Control design for UAV quadrotors via embedded model control

In this paper, a control system for unmanned aerial vehicles (UAVs) is designed, tested in simulation by means of a high-fidelity simulator, and then applied to a real quadrotor UAV. A novel approach is proposed for the control design, based on the combination of two methodologies: feedback linearization (FL) and embedded model control (EMC). FL allows us to properly transform the UAV dynamics into a form suitable for EMC; EMC is then used to control the transformed system. A key feature of EMC is that it encompasses a so-called extended state observer (ESO), which not only recovers the system state but also gives a real-time estimate of all the disturbances/uncertainties affecting the system. This estimate is used by the FL-EMC control law to reject the aforementioned disturbances/uncertainties, including those collected via the FL, allowing a robustness and performance enhancement. This approach allows us to combine FL and EMC strengths. Most notably, the entire process is made systematic and application oriented. To set-up a reliable UAV attitude observer, an effective attitude sensors fusion is proposed and also benchmarked with an enhanced complementary filter. Finally, to enhance the closed-loop performance, a complete tuning procedure, encompassing frequency requirements, is outlined, based on suitably defined stability and performance metrics

Automatic Dti-based Parcellation Of The Corpus Callosum Through The Watershed Transform

Introduction: Parcellation of the corpus callosum (CC) in the midsagittal cross-section of the brain is of utmost importance for the study of diffusion properties within this structure. The complexity of this operation comes from the absence of macroscopic anatomical landmarks to help in dividing the CC into different callosal areas. In this paper we propose a completely automatic method for CC parcellation using diffusion tensor imaging (DTI). Methods: A dataset of 15 diffusion MRI volumes from normal subjects was used. For each subject, the midsagital slice was automatically detected based on the Fractional Anisotropy (FA) map. Then, segmentation of the CC in the midsgital slice was performed using the hierarchical watershed transform over a weighted FA-map. Finally, parcellation of the CC was obtained through the application of the watershed transform from chosen markers. Results: Parcellation results obtained were consistent for fourteen of the fifteen subjects tested. Results were similar to the ones obtained from tractography-based methods. Tractography confirmed that the cortical regions associated with each obtained CC region were consistent with the literature. Conclusions: A completely automatic DTI-based parcellation method for the CC was designed and presented. It is not based on tractography, which makes it fast and computationally inexpensive. While most of the existing methods for parcellation of the CC determine an average behavior for the subjects based on population studies, the proposed method reflects the diffusion properties specific for each subject. Parcellation boundaries are found based on the diffusion properties within each individual CC, which makes it more reliable and less affected by differences in size and shape among subjects.302132143Aboitiz, F., Scheibel, A.B., Fisher, R.S., Zaidel, E., Fiber composition of the human corpus callosum (1992) Brain Research, 598 (1-2), pp. 143-153. , http://dx.doi.org/10.1016/0006-8993(92)90178-CBasser, P.J., Pierpaoli, C., Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI (1996) Journal of Magnetic Resonance, Series B, 111 (3), pp. 209-219. , http://dx.doi.org/10.1006/jmrb.1996.0086Basser, P.J., Mattiello, J., LeBihan, D., MR diffusion tensor spectroscopy and imaging (1994) Biophysical Journal, 66 (1), pp. 259-267. , http://dx.doi.org/10.1016/S0006-3495(94)80775-1Beucher, S., Lantuéjoul, C., (1979) Use of watersheds in contour detection, , In: International Workshop on Image Processing: Proceedings of the International Workshop on Image Processing: Real-time Edge and Motion Detection/EstimationRennes, FranceBeucher, S., Meyer, F., (1992) The morphological approach to segmentation: The Watershed Transformation, pp. 433-481. , Mathematical Morphology in Image Processing (CRC Press)Biegon, A., Eberling, J.L., Richardson, B.C., Roos, M.S., Wong, S.T., Reed, B.R., Jagust, W.J., Human corpus callosum in aging and alzheimer's disease: A magnetic resonance imaging study (1994) Neurobiology of Aging, 15 (4), pp. 393-397. , http://dx.doi.org/10.1016/0197-4580(94)90070-1Chepuri, N.B., Yen, Y.F., Burdette, J.H., Li, H., Moody, D.M., Maldjian, J.A., Diffusion anisotropy in the corpus callosum (2002) American journal of Neuroradiology, 23 (5), pp. 803-808. , PMid: 12006281DeLacoste-Utamsing, C., Holloway, R., Sexual dimorphism in the human corpus callosum (1982) Science, 216 (4553), pp. 1431-1432. , http://dx.doi.org/10.1126/science.7089533Digabel, H., Lantuéjoul, C., Iterative algorithms, pp. 85-99. , In: European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine: Proceedings of the 2nd European Symposium Quantitative Analysis of Microstructures in Material Science, Biology and Medicine1978Dougherty, R.F., Ben-Shachar, M., Bammer, R., Brewer, A.A., Wandell, B.A., Functional organization of human occipital-callosal fiber tracts (2005) Proceedings of the National Academy of Sciences of the United States of America, 102 (20), pp. 7350-7355. , http://dx.doi.org/10.1073/pnas.0500003102, PMid: 15883384 PMCid: PMC1129102Duara, R., Kushch, A., Gross-Glenn, K., Barker, W.W., Jallad, B., Pascal, S., Loewenstein, D.A., Lubs, H., Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans (1991) Archives of Neurology, 48 (4), pp. 410-416. , http://dx.doi.org/10.1001/archneur.1991.00530160078018, PMid: 2012516Falcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29. , http://dx.doi.org/10.1109/TPAMI.2004.1261076, PMid: 15382683Freitas, P., Rittner, L., Appenzeller, S., Lotufo, R.A., Watershed-based segmentation of the midsagittal section of the corpus callosum in diffusion MRI IEEE Computer Society, pp. 274-280. , In: Graphics, Patterns and Images, Conference on: Proceedings of the 24th Conference on Graphics, Patterns and Images2011Grimaud, M., A new measure of contrast: The dynamics (1992) Image Algebra and Morphological Image Processing III, 1769, pp. 292-305. , http://dx.doi.org/10.1117/12.60650Habib, M., Gayraud, D., Oliva, A., Regis, J., Salamon, G., Khalil, R., Effects of handedness and sex on the morphology of the corpus callosum: A study with brain magnetic resonance imaging (1991) Brain and Cognition, 16 (1), pp. 41-61. , http://dx.doi.org/10.1016/0278-2626(91)90084-LHampel, H., Teipel, S.J., Alexander, G.E., Horwitz, B., Teichberg, D., Schapiro, M.B., Rapoport, S.I., Corpus callosum atrophy is a possible indicator of region-and cell type-specific neuronal degeneration in Alzheimer disease: A magnetic resonance imaging analysis (1998) Archives of Neurology, 55 (2), pp. 193-198. , http://dx.doi.org/10.1001/archneur.55.2.193, PMid: 9482361Hofer, S., Frahm, J., Topography of the human corpus callosum revisited-comprehensive fiber tractography using diffusion tensor magnetic resonance imaging (2006) NeuroImage, 32 (3), pp. 989-994. , http://dx.doi.org/10.1016/j.neuroimage.2006.05.044, PMid: 16854598Huang, H., Zhang, J., Jiang, H., Wakana, S., Poetscher, L., Miller, M.I., van Zijl, P.C., Mori, S., DTI tractography based parcellation of white matter: Application to the mid-sagittal morphology of corpus callosum (2005) NeuroImage, 26 (1), pp. 195-205. , http://dx.doi.org/10.1016/j.neuroimage.2005.01.019, PMid: 15862219Johnson, S.C., Farnworth, T., Pinkston, J.B., Bigler, E.D., Blatter, D.D., Corpus callosum surface area across the human adult life span: Effect of age and gender (1994) Brain Research Bulletin, 35 (4), pp. 373-377. , http://dx.doi.org/10.1016/0361-9230(94)90116-3Körbes, A., Lotufo, R.A., Analysis of the watershed algorithms based on the Breadth-First and Depth-First exploring methods (2009) IEEE Computer Society, pp. 133-140. , http://dx.doi.org/10.1109/SIBGRAPI.2009.43, In: Computer Graphics and Image Processing, Brazilian Symposium on: Proceedings of the 22th Brazilian Symposium on Computer Graphics and Image Processing2009Rio de Janeiro, BrazilLarsen, J.P., Höien, T., Odegaard, H., Magnetic resonance imaging of the corpus callosum in developmental dyslexia (1992) Cognitive Neuropsychology, 9 (2), pp. 123-134. , http://dx.doi.org/10.1080/02643299208252055Lotufo, R.A., Falcão, A.X., The ordered queue and the optimality of the watershed approaches Kluwer Academic Publishersv, pp. 341-350. , http://dx.doi.org/10.1007/0-306-47025-X_37, In: Mathematical Morphology and its Applications to Image and Signal Processing: Proceedings of the 5th International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing2000, 18Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.M., Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging (1999) Annals of Neurology, 45 (2), pp. 265-269. , http://dx.doi.org/10.1002/1531-8249(199902)45:2265::AID-ANA213.0.CO;2-3Narr, K.L., Thompson, P.M., Sharma, T., Moussal, J., Cannestra, A.F., Toga, A.W., Mapping morphology of the corpus callosum in schizophrenia (2000) Cerebral cortex (New York, NY, 1991), 10 (1), pp. 40-49. , http://dx.doi.org/10.1093/cercor/10.1.40Narr, K.L., Cannon, T.D., Woods, R.P., Thompson, P.M., Kim, S., Asunction, D., van Erp, T.G., Toga, A.W., Genetic Contributions to Altered Callosal Morphology in Schizophrenia The Journal of Neuroscience, 22 (9), pp. 3720-3729. , PMid: 11978848O'Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., Tanner, C., Mazoyer, B., Hampel, H., Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease (2011) PLoS one, 6 (6), pp. e21745. , http://dx.doi.org/10.1371/journal.pone.0021745, PMid: 21738785 PMCid: PMC3128090Oh, J.S., Suk Park, K., Chan Song, I., Ju Kim, S., Hwang, J., Chung, A., Kyoon Lyoo, I., Fractional anisotropy-based divisions of midsagittal corpus callosum (2005) Neuroreport, 16 (4), pp. 317-320. , http://dx.doi.org/10.1097/00001756-200503150-00002Park, H.J., Kim, J.J., Lee, S.K., Seok, J.H., Chun, J., Kim, D.I., Lee, J.D., Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI (2008) Human Brain Mapping, 29 (5), pp. 503-516. , http://dx.doi.org/10.1002/hbm.20314, PMid: 17133394Park, J.S., Yoon, U., Kwak, K.C., Seo, S.W., Kim, S.I., Na, D.L., Lee, J.M., The relationships between extent and microstructural properties of the midsagittal corpus callosum in human brain (2011) NeuroImage, 56 (1), pp. 174-184. , http://dx.doi.org/10.1016/j.neuroimage.2011.01.065, PMid: 21281715Rajapakse, J.C., Giedd, J.N., Rumsey, J.M., Vaituzis, A.C., Hamburger, S.D., Rapoport, J.L., Regional MRI measurements of the corpus callosum: A methodological and developmental study (1996) Brain and Development, 18 (5), pp. 379-388. , http://dx.doi.org/10.1016/0387-7604(96)00034-4Rumsey, J.M., Casanova, M., Mannheim, G.B., Patronas, N., De Vaughn, N., Hamburger, S.D., Aquino, T., Corpus callosum morphology, as measured with MRI, in dyslexic men Biological Psychiatry, 39 (9), pp. 769-775. , http://dx.doi.org/10.1016/0006-3223(95)00225-1Rosas, H.D., Lee, S.Y., Bender, A.C., Zaleta, A.K., Vangel, M., Yu, P., Fischl, B., Hersch, S.M., Altered white matter microstructure in the corpus callosum in Huntington's disease: Implications for cortical disconnection (2010) NeuroImage, 49 (4), pp. 2995-3004. , http://dx.doi.org/10.1016/j.neuroimage.2009.10.015, PMid: 19850138 PMCid: PMC3725957Thompson, P.M., Narr, K.L., Blanton, R.E., Toga, A.W., Mapping structural alterations of the corpus callosum during brain development and degeneration (2003) Proceedings of the NATO ASI on the corpus callosum, pp. 93-130Von Plessen, K., Lundervold, A., Duta, N., Heiervang, E., Klauschen, F., Smievoll, A.I., Ersland, L., Hugdahl, K., Less developed corpus callosum in dyslexic subjects-a structural MRI study (2002) Neuropsychologia, 40 (7), pp. 1035-1044. , http://dx.doi.org/10.1016/S0028-3932(01)00143-9Wahl, M., Lauterbach-Soon, B., Hattingen, E., Jung, P., Singer, O., Volz, S., Klein, J.C., Ziemann, U., Human motor corpus callosum: Topography, somatotopy, and link between microstructure and function (2007) Journal of Neuroscience, 27 (45), pp. 12132-12138. , http://dx.doi.org/10.1523/JNEUROSCI.2320-07.2007, PMid: 17989279Witelson, S.F., Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study (1989) Brain, 112 (PART 3), pp. 799-835. , http://dx.doi.org/10.1093/brain/112.3.799, PMid: 2731030Witelson, S.F., Goldsmith, C.H., The relationship of hand preference to anatomy of the corpus callosum in men (1991) Brain Research, 545 (1-2), pp. 175-182. , http://dx.doi.org/10.1016/0006-8993(91)91284-

Satellite-to-satellite attitude control of a long-distance spacecraft formation for the Next Generation Gravity Mission

The paperpresentsthedesignandsomesimulatedresultsoftheattitudecontrolofasatelliteformation under studybytheEuropeanSpaceAgencyfortheNextGenerationGravityMission.Theformation consists oftwospacecraftswhich fly morethan200kmapartatanaltitudefromtheEarth'sgroundof between 300and400km.Theattitudecontrolmustkeeptheopticalaxesofthetwospacecraftaligned with amicroradianaccuracy(pointingcontrol).Thisismadepossiblebyspecific opticalsensors accompanyingtheinter-satellitelaserinterferometer,whichisthemainpayloadofthemission.These sensors alloweachspacecrafttoactuateautonomousalignmentafterasuitableacquisitionprocedure. Pointing controlisconstrainedbytheangulardrag-freecontrol,whichisimposedbymissionscience (Earth gravimetryatalowEarthorbit),andmustzerotheangularaccelerationvectorbelow0.01 μrad/s2 in thesciencefrequencyband.Thisismadepossiblebyultrafine accelerometersfromtheGOCE-class, whose measurementsmustbecoordinatedwithattitudesensorstoachievedrag-freeandpointing requirements.EmbeddedModelControlshowshowcoordinationcanbeimplementedaroundthe embedded modelsofthespacecraftattitudeandoftheformationframequaternion.Evidenceand discussion aboutsomecriticalrequirementsarealsoincludedtogetherwithextensivesimulatedresults of twodifferentformationtypes

The Acute-Phase Proteins Serum Amyloid A and C Reactive Protein in Transudates and Exudates

The distinction between exudates and transudates is very important in the patient management. Here we evaluate whether the acute-phase protein serum amyloid A (SAA), in comparison with C reactive protein (CRP) and total protein (TP), can be useful in this discrimination. CRP, SAA, and TP were determined in 36 exudate samples (27 pleural and 9 ascitic) and in 12 transudates (9 pleural and 3 ascitic). CRP, SAA, and TP were measured. SAA present in the exudate corresponded to 10% of the amount found in serum, that is, the exudate/serum ratio (E/S) was 0.10 ± 0.13. For comparison, the exudate/serum ratio for CRP and TP was 0.39 ± 0.37 and 0.68 ± 0.15, respectively. There was a strong positive correlation between serum and exudate SAA concentration (r = 0.764;p < 0.0001). The concentration of SAA in transudates was low and did not overlap with that found in exudates (0.02-0.21 versus 0.8–360.5 g/mL). SAA in pleural and ascitic exudates results mainly from leakage of the serum protein via the inflamed membrane. A comparison of the E/S ratio of SAA and CRP points SAA as a very good marker in discriminating between exudates and transudates

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement

This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquired on scanners from three vendors (Siemens, Philips and General Electric) at both 1.5 T and 3 T. CC-359 is comprised of 359 datasets, approximately 60 subjects per vendor and magnetic field strength. The dataset is approximately age and gender balanced, subject to the constraints of the available images. It provides consensus brain extraction masks for all volumes generated using supervised classification. Manual segmentation results for twelve randomly selected subjects performed by an expert are also provided. The CC-359 dataset allows investigation of 1) the influences of both vendor and magnetic field strength on quantitative analysis of brain MR; 2) parameter optimization for automatic segmentation methods; and potentially 3) machine learning classifiers with big data, specifically those based on deep learning methods, as these approaches require a large amount of data. To illustrate the utility of this dataset, we compared to the results of a supervised classifier, the results of eight publicly available skull stripping methods and one publicly available consensus algorithm. A linear mixed effects model analysis indicated that vendor (p - value < 0.001) and magnetic field strength (p - value < 0.001) have statistically significant impacts on skull stripping results170482494CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP311228/2014-3; 157534/2015-488881.062158/2014-012013/07559-3; 2013/23514-0; 2016/18332-

Enhancement of affective processing induced by bifrontal transcranial direct current stimulation in patients with major depression

ObjectiveOur aim was to evaluate whether one single section of transcranial direct current stimulation (tDCS), a neuromodulatory technique that noninvasively modifies cortical excitability, could induce acute changes in the negative attentional bias in patients with major depression. Subjects and MethodsRandomized, double-blind, sham-controlled, parallel design enrolling 24 age-, gender-matched, drug-free, depressed subjects. Anode and cathode were placed over the left and right dorsolateral prefrontal cortex. We performed a word Emotional Stroop Task collecting the response times (RTs) for positive-, negative-, and neutral-related words. The emotional Stroop effect for negative vs. neutral and vs. positive words was used as the measure of attentional bias. ResultsAt baseline, RTs were significantly slower for negative vs. positive words. We found that active but not sham tDCS significantly modified the negative attentional bias, abolishing slower RT for negative words. ConclusionActive but not sham tDCS significantly modified the negative attentional bias. These findings add evidence that a single tDCS session transiently induces potent changes in affective processing, which might be one of the mechanisms of tDCS underlying mood changes

Consistency Checking for the Evolution of Cardinality-based Feature Models

International audienceFeature models (FMs) are a widely used approach to specify the commonalities and variability in variable systems and software product lines. Various works have addressed edits to FMs for FM evolution and tool support to ensure consistency of FMs. An important extension to FMs are feature cardinalities and related constraints, as extensively used e.g., when modeling variability of cloud computing environments. Since cardinality-based FMs pose additional complexity, additional support for evolution and consistency checking with respect to feature cardinalities would be desirable, but has not been addressed yet. In this paper, we discuss common cardinality-based FM edits and resulting inconsistencies based on experiences with FMs in cloud domain. We introduce tool-support for automated inconsistency detection and explanation based on an off-the-shelf solver. We demonstrate the feasibility of the approach by an empirical evaluation showing the performance of the tool