Three dimensional city models are necessary for supporting numerous management applications. For the determination of city models for visualization purposes, several standardized workflows do exist. They are either based on photogrammetry or on LiDAR or on a combination of both data acquisition techniques. However, the automated determination of reliable and highly accurate city models is still a challenging task, requiring a workflow comprising several processing steps. The most relevant are building detection, building outline generation, building modeling, and finally, building quality analysis. Commercial software tools for building modeling require, generally, a high degree of human interaction and most automated approaches described in literature stress the steps of such a workflow individually. In this article, we propose a comprehensive approach for automated determination of 3D city models from airborne acquired point cloud data. It is based on the assumption that individual buildings can be modeled properly by a composition of a set of planar faces. Hence, it is based on a reliable 3D segmentation algorithm, detecting planar faces in a point cloud. This segmentation is of crucial importance for the outline detection and for the modeling approach. We describe the theoretical background, the segmentation algorithm, the outline detection, and the modeling approach, and we present and discuss several actual projects.
Dinoflagellates are not only important marine primary producers and grazers, but also the major causative agents of harmful algal blooms. It has been reported that many dinoflagellate species can produce various natural toxins. These toxins can be extremely toxic and many of them are effective at far lower dosages than conventional chemical agents. Consumption of seafood contaminated by algal toxins results in various seafood poisoning syndromes: paralytic shellfish poisoning (PSP), neurotoxic shellfish poisoning (NSP), amnesic shellfish poisoning (ASP), diarrheic shellfish poisoning (DSP), ciguatera fish poisoning (CFP) and azaspiracid shellfish poisoning (ASP). Most of these poisonings are caused by neurotoxins which present themselves with highly specific effects on the nervous system of animals, including humans, by interfering with nerve impulse transmission. Neurotoxins are a varied group of compounds, both chemically and pharmacologically. They vary in both chemical structure and mechanism of action, and produce very distinct biological effects, which provides a potential application of these toxins in pharmacology and toxicology. This review summarizes the origin, structure and clinical symptoms of PSP, NSP, CFP, AZP, yessotoxin and palytoxin produced by marine dinoflagellates, as well as their molecular mechanisms of action on voltage-gated ion channels.
This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.
In this paper a stain sensor to measure large strain (80%) in textiles is presented. It consists of a mixture of 50wt-% thermoplastic elastomer (TPE) and 50wt-% carbon black particles and is fiber-shaped with a diameter of 0.315mm. The attachment of the sensor to the textile is realized using a silicone film. This sensor configuration was characterized using a strain tester and measuring the resistance (extension-retraction cycles): It showed a linear resistance response to strain, a small hysteresis, no ageing effects and a small dependance on the strain velocity. The total mean error caused by all these effects was +/- 5.5% in strain. Washing several times in a conventional washing machine did not influence the sensor properties. The paper finishes by showing an example application where 21 strain sensors were integrated into a catsuit. With this garment, 27 upper body postures could be recognized with an accuracy of 97%.
An aflatoxin B-1 (AFB(1)) electrochemical immunosensor was developed by the immobilisation of aflatoxin B-1-bovine serum albumin (AFB(1)-BSA) conjugate on a polythionine (PTH)/gold nanoparticles (AuNP)-modified glassy carbon electrode (GCE). The surface of the AFB(1)-BSA conjugate was covered with horseradish peroxidase (HRP),in order to prevent non-specific binding of the immunosensors with ions in the test solution. The AFB(1) immunosensor exhibited a quasi-reversible electrochemistry as indicated by a cyclic voltammetric (CV) peak separation (Delta E-p) value of 62 mV. The experimental procedure for the detection of AFB(1) involved the setting up of a competition between free AFB(1) and the immobilised AFB(1)-BSA conjugate for the binding sites of free anti-aflatoxin B-1 (anti-AFB(1)) antibody. The immunosensor's differential pulse voltammetry (DPV) responses (peak currents) decreased as the concentration of free AFB(1) increased within a dynamic linear range (DLR) of 0.6 - 2.4 ng/mL AFB(1) and a limit of detection (LOD) of 0.07 ng/mL AFB(1). This immunosensing procedure eliminates the need for enzyme-labeled secondary antibodies normally used in conventional ELISA-based immunosensors.
Ciguatera Fish Poisoning (CFP) is the most frequently reported seafood-toxin illness in the world, and it causes substantial physical and functional impact. It produces a myriad of gastrointestinal, neurologic and/or cardiovascular symptoms which last days to weeks, or even months. Although there are reports of symptom amelioration with some interventions ( e. g. IV mannitol), the appropriate treatment for CFP remains unclear to many physicians. We review the literature on the treatments for CFP, including randomized controlled studies and anecdotal reports. The article is intended to clarify treatment options, and provide information about management and prevention of CFP, for emergency room physicians, poison control information providers, other health care providers, and patients.
In this paper we report the successful development of pressure sensitive textile prototypes based on flexible optical fibers technology. Our approach is based on thermoplastic silicone fibers, which can be integrated into woven textiles. As soon as pressure at a certain area of the textile is applied to these fibers they change their cross section reversibly, due to their elastomeric character, and a simultaneous change in transmitted light intensity can be detected. We have successfully manufactured two different woven samples with fibers of 0.51 and 0.98 mm diameter in warp and weft direction, forming a pressure sensitive matrix. Determining their physical behavior when a force is applied shows that pressure measurements are feasible. Their usable working range is between 0 and 30 N. Small drifts in the range of 0.2 to 4.6%, over 25 load cycles, could be measured. Finally, a sensor array of 2 x 2 optical fibers was tested for sensitivity, spatial resolution and light coupling between fibers at intersections.
Ionophore incorporated PVC membrane sensors are well-established analytical tools routinely used for the selective and direct measurement of a wide variety of different ions in complex biological and environmental samples. Potentiometric sensors have some outstanding advantages including simple design and operation, wide linear dynamic range, relatively fast response and rational selectivity. The vital component of such plasticized PVC members is the ionophore involved, defining the selectivity of the electrodes' complex formation. Molecular recognition causes the formation of many different supramolecules. Different types of supramolecules, like calixarenes, cyclodextrins and podands, have been used as a sensing material in the construction of ion selective sensors. Schiff's bases and crown ethers, which feature prominently in supramolecular chemistry, can be used as sensing materials in the construction of potentiometric ion selective electrodes. Up to now, more than 200 potentiometric membrane sensors for cations and anions based on Schiff's bases and crown ethers have been reported. In this review cation binding and anion complexes will be described. Liquid membrane sensors based on Schiff's bases and crown ethers will then be discussed.
In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a two-layer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.