem railway. Hence it is essential that some condition monitoring schemes are devised to pick up potential defects which could manifest into safety hazards. This paper introduces an innovative condition monitoring system for track profile and, together with an instrumented car to carry out surveillance of the track, will provide a comprehensive railway condition monitoring system which is free from the usual difficulty of electromagnetic compatibility issues in a typical railway environment

Automated object identification using optical video cameras on construction sites

Visual recording devices such as video cameras, CCTVs, or webcams have been broadly used to facilitate work progress or safety monitoring on construction sites. Without human intervention, however, both real-time reasoning about captured scenes and interpretation of recorded images are challenging tasks. This article presents an exploratory method for automated object identification using standard video cameras on construction sites. The proposed method supports real-time detection and classification of mobile heavy equipment and workers. The background subtraction algorithm extracts motion pixels from an image sequence, the pixels are then grouped into regions to represent moving objects, and finally the regions are identified as a certain object using classifiers. For evaluating the method, the formulated computer-aided process was implemented on actual construction sites, and promising results were obtained. This article is expected to contribute to future applications of automated monitoring systems of work zone safety or productivity.

Domestic energy monitoring in Victorian households with PV solar installations : feasibility report for GV Community Energy

Australia requires decisive action on climate change and issues of sustainability. The Urban Informatics Research Lab has been funded by the Queensland State Government to conduct a three year study (2009 – 2011) exploring ways to support Queensland residents in making more sustainable consumer and lifestyle choices. We conduct user-centred design research that inform the development of real-time, mobile, locational, networked information interfaces, feedback mechanisms and persuasive and motivational approaches that in turn assist in-situ decision making and environmental awareness in everyday settings. The study aims to deliver usable and useful prototypes offering individual and collective visualisations of ecological impact and opportunities for engagement and collaboration in order to foster a participatory and sustainable culture of life in Australia. Raising people’s awareness with environmental data and educational information does not necessarily trigger sufficient motivation to change their habits towards a more environmentally friendly and sustainable lifestyle. Our research seeks to develop a better understanding how to go beyond just informing and into motivating and encouraging action and change. Drawing on participatory culture, ubiquitous computing, and real-time information, the study delivers research that leads to viable new design approaches and information interfaces which will strengthen Australia’s position to meet the targets of the Clean Energy Future strategy, and contribute to the sustainability of a low-carbon future in Australia. As part of this program of research, the Urban Informatics Research Lab has been invited to partner with GV Community Energy Pty Ltd on a project funded by the Victorian Government Sustainability Fund. This feasibility report specifically looks at the challenges and opportunities of energy monitoring in households in Victoria that include a PV solar installation. The report is structured into two parts: In Part 1, we first review a range of energy monitoring solutions, both stand-alone and internet-enabled. This section primarily focusses on the technical capacilities. However, in order to understand this information and make an informed decision, it is crucial to understand the basic principles and limitations of energy monitoring as well as the opportunities and challenges of a networked approach towards energy monitoring which are discussed in Section 2.

Crowd monitoring using computer vision

Novel computer vision techniques have been developed for automatic monitoring of crowed environments such as airports, railway stations and shopping malls. Using video feeds from multiple cameras, the techniques enable crowd counting, crowd flow monitoring, queue monitoring and abnormal event detection. The outcome of the research is useful for surveillance applications and for obtaining operational metrics to improve business efficiency.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Tissue thickness calculation in ocular optical coherence tomography

Thickness measurements derived from optical coherence tomography (OCT) images of the eye are a fundamental clinical and research metric, since they provide valuable information regarding the eye’s anatomical and physiological characteristics, and can assist in the diagnosis and monitoring of numerous ocular conditions. Despite the importance of these measurements, limited attention has been given to the methods used to estimate thickness in OCT images of the eye. Most current studies employing OCT use an axial thickness metric, but there is evidence that axial thickness measures may be biased by tilt and curvature of the image. In this paper, standard axial thickness calculations are compared with a variety of alternative metrics for estimating tissue thickness. These methods were tested on a data set of wide-field chorio-retinal OCT scans (field of view (FOV) 60° x 25°) to examine their performance across a wide region of interest and to demonstrate the potential effect of curvature of the posterior segment of the eye on the thickness estimates. Similarly, the effect of image tilt was systematically examined with the same range of proposed metrics. The results demonstrate that image tilt and curvature of the posterior segment can affect axial tissue thickness calculations, while alternative metrics, which are not biased by these effects, should be considered. This study demonstrates the need to consider alternative methods to calculate tissue thickness in order to avoid measurement error due to image tilt and curvature.

A Multi frequency study of Radio Intensity Variations For Active Galactic Nuclei of Different Optical Classes

A study of radio intensity variations at seven frequencies in the range 0.3 to 90 GHz for compact extragalactic radio sources classified as BL Lacs and high- and low-optical polarization quasars (HPQs and LPQs) is presented. This include the results of flux-density monitoring of 33 compact sources for three years at 327 MHz with the Ooty Synthesis Radio Telescope. The degrees of 'short-term' (tau less than about 1 yr) variability for the three optical types are found to be indistinguishable at low frequencies (less than 1 GHz), pointing to an extrinsic origin for the low-frequency variability. At high frequencies, a distinct dependence on optical type is present, the variability increasing from LPQs, through HPQs to BL Lacs. This trend persists even when only sources with ultra-flat radio spectra (alpha greater than -0.2) are considered. Implications of this for the phenomenon of high-frequency variability and the proposed unification schemes for different optical types of active galactic nuclei are discussed.

Water quality estimation by optical remote sensing in boreal lakes

In lake-rich regions, the gathering of information about water quality is challenging because only a small proportion of the lakes can be assessed each year by conventional methods. One of the techniques for improving the spatial and temporal representativeness of lake monitoring is remote sensing from satellites and aircrafts. The experimental material included detailed optical measurements in 11 lakes, air- and spaceborne remote sensing measurements with concurrent field sampling, automatic raft measurements and a national dataset of routine water quality measurements from over 1100 lakes. The analyses of the spatially high-resolution airborne remote sensing data from eutrophic and mesotrophic lakes showed that one or a few discrete water quality observations using conventional monitoring can yield a clear over- or underestimation of the overall water quality in a lake. The use of TM-type satellite instruments in addition to routine monitoring results substantially increases the number of lakes for which water quality information can be obtained. The preliminary results indicated that coloured dissolved organic matter (CDOM) can be estimated with TM-type satellite instruments, which could possibly be utilised as an aid in estimating the role of lakes in global carbon budgets. Based on the results of reflectance modelling and experimental data, MERIS satellite instrument has optimal or near-optimal channels for the estimation of turbidity, chlorophyll a and CDOM in Finnish lakes. MERIS images with a 300 m spatial resolution can provide water quality information in different parts of large and medium-sized lakes, and in filling in the gaps resulting from conventional monitoring. Algorithms that would not require simultaneous field data for algorithm training would increase the amount of remote sensing-based information available for lake monitoring. The MERIS Boreal Lakes processor, trained with the optical data and concentration ranges provided by this study, enabled turbidity estimations with good accuracy without the need for algorithm correction with field measurements, while chlorophyll a and CDOM estimations require further development of the processor. The accuracy of interpreting chlorophyll a via semi empirical algorithms can be improved by classifying lakes prior to interpretation according to their CDOM level and trophic status. Optical modelling indicated that the spectral diffuse attenuation coefficient can be estimated with reasonable accuracy from the measured water quality concentrations. This provides more detailed information on light attenuation from routine monitoring measurements than is available through the Secchi disk transparency. The results of this study improve the interpretation of lake water quality by remote sensing and encourage the use of remote sensing in lake monitoring.

Fiber Bragg Grating Sensors for Real Time Monitoring of Evacuation Process

Fiber bragg grating (FBG) sensors have been widely used for number of sensing applications like temperature, pressure, acousto-ultrasonic, static and dynamic strain, refractive index change measurements and so on. Present work demonstrates the use of FBG sensors in in-situ measurement of vacuum process with simultaneous leak detection capability. Experiments were conducted in a bell jar vacuum chamber facilitated with conventional Pirani gauge for vacuum measurement. Three different experiments have been conducted to validate the performance of FBG sensor in monitoring vacuum creating process and air bleeding. The preliminary results of FBG sensors in vacuum monitoring have been compared with that of commercial Pirani gauge sensor. This novel technique offers a simple alternative to conventional method for real time monitoring of evacuation process. Proposed FBG based vacuum sensor has potential applications in vacuum systems involving hazardous environment such as chemical and gas plants, automobile industries, aeronautical establishments and leak monitoring in process industries, where the electrical or MEMS based sensors are prone to explosion and corrosion.

Health Monitoring Of Composite Structures Based On Acoustic Wave Sensing Using Fiber Optic Sensors

Structural Health Monitoring has gained wide acceptance in the recent past as a means to monitor a structure and provide an early warning of an unsafe condition using real-time data. Utilization of structurally integrated, distributed sensors to monitor the health of a structure through accurate interpretation of sensor signals and real-time data processing can greatly reduce the inspection burden. The rapid improvement of the Fiber Optic Sensor technology for strain, vibration, ultrasonic and acoustic emission measurements in recent times makes it feasible alternative to the traditional strain gauges, PVDF and conventional Piezoelectric sensors used for Non Destructive Evaluation (NDE) and Structural Health Monitoring (SHM). Optical fiber-based sensors offer advantages over conventional strain gauges, and PZT devices in terms of size, ease of embedment, immunity from electromagnetic interference (EMI) and potential for multiplexing a number of sensors. The objective of this paper is to demonstrate the acoustic wave sensing using Extrinsic Fabry-Perot Interferometric (EFPI) sensor on a GFRP composite laminates. For this purpose experiments have been carried out initially for strain measurement with Fiber Optic Sensors on GFRP laminates with intentionally introduced holes of different sizes as defects. The results obtained from these experiments are presented in this paper. Numerical modeling has been carried out to obtain the relationship between the defect size and strain.