Surgical and postmortem pathology studies: contribution for the investigation of temporal lobe epilepsy

Pathology studies in epilepsy patients bring useful information for comprehending the physiopathology of various forms of epilepsy, as well as aspects related to response to treatment and long-term prognosis. These studies are usually restricted to surgical specimens obtained from patients with refractory focal epilepsies. Therefore, most of them pertain to temporal lobe epilepsy (TLE) with mesial temporal sclerosis (MTS) and malformations of cortical development (MCD), thus providing information of a selected group of patients and restricted regions of the brain. Postmortem whole brain studies are rarely performed in epilepsy patients, however they may provide extensive information on brain pathology, allowing the analysis of areas beyond the putative epileptogenic zone. In this article, we reviewed pathology studies performed in epilepsy patients with emphasis on neuropathological findings in TLE with MTS and MCD. Furthermore, we reviewed data from postmortem studies and discussed the importance of performing these studies in epilepsy populations.

Spatio-temporal patterns of tuberculosis incidence in Ribeirão Preto, State of São Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

INTRODUCTION: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirão Preto, State of São Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. METHODS: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. RESULTS: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirão Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. CONCLUSIONS: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Space-temporal dynamics of limnological variables in a floodplain wetland located at Ribeira do Iguape Valley (São Paulo, Brazil) at different hydrological periods

AIM: The main goal of this research was to investigate the influence of the hydrological pulses on the space-temporal dynamics of physical and chemical variables in a wetland adjacent to Jacupiranguinha River (São Paulo, Brazil); METHODS: Eleven sampling points were distributed among the wetland, a tributary by its left side and the adjacent river. Four samplings were carried out, covering the rainy and the dry periods. Measures of pH, dissolved oxygen, electrical conductivity and redox potential were taken in regular intervals of the water column using a multiparametric probe. Water samples were collected for the nitrogen and total phosphorus analysis, as well as their dissolved fractions (dissolved inorganic phosphorus, total dissolved phosphorus, ammoniacal nitrogen and nitrate). Total alkalinity and suspended solids were also quantified; RESULTS: The Multivariate Analysis of Variance showed the influence of the seasonality on the variability of the investigated variables, while the Principal Component Analysis gave rise in two statistical significant axes, which delimited two groups representative of the rainy and dry periods. Hydrological pulses from Jacupiranguinha River, besides contributing to the inputs of nutrients and sediments during the period of connectivity, accounted for the decrease in spatial gradients in the wetland. This "homogenization effect" was evidenced by the Cluster Analysis. The research also showed an industrial raw effluent as the main point source of phosphorus to the Jacupiranguinha River and, indirectly, to the wetland; CONCLUSIONS: Therefore, considering the scarcity of information about the wetlands in the study area, this research, besides contributing to the understanding of the influence of hydrological pulses on the investigated environmental variables, showed the need for adoption of conservation policies of these ecosystems face the increase anthropic pressures that they have been submitted, which may result in lack of their ecological, social and economic functions.

Análisis espacio-temporal de las praderas de Cymodocea nodosa (Ucria) Ascherson en la isla de Gran Canaria

Máster Oficial en Gestión Costera

Variabilidad espacio-temporal de la biomasa de la capa profunda de reflexión en aguas canarias

[ES] La Capa Profunda de Reflexión (Deep Scattering Layer, DSL) es una capa de reflexión bioacústica formada por organismos mesopelágicos y constituye uno de los recursos biológicos marinos más abundantes de las Aguas de Canarias. Utilizando los datos obtenidos mediante técnicas acústicas y de arrastres pelágicos realizados en estas aguas durante diferentes campañas, investigamos la distribución espacio-temporal de la biomasa de las comunidades mesopelágicas que la componen.

Interactions between normative systems and software cognitive agents. A formalization in temporal modal defeasible logic and its implementation

Sustainable computer systems require some flexibility to adapt to environmental unpredictable changes. A solution lies in autonomous software agents which can adapt autonomously to their environments. Though autonomy allows agents to decide which behavior to adopt, a disadvantage is a lack of control, and as a side effect even untrustworthiness: we want to keep some control over such autonomous agents. How to control autonomous agents while respecting their autonomy? A solution is to regulate agents’ behavior by norms. The normative paradigm makes it possible to control autonomous agents while respecting their autonomy, limiting untrustworthiness and augmenting system compliance. It can also facilitate the design of the system, for example, by regulating the coordination among agents. However, an autonomous agent will follow norms or violate them in some conditions. What are the conditions in which a norm is binding upon an agent? While autonomy is regarded as the driving force behind the normative paradigm, cognitive agents provide a basis for modeling the bindingness of norms. In order to cope with the complexity of the modeling of cognitive agents and normative bindingness, we adopt an intentional stance. Since agents are embedded into a dynamic environment, things may not pass at the same instant. Accordingly, our cognitive model is extended to account for some temporal aspects. Special attention is given to the temporal peculiarities of the legal domain such as, among others, the time in force and the time in efficacy of provisions. Some types of normative modifications are also discussed in the framework. It is noteworthy that our temporal account of legal reasoning is integrated to our commonsense temporal account of cognition. As our intention is to build sustainable reasoning systems running unpredictable environment, we adopt a declarative representation of knowledge. A declarative representation of norms will make it easier to update their system representation, thus facilitating system maintenance; and to improve system transparency, thus easing system governance. Since agents are bounded and are embedded into unpredictable environments, and since conflicts may appear amongst mental states and norms, agent reasoning has to be defeasible, i.e. new pieces of information can invalidate formerly derivable conclusions. In this dissertation, our model is formalized into a non-monotonic logic, namely into a temporal modal defeasible logic, in order to account for the interactions between normative systems and software cognitive agents.

Tuzla City (BiH): an example of geohazard induced by salt extraction

Salt deposits characterize the subsurface of Tuzla (BiH) and made it famous since the ancient times. Archeological discoveries demonstrate the presence of a Neolithic pile-dwelling settlement related to the existence of saltwater springs that contributed to make the most of the area a swampy ground. Since the Roman times, the town is reported as “the City of Salt deposits and Springs”; "tuz" is the Turkish word for salt, as the Ottomans renamed the settlement in the 15th century following their conquest of the medieval Bosnia (Donia and Fine, 1994). Natural brine springs were located everywhere and salt has been evaporated by means of hot charcoals since pre-Roman times. The ancient use of salt was just a small exploitation compared to the massive salt production carried out during the 20th century by means of classical mine methodologies and especially wild brine pumping. In the past salt extraction was practised tapping natural brine springs, while the modern technique consists in about 100 boreholes with pumps tapped to the natural underground brine runs, at an average depth of 400-500 m. The mining operation changed the hydrogeological conditions enabling the downward flow of fresh water causing additional salt dissolution. This process induced severe ground subsidence during the last 60 years reaching up to 10 meters of sinking in the most affected area. Stress and strain of the overlying rocks induced the formation of numerous fractures over a conspicuous area (3 Km2). Consequently serious damages occurred to buildings and infrastructures such as water supply system, sewage networks and power lines. Downtown urban life was compromised by the destruction of more than 2000 buildings that collapsed or needed to be demolished causing the resettlement of about 15000 inhabitants (Tatić, 1979). Recently salt extraction activities have been strongly reduced, but the underground water system is returning to his natural conditions, threatening the flooding of the most collapsed area. During the last 60 years local government developed a monitoring system of the phenomenon, collecting several data about geodetic measurements, amount of brine pumped, piezometry, lithostratigraphy, extension of the salt body and geotechnical parameters. A database was created within a scientific cooperation between the municipality of Tuzla and the city of Rotterdam (D.O.O. Mining Institute Tuzla, 2000). The scientific investigation presented in this dissertation has been financially supported by a cooperation project between the Municipality of Tuzla, The University of Bologna (CIRSA) and the Province of Ravenna. The University of Tuzla (RGGF) gave an important scientific support in particular about the geological and hydrogeological features. Subsidence damage resulting from evaporite dissolution generates substantial losses throughout the world, but the causes are only well understood in a few areas (Gutierrez et al., 2008). The subject of this study is the collapsing phenomenon occurring in Tuzla area with the aim to identify and quantify the several factors involved in the system and their correlations. Tuzla subsidence phenomenon can be defined as geohazard, which represents the consequence of an adverse combination of geological processes and ground conditions precipitated by human activity with the potential to cause harm (Rosenbaum and Culshaw, 2003). Where an hazard induces a risk to a vulnerable element, a risk management process is required. The single factors involved in the subsidence of Tuzla can be considered as hazards. The final objective of this dissertation represents a preliminary risk assessment procedure and guidelines, developed in order to quantify the buildings vulnerability in relation to the overall geohazard that affect the town. The historical available database, never fully processed, have been analyzed by means of geographic information systems and mathematical interpolators (PART I). Modern geomatic applications have been implemented to deeply investigate the most relevant hazards (PART II). In order to monitor and quantify the actual subsidence rates, geodetic GPS technologies have been implemented and 4 survey campaigns have been carried out once a year. Subsidence related fractures system has been identified by means of field surveys and mathematical interpretations of the sinking surface, called curvature analysis. The comparison of mapped and predicted fractures leaded to a better comprehension of the problem. Results confirmed the reliability of fractures identification using curvature analysis applied to sinking data instead of topographic or seismic data. Urban changes evolution has been reconstructed analyzing topographic maps and satellite imageries, identifying the most damaged areas. This part of the investigation was very important for the quantification of buildings vulnerability.

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

The temporal pattern of customer satisfaction: a construal-level approach

Customer satisfaction has been traditionally studied and measured regardless of the time elapsed since the purchase. Some studies have recently reopened the debate about the temporal pattern of satisfaction. This research aims to explain why “how you evaluate a service depends on when you evaluate it” on the basis of the theoretical framework proposed by Construal-Level Theory (CLT). Although an empirical investigation is still lacking, the literature does not deny that CLT can be applied also with regard to past events. Moreover, some studies support the idea that satisfaction is a good predictor of future intentions, while others do not. On the basis of CLT, we argue that these inconsistent results are due to the different construal levels of the information pertaining to retrospective and prospective evaluations. Building on the Two-Factor Theory, we explain the persistence of certain attributes’ representations over time according to their relationship with overall performance. We present and discuss three experiments and one field study that were conducted a) to test the extensibility of CLT to past events, b) to disentangle memory and construal effects, c) to study the effect of different temporal perspective on overall satisfaction judgements, and d) to investigate the temporal shift of the determinants of customer satisfaction as a function of temporal distance.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.

Applications of remote sensing and geographic information systems in geomorphological studies: Safaga - El Quseir area, Red Sea coast, Egypt as an example

In den letzten drei Jahrzehnten sind Fernerkundung und GIS in den Geowissenschaften zunehmend wichtiger geworden, um die konventionellen Methoden von Datensammlung und zur Herstellung von Landkarten zu verbessern. Die vorliegende Arbeit befasst sich mit der Anwendung von Fernerkundung und geographischen Informationssystemen (GIS) für geomorphologische Untersuchungen. Durch die Kombination beider Techniken ist es vor allem möglich geworden, geomorphologische Formen im Überblick und dennoch detailliert zu erfassen. Als Grundlagen werden in dieser Arbeit topographische und geologische Karten, Satellitenbilder und Klimadaten benutzt. Die Arbeit besteht aus 6 Kapiteln. Das erste Kapitel gibt einen allgemeinen Überblick über den Untersuchungsraum. Dieser umfasst folgende morphologische Einheiten, klimatischen Verhältnisse, insbesondere die Ariditätsindizes der Küsten- und Gebirgslandschaft sowie das Siedlungsmuster beschrieben. Kapitel 2 befasst sich mit der regionalen Geologie und Stratigraphie des Untersuchungsraumes. Es wird versucht, die Hauptformationen mit Hilfe von ETM-Satellitenbildern zu identifizieren. Angewandt werden hierzu folgende Methoden: Colour Band Composite, Image Rationing und die sog. überwachte Klassifikation. Kapitel 3 enthält eine Beschreibung der strukturell bedingten Oberflächenformen, um die Wechselwirkung zwischen Tektonik und geomorphologischen Prozessen aufzuklären. Es geht es um die vielfältigen Methoden, zum Beispiel das sog. Image Processing, um die im Gebirgskörper vorhandenen Lineamente einwandfrei zu deuten. Spezielle Filtermethoden werden angewandt, um die wichtigsten Lineamente zu kartieren. Kapitel 4 stellt den Versuch dar, mit Hilfe von aufbereiteten SRTM-Satellitenbildern eine automatisierte Erfassung des Gewässernetzes. Es wird ausführlich diskutiert, inwieweit bei diesen Arbeitsschritten die Qualität kleinmaßstäbiger SRTM-Satellitenbilder mit großmaßstäbigen topographischen Karten vergleichbar ist. Weiterhin werden hydrologische Parameter über eine qualitative und quantitative Analyse des Abflussregimes einzelner Wadis erfasst. Der Ursprung von Entwässerungssystemen wird auf der Basis geomorphologischer und geologischer Befunde interpretiert. Kapitel 5 befasst sich mit der Abschätzung der Gefahr episodischer Wadifluten. Die Wahrscheinlichkeit ihres jährlichen Auftretens bzw. des Auftretens starker Fluten im Abstand mehrerer Jahre wird in einer historischen Betrachtung bis 1921 zurückverfolgt. Die Bedeutung von Regentiefs, die sich über dem Roten Meer entwickeln, und die für eine Abflussbildung in Frage kommen, wird mit Hilfe der IDW-Methode (Inverse Distance Weighted) untersucht. Betrachtet werden außerdem weitere, regenbringende Wetterlagen mit Hilfe von Meteosat Infrarotbildern. Genauer betrachtet wird die Periode 1990-1997, in der kräftige, Wadifluten auslösende Regenfälle auftraten. Flutereignisse und Fluthöhe werden anhand von hydrographischen Daten (Pegelmessungen) ermittelt. Auch die Landnutzung und Siedlungsstruktur im Einzugsgebiet eines Wadis wird berücksichtigt. In Kapitel 6 geht es um die unterschiedlichen Küstenformen auf der Westseite des Roten Meeres zum Beispiel die Erosionsformen, Aufbauformen, untergetauchte Formen. Im abschließenden Teil geht es um die Stratigraphie und zeitliche Zuordnung von submarinen Terrassen auf Korallenriffen sowie den Vergleich mit anderen solcher Terrassen an der ägyptischen Rotmeerküste westlich und östlich der Sinai-Halbinsel.

Valorization of pomaces from the mechanical extraction of virgin olive oils in dairy animal feeding

This research work is aimed at the valorization of two types of pomace deriving from the extra virgin olive oil mechanical extraction process, such as olive pomace and a new by-product named “paté”, in the livestock sector as important sources of antioxidants and unsaturated fatty acids. In the first research the suitability of dried stoned olive pomace as a dietary supplement for dairy buffaloes was evaluated. The effectiveness of this utilization in modifying fatty acid composition and improving the oxidative stability of buffalo milk and mozzarella cheese have been proven by means of the analysis of qualitative and quantitative parameters. In the second research the use of paté as a new by-product in dietary feed supplementation for dairy ewes, already fed with a source of unsaturated fatty acids such as extruded linseed, was studied in order to assess the effect of this combination on the dairy products obtained. The characterization of paté as a new by-product was also carried out, studying the optimal conditions of its stabilization and preservation at the same time. The main results, common to both researches, have been the detection and the characterization of hydrophilic phenols in the milk. The analytical detection of hydroxytyrosol and tyrosol in the ewes’ milk fed with the paté and hydroxytyrosol in buffalo fed with pomace showed for the first time the presence in the milk of hydroxytyrosol, which is one of the most important bioactive compounds of the oil industry products; the transfer of these antioxidants and the proven improvement of the quality of milk fat could positively interact in the prevention of some human cardiovascular diseases and some tumours, increasing in this manner the quality of dairy products, also improving their shelf-life. These results also provide important information on the bioavailability of these phenolic compounds.

Secondary Microseisms Characterization and Green’s Function Extraction at the Larderello-Travale Geothermal Field (Italy)

Over the past ten years, the cross-correlation of long-time series of ambient seismic noise (ASN) has been widely adopted to extract the surface-wave part of the Green’s Functions (GF). This stochastic procedure relies on the assumption that ASN wave-field is diffuse and stationary. At frequencies <1Hz, the ASN is mainly composed by surface-waves, whose origin is attributed to the sea-wave climate. Consequently, marked directional properties may be observed, which call for accurate investigation about location and temporal evolution of the ASN-sources before attempting any GF retrieval. Within this general context, this thesis is aimed at a thorough investigation about feasibility and robustness of the noise-based methods toward the imaging of complex geological structures at the local (∼10-50km) scale. The study focused on the analysis of an extended (11 months) seismological data set collected at the Larderello-Travale geothermal field (Italy), an area for which the underground geological structures are well-constrained thanks to decades of geothermal exploration. Focusing on the secondary microseism band (SM;f>0.1Hz), I first investigate the spectral features and the kinematic properties of the noise wavefield using beamforming analysis, highlighting a marked variability with time and frequency. For the 0.1-0.3Hz frequency band and during Spring- Summer-time, the SMs waves propagate with high apparent velocities and from well-defined directions, likely associated with ocean-storms in the south- ern hemisphere. Conversely, at frequencies >0.3Hz the distribution of back- azimuths is more scattered, thus indicating that this frequency-band is the most appropriate for the application of stochastic techniques. For this latter frequency interval, I tested two correlation-based methods, acting in the time (NCF) and frequency (modified-SPAC) domains, respectively yielding esti- mates of the group- and phase-velocity dispersions. Velocity data provided by the two methods are markedly discordant; comparison with independent geological and geophysical constraints suggests that NCF results are more robust and reliable.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.