1,025 research outputs found
Piperolein B, isopiperolein B and piperamide C9:1(8E): total synthesis and cytotoxicities
No full textTotal syntheses of the reported structures of piperolein B, isopiperolein B and piperamide C9:1(8E) have been achieved. The analytical data reported for piperolein B and piperamide C9:1(8E) match the synthetic values, however those for isopiperolein B do not. The cytotoxicities of these three structurally similar compounds against cancer cell lines of different tissue origins were evaluated and the results indicated that these compounds show differential effects on cancer cell viability
Motor directional tuning across brain areas: Directional resonance and the role of inhibition for directional accuracy
No full textMotor directional tuning (Georgopoulos et al., 1982) has been found in every brain area in which it has been sought for during the past 30-odd years. It is typically broad, with widely distributed preferred directions and a population signal that predicts accurately the direction of an upcoming reaching movement or isometric force pulse (Georgopoulos et al., 1992). What is the basis for such ubiquitous directional tuning? How does the tuning come about? What are the implications of directional tuning for understanding the brain mechanisms of movement in space? This review addresses these questions in the light of accumulated knowledge in various sub-fields of neuroscience and motor behavior. It is argued (a) that direction in space encompasses many aspects, from vision to muscles, (b) that there is a directional congruence among the central representations of these distributed directions arising from rough but orderly topographic connectivities among brain areas, (c) that broad directional tuning is the result of broad excitation limited by recurrent and non-recurrent (i.e. direct) inhibition within the preferred direction loci in brain areas, and (d) that the width of the directional tuning curve, modulated by local inhibitory mechanisms, is a parameter that determines the accuracy of the directional command
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
No full text<p> </p>
<h3><strong>Version 2 available! Please make sure to download the latest version of the dataset! <br></strong></h3>
<p> </p>
<p><strong>Description</strong></p>
<p>Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.</p>
<p> </p>
<p><strong>Publication</strong></p>
<p>The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291</p>
<p><strong>If you use this dataset please cite it as R-CAUSTIC</strong> [Reference].<br>[Reference]: <strong>P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in </strong><em><strong>IEEE Journal of Oceanic Engineering</strong></em><strong>, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.</strong></p>
<p>BibTeX:</p>
<p>@ARTICLE{10172291, author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas}, journal={IEEE Journal of Oceanic Engineering}, title={Seafloor-Invariant Caustics Removal From Underwater Imagery}, year={2023}, volume={48}, number={4}, pages={1300-1321}, doi={10.1109/JOE.2023.3277168}}</p>
<p> </p>
R-CAUSTIC: Rippling CAUSTICs underwater Image dataset
No full text<p><strong>Description</strong></p><p>Rippling caustics seem to be the main factor degrading the underwater RGB image quality and affecting the image- based 3D reconstruction process in very shallow waters. These effects are adversely affecting image matching algorithms by throwing off most of them, leading to less accurate matches and causing issues in the Simultaneous Localization and Mapping (SLAM) based navigation of the Remotely Operated Vehicles (ROV) and Autonomous Underwater Vehicles (AUV) on shallow waters. Also, they are the main cause for dissimilarities in the generated textures and orthoimages. In order to fill the gap in the literature regading underwater rippling caustics imagery with real ground truth and reference images, the first real-world underwater caustics benchmark dataset which contains 1465 underwater images is presented. Together with the RGB imagery, the corresponding generated ground truth images are delivered for facilitating the training and testing of machine learning and deep learning methods for image classification. R-CAUSTIC dataset also provides the necessary data to evaluate, at least to some extent, the performance of 3D reconstruction approaches. Data were acquired using a GoPro Hero 4 Black action camera with image dimensions of 4000 x 3000 pixels, focal length of 2.77mm and pixel size of 1.55μm and a tripod. Action cameras are widely used for underwater image acquisition. The dataset was captured in near-shore underwater sites at depths varying from 0.5 to 2m. No artificial light sources were used. Due to the wind, the turbulent surface of the water created dynamic rippling caustics on the seabed. In total 1465 RGB images were collected, separated in 7 different datasets; five of them containing stereo images, one of them tri-stereo images and one consists of multi-stereo imagery acquired in 7 different camera poses.</p><p> </p><p><strong>Publication</strong></p><p>The paper is availbale in Open Access here: https://ieeexplore.ieee.org/document/10172291</p><p><strong>If you use this dataset please cite it as R-CAUSTIC</strong> [Reference].<br>[Reference]: <strong>P. Agrafiotis, K. Karantzalos and A. Georgopoulos, "Seafloor-Invariant Caustics Removal From Underwater Imagery," in </strong><i><strong>IEEE Journal of Oceanic Engineering</strong></i><strong>, vol. 48, no. 4, pp. 1300-1321, Oct. 2023, doi: 10.1109/JOE.2023.3277168.</strong></p><p>BibTeX:</p><p>@ARTICLE{10172291,
author={Agrafiotis, Panagiotis and Karantzalos, Konstantinos and Georgopoulos, Andreas},
journal={IEEE Journal of Oceanic Engineering},
title={Seafloor-Invariant Caustics Removal From Underwater Imagery},
year={2023},
volume={48},
number={4},
pages={1300-1321},
doi={10.1109/JOE.2023.3277168}}</p><p> </p>
Photogrammetric Modelling of Submerged Structures: Influence of Underwater Environment and Lens Ports on Three-Dimensional (3D) Measurements
No full textPrimate motor cortex and free arm movements to visual targets in three- dimensional space. I. Relations between single cell discharge and direction of movement
No full textWe describe the relations between the neuronal activity in primate motor cortex and the direction of arm movement in three-dimensional (3- D) space. The electrical signs of discharge of 568 cells were recorded while monkeys made movements of equal amplitude from the same starting position to 8 visual targets in a reaction time task. The layout of the targets in 3-D space was such that the direction of the movement ranged over the whole 3-D directional continuum in approximately equal angular intervals. We found that the discharge rate of 475/568 (83.6%) cells varied in an orderly fashion with the direction of movement: discharge rate was highest with movements in a certain direction (the cell's “preferred direction”) and decreased progressively with movements in other directions, as a function of the cosine of the angle formed by the direction of the movement and the cell's preferred direction. The preferred directions of different cells were distributed throughout 3-D space. These findings generalize to 3-D space previous results obtained in 2-D space (Georgopoulos et al., 1982) and suggest that the motor cortex is a nodal point in the construction of patterns of output signals specifying the direction of arm movement in extrapersonal space.</jats:p
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