Technological Innovation for Value Creation

Raising awareness among engineering PhD students for the value creation potential of their research work is a crucial element in their education. With this aim, the doctoral conference DoCEIS’12 focused on technological innovation for value creation, challenging the contributors to analyze in which ways their technical and scientific contributions could contribute to creating value in society. The results of this initiative, which was reasonably successful, are briefly analyzed.

In the last years the rapid progress of ICT along with the growing business models have led to the emergence of a large variety of collaborative networks. Collaboration brought new achievements such as the capacity of the involved entities getting into business in a sustainable and stable way. Despite the fact that collaboration has significant benefits, especially in creating value and boosting innovation, some studies indicate that most of them tend to fail. Among others, the lack of specific systems to monitor the emotional state of the network is one of the reasons why collaboration fails. Emotions can interfere in collaboration environments by increasing or decreasing the motivation and performance level and even in conflict resolutions. Therefore, it becomes particularly necessary to find means for supervising the emotional state of collaborative networks in order to achieve the equilibrium for a healthy community. This paper introduces an approach to develop an emotions-oriented system and presents two scenario cases of collaborative networks where this system can be implemented.

In the current economic turbulence, it is crucial for companies and organizations to attain strategic alliances to respond to new business or collaboration opportunities. Besides the importance of understanding the structures and requirements of such alliances, it is also of extreme importance that the process of negotiation to create those alliances is properly modeled so that the elements that constitute the alliance agreements are accurately represented and the involved risks are mitigated. With this aim, this paper outlines the main requirements of an electronic negotiation support environment in a collaborative network, including a brief analysis of how it can help in reducing the risk potential, considering the relevance of the expectations of the involved partners, and highlighting the importance of a technological support environment.

In-robotics functions, performed by the software, include data acquisition, motor control, sensor signals processing, perception of objects and navigation through environment. LabView provides a solution to the limited skills and the limited time problem that appears when we use programming environments like C++ or Java. This document introduces the implementation of a communicative mobile robotic system that is based on two collaborative Pioneer robots controlled by user friendly interfaces developed in LabView. The purpose of developing this type of interfaces was to allow a complex collaborative system to adapt to any platform and be used by any type of user, without depending on his robotics knowledge level.

Modern enterprises are supported by flexible and agile software systems and architectures to respond effectively and quickly to opportunities in a more competitive market. Even small and medium sized enterprises (SME) highly depend on appropriate software architectures to successfully participate in the growth of the economy. For this purpose they must be in a position to upgrade and enhance their ERP at low costs and with small manual efforts. A flexible middleware platform for the integration of future software systems is proposed as a solution to these challenges for innovation.

In this paper, an on-line change detection algorithm for resource allocation in service-oriented systems is presented. The change detection is made basing on a dissimilarity measure between two estimated probability distributions. In our approach we take advantage of the fact that streams of requests in service-oriented systems can be modeled by non-homogenous Poisson processes. Thus, for Bhattacharyya distance measure and Kullback-Leibler divergence analytical expressions can be given. At the end of the paper a simulation study is presented. The aim of the simulation is to demonstrate an effect of applying adaptive approach in resource allocation problem.

The goal of this paper is to propose an ensemble classification method for the credit assignment problem. The idea of the proposed method is based on switching class labels techniques. An application of such techniques allows solving two typical data mining problems: a predicament of imbalanced dataset, and an issue of asymmetric cost matrix. The performance of the proposed solution is evaluated on German Credits dataset.

Information Systems are vital to all kind of business and research activities. In the last years the need for reliable information on time is becoming a differentiating factor for the results of the computer based activities. It is known that data makes possible information then knowledge and, eventually, will result some wisdom. However, much prior to the expected characteristics of such data and its usefulness, would be important to actually find it. On the Internet, search Engines are the key element to make relevant information available to a certain user. By learning about the human’s search capabilities it should be possible to information systems, by enlarging the characteristics of source information towards human perceptive and cognitive functions. This paper describes the application of a research method to explore the development of a framework for knowledge management based on lessons learned from a neuroscience model known as the Two-Stream Hypothesis.

Systems biology of plants offers myriad opportunities and many challenges in modeling. A number of technical challenges stem from paucity of computational methods for discovery of the fundamental properties of complex dynamical systems in biology. In systems engineering, eigen-mode analysis has proved to be a powerful approach to extract system parameters. Following this philosophy, we introduce a new theory that has the benefits of eigen-mode analysis, while it allows investigation of complex dynamics prior to estimation of optimal scales and resolutions.

Information Surfaces

organize the many intricate relationships among “eigen-modes” of gene networks at multiple scales. Via an adaptable multi-resolution analytic approach, one could find the appropriate scale and resolution for discovery of functions of genes in plants. This article pertains the model plant Arabidopsis; however, almost all methods can be applied to investigate development and growth of crops for research on sustainable agriculture.

Recent advancements in Internet technology research, as well as the widespread of commercial content delivery networks, motivates the need for optimization algorithms designed to work in decentralized manner. In this paper we formulate data placement problem, a special case of universal facility location problem with quadratic terms in objective function. The considered combinatorial optimization problem is NP-hard. A randomized algorithm is presented that approximates the solution within factor

O

(log

n

) in decentralized environment, assuming asynchronous message passing of bounded sizes.

Most of the mechatronics equipments and gadgets that we all nowadays rely on incorporate some kind of multidimensional human-machine systems. There is an increasing concern for improving the usability, performance, ergonomics and safety of such devices, and ultimately this will lead to the mass-production of next-generation intelligent machines, which will be capable to assist the human operator and to reduce the global effort by estimating and measuring its skills. This ongoing work introduces a novel human-machine multi-dynamic modeling methodology which can be applied on the development of these Human Adaptive Mechatronic (HAM) machines, able to adapt to the skill/dexterity levels of its users, and to enhance Human’s proficiency. As a new strategy for model development, a number of two-dimensional independent pursuit manual tracking experiments are evaluated. A human-machine state-space linear model is obtained and successfully applied to design an improved closed-loop multivariable control structure.

Basketball has grown into an international sport played and watched by millions of people. This paper describes the implementation of a virtual basketball accelerator. The main purpose of our study was to devise an application that could literary help players in practicing a free throw game indoors. In the development of this project we used Matlab, XVR and 3ds Max. The Matlab trajectory is simulated considering the development of a realistic behavior. The data acquisition from the trackers is adapted both for magnetic and optical markers, therefore extending its usability. The coordinates are then sent via UDP to the XVR environment, which draws the moving parts accordingly. The overall performance is improved by paying a great deal of attention to details.

The aim of the article is to present preliminary results obtained by analysis of the behavior patterns of various driver subjects, in the context of an intelligent assistive driving system. We determined the parameters which are involved in determining the car driver’s interaction intent, and extracted features of interest from various measured parameters of the driver, car, and the environment. We discuss how threshold values can be obtained for the extracted features that can be part of rules to decide on specific interaction intents. The results obtained in this paper will be incorporated in a knowledge base to define the rules of an rule-based expert system that will predict in real-time the driver’s interaction intent, in order to enhance the safe driving experience.

This paper presents a state-space based model-checking framework to test and validate embedded system controllers specified using the IOPT Petri net formalism. The framework is composed of an automatic software code generator, a state-space generator and a query engine, used to define queries applied to the resulting state-space graphs. During state-space generation, the tools collect information required to enable the efficient implementation of hardware/software controllers, including place bounds, deadlocks and conflicts between concurrent transitions. User defined queries can check relevant system properties, as the occurrence of undesired error situations, the reachability of desired states, system liveliness and the occurrence of deadlocks and livelocks. The new tool, available online under a Web based user interface, provides a fast and efficient way to test and validate system controllers, contributing to the reduction of development time.

The evolution of tool-machines shows a trend for integration of different machining functionalities such as, milling, turning, and drilling, on a same machine. These machines are result of the mechatronics development that allows the aggregation of different functionalities into a unique machine (named multifunctional machine tool - MMT). However from the point of view of the production process there is a limitation of the conventional process control solution, i.e., when the systems are composed by MMTs, their functional flexibilities are considered only during scheduling and encapsulated at this level, and during the process execution, MMTs are viewed as traditional ones. Then, the multi-functionalities are not explored for resources allocation. Thus, a new process control approach is proposed here to dynamically allocate the resources exploring the functional flexibilities of MMTs.

A specific class of Petri nets was extended with Asynchronous-Channels (ACs) and Time-Domains (TDs) to support Globally-Asynchronous Locally-Synchronous (GALS) systems’ modeling, analysis and implementation. This non-autonomous class of Petri nets is targeted to support the development of automation and embedded systems using a model-based development approach. It benefits from a tool chain framework previously developed, covering the whole development flow, from specification to hardware and software deployment. With the extended Petri net class is possible to model GALS systems, and use the specification to generate the corresponding state space supporting the behavior verification and providing valuable information for implementation.

This work proposes an approach for course modeling using Petri nets. The proposed modeling method can be applied to support development of e-learning platforms (namely learning management systems - LMS) allowing student guidance when considering reaching a specific goal. This goal could be as simple as getting a set of sequential courses (or a degree), or as complex as combining different modules from different courses having different types of dependencies in order to obtain a qualification. Each course is characterized by a set of modules and their relations. Each module is represented by a Petri net model and the module structure representing the course’s dependency relations is translated into another Petri net model. Additional courses or modules can be included into the offer as their associated Petri net models can be easily composed using net addition operation. The contribution of this paper foresees the usage of common Petri nets analysis techniques (such as state space analysis, invariants, trace finding) to constraint student’s options in order to optimize his/her path to reach a degree or a qualification. A simple example considering a scenario with a few courses and modules is used to illustrate the approach.

Traditional manufacturing solutions, based on centralized structures, are ineffective in unpredictable and volatile scenarios. Recent manufacturing paradigms, such as Holonic Manufacturing Systems, handle better these unpredictable situations but aren’t able to achieve the performance optimization levels displayed by the classical centralized solutions when the system runs without perturbations. This paper introduces a holonic manufacturing architecture that considers biological insights, namely emergence and self-organization, to achieve adaptation and responsiveness without degrading the performance optimization. For this purpose, self-organization and self-learning mechanisms embedded at micro and macro levels play an important role, as well the design of stabilizers to control the system nervousness in such dynamic and adaptive behaviour.

To face globalization challenges, modern production companies need to integrate the monitoring and control of secondary processes into shop floor core system to remain competitive and improve system performance and throughput. The research currently being done under the scope of Self-Learning Production Systems tries to fill this gap. Current work introduces the domain and a generic architecture, while focus over the responsible element for executing system adaptations according to current context: the Adapter. The Adapter architecture and its components are introduced as well as the generic Adaptation process. Early prototype scenarios applied to concrete real-world scenarios are also presented.

This paper evaluates a hybrid neurophysiological interface for controlling a virtual smart home. Electrooculography (EOG) and Electroencephalography (EEG) techniques are used for recording biosignals from the user’s body. The signals are further analyzed and translated into commands for controlling a virtual smart home. The purpose of the study was the evaluation of the implemented interface and the rejection of a set of possible erroneous commands generated by the interface. From a main menu the user is able to select a mask that will reveal a second menu with supplementary commands. The user has the option to cancel the propagation of an erroneous selected mask in the system by selecting another item from the main menu.

This paper presents the kinematics programming for Cooperative Robotic Systems (CRS), based on screw theory approach. It includes a systematic for modeling and programming robotic systems composed by any number of robots (not necessarily identical), working cooperatively to perform different tasks. In order to illustrate the application of the systematic, an example of CRS including four robots is presented. The kinematic computation of a CRS is made through the screw theory approach and its tools, like the Davies method and Assur virtual chains.

This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of Variance (ANOVA) is employed to compare the parameters and identify the ones which give raise to the total dispersion of the data set, at the same time accessing their contribution to the obtained results. It is shown that by applying such technique, it is possible to compute a data-related model for the Multi-Robot Patrolling Problem (MRPP).

This paper presents and describes a new lenticular shaped airship that allows 3D omnidirectional movements. The airship is intended to be part of a cooperative aerial system based in the synergy between an airship and a group of micro-quad rotors capable of long time missions as environmental, biodiversity monitoring, WiSAR missions, road monitoring, detection of forest fires, etc. The emphasis in this paper is the presentation of hardware of the main component of the system: an innovative lenticular airship, the control algorithm and the low size generic hardware platform specially developed for all the robots in the system..

Perceptual geometry refers to the interdisciplinary research whose objectives focuses on study of geometry from the perspective of visual perception, and in turn, applies such geometric findings to the ecological study of vision. Perceptual geometry attempts to answer fundamental questions in perception of form and representation of space through synthesis of cognitive and biological theories of visual perception with geometric theories of the physical world. Perception of form, space and motion are among fundamental problems in vision science. In cognitive and computational models of human perception, the theories for modeling motion are treated separately from models for perception of form.

This article presents a method that without any prior knowledge of a network of cameras, in an automatic and in an on-line mode, estimates their positional relation. The developed method is also able to adapt the estimation of the topology automatically, if some change is made on the network, as adding or removing cameras, and changing the position relative to the adjacent ones. To compute the topological map of a sparse network of cameras, our approach registers events, generated by the entrance and exit of agents (persons or movable objects) from the visible area of the cameras. These events are classified as IN and OUT respectively, and they are stored and associated according to a predefined logic based on the measured entropy level of the overall of events, and joined in relation groups. These groups are then analyzed to infer the topological map. However, it is necessary to develop approaches to avoid and eliminate redundancy of data and remove false relations between the sensors. By this, to achieve reliable results and to prevent loss of data, a method is proposed to adjust the computation of the acquired data based on the entropy evaluation. Then, two algorithms are used to combine the events and to create the relation groups at predefined levels of entropy. Based on the categorization of the relation of the events and groups, is possible to estimate the topological information of sparse networks of cameras. Our experimental results show that, even in situations where there is a significant disorder in the registered events, our method is robust and performs valid estimations.

This paper presents a novel approach to visuo-haptic perception of grasping/manipulative tasks. The proposed approach is founded on a hierarchical Bayesian model which integrates the visual information with the haptic data to reach a reasonable percept of what is happening in grasping tasks. The primary goal of the approach is to identify what type of grasping behaviour is being performed by the human subject, and as a secondary goal, to simultaneously assess the quality of the respective grasping behaviour. For a simple set of grasping behaviours defined in this paper, preliminary experimental results indicate that the proposed approach could result in a robust and efficient perception of grasp behaviours.

In this paper a new approach to rational discrete-time approximations to continuous fractional-order systems of the form 1/(s

α

+p) is proposed. We will show that such fractional-order LTI system can be decomposed into sub-systems. One has the classic behavior and the other is similar to a Finite Impulse Response (FIR) system. The conversion from continuous-time to discrete-time systems will be done using the Laplace transform inversion integral and then applying the Bilinear transformation to obtain the corresponding discrete-time system and the corresponding frequency response. An optimization algorithm will be used to find the FIR type parameters that best approximate the discrete-time system.

This paper proposes a control strategy for Hybrid Active Series Power Filter (HASPF) based on a P+Resonant compensator. The proposed control uses Adaptive Notch Filter to isolate the harmonic content of the source current and a P+Resonant regulator to calculate the harmonic voltage signal necessary to properly tune the hybrid branch. This control strategy ensures multiple frequencies tuning by using only one passive filter. The details of the control algorithm and the design procedure are presented. Experimental results using a single-phase HASPF prototype confirm the effectiveness of the suggested approach.

Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload. In this paper two different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform and Automatic Visual Scoring. The results obtained using both methods are compared with human expert scorers.

The implementation of underground distribution lines has grown significantly over the last decades due to the rapid increase of electric energy demand. But, the life of underground lines is determined by strong single or multiple stresses, developed during manufacturing and operation. Establishing and predicting the end of life of a distribution underground line is a task for researchers, designers and manufacturers, too. In this respect, the multiple stress life analysis may be used as efficient tool to predict the life of lines and cables. In this paper, a multiple stress analysis is developed, applying the Arrhenius-Weibull life-stress model, in order to identify the effects of multiple stresses on power cables. An application to an underground power line in a Romanian Electric Network Company is developed, in ALTA software package. The multiple stresses of temperature, humidity, and vibrations are considered, and the life distributions and reliability plots are obtained.

This paper describes the application of the p-q power theory to the differential protection of power transformers. The information provided by the harmonic content of the differential active and reactive power components is used to detect winding insulation failures and to distinguish them from magnetizing inrush current transients. A variety of test cases is presented in the paper, demonstrating the effectiveness of the protection strategy.

This paper is devoted to the implementation of a parallel active power filter prototype in a nonideal power grid. The control algorithm is described and realized. Simulation and experimental results proved the theoretical prediction. Analytical conditions of the usable implementation are shown. Some guidelines are presented in the conclusion.

The power supply of a Fast Field Cycling Nuclear Magnetic Resonance apparatus is typically a current power source, with characteristics that are not fulfilled by most of the commercially available power supplies. This current source is used to supply a specially designed magnet, and should be able to drive a cycling current with a slew rate lower than 3 A/ms. In this paper, two solutions for this current source are designed and discussed. The first uses two power supplies: the main voltage source and an auxiliary power supply to guarantee the current transitions from the low level to the high level. In the second solution the auxiliary power supply is replaced by a capacitor. To guarantee the required current slew rates, the possibility of using resistors to dissipate the energy stored in the magnet is also discussed.

This paper proposes the use of a Modular Marx Multilevel Converter, as a solution for energy integration between an offshore Wind Farm and the power grid network. The Marx modular multilevel converter is based on the Marx generator, and solves two typical problems in this type of multilevel topologies: modularity and dc capacitor voltage balancing. This paper details the strategy for dc capacitor voltage equalization. The dynamic models of the converter and power grid are presented in order to design the converter ac output voltages and the dc capacitor voltage controller. The average current control is presented and used for power flow control, harmonics and reactive power compensation. Simulation results are presented in order to show the effectiveness of the proposed M

3

C topology.

The intermittence of the renewable sources due to its unpredictability increases the instability of the actual grid and energy supply. Besides, in a deregulated and competitive framework, producers and consumers require short-term forecasting tools to derive their bidding strategies to the electricity market. This paper proposes a novel hybrid computational tool, based on a combination of evolutionary particle swarm optimization with an adaptive-network-based fuzzy inference system, for wind power forecasting and electricity prices forecasting in the short-term. The results from two real-world case studies are presented, in order to illustrate the proficiency of the proposed computational tool.

This paper focuses on the comprehensive modeling, simulation and experimental validation for a photovoltaic system formed by monocrystalline solar modules. The performance of the equivalent circuit model for a solar cell is validated by data measured parameters of photovoltaic modules. Also, this paper brings a novel iterative procedure to find the value of diode ideality factor, series and equivalent shunt resistances.

This paper presents a small-scale prototype of a wind-photovoltaic hybrid system, having batteries as energy storage components. All devices are connected to a common dc-bus using appropriate power converters. The photovoltaic modules are connected through a dc-dc buck converter using Perturb and Observe Maximum Power Point Tracking control. The permanent magnet synchronous generator (wind generator) power interface is composed of a three-phase diode-bridge rectifier followed by a buck converter. A bidirectional buck-boost converter is applied to the batteries in order to control the dc-bus voltage, to which the load is directly connected. The whole hybrid system was built as a laboratory prototype, by using a dSPACE digital controller. Several experimental results are presented in order to analyze the system performance under both steady state operation and transients.

The number of electricity consuming equipment for existing household end-user is continuing to increase, and some residential buildings already consume more energy than existing building regulations prescribe. The uprising SmartGrid technology with alternative energy sources could be a key to solve this problem, but it is demanding also for a “smarter” consumer with ability to monitor and manage his loads. Such a monitoring system can also improve energy efficiency, as it can change consumer non-saving habits, by teaching him possibilities where the energy can be saved. Therefore the paper is devoted to development of new concept of household energy consumption monitoring system. Due to new approach of energy monitoring, the costs of needed metering equipment and total metering system are lowered. The proposed method provides energy consumption apportionment between consumers instead of precise energy consumption metering for each consumer type.

Protection in distributed energy systems depends on the operation of a set of coordinated protective devices spread around the power system. Usually these devices encompass some communication skills and can be remotely operated by energy management systems. Despite this interaction readiness, most of them rely only on non-standard communication protocols and some interoperability issues still remain. This paper presents an innovative plug & play approach for the integration of protective devices, supported by a standard-based and service-oriented software infrastructure entitled NEMO. This approach adopts the IEC 61850 semantic information model and the DPWS specific communication service mapping, to perform a seamless device integration and communication of protective devices.

The future charging of the growing fleet of Electric Vehicles (EV) requires new solutions that guarantee a better efficiency and widely spread chargers. Both in fast charging and in slow charging of the electric vehicle (EV) and hybrid electric vehicle (HEV), the wireless charger is the better choice. The wired charger is limited by its cabling and relatively lower output voltage, while the contactless charger can be more universal and safe. What rests to be discovered is the more efficient energy transfer that would have a stable operation. The strategy is based on guaranteeing the correct portion of energy transported by the resonant tank to the load. Some results are shown from this implementation of the instantaneous energy control.

A solar trigeneration system for off-grid households, based on photovoltaic-thermal (PV/T) collectors, photovoltaic (PV) modules and a heat pump (HP), whose aim is to provide enough electricity, domestic hot water (DHW), heating and cooling power to meet the typical demand of an offgrid single family dwelling, is modeled to predict its performance, enable system sizing and evaluate the impact of different control and management strategies. The system integrates an autonomous photovoltaic system, a solar DHW system and a heating, ventilation and air-conditioning (HVAC) system. Thus, according to local climate data, construction elements, load profiles and storage dynamics, the model computes the system’s net results using a simple supply and demand approach. Additionally, a design space methodology is used to evaluate system performance for different sizes and identify a feasible design region within which reliable and cost-effective system sizing is possible.

This research proposes the use of hardware emulation for inverter plants for the first time. This results in a new low cost, safe and robust solution for teaching control strategies for power electronics inverter circuits, composed of a digital controller (DSP or FPGA) connected to the inverter “hardware emulation circuit”. The inverter dynamical behavior is emulated by cheap operational amplifiers circuits, in such way that the student can safely measure the inverter emulated currents/voltages. The hardware emulation is not beneficial only for educational purposes but can be used for scientific research, testing and fault diagnosis of inverter systems. Substantial part of the control software development and tests can be safely and comfortably done at an office desk, without the high costs, risks and safety measures required for manipulating a real scale inverter.

A Blumlein line is a particular Pulse Forming Line, PFL, configuration that allows the generation of high-voltage sub-microsecond square pulses, with the same voltage amplitude as the dc charging voltage, into a matching load. By stacking

n

Blumlein lines one can multiply in theory by

n

the input dc voltage charging amplitude. In order to understand the operating behavior of this electromagnetic system and to further optimize its operation it is fundamental to theoretically model it, that is to calculate the voltage amplitudes at each circuit point and the time instant that happens. In order to do this, one needs to define the reflection and transmission coefficients where impedance discontinuity occurs. The experimental results of a fast solid-state switch, which discharges a three stage Blumlein stack, will be compared with theoretical ones.

The purpose of this paper is to present and discuss a general HV topology of the solid-state Marx modulator, for unipolar or bipolar generation connected with a step-up transformer to increase the output voltage applied to a resistive load. Due to the use of an output transformer, discussion about the reset of the transformer is made to guarantee zero average voltage applied to the primary. It is also discussed the transformer magnetizing energy recovering back to the energy storage capacitors. Simulation results for a circuit that generates 100 kV pulses using 1000 V semiconductors are presented and discussed regarding the voltage and current stress on the semiconductors and result obtained.

This paper addresses the voltage droop compensation associated with long pulses generated by solid-stated based high-voltage Marx topologies. In particular a novel design scheme for voltage droop compensation in solid-state based bipolar Marx generators, using low-cost circuitry design and control, is described. The compensation consists of adding one auxiliary PWM stage to the existing Marx stages, without changing the modularity and topology of the circuit, which controls the output voltage and a LC filter that smoothes the voltage droop in both the positive and negative output pulses. Simulation results are presented for 5 stages Marx circuit using 1 kV per stage, with 1 kHz repetition rate and 10% duty cycle.

This paper describes and tries to demystify the use of different low-voltage operation devices, such as dynamic threshold MOS transistors (DTMOS) with feedback techniques such as regulated-feedforward (RFF) and self-biasing (SB). Traditionally, DTMOS devices are only used when nominal supply voltages below 0.7 V are envisaged. Moreover, RFF and SB techniques are normally avoided since engineers designing high-performance amplifiers are afraid of additional stability concerns. This work demonstrates, through exhaustive simulation results over process, temperature and supply (PVT) corners using a standard 130 nm 1.2 V CMOS technology that, employing DTMOS in some specific devices can improve some performance parameters such as the open-loop low-frequency gain and, simultaneously, reduce significantly the variability over PVT corners. Moreover, it is also demonstrated that, there is no risk of operating at supply voltages higher than 1.2 V. Combining DTMOS with RFF and SB allows achieving reasonable gain-bandwidth products (GBW) even operating at low-voltage (down to 0.7 V), together averaged power savings of the order of 8% and highly simplifies the design of the circuit (since no biasing circuitry is required).

In this paper a high-level model is proposed for quadrature oscillators that are based on two RC oscillators with capacitive coupling. The capacitive coupling technique has the advantages of being noiseless and not increasing the power requirements, which are important features for low power receivers. This coupling technique was already study for LC oscillators but not for RC oscillators. Simulations showed that the behavior of capacitive coupling in RC oscillators is different than for LC. In opposition to what was expected, in RC oscillators, the oscillation frequency increases almost linearly and proportional to the coupling capacitances. This behavior can be explained by the presented model. Simulations are presented to validate the model in respect to the oscillation frequency and to the output phases of the oscillator.

Recently there is a renewed interest in the development of transistor circuits which emulate the Negative Differential Resistance (NDR) exhibited by different emerging devices like Resonant Tunneling Diodes (RTDs). These MOS-NDR circuits easily allow the prototyping of design concepts and techniques developed for such NDR devices. The importation of those concepts into transistor technologies can result in circuit realizations which are advantageous for some functionalities and application fields. This paper describes a Muller C-element which illustrates this statement which is inspired in an RTD-based topology. The required RTD is implemented by means of the MOS-NDR device. A 4-input Muller C-element has been fabricated and experimentally validated. The proposed circuit compares favorably with respect to a well-known conventional gate realization.

The conventional hard switching half-bridge topology used as a rolling stock auxiliary power supply unit has limited switching frequency due to high power losses in semiconductors. The study analyses LLC and LCC topologies in attempt to improve the performance of the converter and presents a simulation of the operation and an estimation of power losses.

This paper introduces a variability-aware methodology for the design of LC-VCOs in Nano-CMOS technologies. The complexity of the design as well as the necessity for having an environment offering the possibility for exploring design trade-offs has led to the development of design methodologies based multi-objective optimization procedures yielding the generation of Pareto-optimal surfaces. The efficiency of the process is granted by using analytical models for both passive and active devices. Although physics-based analytical expressions have been proposed for the evaluation of the lumped elements, the variability of the process parameters is usually ignored due to the difficulty to formalize it into an optimization performance index. The usually adopted methodology of considering only optimum solutions for the Pareto surface, may lead to pruning quasi-optimal solutions that may prove to be better, should their sensitivity to process parameter variation be accounted for. In this work we propose starting by generating an extended Pareto surface where both optimum and quasi-optimum solutions are considered. Finally information on the sensitivity to process parameter variations, is used for electing the best design solution.

An optimization-based methodology for designing robust high frequency Gm-C filters is presented. In the first step of the design, the topology for high-frequency Gm-C filter is obtained directly from an active RC prototype, using the a transformation method proposed in [1]. where a transformation method for designing Gm-C filters from known RC filters while grounding floating capacitors is proposed. In the above mentioned paper the filter elements values are obtained by a user-defined methodology which can produce high sensitivity to component variations. In the work developed, the analytical characterization of a 2

nd

order Gm-C Sallen-Key LP filter is considered as well as the corresponding symbolic expressions for their sensitivity to component value variations. By using the minimization of the sensitivity as a goal function in the optimization process, parasitic capacitances are canceled and robust designs are obtained. Optimization and simulations results that validate the theoretical results are presented.

This paper addresses an optimization-based approach for the design of RF integrated inductors. The methodology presented deals with the complexity of the design problem by formulating it as a multi-objective optimization. The multi-modal nature of the underlying functions combined with the need to be able to explore design trade-offs leads to the use of niching methods. This allows exploring not only the best trade-off solutions lying on the Pareto-optimum surface but also the quasi-optimum solutions that would be otherwise discarded. In this paper we take advantage of the niching properties of lbest PSO algorithm using ring topology to devise a simple optimizer able to find the local-optima. For the efficiency of the process analytical models are used for the passive/active devices. In spite the use of physics-based analytical expressions for the evaluation of the lumped elements, the variability of the process parameters is ignored in the optimization stage due to the significant computational burden it involves. Thus in the final stage both the Pareto-optimum solutions and the quasi-optimum solutions are evaluated with respect to the sensitivity to process parameter variations.

This work analyzes the use of hybrid continuous-time/discrete-time ∑ Δ modulators for the implementation of analog-to-digital converters intended for wideband mobile applications. Two alternative multirate cascade ∑ Δ architectures are discussed and analyzed, taking into account the impact of main circuit-level error mechanisms, namely: mismatch, finite dc gain and gain-bandwidth product. Both multirate ∑ Δ modulators are compared with conventional single-rate continuous-time cascade ∑ Δ modulators in terms of their sensitivity to non-ideal effects, considering different target specifications. Theoretical predictions match simulation results, showing that the lowest performance degradation is obtained by a new kind of multi-rate hybrid ∑ Δ modulators, in which the continuous-time circuits operate at a higher rate than the discrete-time parts of the modulator.

It is well known that in

ad hoc

networks the interference at a given receiver is expressed by the sum of several random variables representing the distinct sources of interference, and no exact closed-form distribution is known for such a sum. This work characterizes the interference distribution of a Cognitive Radio

ad hoc

Network (CRAHN) based on Code Division Multiple Access (CDMA). The authors start to explore an analytical model for the multiple-access interference of a Primary User (PU), being thereafter extended to embrace the co-existence of a Secondary User (SU) network. Several scenarios are simulated and the results are compared to the proposed analytical model.

Characteristics of tunable wavelength filters based on a-SiC:H multilayered stacked cells are studied both theoretically and experimentally. Results show that the light-activated photonic device combines the demultiplexing operation with the simultaneous photodetection and self amplification of an optical signal. The sensor is a bias wavelength current-controlled device that make use of changes in the wavelength of the background to control the power delivered to the load, acting a photonic active filter. Its gain depends on the background wavelength that controls the electrical field profile across the device.

Characteristics of tunable wavelength pi’n/pin filters based on a-SiC:H multilayered stacked cells are studied both experimental and theoretically. Results show that the device combines the demultiplexing operation with the simultaneous photodetection and self amplification of the signal. An algorithm to decode the multiplex signal is established. A capacitive active band-pass filter model is presented and supported by an electrical simulation of the state variable filter circuit. Experimental and simulated results show that the device acts as a state variable filter. It combines the properties of active high-pass and low-pass filter sections into a capacitive active band-pass filter using a changing photo capacitance to control the power delivered to the load.

Planar diodes fabricated with nano-structured silver oxide thin film show resistive switching effects. These structures can be programmed by voltage into two distinct resistive states and they are promising candidates for resistive random access memory (RRAM) devices. Cycle endurance on/off ration and charge retention are high. This wok provides insight into the mechanisms leading to the memory effects and to charge transport in these memory structures. Temperature dependent measurements show that charge carrier transport has activation energy of 70 meV suggesting that tunneling is the operating mechanism in both resistive states. The change in resistance is caused by a large increase in charge carrier density. This finding is explained by assuming that carrier transport takes place through a percolative network of micro-conducting paths.

Electroforming of an Al/Al

2

O

3

/polymer/Al resistive switching diode is reported. Electroforming is a dielectric soft-breakdown mechanism leading to hysteretic current–voltage characteristics and non–volatile memory behavior. Electron trapping occurs at early stages of electroforming. Trapping is physically located at the oxide/polymer interface. The detrapping kinetics is faster under reverse bias and for thicker oxides layers. Thermally detrapping experiments give a trap depth of 0.65 eV and a density of 5x10

17

/cm

2

. It is proposed that the trapped electrons induce a dipole layer across the oxide. The associated electric field triggers breakdown and ultimately dictate the overall memory characteristics.

Resistive Random Access Memories based on metal-oxide polymer diodes are characterized. The dynamic behavior is studied by recording current-voltage characteristics with varying voltage ramp speed. It is demonstrated that these organic memory devices have an internal capacitive double-layer structure, which inhibits the switching at high ramp rates (1000 V/s). This behavior is modeled and explained in terms of an equivalent circuit.

The work herein presented aims at improving the detection of the fovea and optic disk anatomical structures in color fundus photographs from patients with visible lesions. The detection algorithm consists of five steps: selection of an area in the image where the optic disk is located using Sobel operator, extraction of optic disk boundaries applying the Hough transform to detect center and diameter of optic disk, detection of the ROI (region of interest) where the fovea is located based on the optic disk center and its diameter, detection of the fovea within the ROI. The proposed improvement adds an additional mask based on an estimation of the vessels present so that lesions that lie away from the vessels do not influence the Sobel operator result. The developed algorithm has been tested in public datasets such as STARE and also on proprietary dataset with 1464 images (ground truth generated by experts).

This paper presents the results of the development of an equipment for measuring pupil size variation. A Pupillometer has been developed in order to detect the pupil’s variation in time in a non-invasive way. The output signal of the equipment expresses the pupil’s features variation in time (area, perimeter, vertical diameter, horizontal diameter). The Autonomic Nervous System (ANS) is divided into the Sympathetic and Parasympathetic components and theoretically they are always competing with each other as a two oscillator model. The aim of this project is to identify these components and their frequency bands. With the present results we show that the signal from the pupil’s variation has two major frequency bands, the first being the 0-1 Hz band (LF - Low Frequency) and the second being 1-2Hz band (HF - High Frequency). From the literature we know that the Sympathetic Nervous System (SNS) behaves in a lower frequency than the Parasympathetic (PSNS) one. This means that SNS oscillates in the LF band and PSNS oscillates in the HF band.

The arteriovenous ratio is an objective way to assess the arteriolar narrowing related to several diseases such as hypertension. It is computed as the ratio between the artery and vein mean widths. However, its calculus is not straightforward since the experts do not use all the retinal vessels. This paper presents an automatic, precise and reproducible methodology for the AVR computation. We analyze the way the experts select the vessels in order to build a system which emulates them. The system was evaluated by two ophthalmologists in a data set of 86 images. The correlation results among the system and the experts are an indication of the reproducibility of the results.

Modern epidemiology integrates knowledge from heterogeneous collections of data consisting of numerical, descriptive and imaging. Large-scale epidemiological studies use sophisticated statistical analysis, mathematical models using differential equations and versatile analytic tools that handle numerical data. In contrast, knowledge extraction from images and descriptive information in the form of text and diagrams remain a challenge for most fields, in particular, for diseases of the eye. In this article we provide a roadmap towards extraction of knowledge from text and images with focus on forthcoming applications to epidemiological investigation of retinal diseases, especially from existing massive heterogeneous collections of data distributed around the globe.

Retinopathic changes are common to many ocular diseases and if detected early, much of the change caused by various injuries can be prevented or in some cases reversed. However, detection and quantification of these changes currently requires tedious invasive and manual examination of retinal wholemount images. To remedy the quantitative limitations, we have designed a software system to automatically determine the vascular cell counts and coverage of retina in wholemount trypsin digest images. To verify the utility of the system, retinal trypsin digests from wild type (bcl-2 +/+) and bcl-2 deficient (bcl-2 -/-) mice were compared. Bcl-2 is a critical regulator of apoptosis with a significant role in retinal angiogenesis and vascularization. The bcl-2 -/- mice exhibited significant reduction in retinal vascular density and complexity. Thus, our results show the potential for automated evaluation of retinal trypsin digests delineating the differences between the wild type and bcl-2-deficient retinal vasculature.

Age-related macular degeneration (ARMD) is a common disease in the elderly and is currently the main cause of blindness in developed countries. Drusen are one of its risks factors, which are visible in a retinal examination. Its quantitative analysis is important in the follow up of the ARMD. The authors have previously developed two tools for semi-automatic and automatic drusen quantification. In this paper five tests performed on these tools are presented and discussed in order to evaluate their accuracy, variability and repeatability. The statistical results show that the automatic tool is as accurate as the experts in Drusen quantification, with the advantage of being a reproducible method. The semi-automatic quantification method, which was used by the experts for Drusen quantification, proved statistically to produce a high intra-observer agreement.