Microelectronics

Zagreb, Croatia

Microelectronics

Zagreb, Croatia
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Baric A.,Microelectronics
MIPRO 2012 - 35th International Convention on Information and Communication Technology, Electronics and Microelectronics - Proceedings | Year: 2012

High-gain charge pumps use MOS switches to rearrange the topology of the circuit in the adjacent half periods of the clock signals, which enables the charge pump circuit to achieve high output voltage. The MOS switches must be adequately controlled to enable fast charge transfer while turned "on" and to prevent leakage current while in the "off" state. This paper presents the limitations of the Cascode Voltage Switch Logic (CVSL) circuit as a control circuit for MOS switches. The operation of the CVSL driver is discussed and the negative effects of the capacitive load associated with the CVSL output node are analyzed. © 2012 MIPRO.


Sikora M.,University of Zagreb | Mateljan I.,University of Zagreb | Bogunovic N.,Microelectronics
Automatika | Year: 2011

In order to expand the area of use of the beam tracing method, the beam tracing with the refraction method (BTR) was developed. The BTR is best suited for acoustic and hydro-acoustic simulation of non-homogenous environments. The BTR can trace the refraction as well as the reflection of the sound wave, using triangular beams. The geometry of the scene in the BTR is based on triangle meshes rather than polygons. This enables the BTR to simulate complex, irregular shaped objects, including non-convex volumes. Furthermore, the BTR traces beams through several entities filled with different media. This paper presents algorithms and data structures used to divide beams during the interaction of a beam with the complex, non-convex environment. This paper also brings measurements of the implemented beam division code and the comparison of measured results with results of other methods.


Dogan I.O.,Microelectronics | Yazicioglu Y.,Middle East Technical University
Journal of Computational and Theoretical Nanoscience | Year: 2014

Recent investigations in nanotechnology show that carbon nanotubes have significant mechanical, electrical and optical properties. Interactions between those are also promising in both research and industrial fields. Those unique characteristics are mainly due to the atomistic structure of carbon nanotubes. In this paper, the structural effects of vacant atoms on single walled carbon nanotubes are investigated using matrix stiffness method. In order to use this technique, a linkage between structural mechanics and molecular mechanics is established. A code has been developed to construct the single walled carbon nanotubes with the desired chirality, extracting the vacant atoms with the corresponding atomic bonds between the neighbor nodes and calculating the effect of these vacancies on their vibrational properties. In order to investigate the effect of those vacant nodes, large number of simulations has been carried out with randomly positioned vacant atoms. Also, consecutive vacant nodes have been positioned in order to investigate their effect on the structural properties through the length of a single walled carbon nanotubes. Effects of vacancies on Young's modulus have also been investigated. It is concluded that any amount of vacant atoms have substantial effect on modal frequencies and modulus of elasticity. Chirality and the amount/position of the vacancies are the main parameters determining the structural properties. Copyright © 2014 American Scientific Publishers All rights reserved.


Polat O.M.,Microelectronics | Ozkazanc Y.,Hacettepe University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

In blind scene analysis, the aim is to obtain information about background and targets without any prior information. Blind methods can be considered as pre-processing steps for scene understanding. By means of blind signal separation methodologies, anomalies can be detected and these anomalies can be exploited for target detection. There are many imaging sensor systems which uses different properties of the emittance or the reflectance characteristics of the scene components. Spectral reflectance properties are related to the material composition and these multispectral characteristics can be exploited for detection, identification and classification of the scene components. As the light scattered from the scene elements shows polarization, polarized measurements can be used as extra features. Multispectral and polarimetric images of a scene provide information to some level and this information can be used to get further information on the scene and to facilitate detection. In this study, spectral and polarimetric images of a scene are analyzed via Canonical Correlation Analysis (CCA) which is a powerful multivariate statistical methodology. Multispectral and polarimetric data (spectro-polarimetric data) are treated as two different sets. Canonical variants obtained by CCA give different scene components such as background elements and some man-made objects. The linear relationship of the polarimetric and multispectral data of the same scene is also obtained by CCA. © 2013 SPIE.


Polat O.M.,Microelectronics | Ozkazanc Y.,Hacettepe University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

In hyperspectral data analysis, blind separation of the target and background can be considered as a pre-processing step in the target detection process. Blind Source Separation (BSS) techniques can be used when there is no prior information on the scene for image understanding. Previously, Principal Component Analysis and Independent Component Analysis methods are used as blind techniques for the analysis of hyperspectral data. In this study, we propose a blind analysis methodology based on Canonical Correlation Analysis (CCA) for the analysis of hyperspectral data sets. CCA is a multivariate method of analysis for the exploration of the data structures which extremize the correlations between two data sets. The hyperspectral data analyzed in this study is the HyMap sensor data which is available in the Target Detection Blind Test website. We produce two data sets out of the HyMap data cube which are later subjected to CCA. In the creation of these data sets, two different approaches are used. In first case, the HyMap data cube is simply divided into two sub-cubes by simple spectral separation. As another approach, the second data cube is derived from the HyMap data by a spatial filtering. In both cases, two data sets are analyzed via CCA and canonical variates of these data sets are obtained. The scene components are obtained from images expressed by the canonical variates obtained via CCA. The CCA methodology and its use as a blind analysis tools is presented on the HyMap data. © 2013 SPIE.


Kornaros G.,Technological Educational Institute of Crete | Harteros K.,Technological Educational Institute of Crete | Astrinaki M.,Technological Educational Institute of Crete | Christoforakis I.,Technological Educational Institute of Crete | And 2 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2013

Hardware virtualization is a major challenge in embedded virtualization. The key to improving resource utilization in a virtualized system is to allow maximum possible resource access operations to perform natively with minimal intervention by the virtual machine monitor, while at the same time ensuring protected operation among different virtual machines' address space. An innovative I/O Memory Management Unit component (IOMMU) is architected to enable mapping of virtual addresses from multiple devices to the correct VM's physical memory locations, offering enhanced protection, scatter-gather functions on distributed memory organizations, high performance supported by a configurable TLB and an integrated lightweight hardware monitoring unit to facilitate dynamic system optimizations. This new IOMMU is designed in a modular way supporting address translation along with protection and security extensions. The principal objective is to ensure device isolation by safely mapping a device to a particular guest without risking the integrity of other guests. Additionally, the IOMMU is designed to provide an increased level of security in scenarios without virtualization; with the aid of the IOMMU, the operating system is able to protect itself from malicious device drivers by limiting a device's memory accesses and managing the permissions of peripheral devices. © 2013 SPIE.


Grudenic I.,Microelectronics | Bogunovic N.,Microelectronics
MIPRO 2012 - 35th International Convention on Information and Communication Technology, Electronics and Microelectronics - Proceedings | Year: 2012

Number of internet users as well as number of large distributed computer systems has increased dramatically in the last decade. Large distributed computer systems serving lot of users are usually oversized in order to be able to keep up with user demand surges. However in many usage scenarios workload spikes are rare and systems operate on 20%-30% average utilization. Underutilized systems are shown to be power inefficient and different strategies are proposed to tackle this issue. In this paper we present an overview of different power saving techniques that target different types of systems and workloads. © 2012 MIPRO.


Jovic A.,Microelectronics | Bogunovic N.,Microelectronics
Artificial Intelligence in Medicine | Year: 2011

Objective: The paper addresses a common and recurring problem of electrocardiogram (ECG) classification based on heart rate variability (HRV) analysis. Current understanding of the limits of HRV analysis in diagnosing different cardiac conditions is not complete. Existing research suggests that a combination of carefully selected linear and nonlinear HRV features should significantly improve the accuracy for both binary and multiclass classification problems. The primary goal of this work is to evaluate a proposed combination of HRV features. Other explored objectives are the comparison of different machine learning algorithms in the HRV analysis and the inspection of the most suitable period T between two consecutively analyzed R-R intervals for nonlinear features. Methods and material: We extracted 11 features from 5. min of R-R interval recordings: SDNN, RMSSD, pNN20, HRV triangular index (HTI), spatial filling index (SFI), correlation dimension, central tendency measure (CTM), and four approximate entropy features (ApEn1-ApEn4). Analyzed heart conditions included normal heart rhythm, arrhythmia (any), supraventricular arrhythmia, and congestive heart failure. One hundred patient records from six online databases were analyzed, 25 for each condition. Feature vectors were extracted by a platform designed for this purpose, named ECG Chaos Extractor. The vectors were then analyzed by seven clustering and classification algorithms in the Weka system: K-means, expectation maximization (EM), C4.5 decision tree, Bayesian network, artificial neural network (ANN), support vector machines (SVM) and random forest (RF). Four-class and two-class (normal vs. abnormal) classification was performed. Relevance of particular features was evaluated using 1-Rule and C4.5 decision tree in the cases of individual features classification and classification with features' pairs. Results: Average total classification accuracy obtained for top three classification methods in the two classes' case was: RF 99.7%, ANN 99.1%, SVM 98.9%. In the four classes' case the best results were: RF 99.6%, Bayesian network 99.4%, SVM 98.4%. The best overall method was RF. C4.5 decision tree was successful in the construction of useful classification rules for the two classes' case. EM and K-means showed comparable clustering results: around 50% for the four classes' case and around 75% for the two classes' case. HTI, pNN20, RMSSD, ApEn3, ApEn4 and SFI were shown to be the most relevant features. HTI in particular appears in most of the top-ranked pairs of features and is the best analyzed feature. The choice of the period T for nonlinear features was shown to be arbitrary. However, a combination of five different periods significantly improved classification accuracy, from 70% for a single period up to 99% for five periods. Conclusions: Analysis shows that the proposed combination of 11 linear and nonlinear HRV features gives high classification accuracy when nonlinear features are extracted for five periods. The features' combination was thoroughly analyzed using several machine learning algorithms. In particular, RF algorithm proved to be highly efficient and accurate in both binary and multiclass classification of HRV records. Interpretable and useful rules were obtained with C4.5 decision tree. Further work in this area should elucidate which features should be extracted for the best classification results for specific types of cardiac disorders. © 2010 Elsevier B.V.


Pla D.,Advanced Materials for Energy | Salleras M.,Microelectronics | Garbayo I.,Advanced Materials for Energy | Morata A.,Advanced Materials for Energy | And 4 more authors.
Ceramic Engineering and Science Proceedings | Year: 2014

This work reports the design, fabrication and experimental results of a micro-reformer for hydrogen-rich gas generation from liquid ethanol, for portable-solid oxide fuel cell (SOFC) feeding. Ethanol has been chosen as fuel because its high energy density, easy handling and the possibility of directly obtaining from renewable biomass. The reformer has been designed as a silicon micro monolithic substrate and fabricated by using conventional microelectronics fabrication techniques including photolithography, wet etching, chemical vapor deposition and reactive ion etching. Design and geometry of the system have been optimized for minimizing heat losses in order to satisfy the high temperature requirements of reforming process. The fabricated micro-reformer has dimensions of 15×15 mm2 in area and 500μm in thickness, with an effective reactive area of more than 36 cm2 consisting of an array of ca. 4.6×104 vertical micro channels (50μm diameter). These micro channels have been coated with a noble metal-based catalyst for ethanol steam reforming reaction. The micro-reformer has been successfully tested under diluted feed conditions at the 550-800°C temperature range - optimal operation temperatures for SOFCs - achieving high specific hydrogen production rates, high ethanol conversions (>80%) and adequate selectivity profiles. Results show the increase in contact area between fuel gas and catalyst that leads to a high performance in small volumes and reduced residence times. This functional micro-converter is the basis for a complete gas processing unit to be integrated on an entire micro-SOFC system. Copyright © 2015 by The American Ceramic Society.


Grudenic I.,Microelectronics | Bogunovic N.,Microelectronics
MIPRO 2011 - 34th International Convention on Information and Communication Technology, Electronics and Microelectronics - Proceedings | Year: 2011

Computer clusters are currently the most used distributed computer architecture. Efficient utilization of computer cluster depends on a scheduling policy that is applied. Scheduling of jobs in computer cluster is a complicated task due to frequent changes in the workload. In this paper we present scheduling algorithm that is based on EASY backfilling scheduling strategy. Dynamic programming with time restriction is used to calculate as good schedule as possible within given time constraints. Algorithm is evaluated on several computer cluster workloads and is shown to outperform original backfilling strategy. © 2011 MIPRO.

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