Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios.

Photorealistic Surface Rendering with Microfacet Theory

La synthèse d'images dites photoréalistes nécessite d'évaluer numériquement la manière dont la lumière et la matière interagissent physiquement, ce qui, malgré la puissance de calcul impressionnante dont nous bénéficions aujourd'hui et qui ne cesse d'augmenter, est encore bien loin de devenir une tâche triviale pour nos ordinateurs. Ceci est dû en majeure partie à la manière dont nous représentons les objets: afin de reproduire les interactions subtiles qui mènent à la perception du détail, il est nécessaire de modéliser des quantités phénoménales de géométries. Au moment du rendu, cette complexité conduit inexorablement à de lourdes requêtes d'entrées-sorties, qui, couplées à des évaluations d'opérateurs de filtrage complexes, rendent les temps de calcul nécessaires à produire des images sans défaut totalement déraisonnables. Afin de pallier ces limitations sous les contraintes actuelles, il est nécessaire de dériver une représentation multiéchelle de la matière. Dans cette thèse, nous construisons une telle représentation pour la matière dont l'interface correspond à une surface perturbée, une configuration qui se construit généralement via des cartes d'élévations en infographie. Nous dérivons notre représentation dans le contexte de la théorie des microfacettes (conçue à l'origine pour modéliser la réflectance de surfaces rugueuses), que nous présentons d'abord, puis augmentons en deux temps. Dans un premier temps, nous rendons la théorie applicable à travers plusieurs échelles d'observation en la généralisant aux statistiques de microfacettes décentrées. Dans l'autre, nous dérivons une procédure d'inversion capable de reconstruire les statistiques de microfacettes à partir de réponses de réflexion d'un matériau arbitraire dans les configurations de rétroréflexion. Nous montrons comment cette théorie augmentée peut être exploitée afin de dériver un opérateur général et efficace de rééchantillonnage approximatif de cartes d'élévations qui (a) préserve l'anisotropie du transport de la lumière pour n'importe quelle résolution, (b) peut être appliqué en amont du rendu et stocké dans des MIP maps afin de diminuer drastiquement le nombre de requêtes d'entrées-sorties, et (c) simplifie de manière considérable les opérations de filtrage par pixel, le tout conduisant à des temps de rendu plus courts. Afin de valider et démontrer l'efficacité de notre opérateur, nous synthétisons des images photoréalistes anticrenelées et les comparons à des images de référence. De plus, nous fournissons une implantation C++ complète tout au long de la dissertation afin de faciliter la reproduction des résultats obtenus. Nous concluons avec une discussion portant sur les limitations de notre approche, ainsi que sur les verrous restant à lever afin de dériver une représentation multiéchelle de la matière encore plus générale.

Resting tremor classification and detection in Parkinson's disease patients

Parkinson is a neurodegenerative disease, in which tremor is the main symptom. This paper investigates the use of different classification methods to identify tremors experienced by Parkinsonian patients.Some previous research has focussed tremor analysis on external body signals (e.g., electromyography, accelerometer signals, etc.). Our advantage is that we have access to sub-cortical data, which facilitates the applicability of the obtained results into real medical devices since we are dealing with brain signals directly. Local field potentials (LFP) were recorded in the subthalamic nucleus of 7 Parkinsonian patients through the implanted electrodes of a deep brain stimulation (DBS) device prior to its internalization. Measured LFP signals were preprocessed by means of splinting, down sampling, filtering, normalization and rec-tification. Then, feature extraction was conducted through a multi-level decomposition via a wavelettrans form. Finally, artificial intelligence techniques were applied to feature selection, clustering of tremor types, and tremor detection.The key contribution of this paper is to present initial results which indicate, to a high degree of certainty, that there appear to be two distinct subgroups of patients within the group-1 of patients according to the Consensus Statement of the Movement Disorder Society on Tremor. Such results may well lead to different resultant treatments for the patients involved, depending on how their tremor has been classified. Moreover, we propose a new approach for demand driven stimulation, in which tremor detection is also based on the subtype of tremor the patient has. Applying this knowledge to the tremor detection problem, it can be concluded that the results improve when patient clustering is applied prior to detection.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

How to be a fig wasp down under: the diversity and structure of an Australian fig wasp community

Endophytic insects and their parasitoids provide valuable models for community ecology. The wasp communities in inflorescences of fig trees have great potential for comparative studies, but we must first describe individual communities. Here, we add to the few detailed studies of such communities by describing the one associated with Ficus rubiginosa in Australia. First, we describe community composition, using two different sampling procedures. Overall, we identified 14 species of non-pollinating fig wasp (NPFW) that fall into two size classes. Small wasps, including pollinators, gallers and their parasitoids, were more abundant than large wasps (both galler and parasitoid species). We show that in figs where wasps emerge naturally, the presence of large wasps may partly explain the low emergence of small wasps. During fig development, large gallers oviposit first, before and around the time of pollination, while parasitoids lay eggs after pollination. We further show that parasitoids in the subfamily Sycoryctinae, which comprise the majority of all individual NPFWs, segregate temporally by laying eggs at different stages of fig development. We discuss our results in terms of species co-existence and community structure and compare our findings to those from fig wasp communities on other continents.

Comparing methods for sampling large- and medium-sized mammals: camera traps and track plots

Activities involving fauna monitoring are usually limited by the lack of resources; therefore, the choice of a proper and efficient methodology is fundamental to maximize the cost-benefit ratio. Both direct and indirect methods can be used to survey mammals, but the latter are preferred due to the difficulty to come in sight of and/or to capture the individuals, besides being cheaper. We compared the performance of two methods to survey medium and large-sized mammal: track plot recording and camera trapping, and their costs were assessed. At Jatai Ecological Station (S21 degrees 31`15 ``- W47 degrees 34`42 ``-Brazil) we installed ten camera traps along a dirt road directly in front of ten track plots, and monitored them for 10 days. We cleaned the plots, adjusted the cameras, and noted down the recorded species daily. Records taken by both methods showed they sample the local richness in different ways (Wilcoxon, T=231; p;;0.01). The track plot method performed better on registering individuals whereas camera trapping provided records which permitted more accurate species identification. The type of infra-red sensor camera used showed a strong bias towards individual body mass (R(2)=0.70; p=0.017), and the variable expenses of this method in a 10-day survey were estimated about 2.04 times higher compared to track plot method; however, in a long run camera trapping becomes cheaper than track plot recording. Concluding, track plot recording is good enough for quick surveys under a limited budget, and camera trapping is best for precise species identification and the investigation of species details, performing better for large animals. When used together, these methods can be complementary.

Versatile microanalytical system with porous polypropylene capillary membrane for calibration gas generation and trace gaseous pollutants sampling applied to the analysis of formaldehyde, formic acid, acetic acid and ammonia in outdoor air

The analytical determination of atmospheric pollutants still presents challenges due to the low-level concentrations (frequently in the mu g m(-3) range) and their variations with sampling site and time In this work a capillary membrane diffusion scrubber (CMDS) was scaled down to match with capillary electrophoresis (CE) a quick separation technique that requires nothing more than some nanoliters of sample and when combined with capacitively coupled contactless conductometric detection (C(4)D) is particularly favorable for ionic species that do not absorb in the UV-vis region like the target analytes formaldehyde formic acid acetic acid and ammonium The CMDS was coaxially assembled inside a PTFE tube and fed with acceptor phase (deionized water for species with a high Henry s constant such as formaldehyde and carboxylic acids or acidic solution for ammonia sampling with equilibrium displacement to the non-volatile ammonium ion) at a low flow rate (8 3 nLs(-1)) while the sample was aspirated through the annular gap of the concentric tubes at 25 mLs(-1) A second unit in all similar to the CMDS was operated as a capillary membrane diffusion emitter (CMDE) generating a gas flow with know concentrations of ammonia for the evaluation of the CMDS The fluids of the system were driven with inexpensive aquarium air pumps and the collected samples were stored in vials cooled by a Peltier element Complete protocols were developed for the analysis in air of NH(3) CH(3)COOH HCOOH and with a derivatization setup CH(2)O by associating the CMDS collection with the determination by CE-C(4)D The ammonia concentrations obtained by electrophoresis were checked against the reference spectrophotometric method based on Berthelot s reaction Sensitivity enhancements of this reference method were achieved by using a modified Berthelot reaction solenoid micro-pumps for liquid propulsion and a long optical path cell based on a liquid core waveguide (LCW) All techniques and methods of this work are in line with the green analytical chemistry trends (C) 2010 Elsevier B V All rights reserved

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Benthic–pelagic links and rocky intertidal communities: Bottom-up effects on top-down control?

Insight into the dependence of benthic communities on biological and physical processes in nearshore pelagic environments, long considered a “black box,” has eluded ecologists. In rocky intertidal communities at Oregon coastal sites 80 km apart, differences in abundance of sessile invertebrates, herbivores, carnivores, and macrophytes in the low zone were not readily explained by local scale differences in hydrodynamic or physical conditions (wave forces, surge flow, or air temperature during low tide). Field experiments employing predator and herbivore manipulations and prey transplants suggested top-down (predation, grazing) processes varied positively with bottom-up processes (growth of filter-feeders, prey recruitment), but the basis for these differences was unknown. Shore-based sampling revealed that between-site differences were associated with nearshore oceanographic conditions, including phytoplankton concentration and productivity, particulates, and water temperature during upwelling. Further, samples taken at 19 sites along 380 km of coastline suggested that the differences documented between two sites reflect broader scale gradients of phytoplankton concentration. Among several alternative explanations, a coastal hydrodynamics hypothesis, reflecting mesoscale (tens to hundreds of kilometers) variation in the interaction between offshore currents and winds and continental shelf bathymetry, was inferred to be the primary underlying cause. Satellite imagery and offshore chlorophyll-a samples are consistent with the postulated mechanism. Our results suggest that benthic community dynamics can be coupled to pelagic ecosystems by both trophic and transport linkages.

“Deep-Arvor”: A new profiling float to extend the Argo observations down to 4000m depth.

The international Argo program, consisting of a global array of more than 3000 free-drifting profiling floats, has now been monitoring the upper 2000 meters of the ocean for several years. One of its main proposed evolutions is to be able to reach the deeper ocean in order to better observe and understand the key role of the deep ocean in the climate system. For this purpose, Ifremer has designed the new “Deep-Arvor” profiling float: it extends the current operational depth down to 4000 meters, and measures temperature and salinity for up to 150 cycles with CTD pumping continuously and 200 cycles in spot sampling mode. High resolution profiles (up to 2000 points) can be transmitted and data are delivered in near real time according to Argo requirements. Deep-Arvor can be deployed everywhere at sea without any pre-ballasting operation and its light weight (~ 26kg) makes its launching easy. Its design was done to target a cost effective solution. Predefined spots have been allocated to add an optional oxygen sensor and a connector for an extra sensor. Extensive laboratory tests were successful. The results of the first at sea experiments showed that the expected performances of the operational prototypes had been reached (i.e. to perform up to 150 cycles). Meanwhile, the industrialization phase was completed in order to manufacture the Deep-Arvor float for the pilot experiment in 2015. In this paper, we detail all the steps of the development work and present the results from the at sea experiments.

Down-regulation Of Slc8a1 As A Putative Apoptosis-evasion Mechanism By Modulation Of Calcium Levels In Penile Carcinoma.

The SLC8A1 gene, which encodes the Na(+)/Ca(2+) exchanger, plays a key role in calcium homeostasis. Our previous gene expression oligoarray data revealed SLC8A1 underexpression in penile carcinoma (PeCa). The aim of this study was to investigate whether the dysregulation of SLC8A1 expression is associated with apoptosis and cell proliferation in PeCa, via modulation of calcium concentration. The underlying mechanisms of SLC8A1 underexpression were also explored, focusing on copy number alteration and microRNA. Transcript levels of SLC8A1 gene and miR-223 were evaluated by quantitative PCR, comparing PeCa samples with normal glans tissues. SLC8A1 copy number was evaluated by microarray-based comparative genomic hybridization (array-CGH). Caspase-3 and Ki-67 immunostaining, as well as calcium distribution by Laser Ablation Imaging Inductively Coupled Plasma Mass Spectrometry [LA(i)-ICP-MS], were investigated in both normal and tumor samples. Confirming our previous data, SLC8A1 underexpression was detected in PeCa samples (P=0.001) and was not associated with gene copy number loss. In contrast, overexpression of miR-223 (P=0.002) was inversely correlated with SLC8A1 (P=0.015, r=-0.426), its putative repressor. In addition, SLC8A1 underexpression was associated with decreased calcium distribution, high Ki-67 and low caspase-3 immunoexpression in PeCa when compared with normal tissues. Down-regulation of the SLC8A1 gene, most likely mediated by its regulator miR-223, can lead to reduced calcium levels in PeCa and, consequently, to suppression of apoptosis and increased tumor cell proliferation. These data suggest that the miR-223-NCX1-calcium-signaling axis may represent a potential therapeutic approach in PeCa.

Chasing Cardiac Physiology And Pathology Down The Camkii Cascade.

Calcium dynamics is central in cardiac physiology, as the key event leading to the excitation-contraction coupling (ECC) and relaxation processes. The primary function of Ca(2+) in the heart is the control of mechanical activity developed by the myofibril contractile apparatus. This key role of Ca(2+) signaling explains the subtle and critical control of important events of ECC and relaxation, such Ca(2+) influx and SR Ca(2+) release and uptake. The multifunctional Ca(2+)-calmodulin-dependent protein kinase II (CaMKII) is a signaling molecule that regulates a diverse array of proteins involved not only in ECC and relaxation, but also in cell death, transcriptional activation of hypertrophy, inflammation and arrhythmias. CaMKII activity is triggered by an increase in intracellular Ca(2+) levels. This activity can be sustained, creating molecular memory after the decline in Ca(2+) concentration, by autophosphorylation of the enzyme, as well as by oxidation, glycosylation and nitrosylation at different sites of the regulatory domain of the kinase. CaMKII activity is enhanced in several cardiac diseases, altering the signaling pathways by which CaMKII regulates the different fundamental proteins involved in functional and transcriptional cardiac processes. Dysregulation of these pathways constitutes a central mechanism of various cardiac disease phenomena, like apoptosis and necrosis during ischemia/reperfusion injury, digitalis exposure, post-acidosis and heart failure arrhythmias, or cardiac hypertrophy. Here we summarize significant aspects of the molecular physiology of CaMKII and provide a conceptual framework for understanding the role of the CaMKII cascade on Ca(2+) regulation and dysregulation in cardiac health and disease.