@article{HampfNendelStreyetal.2021,
  author    = {Hampf, Anna and Nendel, Claas and Strey, Simone and Strey, Robert},
  title     = {Biotic yield losses in the Southern Amazon, Brazil},
  series = {Frontiers in plant science : FPLS},
  volume    = {12},
  journal   = {Frontiers in plant science : FPLS},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2021.621168},
  pages     = {16},
  year      = {2021},
  abstract  = {Pathogens and animal pests (P\&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P\&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P\&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P\&A, (2) map the spatial distribution of P\&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P\&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P\&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P\&A, whereas soybean is mainly affected by P\&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16\%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US\$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.},
  language  = {en}
}
@article{KreowskyStabernack2021,
  author    = {Kreowsky, Philipp and Stabernack, Christian Benno},
  title     = {A full-featured FPGA-based pipelined architecture for SIFT extraction},
  series = {IEEE access : practical research, open solutions / Institute of Electrical and Electronics Engineers},
  volume    = {9},
  journal   = {IEEE access : practical research, open solutions / Institute of Electrical and Electronics Engineers},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {New York, NY},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2021.3104387},
  pages     = {128564 -- 128573},
  year      = {2021},
  abstract  = {Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps.},
  language  = {en}
}
@article{VermaKummerowDenker2018,
  author    = {Verma, Meetu and Kummerow, P. and Denker, Carsten},
  title     = {On the extent of the moat flow in axisymmetric sunspots},
  series = {Astronomische Nachrichten = Astronomical notes},
  volume    = {339},
  journal   = {Astronomische Nachrichten = Astronomical notes},
  number    = {4},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {0004-6337},
  doi       = {10.1002/asna.201813482},
  pages     = {268 -- 276},
  year      = {2018},
  abstract  = {Unipolar, axisymmetric sunspots are figuratively called "theoretician's sunspots" because their simplicity supposedly makes them more suitable for theoretical descriptions or numerical models. On November 18, 2013, a very large specimen (active region NOAA 11899) crossed the central meridian of the sun. The moat flow associated with this very large spot is quantitatively compared to that of a medium and a small sunspot to determine the extent of the moat flow in different environments. We employ continuum images and magnetograms of the Helioseismic and Magnetic Imager (HMI) as well as extreme ultraviolet (EUV) images at λ160 nm of the Atmospheric Imaging Assembly (AIA), both on board the Solar Dynamics Observatory (SDO), to measure horizontal proper motions with Local Correlation Tracking (LCT) and flux transport velocities with the Differential Affine Velocity Estimator (DAVE). We compute time-averaged flow maps (±6 hr around meridian passage) and radial averages of photometric, magnetic, and flow properties. Flow fields of a small- and a medium-sized axisymmetric sunspot provide the context for interpreting the results. All sunspots show outward moat flow and the advection of moving magnetic features (MMFs). However, the extent of the moat flow varies from spot to spot, and a correlation of flow properties with size is tenuous, if at all present. The moat flow is asymmetric and predominantly in the east-west direction, whereby deviations are related to the tilt angle of the sunspot group as well as to the topology and activity level of the trailing plage.},
  language  = {en}
}
@article{KonakWegnerArnrich2020,
  author    = {Konak, Orhan and Wegner, Pit and Arnrich, Bert},
  title     = {IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {24},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s20247179},
  pages     = {15},
  year      = {2020},
  abstract  = {Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.},
  language  = {en}
}
@article{BreuerNowakIvakovetal.2017,
  author    = {Breuer, David and Nowak, Jacqueline and Ivakov, Alexander and Somssich, Marc and Persson, Staffan and Nikoloski, Zoran},
  title     = {System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells},
  series = {Proceedings of the National Academy of Sciences of the United States of America},
  volume    = {114},
  journal   = {Proceedings of the National Academy of Sciences of the United States of America},
  publisher = {National Acad. of Sciences},
  address   = {Washington},
  issn      = {0027-8424},
  doi       = {10.1073/pnas.1706711114},
  pages     = {E5741 -- E5749},
  year      = {2017},
  abstract  = {The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.},
  language  = {en}
}