Markbaserade sensorer för insamling av skogliga data : en förstudie

En förutsättning för skoglig planering på alla nivåer är att man har en god uppfattning om tillståndet i den stående skogen. K valiten på de beslut som fattas kommer därför att vara direkt beroende av mängden och kvaliten på den information som samlats in. Sensorer som radar, lidar och olika typer av digitala kameror används idag med framgång för fjärranalys där skogen avbildas från ovan. Föreliggande arbete syftar till att belysa de tekniska förutsättningarna för att utnyttja modem sensorteknik även för markbaserade mätningar. Detta skulle i så fall öppna möjligheter att automatisera fångsten av skogliga data som därmed skulle bli kostnadseffektivare samtidigt som nya typer av data skulle bli tillgängliga. På ett tidigt stadium valdes följande tekniker ut för att närmare studeras med avseende på lämplighet att ingå i ett inventeringskoncept: • Lidar. Lidar är en laserbaserad teknik för att mäta avstånd och riktning till objekt. Laserns fördelar gentemot andra typer av sensorer är att mycket hög vinkelupplösning kan erhållas på signalen. Laser baserade sensorer framstår därför som mycket lämpliga för olika typer av scanning eller avståndsmätning. Nackdelen är att laserstrålen måste ha fri sikt för att kunna registrera ett objekt samt att det i dagsläget handlar om dyr och avancerad teknik. • Fotogrammetri i digitala bilder. Om avståndet till objektet i en bild och kamerans inre geometri är kända kan geometriska mätningar av exempelvis stamdiametrar göras i bilden. Genom att använda digitala bilder och bildanalys borde det vara möjligt att skapa program som mer eller mindre automatiskt detekterar och mäter diametrar på de stammar som är synliga i en bild. Objektavstånden tas lämpligen ut genom separat avståndsmätning med lidar eller genom stereomatchning av två eller flera bilder. • Radar. Radarsignalen använder betydligt lägre frekvenser än laser, vilket ger den intressanta egenskaper i skogsuppskatt ningssammanhang då man kan se igenom objekt mindre än halva våglängden. Nackdelen med lågfrekventa signaler är att man får en för dålig vinkelupplösning om man försöker att genom scanning ta ut vinkel och avstånd till de enskilda stammarna. Radar verkar däremot vara en mer framkomlig väg om man avser att hämta information ur den totala retursignalen. En viss uppfattning om diameter fördelningen skulle i så fall kunna fås genom att studera skillnaden i retursignalen från olika våglängder. • Ultraljud. Ultraljud kan användas enligt samma principer som radar. Fördelarna med ultraljudssensorer är att det finns enkla och billiga standardkomponenter. Nackdelen är att signalen dämpas under fården genom luften och måste därför kalibreras för förändringar i luftens temperatur och fuktighet. Inventeringens uppläggning har också betydelse för de olika teknikernas användbarhet. Om man inventerar enligt principen för tvåfassampling ställs olika krav på utrustningen beroende på om det är det stora primära samplet eller det mer noggranna sekundära samplet man samlar in. Vid insamlingen av det primära samplet försöker man samla in stora mängder data som är korrelerat med den variabel som man önskar mäta för att få ett så lågt representativt fel som möjligt. Generellt kan man därför säga att kvantiteten data är viktigare än kvaliteten på det samma vid insamling av det primära samplet. Detta gör att tekniker som samlar in data kostnadseffektivt men med låg precision blir intressanta, exempelvis radar och ultraljuds sensorer som registrerar ekon kontinuerligt medan utrustningen förs längs en linje. Om man däremot vill mäta in det sekundära samplet med sensorer krävs utrustning som mäter med hög precision på den enskilda provytan, vilket talar för tekniker som lidar och fotogrammetri i digitala bilder. Ett kanske mer realistiskt alternativ är annars att inventera det primära samplet med automatiska kostnadseffektiva metoder medan det sekundära samplet mäts in med traditionella manuella metoder.This MSc thesis was done at the Department of Forest Resources and Geomatics SLU, Umeå during the fall 1996 and spring 1997. A requirement for accurate forestry planning at all levels is knowledge about the condition of the forests today. The quality of the decisions made will therefore be proportional to the quality and amount of information collected. Sensors like radar, lidar and different types of digital cameras are today used quite successfully for remote sensing from aircraft or satellites. The purp ose of the thesis is to make a feasibility-study about ground-based use of these kinds of sensors in order to rationalise and improve forest inventory work. Following techniques where selected for a closer study oftheir qualities as ingredients in an inventory concept: • Lidar. Lidar is an abbreviation for light detection and ranging and is a laser­ hased technique to measure distance and reflection of an object. Compared to other kinds of sensors the lidar has a very high angular resolution. Therefor it is well suited for scanning and ranging where high accuracy is required. The disadvantage of the method isthat a clear sight to the object is needed and that the technique must still be considered advanced and expensive. • Photogrammetry in digital images. If the distance to the object and the intemal geometry of the camera is known for an image, measurements of for example tree diameters or the shape of the whole trunk can be made. By using digital pietmes and image processing it seerus possible to develop programs that more or less automatically detects and measure the desired variables. • Radar. The radar-based sensors are using much lower frequencies than the laser. This gives radar the characteristic of being able to see through objects half the size of the wavelength or less. This could be very valuable if radar are supposed to be used for forest inventory work since there will be a problem with undergrowth covering stems if methods that demands visual sight will be used. The disadvantage with using the appropriate wavelengths is that the angular resolution will be to low for scanning where the purpose is to measure each stem individually. If on the other hand the total sum of the signals in a seanned sector is analysed in order to extract information about the standing volume, radar seems like a very feasible technique for developing an effective forest inventory concept. • Ultras o nie. Ultrasonic can be used by the same theories as for radar. The advantage for ultrasonic compared to radar is that there is a supply of comparatively cheap and simple standard components. The disadvantage is that how much the signal will be reduced while transmitting through the air depends to a high grade of the atmospheric humidity and temperature, which means that calibration must be made. Another negative characteristic of ultrasonic is that the signal is sensitive for wind. The methods of in ventory used al so have an effect on the feasibility of the techniques. If double sampling is used there will be different requirements for the equipment depending on if it is data from the large primary sample or the more accurate secondary sample being collected. When collecting the primary sample i t' s necessary to collect a large amount of data to prevent errors that originates from the representation of the population. The quality of the collected data will therefore be less important. This means that techniques that collects large arnounts of data with high cost-efficiency but lacks in precision will be interesting, for example radar or ultra-sonie sensors that collects data continuously while moving. On the other hand if the purpose is to collect the secondary sample, equipment that measures with high accuracy, like lidar or photogrammetry in digital pictures will be required. Another, maybe more realistic alternative, is to use a cost-efficient sensor-hased technique to collect data from the primary sample and then use traditional manual circular plot sampling for the secondary sample

The hiphive package for the extraction of high-order force constants by machine learning

The efficient extraction of force constants (FCs) is crucial for the analysis of many thermodynamic materials properties. Approaches based on the systematic enumeration of finite differences scale poorly with system size and can rarely extend beyond third order when input data is obtained from first-principles calculations. Methods based on parameter fitting in the spirit of interatomic potentials, on the other hand, can extract FC parameters from semi-random configurations of high information density and advanced regularized regression methods can recover physical solutions from a limited amount of data. Here, we present the hiPhive Python package, that enables the construction of force constant models up to arbitrary order. hiPhive exploits crystal symmetries to reduce the number of free parameters and then employs advanced machine learning algorithms to extract the force constants. Depending on the problem at hand both over and underdetermined systems are handled efficiently. The FCs can be subsequently analyzed directly and or be used to carry out e.g., molecular dynamics simulations. The utility of this approach is demonstrated via several examples including ideal and defective monolayers of MoS$_2$ as well as bulk nickel

Cooja TimeLine: A Power Visualizer for Sensor Network Simulation

Power consumption is one of the most important factors in wireless sensor network research, but most simulators do not provide support for visualizing the power consumption of an entire sensor network. This makes it hard to develop, debug, and understand mechanisms and protocols based on power-saving mechanisms. We present Cooja TimeLine, an extension to Contiki’s Cooja network simulator, that visualizes radio traffic and radio usage of sensor networks. Cooja TimeLine makes is possible to visually see the behavior of low-power protocols and mechanisms thereby increasing the understanding of the behavior of sensor networks. We see this as an important tool for the field moving forward

Efficient construction of linear models in materials modeling and applications to force constant expansions

Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science.Comment: 15 pages, 12 figure

Constructive Alignment as a Means to Establish Information Literacy in the Curriculum

Constructive alignment is the pedagogical principal which connects learning goals and learning activities with assessment. Learning goals should inform the student about what they’ll know once they finish the course. Learning activities is what the student needs to do to fulfill the goals. The assessment measures to what degree the student reaches the learning goals. The principal of constructive alignment permeates Swedish higher education. Regulatory documents at both national and local level talk about the importance of having a logical connection between the three parts that make out the principle of constructive alignment. But can we use Constructive Alignment as a means to establish courses in information literacy? I think we can if we align the structure of IL-courses with existing syllabus and formalize this connection.

Development and application of techniques for predicting and analysing phonon-derived materials properties

The thermodynamic properties of materials are of great interest for both scientists and engineers. A large contribution to many properties stems from the vibrational motion of the atoms in the material. An understanding of the dynamics of the vibrating atoms is therefore important for many other areas as well, including, e.g., electronic and optical properties. Since many materials of particular technological interest are crystalline, the vibrations can be studied in the framework of lattice dynamics. One of the main challenges in lattice dynamics is to acquire the force constants that describe the atomic interactions. Using crystal symmetries it is possible to reduce and cast this problem to a linear regression problem. This approach has been implemented in the present work in the hiphive package. The force constants (an interatomic potential) can be fitted to forces obtained from, e.g., density functional theory calculations.Although the problem of linear regression is well studied from a theoretical point of view the number of unknown coefficients in the force constant expansion is typically very large. Obtaining good models from limited data is possible via regularized regression, which has been successfully applied in many areas of physics. However, how well these techniques work in general for practical problems involving force constants is not well understood. By interfacing with the scikit-learn package, here, the hiphive package has been used to explore how well these techniques work in practice. It is found that many concepts from machine (or statistical) learning can be useful in order to predict macroscopic properties and quantify model uncertainties.Moving beyond the domain of pure lattice dynamics we also studied the thermal conductivity of rotationally disordered layered materials, which feature weak van-der-Waals interactions between the layers. These structures exhibit a remarkably low through-plane thermal conductivity and their dynamic properties can be described as one-dimensional glasses (a property worth further studies). By performing molecular dynamics simulations on state-of-the-art graphical processing units using the Green-Kubo formalism excellent agreement with experiments could be achieved

Rotation Averaging and Strong Duality

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of computer vision applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that out-performs general purpose numerical solvers and is able to handle the large problem instances commonly occurring in structure from motion settings. The potential of this proposed method is demonstrated on a number of different problems, consisting of both synthetic and real-world data

Haptic Interface for a Contact Force Controlled Robot

The use of haptics in teleoperation and robotics can help the human operator to greatly improve the performance. This master thesis presents a haptic interface between a haptic device, a Phantom Premium A, and an industrial robot, an IRB140B, on which a force sensor was mounted. This will enable a human operator controlling the Phantom to get information about the environment surrounding the IRB140B through the sense of touch. An impedance controller is also introduced in order to avoid too large contact forces in the robot-environment interaction and to obtain a good behavior of the Phantom. The haptic interface was achieved by deriving a general mathematical mapping from the states of a haptic device to these of an industrial robot and by implementing that mapping for the Phantom and the IRB140B. To handle the Phantom e.g., the force feedback, a C++ program was developed. The program also features a virtual representation of the IRB140B. The impedance controller for the robot-environment interaction was implemented in Matlab's Simulink. That controller was translated into C code and downloaded to the axis computer of the IRB140B. Everything was connected via a TCP/IP network. Tuning the impedance controller resulted in a stable teleoperation system with haptic feedback to the human operator for the materials touched by the IRB140B

Cross-level sensor network simulation with COOJA

Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach