Sparse time-frequency data analysis : a multi-scale approach

In this work, we further extend the recently developed adaptive data analysis method, the Sparse Time-Frequency Representation (STFR) method. This method is based on the assumption that many physical signals inherently contain AM-FM representations. We propose a sparse optimization method to extract the AM-FM representations of such signals. We prove the convergence of the method for periodic signals under certain assumptions and provide practical algorithms specifically for the non-periodic STFR, which extends the method to tackle problems that former STFR methods could not handle, including stability to noise and non-periodic data analysis. This is a significant improvement since many adaptive and non-adaptive signal processing methods are not fully capable of handling non-periodic signals. Moreover, we propose a new STFR algorithm to study intrawave signals with strong frequency modulation and analyze the convergence of this new algorithm for periodic signals. Such signals have previously remained a bottleneck for all signal processing methods. Furthermore, we propose a modified version of STFR that facilitates the extraction of intrawaves that have overlaping frequency content. We show that the STFR methods can be applied to the realm of dynamical systems and cardiovascular signals. In particular, we present a simplified and modified version of the STFR algorithm that is potentially useful for the diagnosis of some cardiovascular diseases. We further explain some preliminary work on the nature of Intrinsic Mode Functions (IMFs) and how they can have different representations in different phase coordinates. This analysis shows that the uncertainty principle is fundamental to all oscillating signals.

Adaptive coatings based on polyaniline for direct 2D observation of diffusion processes in microfluidic systems

[EN] Therefore the understanding and proper evaluation of the flow and mixing behaviour at microscale becomes a very important issue. In this study, the diffusion behaviour of two reacting solutions of HCI and NaOH were directly observed in a glass/polydimethylsiloxane microfluidic device using adaptive coatings based on the conductive polymer polyaniline that are covalently attached to the microchannel walls. The two liquid streams were combined at the junction of a Y-shaped microchannel, and allowed to diffuse into each other and react. The results showed excellent correlation between optical observation of the diffusion process and the numerical results. A numerical model which is based on finite volume method (FVM) discretisation of steady Navier-Stokes (fluid flow) equations and mass transport equations without reactions was used to calculate the flow variables at discrete points in the finite volume mesh element. The high correlation between theory and practical data indicates the potential of such coatings to monitor diffusion processes and mixing behaviour inside microfluidic channels in a dye free environment.

Adaptive lighting design as a holistic approach to public lighting

Dissertação para obtenção do grau de Doutor em Design, apresentada na Universidade de Lisboa - Faculdade de Arquitetura.

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.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

Prevalence of SOS-mediated control of integron integrase expression as an adaptive trait of chromosomal and mobile integrons

BACKGROUND Integrons are found in hundreds of environmental bacterial species, but are mainly known as the agents responsible for the capture and spread of antibiotic-resistance determinants between Gram-negative pathogens. The SOS response is a regulatory network under control of the repressor protein LexA targeted at addressing DNA damage, thus promoting genetic variation in times of stress. We recently reported a direct link between the SOS response and the expression of integron integrases in Vibrio cholerae and a plasmid-borne class 1 mobile integron. SOS regulation enhances cassette swapping and capture in stressful conditions, while freezing the integron in steady environments. We conducted a systematic study of available integron integrase promoter sequences to analyze the extent of this relationship across the Bacteria domain. RESULTS Our results showed that LexA controls the expression of a large fraction of integron integrases by binding to Escherichia coli-like LexA binding sites. In addition, the results provide experimental validation of LexA control of the integrase gene for another Vibrio chromosomal integron and for a multiresistance plasmid harboring two integrons. There was a significant correlation between lack of LexA control and predicted inactivation of integrase genes, even though experimental evidence also indicates that LexA regulation may be lost to enhance expression of integron cassettes. CONCLUSIONS Ancestral-state reconstruction on an integron integrase phylogeny led us to conclude that the ancestral integron was already regulated by LexA. The data also indicated that SOS regulation has been actively preserved in mobile integrons and large chromosomal integrons, suggesting that unregulated integrase activity is selected against. Nonetheless, additional adaptations have probably arisen to cope with unregulated integrase activity. Identifying them may be fundamental in deciphering the uneven distribution of integrons in the Bacteria domain.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.

The conjugate gradient partitioned block frequency-domain for multichannel adaptive filtering

The conjugate gradient is the most popular optimization method for solving large systems of linear equations. In a system identification problem, for example, where very large impulse response is involved, it is necessary to apply a particular strategy which diminishes the delay, while improving the convergence time. In this paper we propose a new scheme which combines frequency-domain adaptive filtering with a conjugate gradient technique in order to solve a high order multichannel adaptive filter, while being delayless and guaranteeing a very short convergence time.

Adaptive Neural Translation for Enterprises (ANTE). La traduzione automatica neurale a sostegno delle imprese emiliano-romagnole

Il progetto ANTE riguarda i nuovi sistemi di traduzione automatica (TA) e la loro applicazione nel mondo delle imprese. Lo studio prende spunto dai recenti sviluppi legati all’intelligenza artificiale e ai Big Data che negli ultimi anni hanno permesso alla TA di raggiungere livelli qualitativi molto elevati, al punto tale da essere impiegata da grandi multinazionali per raggiungere nuove quote di mercato. La TA può rispondere positivamente anche ai bisogni delle imprese di piccole dimensioni e a basso tenore tecnologico, migliorando la qualità delle comunicazioni multilingue attraverso delle traduzioni in tempi brevi e a costi contenuti. Lo studio si propone quindi di contribuire al rafforzamento della competitività internazionale delle piccole e medie imprese (PMI) emiliano- romagnole, migliorando la loro capacità di comunicazione in una o più lingue straniere attraverso l’introduzione e l’utilizzo efficace e consapevole di soluzioni ICT di ultima generazione e fornire, così, nuove opportunità di internazionalizzazione.

1° level of automation: the effectiveness of adaptive cruise control on driving and visual behaviour

The research activities have allowed the analysis of the driver assistance systems, called Advanced Driver Assistance Systems (ADAS) in relation to road safety. The study is structured according to several evaluation steps, related to definite on-site tests that have been carried out with different samples of users, according to their driving experience with the ACC. The evaluation steps concern: •The testing mode and the choice of suitable instrumentation to detect the driver’s behaviour in relation to the ACC. •The analysis modes and outputs to be obtained, i.e.: - Distribution of attention and inattention; - Mental workload; - The Perception-Reaction Time (PRT), the Time To Collision (TTC) and the Time Headway (TH). The main purpose is to assess the interaction between vehicle drivers and ADAS, highlighting the inattention and variation of the workloads they induce regarding the driving task. The research project considered the use of a system for monitoring visual behavior (ASL Mobile Eye-XG - ME), a powerful GPS that allowed to record the kinematic data of the vehicle (Racelogic Video V-BOX) and a tool for reading brain activity (Electroencephalographic System - EEG). Just during the analytical phase, a second and important research objective was born: the creation of a graphical interface that would allow exceeding the frame count limit, making faster and more effective the labeling of the driver’s points of view. The results show a complete and exhaustive picture of the vehicle-driver interaction. It has been possible to highlight the main sources of criticalities related to the user and the vehicle, in order to concretely reduce the accident rate. In addition, the use of mathematical-computational methodologies for the analysis of experimental data has allowed the optimization and verification of analytical processes with neural networks that have made an effective comparison between the manual and automatic methodology.

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.

Applicazione della Metodologia Agile e di tecniche di Data Analysis nello sviluppo di un sistema di e-grocery

I principi Agile, pubblicati nell’omonimo Manifesto più di 20 anni fa, al giorno d’oggi sono declinati in una moltitudine di framework: Scrum, XP, Kanban, Lean, Adaptive, Crystal, etc. Nella prima parte della tesi (Capitoli 1 e 2) sono stati descritti alcuni di questi framework e si è analizzato come un approccio Agile è utilizzato nella pratica in uno specifico caso d’uso: lo sviluppo di una piattaforma software a supporto di un sistema di e-grocery da parte di un team di lab51. Si sono verificate le differenze e le similitudini rispetto alcuni metodi Agile formalizzati in letteratura spiegando le motivazioni che hanno portato a differenziarsi da questi framework illustrando i vantaggi per il team. Nella seconda parte della tesi (Capitoli 3 e 4) è stata effettuata un’analisi dei dati raccolti dal supermercato online negli ultimi anni con l’obiettivo di migliorare l’algoritmo di riordino. In particolare, per prevedere le vendite dei singoli prodotti al fine di avere degli ordini più adeguati in quantità e frequenza, sono stati studiati vari approcci: dai modelli statistici di time series forecasting, alle reti neurali, fino ad una metodologia sviluppata ad hoc.

Iis--integrated Interactome System: A Web-based Platform For The Annotation, Analysis And Visualization Of Protein-metabolite-gene-drug Interactions By Integrating A Variety Of Data Sources And Tools.

High-throughput screening of physical, genetic and chemical-genetic interactions brings important perspectives in the Systems Biology field, as the analysis of these interactions provides new insights into protein/gene function, cellular metabolic variations and the validation of therapeutic targets and drug design. However, such analysis depends on a pipeline connecting different tools that can automatically integrate data from diverse sources and result in a more comprehensive dataset that can be properly interpreted. We describe here the Integrated Interactome System (IIS), an integrative platform with a web-based interface for the annotation, analysis and visualization of the interaction profiles of proteins/genes, metabolites and drugs of interest. IIS works in four connected modules: (i) Submission module, which receives raw data derived from Sanger sequencing (e.g. two-hybrid system); (ii) Search module, which enables the user to search for the processed reads to be assembled into contigs/singlets, or for lists of proteins/genes, metabolites and drugs of interest, and add them to the project; (iii) Annotation module, which assigns annotations from several databases for the contigs/singlets or lists of proteins/genes, generating tables with automatic annotation that can be manually curated; and (iv) Interactome module, which maps the contigs/singlets or the uploaded lists to entries in our integrated database, building networks that gather novel identified interactions, protein and metabolite expression/concentration levels, subcellular localization and computed topological metrics, GO biological processes and KEGG pathways enrichment. This module generates a XGMML file that can be imported into Cytoscape or be visualized directly on the web. We have developed IIS by the integration of diverse databases following the need of appropriate tools for a systematic analysis of physical, genetic and chemical-genetic interactions. IIS was validated with yeast two-hybrid, proteomics and metabolomics datasets, but it is also extendable to other datasets. IIS is freely available online at: http://www.lge.ibi.unicamp.br/lnbio/IIS/.

Laparoscopic Antireflux Surgery In Patients With Extra Esophageal Symptoms Related To Asthma.

Asthma, laryngitis and chronic cough are atypical symptoms of the gastroesophageal reflux disease. To analyze the efficacy of laparoscopic surgery in the remission of extra-esophageal symptoms in patients with gastroesophageal reflux, related to asthma. Were reviewed the medical records of 400 patients with gastroesophageal reflux disease submitted to laparoscopic Nissen fundoplication from 1994 to 2006, and identified 30 patients with extra-esophageal symptoms related to asthma. The variables considered were: gender, age, gastroesophageal symptoms (heartburn, acid reflux and dysphagia), time of reflux disease, treatment with proton pump inhibitor, use of specific medications, treatment and evolution, number of attacks and degree of esophagitis. Data were subjected to statistical analysis, comparing the pre- and post-surgical findings. The comparative analysis before surgery (T1) and six months after surgery (T2) showed a significant reduction on heartburn and reflux symptoms. Apart from that, there was a significant difference between the patients with daily crises of asthma (T1 versus T2, 45.83% to 16.67%, p=0.0002) and continuous crises (T1, 41.67% versus T2, 8.33%, p=0.0002). Laparoscopic Nissen fundoplication was effective in improving symptoms that are typical of reflux disease and clinical manifestations of asthma.

[gait Speed, Grip Strength And Self-rated Health Among The Elderly: Data From The Fibra Campinas Network, São Paulo, Brazil].

The article seeks to investigate patterns of performance and relationships between grip strength, gait speed and self-rated health, and investigate the relationships between them, considering the variables of gender, age and family income. This was conducted in a probabilistic sample of community-dwelling elderly aged 65 and over, members of a population study on frailty. A total of 689 elderly people without cognitive deficit suggestive of dementia underwent tests of gait speed and grip strength. Comparisons between groups were based on low, medium and high speed and strength. Self-related health was assessed using a 5-point scale. The males and the younger elderly individuals scored significantly higher on grip strength and gait speed than the female and oldest did; the richest scored higher than the poorest on grip strength and gait speed; females and men aged over 80 had weaker grip strength and lower gait speed; slow gait speed and low income arose as risk factors for a worse health evaluation. Lower muscular strength affects the self-rated assessment of health because it results in a reduction in functional capacity, especially in the presence of poverty and a lack of compensatory factors.