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This paper investigates four different mobile robots with respect to their drivingcharacteristics and soil preservation properties in an agricultural environment.Thereby, robots of classical design from agriculture as well as systems from spacerobotics with advanced locomotion concepts are considered to determine theindividual advantages of each rover concept with respect to the application domain.Locomotion experiments were conducted to analyze the general driving behavior,tensile force, and obstacle‐surmounting capability and ground interaction of eachrobot. Various soil conditions typical for the area of application are taken intoaccount, which are varied in terms of moisture and density. The presented workcovers the specification of the conducted experiments, documentation of theimplementation as well as analysis and evaluation of the collected data. In theevaluation, particular attention is paid to the change in driving characteristics underdifferent soil conditions, as well as to the soil stress caused by driving, since soilquality is of critical importance for agricultural applications. The analysis shows thatthe advanced locomotion concepts, as used in space robotics, also have positiveimplications for certain requirements in agricultural applications, such as maneuver-ability in wet conditions and soil conservation. The results show potential for designinnovations in agricultural robotics that can be used, to open up new fields ofapplication for instance in the context of precision farming.
The usage of high-level synthesis (HLS) tools for FPGAs has increased significantly over the last years since they matured and allow software programmers to take advantage of reconfigurable hardware technology.
Most HLS tools employ methods to optimize for loops, e. g. by unrolling or pipelining them. But there is hardly any work on the optimization of while loops. This comes at no surprise since most while loops have loop-carried dependences involving the loop condition which result in large recurrence cycles in the dataflow graphs. Therefore typical while loops cannot be parallelized or pipelined.
We propose a novel transformation which allows to optimize while loops nested within a for loop. By interchanging the two loops, it is possible to pipeline (and thereby parallelize) the inner loop, resulting in a reduced execution time. We present two case studies on different hardware platforms and show the speedup factors - compared to a host processor and to an unoptimized hardware implementation - achieved by our while loop optimization method.
Es ist davon auszugehen, dass weltweit etwa die Hälfte der industriell eingesetzten Wärme als Abwärme ungenutzt verloren geht (Quelle: Effiziente Energieversorgung durch Abwärme, Fachmagazin Energy 2.0, April 2012). Vor dem Hintergrund der Nachhaltigkeit und Energieeffizienz ist es eine verantwortungsvolle Aufgabe, diese ungenutzte Energieressource schrittweise zu erschließen. Für die bisherige Vernachlässigung verfügbarer Energiequellen gibt es spezifische Gründe, die erkannt und projektbezogen möglichst ausgeräumt werden müssen. Dazu hat die Hochschule Osnabrück in Kooperation mit dem Kompetenzzentrum Energie und dem Landkreis Osnabrück eine Studie erstellt.
Regionales Wärmekataster Industrie - ReWIn
Diese Konzeptstudie schafft durch eine vorangestellte Recherche der bereits entwickelten Methoden und Technologien zur Abwärmenutzung eine Grundlage zur Potenzialabschätzung und Aufstellung eines Wärmekatasters für den Landkreis Osnabrück.
In der Studie werden für die typisch energieintensiven Branchen des Landkreises methodische Berechnungsansätze mit statistischen, branchenbezogenen Energiekennwerten und vorerst anonymisierten Unternehmensdaten neuartig kombiniert, um eine regionale Potenzialkarte der Abwärme zu erstellen. Die Studie wurde vom Europäischen Fonds für regionale Entwicklung (EFRE) gefördert.
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
Der Einsatz paralleler Hardware-Architekturen betrifft alle Software-Entwickler und -Entwicklerinnen: vom Supercomputer bis zum eingebetteten System werden Multi- und Manycore-Systeme inzwischen eingesetzt. Die Herausforderungen an das Software Engineering sind vielfältig. Zum einen ist (wieder) ein stärkeres Verständnis für die Hardware notwendig. Ohne eine skalierbare Partitionierung der Software und parallele Algorithmen bleibt die Rechenleistung ungenutzt. Zum anderen stehen neue Programmiersprachen im Vordergrund, die die Ausführung von parallelen Anweisungen ermöglichen.
Dieses Buch betrachtet unterschiedliche Aspekte bei der Entwicklung paralleler Systeme und berücksichtigt dabei auch eingebettete Systeme. Es verbindet Theorie und praktische Anwendung und ist somit für Studierende und Anwender in der Praxis gleichermaßen geeignet. Durch die programmiersprachenunabhängige Darstellung der Algorithmen können sie leicht für die eigene Anwendung angepasst werden. Viele praktische Projekte erleichtern das Selbststudium und vertiefen das Gelernte.
he development of context-aware applications is a difficult and error-prone task. The dynamics of the environmental context combined with the complexity of the applications poses a vast number of possibilities for mistakes during the creation of new applications. Therefore it is important to test applications before they are deployed in a life system. For this reason, this paper proposes a testing tool, which will allow for automatic generation of various test cases from application description documents. Semantic annotations are used to create specific test data for context-aware applications. A test case reduction methodology based on test case diversity investigations ensures scalability of the proposed automated testing approach.
In this experimental work, the quasi static and fatigue properties of a 40 wt.% long carbon fiber reinforced partially aromatic polyamide (Grivory GCL-4H) were investigated. For this purpose, microstructural parameter variations in the form of different thicknesses and different removal directions from injectionmolded plates were evaluated. Mechanical properties decreased by increasing misalignment away from the melt flow direction. By changing the specimen thickness, no change in the general fiber distribution pattern transversal and normal to the axis of melt flow was observed. It has shown that with increasing specimen thickness the quasi static properties along the melt flow direction decreased and vice versa resulting in superior properties normal to the melt flow axis. At around 5 mm, an intersection suggests quasi-isotropic behavior. In addition, the fatigue strength of the material was significantly higher in the flow direction than normal to the flow direction. No change in fatigue life was observed while changing specimen thickness. The Basquin equation seems to describe the effect of stress amplitude on the fatigue strength of this composite. Scanning electron microscopy was used to investigate fracture surfaces of tested specimens. Results show that mechanical properties and morphological structures depend highly on fiber orientation.
Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.