The implementation of an Enterprise Geographical Information System to support Cadastre and Expropriation activities.

The processes of mobilization of land for infrastructures of public and private domain are developed according to proper legal frameworks and systematically confronted with the impoverished national situation as regards the cadastral identification and regularization, which leads to big inefficiencies, sometimes with very negative impact to the overall effectiveness. This project report describes Ferbritas Cadastre Information System (FBSIC) project and tools, which in conjunction with other applications, allow managing the entire life-cycle of Land Acquisition and Cadastre, including support to field activities with the integration of information collected in the field, the development of multi-criteria analysis information, monitoring all information in the exploration stage, and the automated generation of outputs. The benefits are evident at the level of operational efficiency, including tools that enable process integration and standardization of procedures, facilitate analysis and quality control and maximize performance in the acquisition, maintenance and management of registration information and expropriation (expropriation projects). Therefore, the implemented system achieves levels of robustness, comprehensiveness, openness, scalability and reliability suitable for a structural platform. The resultant solution, FBSIC, is a fit-for-purpose cadastre information system rooted in the field of railway infrastructures. FBSIC integrating nature of allows: to accomplish present needs and scale to meet future services; to collect, maintain, manage and share all information in one common platform, and transform it into knowledge; to relate with other platforms; to increase accuracy and productivity of business processes related with land property management.

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Botnet cluster identification

Botnets are a group of computers infected with a specific sub-set of a malware family and controlled by one individual, called botmaster. This kind of networks are used not only, but also for virtual extorsion, spam campaigns and identity theft. They implement different types of evasion techniques that make it harder for one to group and detect botnet traffic. This thesis introduces one methodology, called CONDENSER, that outputs clusters through a self-organizing map and that identify domain names generated by an unknown pseudo-random seed that is known by the botnet herder(s). Aditionally DNS Crawler is proposed, this system saves historic DNS data for fast-flux and double fastflux detection, and is used to identify live C&Cs IPs used by real botnets. A program, called CHEWER, was developed to automate the calculation of the SVM parameters and features that better perform against the available domain names associated with DGAs. CONDENSER and DNS Crawler were developed with scalability in mind so the detection of fast-flux and double fast-flux networks become faster. We used a SVM for the DGA classififer, selecting a total of 11 attributes and achieving a Precision of 77,9% and a F-Measure of 83,2%. The feature selection method identified the 3 most significant attributes of the total set of attributes. For clustering, a Self-Organizing Map was used on a total of 81 attributes. The conclusions of this thesis were accepted in Botconf through a submited article. Botconf is known conferênce for research, mitigation and discovery of botnets tailled for the industry, where is presented current work and research. This conference is known for having security and anti-virus companies, law enforcement agencies and researchers.

PV systems linked to the grid: parameter identification with a heuristic procedure

This paper focuses on a PV system linked to the electric grid by power electronic converters, identification of the five parameters modeling for photovoltaic systems and the assessment of the shading effect. Normally, the technical information for photovoltaic panels is too restricted to identify the five parameters. An undemanding heuristic method is used to find the five parameters for photovoltaic systems, requiring only the open circuit, maximum power, and short circuit data. The I–V and the P–V curves for a monocrystalline, polycrystalline and amorphous photovoltaic systems are computed from the parameters identification and validated by comparison with experimental ones. Also, the I–V and the P–V curves under the effect of partial shading are obtained from those parameters. The modeling for the converters emulates the association of a DC–DC boost with a two-level power inverter in order to follow the performance of a testing commercial inverter employed on an experimental system.

Rapid detection and differentiation of mycobacterial species using a multiplex PCR system

Introduction The early diagnosis of mycobacterial infections is a critical step for initiating treatment and curing the patient. Molecular analytical methods have led to considerable improvements in the speed and accuracy of mycobacteria detection. Methods The purpose of this study was to evaluate a multiplex polymerase chain reaction system using mycobacterial strains as an auxiliary tool in the differential diagnosis of tuberculosis and diseases caused by nontuberculous mycobacteria (NTM) Results Forty mycobacterial strains isolated from pulmonary and extrapulmonary origin specimens from 37 patients diagnosed with tuberculosis were processed. Using phenotypic and biochemical characteristics of the 40 mycobacteria isolated in LJ medium, 57.5% (n=23) were characterized as the Mycobacterium tuberculosis complex (MTBC) and 20% (n=8) as nontuberculous mycobacteria (NTM), with 22.5% (n=9) of the results being inconclusive. When the results of the phenotypic and biochemical tests in 30 strains of mycobacteria were compared with the results of the multiplex PCR, there was 100% concordance in the identification of the MTBC and NTM species, respectively. A total of 32.5% (n=13) of the samples in multiplex PCR exhibited a molecular pattern consistent with NTM, thus disagreeing with the final diagnosis from the attending physician. Conclusions Multiplex PCR can be used as a differential method for determining TB infections caused by NTM a valuable tool in reducing the time necessary to make clinical diagnoses and begin treatment. It is also useful for identifying species that were previously not identifiable using conventional biochemical and phenotypic techniques.

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving.

Detection of dangerous situations using a smart internet of things system

The Internet of Things (IoT) is a concept that can foster the emergence of innovative applications. In order to minimize parents’s concerns about their children’s safety, this paper presents the design of a smart Internet of Things system for identifying dangerous situations. The system will be based on real time collection and analysis of physiological signals monitored by non-invasive and non-intrusive sensors, Frequency IDentification (RFID) tags and a Global Positioning System (GPS) to determine when a child is in danger. The assumption of a state of danger is made taking into account the validation of a certain number of biometric reactions to some specific situations and according to a self-learning algorithm developed for this architecture. The results of the analysis of data collected and the location of the child will be able in real time to child’s care holders in a web application.

Parameter identification of the fermentative production of fructo-oligosaccharides by Aureobasidium pullulans

In this study, a mathematical model for the production of Fructo-oligosaccharides (FOS) by Aureobasidium pullulans is developed. This model contains a relatively large set of unknown parameters, and the identification problem is analyzed using simulation data, as well as experimental data. Batch experiments were not sufficiently informative to uniquely estimate all the unknown parameters, thus, additional experiments have to be achieved in fed-batch mode to supplement the missing information. © 2015 IEEE.

apoptosis-regulator c-FLIP : functional role in urothelial carcinoma and autoimmunity and identification of novel CD95 DISC-interacting proteins

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2013

Visual recognition systems in a car passenger compartment with the focus on facial driver identification

Magdeburg, Univ., Fak. für Informatik, Diss., 2014

Optimal experimental design for parameter identification and model selection

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Identification technique of mechanism-based constitutive model for cast iron under thermo-mechanical loads

Magdeburg, Univ., Fak. für Maschinenbau, Diss., 2015

Structure-function relationships for the interleukin 2 receptor system

Receptors for interleukin 2 (IL-2) esit in at least three forms which differ in their subunit compositio, their affinity for ligand and their ability to mediate a cellular reponse. Type I receptors occur following cellular acitivation and consist of the 55,000 m. w. glycoprotein Tac. These receptors bind IL-2 with a low affinity, do not internalize ligand and have not been definitively associated with any response. Type II receptors, on the other hand, conssit of one or more glycoproteins of 70,000 m. w. which have been termed "beta ([beta]) chains." They bind IL-2 with an intermediate affinity and rapidly internalize the ligand. [Beta] proteins mediate many cellular IL-2-dependent reponses, including the short-term activation of natural killer cells and the induction of Tac protein expression. Type III receptors consist of a ternary complex of the Tac protein, the [beta] chain(s) and IL-2. They are characterized by a paricularly high affinity for ligand association. Type III receptors also internalize ligand and mediate IL-2-dependent responses at low factor concentrations. The identification of two independent IL-2-binding molecules, Tac and [beta], thus provides the elusive molecular explanation for the differences in IL-2 receptor affinity and suggests the potential for selective therapeutic manipulation of IL-2 reponses.