Μεταπτυχιακές Διατριβές

Μόνιμο URI για αυτήν τη συλλογήhttps://dspace.library.tuc.gr/handle/123456789/121

Νέα

Περιηγούμαι

Τώρα δείχνει 1 - 20 από 41

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
Depth perception from a single camera and multiple light sources
(Technical University of Crete, 2015) Rematska Georgia; Ρεματσκα Γεωργια; Dollas Apostolos; Δολλας Αποστολος; Papaefstathiou Ioannis; Παπαευσταθιου Ιωαννης; Zervakis Michalis; Ζερβακης Μιχαλης
3D Vision has always been a subject of research for many decades. The use of images results in the loss of the 3rd dimension. Many techniques have been developed over the years that aim in acquiring depth from images. Among the most widespread are stereo vision, fringe projection, laser scanning or combinations of them, all of which have shown high quality results but unsuitable for some classes of applications. For example, laser scanning rangefinders are not low-cost system. Hence, there is a need to develop a low cost, high speed, high resolution system for 3D vision. In this thesis a novel approach is presented to estimate depth using a single camera combined with two spectrally distinct light sources for road surface measurements. The light sources consist of two sets of LED arrays. Depth information can be extracted, by processing the different reflections in the image from the two light sources. The basic information of depth estimation lies in their blue to red ratio, which with appropriate calibration is correlated to depth. The methodology was first developed in MATLAB and then was implemented on FPGA. The system is real time and fully optimized to have the minimum hardware resources and high frequency of operation for high resolution images.
Design optimization of an electric energy production system for power supplying the nodes of wireless sensor networks
(Technical University of Crete, 2015) Mandourarakis Ioannis; Μανδουραρακης Ιωαννης; Koutroulis Eftychios; Κουτρουλης Ευτυχιος; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Stavrakakis Georgios; Σταυρακακης Γεωργιος
The widespread utilization of the Wireless Sensor Networks (WSNs) that are power supplied by Renewable Energy Sources (RES) is now leading scientific research towards the development of innovative sizing optimization techniques and configurations that aim in the service of multi-criteria objectives of economical and/or technical nature. The most frequently used ones have to do with the minimization of the overall cost and the maximization of the overall system efficiency, in terms of energy redundancy and operational reliability. Sizing optimization techniques are being introduced, where there is a tradeoff amongst similar to the aforementioned criteria that often contradict to each other (e.g. reliability is improved when economical cost or energy redundancy are increased). In this thesis, two complementary design optimization methods (a circuit level and a system level study) are presented for deriving the optimal configuration of the RES based energy production system of a WSN node, such that its total lifetime cost is minimized, while simultaneously guaranteeing that the data acquisition equipment is uninterruptedly power supplied during the entire year. The experimental results verify that, by applying the design variables as they were derived by the proposed optimization techniques at both the circuit and the system level, RES-based power-supply structures with a lower lifetime cost and higher power-processing efficiency are derived, compared to the non-optimally designed configurations. The design optimization and experimental results indicate that by using the proposed techniques, the total cost of the RES-based power supply system is reduced by 15.7 % and the DC DC converter efficiency is increased by 5.5 % compared to the corresponding results obtained by non-optimized power-supply structures.
Self-powered plant sensor for scatter radio
(Technical University of Crete, 2015) Konstantopoulos Christos; Κωνσταντοπουλος Χρηστος; Koutroulis Eftychios; Κουτρουλης Ευτυχιος; Bletsas Aggelos; Μπλετσας Αγγελος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
In agriculture applications (e.g. greenhouses, vineyards etc.) it is required to automatically gather information about environmental variables such as soil and air humidity, as well as temperature in the vicinity of plants within the same field, with low-cost and high-scalability. Thus, sensor networks that are extending over a broad area and gather environmental data for microclimate monitoring, are indispensable for the application of optimal crop management techniques. The field of plant electro-physiology investigates the correlation of environmental variables with the electrical signals that are produced by diverse types of plants. Existing research in measurement of electrical signals generated by plants has been conducted using high-cost equipment, such as laboratory multi-meters and data-loggers, in order to perform the signal-conditioning and data acquisition operations required. This thesis introduces for first time in the existing research literature a novel low cost and self-powered sensor node that belongs to a large-scale scatter radio network and simultaneously is powered in a parasitic way to the plants, as well as is able to acquire and transmit these types of signals from each plant. Furthermore, in the context of this thesis, several experimental prototypes of the proposed node were developed, as well as used to gather measurements of electrical signals that are generated from multiple Avocado plants. The experimental results demonstrate the successful operation of the proposed WSN node, as well as indicate the correlation of plants signals with solar irradiation and plant irrigation events. Thus, the proposed system can be employed in precision agriculture applications for automated irrigation scheduling, control of the plant ambient conditions etc. based on data derived directly by the plants.
Design and layout techniques in analog/RF integrated circuits
(Technical University of Crete, 2014) Papathanasiou Konstantinos; Παπαθανασιου Κωνσταντινος; Bucher Matthias; Bucher Matthias; Kalaitzakis Kostas; Καλαϊτζακης Κωστας; Balas Costas; Μπαλας Κωστας
Τα τελευταία χρόνια, η ραγδαία ανάπτυξη στα αναλογικά ολοκληρωμένα κυκλώματα πολύ υψηλών συχνοτήτων (RFIC) οφείλεται κυρίως στην εξέλιξη της τεχνολογίας CMOS. Οι λόγοι που οδήγησαν σε αυτήν την ανάπτυξη συνοψίζονται κυρίως στους εξής: την υψηλή απόδοση, που απορρέει από τη σμίκρυνση του μήκους καναλιού των CMOS τεχνολογιών, το χαμηλότερο κόστος τους, συγκριτικά με άλλες τεχνολογίες, καθώς και την γρήγορη ενσωμάτωση τους στον τομέα της μικροηλεκτρονικής. Αποτέλεσμα των παραπάνω, είναι τεχνολογίες CMOS με μήκος καναλιού κάτω από τα 45nm να θεωρούνται, πλέον, αιχμή της τεχνολογίας. Λόγω του υψηλού λόγου απόδοσης προς κόστος και της αξιοπιστίας (reliability) τους, τεχνολογίες με μεγαλύτερα μήκη καναλιού (90nm ή και 180nm) χρησιμοποιούνται ακόμα και στις μέρες μας κατά κόρον στην σχεδίαση και κατασκευή RFICs. Σκοπός της παρούσας μεταπτυχιακής εργασίας είναι η μελέτη και αξιοποίηση τεχνικών σχεδίασης για RFICs υψηλής απόδοσης, καλύπτοντας ένα μεγάλο εύρος CMOS τεχνολογιών, από τα 90nm ως τα 30nm. Η παρούσα έρευνα εκτείνεται από την ορθή υλοποίηση του φυσικού σχεδίου (layout) RF διατάξεων και κυκλωμάτων έως τον καθορισμό του βέλτιστου σημείου λειτουργίας τους και βρίσκει εφαρμογή σε κυκλώματα ενισχυτών χαμηλού θορύβου (LNA). Για τον σκοπό αυτό σχεδιάστηκε, υλοποιήθηκε και κατασκευάστηκε ένα RF τεστ τσιπ σε τεχνολογία 90nm της TSMC. Οι δομές που υλοποιήθηκαν, στη συνέχεια μετρήθηκαν on wafer,, χαρακτηρίσθηκαν και μοντελοποιήθηκαν με το EKV3 μοντέλο έως τα 26.5GHz. Εν συνεχεία, μελετήθηκε και παρουσιάζεται η επίδραση βασικών παραμέτρων σχεδίασης, όπως μήκος (L), πλάτος (W) καναλιού, αριθμός δακτύλων (NF) στην απόδοση των MOSFET δομών και των ενισχυτών χαμηλού θορύβου, μέσω αντιπροσωπευτικών δεικτών απόδοσης (figures of merit), σε τεχνολογία με μήκος καναλιού 90nm. Οι συγκεκριμένοι δείκτες απόδοσης μελετήθηκαν επίσης σε προηγμένη CMOS τεχνολογία με μήκος καναλιού 30nm. Tα αποτελέσματα, τα οποία επικυρώθηκαν με το EKV3 μοντέλο, ανέδειξαν την μετατόπιση του βέλτιστου σημείου λειτουργίας RF κυκλωμάτων προς το μέσο της περιοχής μέτριας αναστροφής, με την μείωση του μήκους καναλιού. Το γεγονός αυτό είναι πολύ σημαντικό, καθώς καθιστά εφικτή την μείωση της κατανάλωσης ισχύος, με ταυτόχρονη αύξηση της συνολικής απόδοσης των εν λόγω κυκλωμάτων.
Channel coding and detection for increased range bistatic scatter radio
(Technical University of Crete, 2014) Alevizos Panagiotis; Αλεβιζος Παναγιωτης; Bletsas Aggelos; Μπλετσας Αγγελος; Karystinos Georgios; Καρυστινος Γεωργιος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Scatter radio, i.e communication by means of reflection, has emerged as a potential key-enabling technology for ultra low-cost, large-scale, ubiquitous sensor networking. This work jointly studies noncoherent detection and channel coding for scatter radio networks, with the ultimate goal to further extend communication range, compared to prior art. Specifically, this work focuses on frequency shift keying modulation (FSK) in bistatic scatter radio architectures, where carrier emitter is dislocated from the software defined radio receiver (SDR). FSK is ideal for the power limited regime and allows for simple, frequency division multiple access (FDMA) of simultaneously operating receiver-less sensors. A novel composite hypothesis testing decoding rule is derived for noncoherent channel-encoded FSK, in bistatic scatter radio architectures. Such decoding rule is evaluated with short block length channel codes; the latter offer ultra-low encoding complexity, and thus, they are appropriate for resource-constraint scatter radio sensors. Reed-Muller and BCH codes are studied, due to their strong algebraic structure. It is shown that the proposed decoding scheme achieves high diversity order through interleaving. Extensive simulations under Rician fading scenarios include the impact of carrier frequency offset estimation errors, channel coherence time and interleaving depth. Closed-form performance analysis is also provided. Theoretical analysis for maximum likelihood coherent detection and decoding in on-off keying modulation (OOK) is also presented. Furthermore, experimental measurements are conducted outdoors, with a commodity SDR reader and custom scatter radio sensor. Sensor-to-reader ranges up to 134 meters are experimentally demonstrated with omnidirectional antennas and 13 dBm (20 milliWatt) transmission power. Coded setup offered 10 additional meters range extension compared to the state-of-the-art uncoded noncoherent detection. As a result, this thesis provided a simple solution that could further leverage the adoption of scatter radio in large-scale, ultra low-cost wireless sensor networks
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
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
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
Smart sensors of RF and backscatter signals with localization
(Πολυτεχνείο Κρήτης, 2014) Alimpertis Emmanouil; Αλιμπερτης Εμμανουηλ; Bletsas Aggelos; Μπλετσας Αγγελος; Karystinos Georgios; Καρυστινος Γεωργιος; Lagoudakis Michael; Λαγουδακης Μιχαηλ
Information may be more valuable when the location of its source is known. This thesis develops localization algorithms based on received signal strength (RSS) measurements for unknown radio frequency (RF) sources and bistatic scatter radio tags (sensors). This thesis demonstrates RF source location es- timation utilizing RSS measurements by a community of smartphone users, within 800m (or more) from the source. The location estimation algorithm incorporates careful modeling of the time-varying source transmission power, source antenna directionality (even with a single 4-parameter model) and different path loss exponents among the various source-user links. More im- portantly, a vast number of measurements is collected and exploited through an automated community of smartphones. Location estimation error on the order of 50m is achieved, even when users are within 800m or more from the RF source. Furthermore, multiple input single output RSS localization for bistatic scatter radio is also considered. The RF scatter radio tag is illuminated by multiple low-cost carrier emitters, operating consecutively. Experimental validation of the proposed algorithm reports localization error on the order of 3m for tag and emitters placed at an area of 70m x 70m. Both estimation algorithms on real-world data exploit non-parametric estimation based on particle filtering.
Oλοκληρωμένο σύστημα μέτρησης φθοράς κοπτικών εργαλείων με τη βοήθεια ψηφιακής επεξεργασίας εικόνας
(Technical University of Crete, 2014) Lyronis Antonios; Λυρωνης Αντωνιος; Zervakis Michalis; Ζερβακης Μιχαλης; Antoniadis Aristomenis; Αντωνιαδης Αριστομενης; Kalaitzakis Kostas; Καλαϊτζακης Κωστας
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
Stochastic PageRank maintenance over shared-nothing architectures
(Technical University of Crete, 2014) Perros Ioakeim; Περρος Ιωακειμ; Garofalakis Minos; Γαροφαλακης Μινως; Lagoudakis Michael; Λαγουδακης Μιχαηλ; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Real-time stream data processing with FPGA-based SuperComputer
(Technical University of Crete, 2015) Nikolakaki Sofia-Maria; Νικολακακη Σοφια-Μαρια; Dollas Apostolos; Δολλας Αποστολος; Garofalakis Minos; Γαροφαλακης Μινως; Papaefstathiou Ioannis; Παπαευσταθιου Ιωαννης
It is a foregone conclusion that contemporary applications are bounded by massive computational demands. The semiconductor industry has announced that physical constraints restrict the community from surpassing the currently upper frequency limit of modern processors, thus leading to the emergence of multi-core platforms. This thesis explores the recently emergent paradigm of the Maxeler multi-FPGA platform for dataflow computing to efficiently map computationally intensive algorithms on modern hardware. We tackle two challenging problems within this framework, the first being classification by focusing on the kernel computation of the broadly used Support Vector Machines (SVM) classifier, and the second being time-series analysis by focusing on the calculation of the Mutual Information (MI) value between two time-series. Prior art on modern hardware has indicated the parallelism opportunities offered by the SVM method, but mainly for low-dimensional datasets, while no work has contemplated the performance of the algorithm on dataflow processors. Moreover, the problem of MI computation between two time-series on special-purpose platforms has been addressed by the research community for low-precision arithmetic applications, and again the performance of the specific method has not been evaluated on the emerging dataflow platforms. This is the first work to extensively study the pros and cons of using the Maxeler platform, by identifying the most essential dataflow elements and describing how they can be efficiently utilized. Thus, it can be employed as an independent point of reference for similar future endeavors. In terms of results, while the SVM kernel computation reached the same performance as the reference software for high-dimensional data, the know-how acquired during this process was leveraged towards the design of the MI FPGA-based architecture that yielded 9.4x speedup using two parallel cores and 32-precision arithmetic.
Machine learning methods for genomic signature extraction
(Technical University of Crete, 2015) Chlis Nikolaos-Kosmas; Χλης Νικολαος-Κοσμας; Zervakis Michalis; Ζερβακης Μιχαλης; Balas Costas; Μπαλας Κωστας; Mania Aikaterini; Μανια Αικατερινη
The application of machine learning methodologies for the analysis of DNA microarray data has become a common practice in the field of bioinformatics. DNA microarrays can be used in order to simultaneously measure the expression value of thousands of genes. Given the measurements of gene expression, machine learning methods can be employed in order to identify candidate genes that are related to a biological state or phenotype of interest, such as cancer. These lists of candidate genes are often called “genomic signatures” in literature. The application of machine learning methods for the extraction of genomic signatures is a necessity, since it is practically impossible for field experts to assess the importance of each gene individually by manual inspection due to the large size of the genome, which consists of approximately 25,000 genes. Machine learning methods such as feature subset selection and classification algorithms are popular choices for the extraction of genomic signatures. Univariate feature selection methods filter genes according to difference in their gene expression profiles among samples belonging to different classes of interest, such as control and disease. Since they test each gene individually, univariate methods are computationally efficient and they select genes with high discrimination ability. However, they ignore associations among genes. On the other hand, multivariate methods simultaneously assess groups of genes and select candidate genes based on their predictive performance when used in conjunction with a classifier. As such, they are more efficient at capturing the latent associations among genes and select genes with high predictive capability, at the cost of being computationally expensive. While the applied feature selection and classification methodologies have matured and several state of the art algorithms have been established, the stability of the extracted genomic signatures is often overlooked. As a result, the genomic signatures extracted by many methodologies are unstable under sample variations. That is, the extracted signatures differ significantly under variations of the training data. Since result stability is related to generalization, this instability raises skepticism in the expert community and hinders the validity and clinical application of research findings extracted from such gene expression studies. This thesis deals with the following three aspects of the selection and evaluation of gene signatures, namely stability, predictive capability and statistical significance. First, a framework for the extraction of stable genomic signatures, called Stable Bootstrap Validation (SBV) is introduced. The proposed methodology enforces stability at the validation step. As a result, it can be combined with any classification method, as long as it supports feature selection. Three publicly available gene expression datasets are used in order to test the proposed methodology. First the dimensionality of the datasets is reduced using a filtering method. Then, bootstrap resampling is utilized in order to generate a list of candidate signatures according to the selection frequency of genes across all bootstrap datasets. Then, a stable signature which has maximal predictive performance in terms of accuracy, sensitivity and specificity is extracted and the predictive performance of all candidate signatures is plotted in an elaborate manner for further inspection. Additionally, the application of random sampling methods for countering the negative effects of imbalanced datasets in classification was investigated, since imbalanced datasets are frequently found in DNA microarray studies where control samples are usually scarce. Moreover, a proper statistical framework was implemented that includes two separate statistical tests, in order to assess the statistical significance of the extracted signature in terms of classification accuracy as well as association to the response variable (phenotype/biological state). Finally, the robustness of the methodology is assessed by testing the degree of “agreement” among signatures extracted from independent executions of the methodology.
Practical secure and efficient range search
(Technical University of Crete, 2015) Demertzis Ioannis; Δεμερτζης Ιωαννης; Garofalakis Minos; Γαροφαλακης Μινως; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος; Christodoulakis Stavros; Χριστοδουλακης Σταυρος
Due to their potential for near-infinite scalability, cloud computing platforms are rapidly becoming the defacto standard for large-scale, big data analytics. Still, serious concerns regarding the outsourcing and querying of private company and personal data remain a key roadblock in the adoption of such cloud platforms for numerous big-data applications. In this work, we extend cryptographic Searchable Symmetric Encryption (SSE) schemes to create the first adaptive Range Searchable Symmetric Encryption (RSSE) schemes that allow the execution of range queries in a practical, efficient, and secure manner. We propose a number of new RSSE schemes, that we analytically prove to be adaptively secure according to a novel, cryptographic security definition (RQ-CKA2), and also exhibit interesting security and performance trade-offs. We also tackle the challenge of updates in our RSSE schemes by proposing a general solution that does not introduce any additional leakage over the static case, other than the number of inserts/deletes. The practicality and scalability of our proposed schemes is demonstrated both theoretically and experimentally. More specifically, our techniques outperform state-of-the-art Privacy Preserving Range Querying approaches in terms of both security and efficiency and, at the same time, offer worst-case guarantees on possible leakages and also protect sensitive information regarding the order of encrypted values.
Revenue and irritation-based call admission control using road maps over wireless cellular networks
(Πολυτεχνείο Κρήτης, 2014) Dimitriou Konstantina; Δημητριου Κωνσταντινα; Koutsakis Polychronis; Κουτσακης Πολυχρονης; Paterakis Michalis; Πατερακης Μιχαλης; Bletsas Aggelos; Μπλετσας Αγγελος
Bandwidth remains the most valuable commodity in wireless networks, even more so today, as new bandwidth-hungry multimedia applications emerge constantly and their users have high Quality of Service (QoS) requirements. Hence, the ability of a wireless network to efficiently allocate its bandwidth resources is crucial. In this thesis, we propose a new mechanism which controls the admission of users moving from cell to cell in a wireless cellular network. The Call Admission Control (CAC) mechanism is defined as a sequence of activities that are realized from the network in order to check if a user’s request, to use a specific service of the network, can be admitted or not. This request will be admitted if the desirable level of QoS can be accomplished without causing violation on the QoS that existing users enjoy. The design and simulation of the CAC mechanism were realized for the simultaneous use of the channel both from voice and e-mail users and video users downloading movies encoded with MPEG-4. The CAC mechanism aims to maximize channel throughput without allowing network congestion which would lead to the violation of the users’ QoS requirements. This is difficult to achieve because of the contradictory nature of the different types of traffic. Our work is one of the first in the relevant literature, to the best of our knowledge, to simultaneously consider both the provider revenue and user irritation in making its admission decisions. Our results show the resource allocation efficiency of the proposed mechanism in two different implementations regarding the degradation/upgrades of video users. Our CAC mechanism is shown to provide voice and video users long term satisfaction with the QoS they receive, even for very high traffic loads, while at the same time allowing the provider to increase its profit.
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.
SDR readers for Gen2 RFID and backscatter sensor networks
(Technical University of Crete, 2015) Kargas Nikolaos; Καργας Νικολαος; Bletsas Aggelos; Μπλετσας Αγγελος; Karystinos Georgios; Καρυστινος Γεωργιος; Deligiannakis Antonios; Δεληγιαννακης Αντωνιος
Scatter radio has emerged as a key enabling technology for low-cost and large scale ubiquitous sensing. Radio frequency identification (RFID) tags/sensors utilize scatter radio technology to transfer sensed information to readers, typically employing Gen2, the industrial RFID protocol. This work offers a complete software-defined radio Gen2 reader, based on GNU Radio and USRP2 commodity software defined radio (SDR) platform. In sharp contrast to prior art, a single radio front end card is used with coherent detection and optimal exploitation of the FM0 line coding memory. The reader can act as a research tool to experiment with state-of-the-art signal processing algorithms and RFID devices. The two tag collision problem is studied and problems that arise in a real world system, such as channel estimation and tag symbol synchronization are highlighted. Experimental measurements are conducted and it is shown that the reader can identify a commercial, passive UHF RFID tag up to 6 meters with acceptable reliability. In addition, it is shown that collision recovery algorithms can increase performance of the implemented reader. Furthermore, an implementation of a SDR reader for a wireless backscatter sensor network (BSN) is presented. The developed reader implements noncoherent frequency shift keying (FSK) detection. The reader can decode multiple tags in real time and achieves communication ranges with semi-passive tags/sensors up to 130 meters.
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.

Περιηγούμαι

Πρόσφατες Υποβολές