| 11. |
- Ekvall, Mikael T., et al.
(författare)
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Behavioural responses to co-occurring threats of predation and ultraviolet radiation in Daphnia
- 2020
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Ingår i: Freshwater Biology. - : Wiley. - 0046-5070 .- 1365-2427. ; 65:9, s. 1509-1517
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Tidskriftsartikel (refereegranskat)abstract
- Organisms in the wild are faced with multiple threats and a common response is a change in behaviour. To disentangle responses to several threats, we exposed two differently sized species of the freshwater invertebrate Daphnia to solar ultraviolet radiation (UVR) and predation from either moving pelagic or benthic ambush predators. Using an advanced nanotechnology-based method, we tracked the three-dimensional movements of those mm-sized animals at the individual level. Each behavioural trial was performed both under conditions resembling night (no UVR) and day (UVR) and we examined patterns of the depth distribution and swimming speed by Daphnia across three treatments: no predator (control); bottom-dwelling damselfly (Calopteryx sp.); and fish (stickleback, Pungitius pungitius) predators. We also quantified the actual predation rate by the two predators on the two Daphnia species, Daphnia manga and Daphnia pulex. We show that individual Daphnia are able to identify predators with different feeding habitats, rank multiple and simultaneously occurring risks and respond in accordance with the actual threat; complex responses that are generally associated with larger animals. In a broader context, our results highlight and quantify how a cocktail of everyday threats is perceived and handled by invertebrates, which advances our understanding of species distribution in space and time, and thereby of population dynamics and ecosystem function in natural ecosystems.
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| 12. |
- Ferranti, Luca, et al.
(författare)
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Sensor Networks Tdoa Self-Calibration : 2d Complexity Analysis and Solutions
- 2021
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781728176062 - 9781728176055 ; 2021-June, s. 4635-4639
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Konferensbidrag (refereegranskat)abstract
- Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques, known as Time-Difference-Of-Arrival (TDOA) techniques. Despite previous work, TDOA self-calibration is computationally challenging. Iterative algorithms are very sensitive to the initialization, causing convergence issues. In this paper, we present a novel approach, which gives an algebraic solution to two previously unsolved scenarios. We also demonstrate that our solvers produce an excellent initial value for non-linear optimisation algorithms, leading to a full pipeline robust to noise.
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| 13. |
- Ferreira, Lucas, et al.
(författare)
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Design of an Application-specific VLIW Vector Processor for ORB Feature Extraction
- 2023
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Ingår i: Journal of Signal Processing Systems. - : Springer Science and Business Media LLC. - 1939-8018 .- 1939-8115. ; 95:7, s. 863-875
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Tidskriftsartikel (refereegranskat)abstract
- In computer-vision feature extraction algorithms, compressing the image into a sparse set of trackable keypoints, empowers navigation-critical systems such as Simultaneous Localization And Mapping (SLAM) in autonomous robots, and also other applications such as augmented reality and 3D reconstruction. Most of those applications are performed in battery-powered gadgets featuring in common a very stringent power-budget. Near-to-sensor computing of feature extraction algorithms allows for several design optimizations. First, the overall on-chip memory requirements can be lessened, and second, the internal data movement can be minimized. This work explores the usage of an Application Specific Instruction Set Processor (ASIP) dedicated to perform feature extraction in a real-time and energy-efficient manner. The ASIP features a Very Long Instruction Word (VLIW) architecture comprising one RV32I RISC-V and three vector slots. The on-chip memory sub-system implements parallel multi-bank memories with near-memory data shuffling to enable single-cycle multi-pattern vector access. Oriented FAST and Rotated BRIEF (ORB) are thoroughly explored to validate the proposed architecture, achieving a throughput of 140 Frames-Per-Second (FPS) for VGA images for one scale, while reducing the number of memory accesses by 2 orders of magnitude as compared to other embedded general-purpose architectures.
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| 14. |
- Finn, Eric Lyckegård, et al.
(författare)
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The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period – an explorative observational study
- 2024
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Ingår i: Journal of NeuroEngineering and Rehabilitation. - 1743-0003. ; 21:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient’s progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. Methods: Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. Results: 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman’s rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 − 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. Conclusions: The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability.
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| 15. |
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| 16. |
- Flood, Gabrielle, et al.
(författare)
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Minimal Solvers for Point Cloud Matching with Statistical Deformations
- 2022
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Ingår i: 2022 26th International Conference on Pattern Recognition (ICPR). - 9781665490634
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Konferensbidrag (refereegranskat)abstract
- An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the local maps it is important to find feature matches that capture such deformations. In this paper we present minimal solvers for point cloud matching using statistical deformations. The solvers use either three or four point matches. These solve for either rigid or similarity transformation as well as shape deformation in the direction of the most important modes of variation. Given an initial set of tentative matches based on, for example, feature descriptors or machine learning we use these solvers in a RANSAC loop to remove outliers among the tentative matches. We evaluate the methods on both synthetic and real data and compare them to RANSAC methods based on Procrustes and demonstrate that the proposed methods improve on the current state-of-the-art.
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| 17. |
- Gillsjö, David, et al.
(författare)
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In Depth Bayesian Semantic Scene Completion
- 2021
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Ingår i: 2020 25th International Conference on Pattern Recognition (ICPR). - 1051-4651. - 9781728188089 ; , s. 6335-6342
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Konferensbidrag (refereegranskat)
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| 18. |
- Gillsjö, David, et al.
(författare)
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Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes
- 2023
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Ingår i: Proceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023. - 9798350307443 ; , s. 1-10
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Konferensbidrag (refereegranskat)abstract
- This paper presents a neural network based semantic plane detection method utilizing polygon representations. The method can for example be used to solve room layout estimations tasks and is built on, combines and further develops several different modules from previous research. The network takes an RGB image and estimates a wireframe as well as a feature space using an hourglass backbone. From these, line and junction features are sampled. The lines and junctions are then represented as an undirected graph, from which polygon representations of the sought planes are obtained. Two different methods for this last step are investigated, where the most promising method is built on a heterogeneous graph transformer. The final output is in all cases a projection of the semantic planes in 2D. The methods are evaluated on the Structured3D dataset and we investigate the performance both using sampled and estimated wireframes. The experiments show the potential of the graph-based method by outperforming state of the art methods in Room Layout estimation in the 2D metrics using synthetic wireframe detections.
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| 19. |
- Gillsjö, David, et al.
(författare)
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Semantic Room Wireframe Detection from a Single View
- 2022
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Ingår i: 26th International Conference on Pattern Recognition, 2022. - 9781665490634 - 9781665490627 ; , s. 1886-1893
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Konferensbidrag (refereegranskat)abstract
- Reconstruction of indoor surfaces with limited texture information or with repeated textures, a situation common in walls and ceilings, may be difficult with a monocular Structure from Motion system. We propose a Semantic Room Wireframe Detection task to predict a Semantic Wireframe from a single perspective image. Such predictions may be used with shape priors to estimate the Room Layout and aid reconstruction. To train and test the proposed algorithm we create a new set of annotations from the simulated Structured3D dataset. We show qualitatively that the SRW-Net handles complex room geometries better than previous Room Layout Estimation algorithms while quantitatively out-performing the baseline in non-semantic Wireframe Detection.
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| 20. |
- Gulin, Jens, et al.
(författare)
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GCC-PHAT Re-Imagined - A U-Net Filter for Audio TDOA Peak-Selection
- 2024
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Ingår i: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). - 9798350344868 - 9798350344851 ; , s. 8806-8810
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Konferensbidrag (refereegranskat)abstract
- Time-difference-of-arrival (TDOA) estimation from GCC-PHAT is not always as straight forward as finding the maximum peak. This work views the GCC output as an image, with time on the vertical axis and TDOA horizontally, to explore if image-to-image machine learning methods can make a more robust filter. The Structure from Sound Database provides audio recorded with a distributed microphone setup and a moving sound source. The audio was fed to GCC-PHAT without pre-processing, and images were produced for batch processing. The ground truth, the direct-path TDOA, shows a continuous curve through time. The GCC output image has a similar curve, but obscured by noise and not at all times texturally different from the multi-path components. The main approach tested is binary semantic segmentation with a U-Net. A challenge is the extreme class imbalance within the image. Preliminary results indicate that the method is valid to detect curves, yet more work is needed to single out the direct path TDOA with confidence.
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