Μεταπτυχιακές Διατριβές

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Τώρα δείχνει 1 - 20 από 41

Full-rate fifferential M-PSK Alamouti modulation with polunomial-complexity maximum-likelihood noncoherent detection
(Technical University of Crete, 2012) Markopoulos Panagiotis; Μαρκοπουλος Παναγιωτης; Karystinos Georgios; Καρυστινος Γεωργιος; Liavas Athanasios; Λιαβας Αθανασιος; Bletsas Aggelos; Μπλετσας Αγγελος
Sketch-based geometric monitoring of distributed stream queries
(Technical University of Crete, 2012) Athanasoglou Konstantinos; Αθανασογλου Κωνσταντινος; Garofalakis Minos; Γαροφαλακης Μινως; Samoladas Vasilis; Σαμολαδας Βασιλης; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Βελτιστοποίηση συστήματος αφαλάτωσης αντίστροφης όσμωσης με τη χρήση κυλινδροπαραβολικών ηλιακών συλλεκτών
(Πολυτεχνείο Κρήτης, 2012) Vasilomichelaki Ariadni; Βασιλομιχελακη Αριαδνη; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Koutroulis Eftychios; Κουτρουλης Ευτυχιος; Stavrakakis Georgios; Σταυρακακης Γεωργιος
Βλάβες, καταστροφές και υποβαθμίσεις σε φωτοβολταϊκά πλαίσια κρυσταλλικού πυριτίου (c-Si) που αποτελούν μέρη φωτοβολταϊκών εγκαταστάσεων διασυνδεδεμένων με δίκτυα διανομής ηλεκτρικής ενέργειας
(Πολυτεχνείο Κρήτης, 2013) Perakis Georgios; Περακης Γεωργιος; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Zervakis Michalis; Ζερβακης Μιχαλης; Mania Aikaterini; Μανια Αικατερινη
Simultaneous multi-spectral imaging system: application in real-time, unsupervised classification in endometrial endoscopy
(Technical University of Crete, 2013) Kavvadias Vasileios; Καββαδιας Βασιλειος; Balas Costas; Μπαλας Κωστας; Zervakis Michalis; Ζερβακης Μιχαλης; Bucher Matthias; Bucher Matthias
Quantum walk on integers and maximum likelihood parametric estimation
(Technical University of Crete, 2013) Moutzianou Georgios; Μουτζιανου Γεωργιος; Ellinas Dimosthenis; Ελληνας Δημοσθενης; Aggelakis Dimitrios; Αγγελακης Δημητριος; Dollas Apostolos; Δολλας Αποστολος
Grammatical inference for event recognition
(Πολυτεχνείο Κρήτης, 2014) Kofinas Nikolaos; Κοφινας Νικολαος; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Bletsas Aggelos; Μπλετσας Αγγελος; Garofalakis Minos; Γαροφαλακης Μινως
As robot technology finds applications in the real world (search and rescue, daily household tasks, etc.), huge amounts of data are generated during autonomous robot missions. In such applications, it is often desirable to recognize high-level events that may have occurred during a mission either online or offline. Event Recognition in robot missions currently relies on human expertise and time-consuming data annotation. A modern method to recognize events is to employ Probabilistic Context-Free Grammars (PCFGs), which are formal models that can capture complex patterns in discrete sequences and can be used to parse incoming sensor data streams in order to detect patterns that may signal the occurrence of some event of interest. Recent experimentation with such methods on data from Autonomous Underwater Vehicle (AUV) missions indicated that interesting events can be recognized by parsing sequences of sensor data using an intuitive hand-written PCFG. This thesis introduces a generic procedure which can be used to automatically construct PCFGs which encode sensor data sequences that typically appear during normal robot operation using recorded logs from past missions. The resulting PCFGs can be used to recognize abnormal events in new missions evidenced by sensor data sequences which cannot be interpreted as normal. The proposed procedure consists of two parts: (a) the transformation of sensor streams into discrete sequences either to form a training corpus offline or to generate input for online parsing and (b) a Grammatical Inference algorithm in order to learn a compact PCFG consistent with a given training corpus. The learning part relies on a local search method over the space of possible grammars using chunk and merge operations. The search method aims to find a compact grammar that also maximizes its posterior probability, in a Bayesian sense, with respect to a given training corpus. The proposed procedure is evaluated on a variety of domains ranging from data-sets generated by typical context-free grammars to data-sets generated from real robot missions (NAO robot walk and AUV navigation). The results indicate that our approach is capable of producing reliable PCFG-based event recognizers, which may yield some false positive signals, but in general succeed in capturing abnormalities.
Coherent detection and channel coding for backscatter sensor networks
(Πολυτεχνείο Κρήτης, 2014) Fasarakis-Chilliarnt Nikos; Φασαρακης-Χιλλιαρντ Νικος; Bletsas Aggelos; Μπλετσας Αγγελος; Karystinos Georgios; Καρυστινος Γεωργιος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Βελτιστοποίηση σχεδιασμού φωτοβολταϊκών μετατροπέων DC/AC
(Technical University of Crete, 2014) Saridakis Stefanos; Σαριδακης Στεφανος; Koutroulis Eftychios; Κουτρουλης Ευτυχιος; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Bucher Matthias; Bucher Matthias
Τα τελευταία χρόνια το ποσοστό της ενέργειας που παράγεται από Ανανεώσιμες Πηγές Ενέργειας αυξάνεται σημαντικά. Τα φωτοβολταϊκά συστήματα είναι αυτά με την πιο δυναμική και αξιόπιστη συμβολή στην παραγωγή ηλεκτρικής ενέργειας. Οι αντιστροφείς DC/AC χωρίς μετασχηματιστή αποτελούν το κύριο στοιχείο ενός φωτοβολταϊκού συστήματος στην ταχέως αναπτυσσόμενη αγορά των διασυνδεδεμένων φωτοβολταϊκών συστημάτων. Οι αντιστροφείς αυτοί μπορούν να σχεδιαστούν χρησιμοποιώντας διάφορες εναλλακτικές λύσεις που είναι διαθέσιμες, όπως είναι η τοπολογία του αντιστροφέα, η τεχνολογία κατασκευής των ημιαγωγών ισχύος και η δομή του φίλτρου εξόδου. Επίσης, οι ημιαγωγοί που βασίζονται στο καρβίδιο του πυριτίου (SiC) έχουν αρχίσει να χρησιμοποιούνται τα τελευταία χρόνια σε φωτοβολταϊκές εφαρμογές, έναντι των ημιαγωγών που βασίζονται στο πυρίτιο (Si), λόγω της ικανότητας τους να λειτουργούν αξιόπιστα σε υψηλά επίπεδα θερμοκρασίας και συχνοτήτων μεταγωγής, παρέχοντας ταυτόχρονα υψηλή απόδοση. Σε αυτή την μεταπτυχιακή εργασία, παρουσιάζεται μια νέα τεχνική σχεδιασμού με χρήση γενετικού αλγορίθμου για τη βελτιστοποίηση της συχνότητας μεταγωγής και της δομής του φίλτρου εξόδου (είτε LCL ή LLCL) σε διάφορους εμπορικούς φωτοβολταϊκούς αντιστροφείς, όπως οι Η5, Η6, NPC, ANPC και Conergy-NPC χωρίς την χρήση μετασχηματιστή και οι οποίοι ως υλικό κατασκευής των ημιαγωγών έχουν είτε το SiC, είτε το Si. Τα αποτελέσματα του σχεδιασμού αποδεικνύουν ότι οι βελτιστοποιημένοι φωτοβολταϊκοί αντιστροφείς που βασίζονται στην τεχνολογία SiC είναι πιο αποτελεσματικοί από την άποψη της παραγωγής ενέργειας από αυτούς που βασίζονται στο Si. Έτσι, η προτεινόμενη μεθοδολογία επιτρέπει τη βέλτιστη σχεδίαση για τη μεγιστοποίηση του οικονομικού οφέλους που προκύπτει κατά τη διάρκεια ζωής του εγκατεστημένου φωτοβολταϊκού συστήματος. Τέλος, για την εκτέλεση του αλγορίθμου χρησιμοποιήθηκε ο υπολογιστής πλέγματος του Πολυτεχνείου Κρήτης.
A bayesian personalized recommendation system
(Πολυτεχνείο Κρήτης, 2014) Babas Konstantinos; Μπαμπας Κωνσταντινος; Chalkiadakis Georgios; Χαλκιαδακης Γεωργιος; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
In this research, we propose a novel Bayesian approach for personalized recommendations. We succeed in providing recommendations that are entirely personalized, based on a user’s past item “consumptions”, building a representative user model which reflects agent’s corresponding beliefs. Having a set of items, our agent has to select the one which better matches her beliefs about a specific user, in order to recommend it and receive the corresponding reward. In our approach, we model both user preferences and items under recommendation as multivariate Gaussian distributions; and make use of Normal-Inverse Wishart priors to model the recommendation agent beliefs about user types. We interpret user ratings in an innovative way, using them to guide a Bayesian updating process that helps us both capture a user’s current mood, and maintain her overall user type. We produced several variants of our approach, and applied them in the movie recommendations domain, evaluating them on data from the MovieLens dataset. We developed a generic & domain independent system, able to face the scalability challenge and able to capture user preferences (long-term and short-term). Moreover, we dealt with the exploration vs exploitation dilemma in this domain, via the application of various exploration algorithms (e.g., VPI exploration). Ours is a completely personalized approach, which exploits Bayesian Reinforcement Learning in order to recommend an item or a top-N group of items, without the need of ratings prediction. We do not employ a Collaborative Filtering or Content-based or Preference Elicitation technique, but we are still able to provide successful recommendations. Furthermore, we tackle the famous “cold-start” problem via the use of Bayesian and VPI explorations. Our algorithms are shown to be competitive against a state-of-the-art method, which nevertheless requires a minimum set of ratings from various users to provide recommendations --- unlike our entirely personalized approach.
Crowdsourcing and management of nature observational data
(Πολυτεχνείο Κρήτης, 2014) Skevakis Giannis; Σκευακης Γιαννης; Christodoulakis Stavros; Χριστοδουλακης Σταυρος; Mania Aikaterini; Μανια Αικατερινη; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Observations of plants and animals in nature is highly valued information for the experts in the area of biodiversity. They can be used to define changes in the population of animals, plants, or track their movements throughout long periods of time. Moreover, the richer the information following the observations, the more knowledge can be extracted from them. However, the limited number of experts and the limited funding in the area, makes the observation gathering procedure almost impossible. We present the design and implementation of a framework for the management of bio- diversity observations captured by users roaming in the nature. This aims to alleviate the need for experts capturing biodiversity information, and propagates the collection of information to simple users wandering in the nature. Our framework consists of a model supporting the observations, and an infrastructure that allows the capturing, enrichment and storage of the observations using state- of-the-art technologies. Our architecture provides a scalable, highly efficient management of the collected data. The collection of the observational data is performed in real-time using mobile de- vices that most of the people have available with them, like mobile phones and tablets. Additionally, we describe the meta-model that we have defined, allowing the personalization of the metadata that follow the observations. This provides our framework with the freedom and extensibility needed so as to be implemented for various domains other than biodiversity. Finally, we describe the process of migrating the data collected by the Natural Europe project to our infrastructure.
Non-coherent receivers for zero-feedback distributed beamforming in connectivity-constrained wireless sensor networks (WSNs)
(Πολυτεχνείο Κρήτης, 2014) Alexandris Konstantinos; Αλεξανδρης Κωνσταντινος; Bletsas Aggelos; Μπλετσας Αγγελος; Karystinos Georgios; Καρυστινος Γεωργιος; Liavas Athanasios; Λιαβας Αθανασιος
Power-constrained wireless sensor networks (WSNs) suffer from network partitioning problems. In many cases, each node among a network subset, cannot reliably communicate with a distant receiver even when transmitting at maximum power. Thus, a collaborative beamforming scheme among the distributed adjacent terminals is needed in terms of power addition. Prior art on distributed beamforming has mainly focused on feedback messages for channel estimation (CSI) or physical layer carrier phase adjustments. In sharp constrast, this thesis assumes commodity radios and studies the low signal to-noise-ratio (SNR) regime, where accurate channel estimation is not feasible and no reliable feedback exists. The main idea is to exploit recently proposed zero-feedback distributed beamforming and design specific non-coherent receivers. Towards that goal, three concrete non-coherent receivers are presented for zero-feedback distributed beamforming (ZF-DBF); one based on energy detection, one based on maximum-likelihood for a specific condition (i.e., full correlation among the received samples), and finally, one non-coherent receiver for all other cases. A non-coherent receiver for energy harvesting through time division multiple access (TDMA) is also provided for comparison purposes. Analytical and numerical bit-error-rate results are presented. It is shown that the ZF-DBF receiver outperforms the energy harvesting one at the low-SNR regime and overcomes connectivity adversities by exploiting signal alignment from the distributed terminals, at the expense of total (network) power transmission.
Hyper spectral data estimation from power dimensionality experimental imaging
(Technical University of Crete, 2014) Iliou Dimitrios; Ηλιου Δημητριος; Balas Costas; Μπαλας Κωστας; Garofalakis Minos; Γαροφαλακης Μινως; Digalakis Vasilis; Διγαλακης Βασιλης
We report the first real time modular spectral and color imaging system based on the combination of snapshot spectral imaging, spectral estimation and color reproduction algorithms. A limited number of spectral bands are captured simultaneously, with the aid of specially designed camera, which are subsequently processed with spectral estimation algorithms to obtain a full spectrum per image pixel. We have succeeded to demonstrate complete spectral cube calculation and display of millions of spectra in real-time and to remove trade-off between spectral and spatial resolution. Besides accurate spectral mapping, our approach enables also reliable and device-independent color reproduction based on complete, per-pixel spectra. These achievements hold the promise to provide an indispensable tool in nondestructive analysis and in noninvasive diagnosis.
Predictive control for stable dynamic locomotion of real humanoid robots
(Πολυτεχνείο Κρήτης, 2014) Piperakis Stylianos; Πιπερακης Στυλιανος; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Liavas Athanasios; Λιαβας Αθανασιος; Bletsas Aggelos; Μπλετσας Αγγελος
Robust stable omnidirectional locomotion for humanoid robots is a crucial problem and an active research area nowadays. In general, biped locomotion relies on distinct gait phases, during which it must be ensured that the sum of the forces acting on the robot do not result in a loss of balance. To generate stable walking patterns, the need of a stability measure is evident to ensure upright locomotion. State-of-the-art work on this problem uses the Zero Moment Point (ZMP) as a criterion to measure stability. The ZMP approach is a formal representation of the problem, which makes full use of sensor information commonly available on humanoid robots and allows for rigorous solutions to be constructed. This thesis presents a complete formulation of the challenging task of stable humanoid robot omnidirectional walk, based on the Cart and Table model for approximating the robot dynamics. For the control task, two novel approaches are proposed: (i) Preview Control augmented with the inverse system for negotiating strong disturbances and uneven terrain and (ii) Linear Model-Predictive Control (LMPC) approximated by an orthonormal basis for computational efficiency coupled with constraints for improved stability. For the generation of smooth feet trajectory, a new approach based on rigid body interpolation is proposed, enhanced by adaptive step correction. Finally, we present a sensor fusion approach for sensor-based state estimation and an effective solution to sensors' noise, delay, and bias issues, as well as to errors induced by the simplified dynamics and actuation imperfections. The proposed formulation is applied on a real Aldebaran Nao humanoid robot, where it achieves real-time onboard execution and yields smooth and stable gaits.
BucDoop: Bottom Up Computation of Iceberg Data Cubes with Hadoop
(Πολυτεχνείο Κρήτης, 2014) Tsakonas Konstantinos; Τσακωνας Κωνσταντινος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος; Garofalakis Minos; Γαροφαλακης Μινως; Christodoulakis Stavros; Χριστοδουλακης Σταυρος
Big Data analysis has been a key matter during the recent years for the study of various phenomena in various science contexts as well as in business intelligence. Furthermore it appears for good reason to remain in focus for the future. Online Analytical processing methods and Data Cubes need to be further studied in order to reduce time used for efficient data analysis. This study introduces BucDoop, a novel algorithm that exploits the parallelism benefits of Hadoop Map Reduce, for the efficient iceberg data cube creation in reasonable time. BucDoop includes the use of the Bottom Up Computation (BUC) idea in the context of iceberg cube data lattice traversal, managing to reduce the amount of data handled with early pruning architecture and producing the portion of the cube needed for analysis purposes (iceberg problem). Experiments conducted herein present an efficient scalability factor for the creation of the iceberg cube for very big data, by-passing the data explosion and memory constraints problem while using only commodity hardware.
Scatter radio sensor network with analog frequency modulation principles
(Technical University of Crete, 2014) Kabianakis Eleftherios; Καμπιανακης Ελευθεριος; Bletsas Aggelos; Μπλετσας Αγγελος; Dollas Apostolos; Δολλας Αποστολος; Koutroulis Eftychios; Κουτρουλης Ευτυχιος
Scatter radio communication is implemented with very simple, low-power and low-cost front-ends that only consist of a single radio frequency (RF) switch. This work develops a bistatic scatter radio wireless sensor network (WSN) with analog energy-assisted tags that monitor relative humidity percentage (%RH) and consume less than 1 mWatt power. Particularly, the tags em- ploy a capacitance-to-frequency converter, that is implemented with a 555 timer and modulates the capacitance of the HCH-1000 %RH sensor. The frequency-modulated pulses are routed to the tag’s RF front-end which is designed to increase communication range. In order to convert the out- put frequency of the tags to %RH, a transfer function is estimated using careful polynomial surface fitting calibration and including the ambient tem- perature. Frequency division multiple access (FDMA) networking is im- plemented with the utilization of different passive components on each tag. Moreover the receiver that is implemented on a software defined radio (SDR) platform exploits carefully designed software filters based on histogram and Savitsky-Golay smoothing techniques. The achieved communication range is over 130 meters at an end-to-end root mean squared error (RMSE) of less than 5 %RH. For the network evaluation, a testbed is calibrated and deployed in a tomato greenhouse demonstrating a novel analog bistatic scatter radio WSN. Finally, an over the air programmable (OTAP) testbed was developed, employing nodes that utilize both an active radio front-end and scatter radio front-end in order to facilitate remote monitoring and debugging.
Optimal blind detection of APSK in polynomial time
(Technical University of Crete, 2014) Fountzoulas Ioannis; Φουντζουλας Ιωαννης; Karystinos Georgios; Καρυστινος Γεωργιος; Bletsas Aggelos; Μπλετσας Αγγελος
The SPARQL-RW framework: mapping modeling and query rewriting for ontology based mediators
(Πολυτεχνείο Κρήτης, 2014) Makris Konstantinos; Μακρης Κωνσταντινος; Christodoulakis Stavros; Χριστοδουλακης Σταυρος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος; Lagoudakis Michael; Λαγουδακης Μιχαηλ
The Web of Data is an open environment consisting of heterogeneous, distributed and highly structured information sources. Uniform information access in this kind of setting is of major importance for data consumer applications and end users. To this end, ontology based mediator systems supporting transparent query access over federated data sources are considered essential. In this thesis we present SPARQL-RW, a Framework supporting mapping modeling and query rewriting in the context of ontology based mediator architectures. SPARQL-RW provides a formal model for describing mappings between ontology schemas, as well as a generic method for SPARQL 1.1 query rewriting, with respect to a set of predefined ontology mappings and data source endpoints. The mapping model supports a set of rich and flexible mapping types based on Description Logic semantics, as well as notable mapping formalisms, including Global-As-View (GAV), Local-As-View (LAV), and Global-and-Local-As-View (GLAV). Additionally, it defines a mapping language capable of representing all the supported types of inter-schema correspondences. The Framework provides functionality for performing mapping inference and for identifying inconsistencies in a given set of mappings and ontology schemas. Regarding query rewriting, the proposed algorithms are proved to provide semantics preserving queries with respect to the GAV mapping types supported by the model. The reformulated queries can be executed directly on any SPARQL federated query engine, or exploited as logical query plans by any ontology based mediator system. SPARQL-RW has been implemented, formally evaluated and tested in a prototype mediator system integrating several data providers from the biodiversity community along with DBpedia.
Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models
(Πολυτεχνείο Κρήτης, 2014) Georgogiannis Alexandros; Γεωργογιαννης Αλεξανδρος; Digalakis Vasilis; Διγαλακης Βασιλης; Liavas Athanasios; Λιαβας Αθανασιος; Lagoudakis Michael; Λαγουδακης Μιχαηλ
Since its earliest days as a discipline, machine learning has made use of optimization formulations and algorithms. Likewise, machine learning has contributed to optimization, driving the develop- ment of new optimization approaches that address the significant challenges presented by machine learning applications. This influence continues to deepen, producing a growing literature at the intersection of the two fields while attracting leading researchers to the effort. While techniques proposed twenty years ago continue to be refined, the increased complexity, size, and variety of today’s machine learning models demand a principled reassessment of existing assumptions and techniques. This thesis makes a small step toward such a reassessment. It describes novel contexts of established frameworks such as convex relaxation, splitting methods, and regularized estimation and how we can use them to solve significant problems in data mining and statistical learning. The thesis is organised in two parts. In the first part, we present a new clustering algorithm. The task of clustering aims at discovering structures in data. This algorithm is an extension of recently proposed convex relaxations of k-means and hierarchical clustering. In the second part, we present a new algorithm for discovering dependencies among common variables in multiple undirected graphical models. Graphical models are useful for the description and modelling of multivariate systems. In the appendix, we comment on a core problem underlying the whole study and we give an alternative solution based on recent advances in convex optimization.
Hardware accelerated basic blocks for power-aware intercommunication in HPC and embedded systems
(Πολυτεχνείο Κρήτης, 2014) Tampouratzis Nikolaos; Ταμπουρατζης Νικολαος; Papaefstathiou Ioannis; Παπαευσταθιου Ιωαννης; Dollas Apostolos; Δολλας Αποστολος; Pnevmatikatos Dionysios; Πνευματικατος Διονυσιος
In the past, a transition to the next fabrication process typically translated to more transistors and frequency and less power. The higher frequencies paired with innovations in computer architecture defined the semiconductor industry and research until the mid-90s. At that point architecture research saturated and industry resided to the technology scaling for performance gains. During the mid-00s frequency scaling saturated as well. Transistor count, the only resource which reliably kept scaling, along with intra-chip parallelism, which could leverage and extend the existing knowledge of old-days supercomputers, emerged as the only solution to keep Moore’s law live. In parallel systems, computing nodes cooperate to solve processing intensive problems. The communication between nodes is achieved through a variety of protocols. Traditionally, research has focused on optimizing these protocols and identifying the most suitable ones per system and application. Recently, an attempt to unify the primitive operations of the proposed intercommunication protocols has been realized through the Portals system. Portals offer a set of low level communication routines which can be composed to model complex protocols. However, Portals modularity comes at a performance cost, as communication protocols have been tuned and many of their timing critical parts have been decoupled from the main execution thread and in many cases accelerated as dedicated hardware. This work targets to close the performance gap between a generic and reusable intercommunication layer, Portals, and the several monolithic but highly tuned protocols. A software driven hardware accelerated system is suggested which resides on execution of actual software to highlight the critical parts of the communication routines. Accelerating the bottlenecks starts by modeling the hardware in untimed virtual prototypes and the software in a range of candidate embedded processors. A novel path from hardware prototypes to actual silicon allows rapid characterization of the accelerator in terms of power, performance and area. The suggested approach triggers a speedup from one order of magnitude in bottleneck components of Portals, while it is up to two orders of magnitude faster in both MPI and GA baseline implementations in a recent embedded processor.

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