Hardware accelerated volume texturing.

The emergence of volume graphics, a sub field in computer graphics, has been evident for the last 15 years. Growing from scientific visualization problems, volume graphics has established itself as an important field in general computer graphics. However, the general graphics fraternity still favour the established surface graphics techniques. This is due to well founded and established techniques and a complete pipeline through software onto display hardware. This enables real-time applications to be constructed with ease and used by a wide range of end users due to the readily available graphics hardware adopted by many computer manufacturers. Volume graphics has traditionally been restricted to high-end systems due to the complexity involved with rendering volume datasets. Either specialised graphics hardware or powerful computers were required to generate images, many of these not in real-time. Although there have been specialised hardware solutions to the volume rendering problem, the adoption of the volume dataset as a primitive relies on end-users with commodity hardware being able to display images at interactive rates. The recent emergence of programmable consumer level graphics hardware is now allowing these platforms to compute volume rendering at interactive rates. Most of the work in this field is directed towards scientific visualisation. The work in this thesis addresses the issues in providing real-time volume graphics techniques to the general graphics community using commodity graphics hardware. Real-time texturing of volumetric data is explored as an important set of techniques in delivering volume datasets as a general graphics primitive. The main contributions of this work are; The introduction of efficient acceleration techniques; Interactive display of amorphous phenomena modelled outside an object defined in a volume dataset; Interactive procedural texture synthesis for volume data; 2D texturing techniques and extensions for volume data in real-time; A flexible surface detail mapping algorithm that removes many previous restrictions Parts of this work have been presented at the 4th International Workshop on Volume Graphics and also published in Volume Graphics 2005

A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great deal of attention has been devoted to improving it and thereby ensuring reliable transmission. In this paper, a predictive maintenance framework using machine learning techniques is proposed for real-time heath monitoring and prognosis of semiconductor laser and thus enhancing its reliability. The proposed approach is composed of three stages: i) real-time performance degradation prediction, ii) degradation detection, and iii) remaining useful life (RUL) prediction. First of all, an attention based gated recurrent unit (GRU) model is adopted for real-time prediction of performance degradation. Then, a convolutional autoencoder is used to detect the degradation or abnormal behavior of a laser, given the predicted degradation performance values. Once an abnormal state is detected, a RUL prediction model based on attention-based deep learning is utilized. Afterwards, the estimated RUL is input for decision making and maintenance planning. The proposed framework is validated using experimental data derived from accelerated aging tests conducted for semiconductor tunable lasers. The proposed approach achieves a very good degradation performance prediction capability with a small root mean square error (RMSE) of 0.01, a good anomaly detection accuracy of 94.24% and a better RUL estimation capability compared to the existing ML-based laser RUL prediction models.Comment: Published in Journal of Lightwave Technology (Volume: 40, Issue: 14, 15 July 2022

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.Comment: Pre-print under review, submitted to Proceedings of Machine Learning Researc

A methodology for probabilistic real-time forecasting – an urban case study

Copyright © IWA Publishing 2013. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, Volume 15 (3), pp. 751-762 (2013), DOI:10.2166/hydro.2012.031 and is available at www.iwapublishing.comThe phenomenon of urban flooding due to rainfall exceeding the design capacity of drainage systems is a global problem and can have significant economic and social consequences. The complex nature of quantitative precipitation forecasts (QPFs) from numerical weather prediction (NWP) models has facilitated a need to model and manage uncertainty. This paper presents a probabilistic approach for modelling uncertainty from single-valued QPFs at different forecast lead times. The uncertainty models in the form of probability distributions of rainfall forecasts combined with a sewer model is an important advancement in real-time forecasting at the urban scale. The methodological approach utilized in this paper involves a retrospective comparison between historical forecasted rainfall from a NWP model and observed rainfall from rain gauges from which conditional probability distributions of rainfall forecasts are derived. Two different sampling methods, respectively, a direct rainfall quantile approach and the Latin hypercube sampling based method were used to determine the uncertainty in forecasted variables (water level, volume) for a test urban area, the city of Aarhus. The results show the potential for applying probabilistic rainfall forecasts and their subsequent use in urban drainage forecasting for estimation of prediction uncertainty

Biosensors to diagnose Chagas disease: A review

International audienceChagas disease (CD), which mostly affects underprivileged people, has turned into one 9 of Latin America's main public health problems. Prevention of the disease requires early diagnosis, 10 initiation of therapy, and regular blood monitoring of the infected individual. However, the majority 11 of the infections go undiagnosed because of general mild symptoms and lack of access to medical 12 care. Therefore, more affordable and accessible detection technologies capable of providing early 13 diagnosis and parasite load measurements in settings where CD is prevalent are needed to enable 14 enhanced intervention strategies. This review discusses currently available detection technologies 15 and emerging biosensing technologies for a future application to CD. Even if biosensing 16 technologies still require further research efforts to develop portable systems, we arrive to the 17 conclusion that biosensors could improve diagnosis and the patients' treatment follow-up, in terms 18 of rapidity, small sample volume, high integration, ease of use, real-time and low cost detection 19 compared to current conventional technologies. 2

Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results

BrachyView, A novel inbody imaging system for HDR prostate brachytherapy: Design and Monte Carlo feasibility study

Purpose: High dose rate (HDR) brachytherapy is a form of radiation therapy for treating prostate cancer whereby a high activity radiation source is moved between predefined positions inside applicators inserted within the treatment volume. Accurate positioning of the source is essential in delivering the desired dose to the target area while avoiding radiation injury to the surrounding tissue. In this paper, HDR BrachyView, a novel inbody dosimetric imaging system for real time monitoring and verification of the radioactive seed position in HDR prostate brachytherapy treatment is introduced. The current prototype consists of a 15 × 60 mm2 silicon pixel detector with a multipinhole tungsten collimator placed 6.5 mm above the detector. Seven identical pinholes allow full imaging coverage of the entire treatment volume. The combined pinhole and pixel sensor arrangement is geometrically designed to be able to resolve the three-dimensional location of the source. The probe may be rotated to keep the whole prostate within the transverse plane. The purpose of this paper is to demonstrate the efficacy of the design through computer simulation, and to estimate the accuracy in resolving the source position (in detector plane and in 3D space) as part of the feasibility study for the BrachyView project. Methods: Monte Carlo simulations were performed using the GEANT4 radiation transport model, with a 192Ir source placed in different locations within a prostate phantom. A geometrically accurate model of the detector and collimator were constructed. Simulations were conducted with a single pinhole to evaluate the pinhole design and the signal to background ratio obtained. Second, a pair of adjacent pinholes were simulated to evaluate the error in calculated source location. Results: Simulation results show that accurate determination of the true source position is easily obtainable within the typical one second source dwell time. The maximum error in the estimated projection position was found to be 0.95 mm in the imaging (detector) plane, resulting in a maximum source positioning estimation error of 1.48 mm. Conclusions: HDR BrachyView is a feasible design for real-time source tracking in HDR prostate brachytherapy. It is capable of resolving the source position within a subsecond dwell time. In combination with anatomical information obtained from transrectal ultrasound imaging, HDR BrachyView adds a significant quality assurance capability to HDR brachytherapy treatment systems. © 2013 American Association of Physicists in Medicine

Generating Hydrants’ Configurations for Efficient Analysis and Management of Pressurized Irrigation Distribution Systems

Water scarcity is a mounting problem in arid and semi-arid regions such as the Mediterranean. Therefore, smarter and more effective water management is required, especially in irrigated agriculture. One of the most challenging uncertainties in the operation of on-demand collective Pressurized Irrigation Distribution Systems (PIDSs) is to know, a priori, the number and the position of hydrants in simultaneous operation. To this end, a model was developed to generate close to reality operating hydrants configurations, with 15, 30 or 60 min time steps, by estimating the irrigation scheduling for the entire irrigation season, using climatic, crop and soil data. The model is incorporated in an integrated DSS called Decision Support for Irrigation Distribution Systems (DESIDS) and links two of its modules, namely, the irrigation demand and scheduling module and the hydraulic analysis module. The latter is used to perform two types of analyses for the performance assessment and decision-making processes. The model was used in a real case study in Italy to generate hydrants’ operation taking into consideration irrigation scheduling. The results show that during the peak period, hydrants simultaneity topped 62%. The latter created pressure deficit in some hydrants, thus reducing the volume of water supplied for irrigation by up to 87 m3 in a single hydrant during the peak demand day. The developed model proved to be an important tool for irrigation managers, as it provides vital information with great flexibility and the ability to assess and predict the operation of PIDSs at any period during the irrigation season