Jeffrey A. Tuhtan

Joint Estimation of Bulk Flow Velocity and Angle Using a Lateral Line Probe

Measurement of complex natural flows, especially those occurring in rivers due to man-made structures, is often hampered by the limitations of existing flow measurement methods. Furthermore, there is a growing need for new measurement devices that are capable of measuring the hydrodynamic characteristics of complex natural flows required in environmental studies that often use fish as an indicator of ecological health. In this paper, we take the first step toward in situ natural flow measurements with a new biologically inspired probe design in conjunction with signal processing methods. The device presented in this paper is a dedicated hydrodynamically sensitive sensor array following the fish lateral line sensor modality. Low-level multidimensional sensor signals are transformed to the two key hydrodynamic primitives, bulk flow velocity and bulk flow angle. We show that this can be achieved via canonical signal transformation and kernel ridge regression, allowing velocity estimates with a less than 10 cm/s error. The approach provides robust velocity estimates not only when the sensor is ideally oriented parallel to the bulk flow, but also across the full range of angular deviations up to a completely orthogonal orientation by correcting the pressure field asymmetry for large angular deviations. Furthermore, we show that their joint estimation becomes feasible above a threshold current velocity of 0.45 m/s. The method demonstrated an error of 14 cm/s in velocity estimation in a river environment after training in laboratory conditions.

Flow feature extraction for underwater robot localization: Preliminary results

Underwater robots conventionally use vision and sonar sensors for perception purposes, but recently bio-inspired sensors that can sense flow have been developed. In literature, flow sensing has been shown to provide useful information about an underwater object and its surroundings. In the light of this, we develop an underwater landmark recognition technique which is based on the extraction and comparison of compact flow features. The proposed features are based on frequency spectrum of a pressure signal acquired by a piezo-resistive sensor. We report experiments in semi-natural (human-made flume with obstacles) and natural (river) underwater conditions where the proposed technique successfully recognizes previously visited locations.

Design and application of a fish-shaped lateral line probe for flow measurement

We introduce the lateral line probe (LLP) as a measurement device for natural flows. Hydraulic surveys in rivers and hydraulic structures are currently based on time-averaged velocity measurements using propellers or acoustic Doppler devices. The long-term goal is thus to develop a sensor system, which includes spatial gradients of the flow field along a fish-shaped sensor body. Interpreting the biological relevance of a collection of point velocity measurements is complicated by the fact that fish and other aquatic vertebrates experience the flow field through highly dynamic fluid-body interactions. To collect body-centric flow data, a bioinspired fish-shaped probe is equipped with a lateral line pressure sensing array, which can be applied both in the laboratory and in the field. Our objective is to introduce a new type of measurement device for body-centric data and compare its output to estimates of conventional point-based technologies. We first provide the calibration workflow for laboratory investigations. We then provide a review of two velocity estimation workflows, independent of calibration. Such workflows are required as existing field investigations consist of measurements in environments where calibration is not feasible. The mean difference for uncalibrated LLP velocity estimates from 0 to 50 cm/s under in a closed flow tunnel and open channel flume was within 4 cm/s when compared to conventional measurement techniques. Finally, spatial flow maps in a scale vertical slot fishway are compared for the LLP, direct measurements, and 3D numerical models where it was found that the LLP provided a slight overestimation of the current velocity in the jet and underestimated the velocity in the recirculation zone.

An investigation of image processing techniques for substrate classification based on dominant grain size using RGB images from UAV

ABSTRACT Imagery collected with an unmanned aerial vehicle (UAV) in conjunction with image processing provides new sources of environmental intelligence data and can be implemented in river habitat studies. High-resolution RGB orthomosaic images with 1 cm/px resolution are generated from RGB images acquired with a UAV. Ground truth mapping of the dominant substrate of the river bottom is then used to classify each spatial region. Several texture parameters are examined using image processing techniques to determine the presence and extent of each of the dominant grain classes, providing a method to classify and map the river bed. The method differentiates between submerged, dry exposed, and vegetated regions. The image cover was classified via application and examination of a variety of pixel-based image classification methods. The highest classification accuracy for pixel based analysis was achieved using the thresholding and masking algorithm which achieved an overall 97% correct classification. In addition, object-based image classification was applied using different grey-level co-occurrence matrices (GLCM) in all directions. The classification accuracy for segmentation based classification was found to be lower, at 61%.

Estimation of Flow Turbulence Metrics With a Lateral Line Probe and Regression

The time-averaged velocity of water flow is the most commonly measured metric for both laboratory and field applications. Its employment in scientific and engineering studies often leads to an oversimplification of the underlying flow physics. In reality, complex flows are ubiquitous, and commonly arise from fluid-body interactions with man-made structures, such as bridges as well as from natural flows along rocky river beds. Studying flows outside of laboratory conditions requires more detailed information in addition to time-averaged flow properties. The choice of in situ measuring device capable of delivering turbulence metrics is determined based on site accessibility, the required measuring period, and overall flow complexity. Current devices are suitable for measuring turbulence under controlled laboratory conditions, and thus there remains a technology gap for turbulence measurement in the field. In this paper, we show how a bioinspired fish-shaped probe outfitted with an artificial lateral line can be utilized to measure turbulence metrics under challenging conditions. The device and proposed signal processing methods are experimentally validated in a scale vertical slot fishway, which represents an extreme turbulent environment, such as those commonly encountered in the field. Optimal performance is achieved after 10 s of sampling using a standard deviation feature.

RAPTOR-UAV: Real-time particle tracking in rivers using an unmanned aerial vehicle

River system measurement and mapping using UAVs is both lean and agile, with the added advantage of increased safety for the surveying crew. A common parameter of fluvial geomorphological studies is the flow velocity, which is a major driver of sediment behavior. Advances in fluid mechanics now include metrics describing the presence and interaction of coherent structures within a flow field and along its boundaries. These metrics have proven to be useful in studying the complex turbulent flows but require time-resolved flow field data, which is normally unavailable in geomorphological studies. Contactless UAV-based velocity measurement provides a new source of velocity field data for measurements of extreme hydrological events at a safe distance, and could allow for measurements of inaccessible areas. Recent works have successfully applied large-scale particle image velocimetry (LSPIV) using UAVs in rivers, focusing predominantly on surficial flow estimation by tracking intensity differences between georeferenced images. The objective of this work is to introduce a methodology for UAV based real-time particle tracking in rivers (RAPTOR) in a case study along a short test reach of the Brigach River in the German Black Forest. This methodology allows for large scale particle tracking velocimetry (LSPTV) using a combination of floating, infrared light-emitting particles and a programmable embedded color vision sensor in order to simultaneously detect and track the positions of objects. The main advantage of this approach is its ability to rapidly collect and process the position data, which can be done in real-time. The disadvantages are that the method requires the use of specialized light-emitting particles, which in some cases cannot be retrieved from the investigation area, and that the method returns velocity data in unscaled units of px/s. This work introduces the RAPTOR system with its hardware, data processing workflow, and provides an example of unscaled velocity field estimation using the proposed method. First experiences with the method show that the tracking rate of 50 Hz allows for position estimation with sub-pixel accuracy, even considering UAV self-motion. A comparison of the unscaled tracks after Savitzky-Golay filtering shows that although the time-averaged velocities remain virtually the same, the filter reduces the standard deviation by more than 40% and the maxima by 20%.

Flussabwärts gerichtete Fischwanderung an mittelgroßen Fließgewässern in Österreich@@@Downstream fish migration in middle-sized rivers in Austria

Fragen zur flussabwärts gerichteten Wanderung von Fischen und die dabei auftretenden Einflüsse von Wasserkraftwerken gewinnen in letzter Zeit zunehmend an Bedeutung. Gerade bei potamodromen Fischarten sind erhebliche Wissensdefizite vorhanden. Hauptziele des hier vorge stellten Projektes sind daher die Erfassung des Ausmaßes stromab gerichteter Wanderungen sowie die Dokumenta tion der Schädigungen ausgewählter heimischer Fischarten bei der Turbinenpassage. Durch Feldund Laborversuche sowie Modelle wird der Einfluss der Turbinenpassage auf Fischpopulationen bewertet.

Genetische Analysen von Fischbeständen: Populationsgenetik und eDNA

Genetische Methoden können schökologische Managementmaßnahmen und Monitoring-Projekte wesentlich unterstützen: Populationsgenetische Studien, wie z. B. die Analyse und Di erenzierung von danubischen und atlantischen Bachforellenpopulationen, liefern einen wesentlichen Beitrag für gezielte Besatzprogramme und Artenschutzprojekte. Eine neue – nicht invasive – Methode ermöglicht Artnachweise anhand von eDNA (environmental DNA, Umwelt DNA), was ein vielversprechendes Instrument für Monitoringprogramme darstellt.

Forschung und Technik

Seit 2011 wurden durch die EnBW in Zusammenarbeit mit dem Institut fur Umwelt studien Versuche zum Scheuchen und Leiten von Fisch en mit elektrischem Strom durchgefuhrt. Dabei wurde neben der Barrierewirkung von elektrischen Feldern auch die Moglichkeit zum Stoppen und Leiten von Fisch en entlang elektrifizierter Rechenanlagen untersucht. Die Versuche wurden sowohl unter Freilandbedingungen vor dem Einlaufbauwerk eines Kraftwerks als auch in einem Versuchsbecken durchgefuhrt. Die Ergebnisse zeigen, dass durch den Einsatz elektrischer Felder eine erhohte Abweise- oder Leitwirkung erzielt werden kann.

CLASSIFICATION OF BENTHIC BIOCENOSES OF THE LOWLAND RIVER TUDOVKA (TVER REGION, RUSSIA) USING COMMUNITY FEATURES

Within the joint Russian-Austrian monitoring programme “REFCOND_VOLGA (2006 – 20XX)”, monitoring sites were established in the headwaters of the Volga (Tver Region). River Tudovka, a right tributary to the Volga River, was included within this monitoring programme as its catchment is partly protected and has only few anthropogenic activities. The monitoring activities include physico-chemical and hydraulic parameters as well as biota with a focus is on benthic organisms (diatoms and macrozoobenthos). In this work, the longitudinal patterns in community structure are classified in the lowland river Tudovka using a novel feature-based approach taken from signal processing theory. The method first clusters field sampling data into longitudinal classes (upper, middle, lower course). Community features based on the relative frequency of individual species occurring per class are then generated. We apply both generative and discriminative classification methods. The application of generative methods provides data models which predict the probability of a new sample to belong to an existing class. In contrast, discriminative approaches search for differences between classes and allocate new data accordingly. Leveraging both methods allows for the creation of stable classifications. On this basis we show how the community features can be used to predict the longitudinal class. The community features approach also allows for a robust cross-comparison of investigation reaches over time. In cases where suitable long-term data set are available, predictive models using this approach can also be developed.