Copper ions binding in cu‐alginate gelation

Alginate polysaccharide forms viscous aqueous dispersions and has the ability to form gels in the presence of divalent cations such as calcium and copper. In this work, we have studied cooper ions binding during Cu‐alginate gelation, obtaining quantitative information about the amount and kinetics of cation binding. Our results indicate that copper binding during gelation occurs until a Langmuir‐type equilibrium is reached between bound and free ions in the gel‐contacting solution. The kinetics of metal ions binding can be modeled using Ritchie equation–derived models, allowing the prediction of ionic binding and gel formation temporal evolution. The ratio between cationic and polysaccharide quantities in the gelation system determines the kinetics of gelation and the characteristics of the gel formed. The experimental results and models applied in the work give more insights on alginate gelation and contribute to a reliable design and control of production methods for alginate gel structures.

Proton spectroscopy in Alzheimer`s disease and cognitive impairment no dementia: A community-based study

Objective: To describe the findings of proton magnetic resonance spectroscopy (H-1-MRS) in Alzheimer`s disease (AD) and cognitive impairment, no dementia (CIND) elderly from a community-based sample. Methods: Thirteen patients with AD, 12 with CIND and 15 normal individuals were evaluated. The H-1-MRS was performed in the right temporal, left parietal and medial occipital regions studying the metabolites N-acetylaspartate (NAA), creatine (Cr), choline (Cho) and myoinositol (ml). The clinical diagnosis was based on standardized cognitive tests - MMSE and CAMDEX - and the results correlated with the H-1-MRS. Results: Parietal Cho was higher in control individuals and lower in CIND subjects. AD and control groups were better identified by temporal and parietal ml combined with the temporal NAA/Cr ratio. CIND was better identified by parietal Cho. Conclusion: The H-1-MRS findings confirmed the hypothesis that metabolic alterations are present since the first symptoms of cognitively impaired elderly subjects. These results suggest that combining MRS from different cerebral regions can help in the diagnosis and follow-up of community elderly individuals with memory complaints and AD. Copyright (C) 2008 S. Karger AG, Basel.

Caracterização da ocupação do solo com recurso à aplicação de modelos de misturas espectrais em séries multi-temporais de imagens MODIS

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

Influence of hip-flexion angle on hamstrings isokinetic activity in sprinters.

CONTEXT: Hamstrings strains are common and debilitating injuries in many sports. Most hamstrings exercises are performed at an inadequately low hip-flexion angle because this angle surpasses 70° at the end of the sprinting leg's swing phase, when most injuries occur. OBJECTIVE: To evaluate the influence of various hip-flexion angles on peak torques of knee flexors in isometric, concentric, and eccentric contractions and on the hamstrings-to-quadriceps ratio. DESIGN: Descriptive laboratory study. SETTING: Research laboratory. Patients and Other Participants: Ten national-level sprinters (5 men, 5 women; age = 21.2 ± 3.6 years, height = 175 ± 6 cm, mass = 63.8 ± 9.9 kg). Intervention(s): For each hip position (0°, 30°, 60°, and 90° of flexion), participants used the right leg to perform (1) 5 seconds of maximal isometric hamstrings contraction at 45° of knee flexion, (2) 5 maximal concentric knee flexion-extensions at 60° per second, (3) 5 maximal eccentric knee flexion-extensions at 60° per second, and (4) 5 maximal eccentric knee flexionextensions at 150° per second. Main Outcome Measure(s): Hamstrings and quadriceps peak torque, hamstrings-to-quadriceps ratio, lateral and medial hamstrings root mean square. RESULTS: We found no difference in quadriceps peak torque for any condition across all hip-flexion angles, whereas hamstrings peak torque was lower at 0° of hip flexion than at any other angle (P < .001) and greater at 90° of hip flexion than at 30° and 60° (P < .05), especially in eccentric conditions. As hip flexion increased, the hamstrings-to-quadriceps ratio increased. No difference in lateral or medial hamstrings root mean square was found for any condition across all hip-flexion angles (P > .05). CONCLUSIONS: Hip-flexion angle influenced hamstrings peak torque in all muscular contraction types; as hip flexion increased, hamstrings peak torque increased. Researchers should investigate further whether an eccentric resistance training program at sprint-specific hip-flexion angles (70° to 80°) could help prevent hamstrings injuries in sprinters. Moreover, hamstrings-to-quadriceps ratio assessment should be standardized at 80° of hip flexion.

Intraoral open reduction and internal fixation of displaced mandibular angle fractures using a specific ad hoc reduction-compression forceps: a preliminary study

OBJECTIVES: To preliminarily evaluate prospectively the accuracy and reliability of a specific ad hoc reduction-compression forceps in intraoral open reduction of transverse and displaced mandibular angle fractures. STUDY DESIGN: We analyzed the clinical and radiologic data of 7 patients with 7 single transverse and displaced angle fractures. An intraoral approach was performed in all of the patients without using perioperative intermaxillary fixation. A single Arbeitsgemeinschaft Osteosynthese (AO) unilock reconstruction plate was fixed to each stable fragment with 3 locking screws (2.0 mm in 5 patients and 2.4 mm in 2 patients) at the basilar border of the mandible, according to AO/American Society of Internal Fixation (ASIF) principles. Follow-up was at 1, 3, 6, and 12 months, and we noted the status of healing and complications, if any. RESULTS: All of the patients had satisfactory fracture reduction as well as a successful treatment outcome without complications. CONCLUSION: This preliminary study demonstrated that the intraoral reduction of transverse and displaced angle fractures using a specific ad hoc reduction-forceps results in a high rate of success.

A multisensor, multitemporal approach for monitoring herbaceous vegetation growth in the Amazon floodplain

The Amazon River floodplain is an important source of atmospheric CO2 and CH4. Aquatic herbaceous vegetation (macrophytes) have been shown to contribute significantly to floodplain net primary productivity (NPP) and methane emission in the region. Their fast growth rates under both flooded and dry conditions make herbaceous vegetation the most variable element in the Amazon floodplain NPP budget, and the most susceptible to environmental changes. The present study combines multitemporal Radarsat-1 and MODIS images to monitor spatial and temporal changes in herbaceous vegetation cover in the Amazon floodplain. Radarsat-1 images were acquired from Dec/2003 to Oct/2005, and MODIS daily surface reflectance products were acquired for the two cloud-free dates closest to each Radarsat-1 acquisition. An object-based, hierarchical algorithm was developed using the temporal SAR information to discriminate Permanent Open Water (OW), Floodplain (FP) and Upland (UL) classes at Level 1, and then subdivide the FP class into Woody Vegetation (WV) and Possible Macrophytes (PM) at Level 2. At Level 3, optical and SAR information were combined to discriminate actual herbaceous cover at each date. The resulting maps had accuracies ranging from 80% to 90% for Level 1 and 2 classifications, and from 60% to 70% for Level 3 classifications, with kappa values ranging between 0.7 and 0.9 for Levels 1 and 2 and between 0.5 and 0.6 for Level 3. All study sites had noticeable variations in the extent of herbaceous cover throughout the hydrological year, with maximum areas up to four times larger than minimum areas. The proposed classification method was able to capture the spatial pattern of macrophyte growth and development in the studied area, and the multitemporal information was essential for both separating vegetation cover types and assessing monthly variation in herbaceous cover extent.

Ultra-low power WSNs: distributed signal processing and dynamic resource management

This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications

Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.

Campionamento e ricostruzione di immagini full wavefield per il monitoraggio strutturale

L'elaborato affronta la definizione di differenti strategie per il campionamento e la ricostruzione di segnali wavefield per applicazioni di monitoraggio strutturale. In accordo con quanto indicato dalla teoria del Compressive Sensing, obiettivo della tesi è la minimizzazione del numero di punti di acquisizione al fine di ridurre lo sforzo energetico del campionamento. I risultati sono validati in ambiente Matlab utilizzando come riferimento segnali acquisiti su setup sperimentali in alluminio o materiale composito in presenza di diverse tipologie di difetto.

Measuring population mobility speed from space

Ad-hoc population dynamics in Krugman’s type core and periphery models adjust population share of a region, based on its real wage rate deviation from national average, at pre-specified speed of population mobility. Whereas speed of population mobility is expected to be different across countries, for geographical, cultural, technological, etc. reasons, one common speed is often applied in theoretical and simulation analysis, due to spatially patchy, and temporally infrequent, availability of sub-national regional data. This article demonstrates how, increasingly available, high definition spatio-temporal remote-sensing data, and their by-products, can be used to measure speed of population mobility in national and sub-national level.

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.

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies.

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.

Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals

Many problems in digital communications involve wideband radio signals. As the most recent example, the impressive advances in Cognitive Radio systems make even more necessary the development of sampling schemes for wideband radio signals with spectral holes. This is equivalent to considering a sparse multiband signal in the framework of Compressive Sampling theory. Starting from previous results on multicoset sampling and recent advances in compressive sampling, we analyze the matrix involved in the corresponding reconstruction equation and define a new method for the design of universal multicoset codes, that is, codes guaranteeing perfect reconstruction of the sparse multiband signal.

Effect of aspect ratio on strain sensing capacity of carbon fiber reinforced cement composites

The electrical resistivity of carbon fiber reinforced cement composites (CFRCCs) has been widely studied, because of their utility as multifunctional materials. The percolation phenomenon has also been reported and modeled when the electrical behavior of those materials had to be characterized. Amongst the multiple applications of multifunctional cement composites the ability of a CFRCC to act as a strain sensor is attractive. This paper provides experimental data relating self-sensing function and percolation threshold, and studying the effect of fiber aspect ratio on both phenomena. Higher fiber slenderness permitted percolation at lower carbon fiber addition, affected mechanical properties and improved strain-sensing sensitivity of CFRCC, which was also improved if percolation had not been achieved.