Achieving a robust long-term deployment with mobile robots in the agriculture domain is both a demanded and challenging task. The possibility to have autonomous platforms in the field performing repetitive tasks, such...
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Achieving a robust long-term deployment with mobile robots in the agriculture domain is both a demanded and challenging task. The possibility to have autonomous platforms in the field performing repetitive tasks, such as monitoring or harvesting crops, collides with the difficulties posed by the always-changing appearance of the environment due to seasonality. With this scope in mind, we report an ongoing effort in the long-term deployment of an autonomous mobile robot in a vineyard, with the main objective of acquiring what we called the Bacchus Long-Term (BLT) data set. This data set consists of multiple sessions recorded in the same area of a vineyard but at different points in time, covering a total of 7 months to capture the whole canopy growth from March until September. The multimodal data set recorded is acquired with the main focus put on pushing the development and evaluations of different mapping and localization algorithms for long-term autonomous robots operation in the agricultural domain. Hence, besides the data set, we also present an initial study in long-term localization using four different sessions belonging to four different months with different plant stages. We identify that state-of-the-art localization methods can only cope partially with the amount of change in the environment, making the proposed data set suitable to establish a benchmark on which the robotics community can test its methods. On our side, we anticipate two solutions pointed at extracting stable temporal features for improving long-term 4D localization results. The BLT data set is available at https://***/lcas-blt.
Many visual scene understanding applications, especially in visual servoing settings, may require high quality object mask predictions for the accurate undertaking of various robotic tasks. In this work we investigate...
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Background: Detecting impaired naming capacity is valuable in diagnosing neurocognitive disorders (ND). A. clinical practice- oriented overview of naming tests validated in ND is not available yet. Here, features of n...
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Background: Detecting impaired naming capacity is valuable in diagnosing neurocognitive disorders (ND). A. clinical practice- oriented overview of naming tests validated in ND is not available yet. Here, features of naming tests with validated utility in ND which are open access or available for purchase are succinctly presented and compared. Methods: Searches were carried out across Pubmed, Medline and Google Scholar. Additional studies were identified by searching reference lists. Only peer-reviewed journal articles were eligible. A narrative- and tabullar synthesis was used to summarize different aspects of the naming assessment instruments used in patients with ND such as stimuli type, administration time, assessment parameters and accessibility. Based on computational word frequency calculations, the tests were compared in terms of the average frequency of their linguistic content. Results: Twelve naming tests, relying either on visual or auditory stimuli have been validated in ND. Their content and administration time vary between three and 60 items and one and 20 minutes, respectively. The average frequency of the words of each considered test was two or lower, pointing to low frequency of most items. In all but one test, scoring systems are exclusively based on correctly named items. Seven instruments are open access and four are available in more than one language. Conclusions: Gaining insights into naming tests' characteristics may catalyze the wide incorporation of those with short administration time but high diagnostic accuracy into the diagnostic workup of ND at primary healthcare and of extensive, visual or auditory ones into the diagnostic endeavors of memory clinics, as well as of secondary and tertiary brain healthcare settings.
BackgroundDetecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer's disease (AD). The Test for Finding Word retrieval deficits ...
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BackgroundDetecting impaired naming capacity contributes to the detection of mild (MildND) and major (MajorND) neurocognitive disorder due to Alzheimer's disease (AD). The Test for Finding Word retrieval deficits (WoFi) is a new, 50-item, auditory stimuli-based *** study aimed to adapt WoFi to the Greek language, to develop a short version of WoFi (WoFi-brief), to compare the item frequency and the utility of both instruments with the naming subtest of the widely used Addenbrooke's cognitive examination III (ACEIIINaming) in detecting MildND and MajorND due to *** cross-sectional, validation study included 99 individuals without neurocognitive disorder, as well as 114 and 49 patients with MildND and MajorND due to AD, respectively. The analyses included categorical principal components analysis using Cramer's V, assessment of the frequency of test items based on corpora of television subtitles, comparison analyses, Kernel Fisher discriminant analysis models, proportional odds logistic regression (POLR) models and stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio).ResultsWoFi and WoFi-brief, which consists of 16 items, have comparable item frequency and utility and outperform ACEIIINaming. According to the results of the discriminant analysis, the misclassification error was 30.9%, 33.6% and 42.4% for WoFi, WoFi-brief and ACEIIINaming, respectively. In the validation regression model including WoFi the mean misclassification error was 33%, while in those including WoFi-brief and ACEIIINaming it was 31% and 34%, *** and WoFi-brief are more effective in detecting MildND and MajorND due to AD than ACEIIINaming.
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