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In 5G networks of high complexity, autonomous management of the end-to-end (E2E) services is desired. Closed-loop (CL) control is a future-proof means to enable this autonomy. In the multi-provider, multi-vendor, and multi-tenant environment, the autonomous service management demands collaborations of all providers and vendors. In this paper, the collaboration problem is formulated as cross-CL collaboration involving multiple CLs participating in the E2E service management. We present a cross-CL collaboration framework with key elements, e.g., APIs, governance models, intent and policies management. Then a representative use case of service assurance with edge clouds is described to exemplify how the framework is applied to implement cross-CL collaborations and answer key questions: WHEN collaboration is needed; and HOW collaboration is enabled.

This paper uses non-traditional data, from a MicroFinance Institution (MFI), in a Credit Scoring loan classification problem and addresses a common problem in emerging markets of the lack of a verifiable customers’ credit history. We perform a set of experiments to define a baseline model and prove the relevance of node embedding features, in credit scoring models, using a real world dataset.

Scenarios for distributed cloud with multiple edge clouds and centralized data centers are being investigated as the computing and networking underpinnings of next-generation network services such as augmented reality, self-driving vehicles, drones, and more. In such distributed environments, service providers will typically face tens, hundreds, or thousands of compute location candidates (edge, regional, and central) where network service components can be placed. To take optimized placement decisions of network services and execute the management workflows, orchestration systems require up-to-date and accurate resource availability representation, in the form of a network inventory that can be immense in distributed cloud scenarios. As a result, the service management and placement problems may become not tractable. In this work, we propose the Abstracted Network Inventory (ANI) component to generate service-optimized network views over the same network inventory. ANI implements a novel abstraction method where network service requirements are used as an input to generate an optimized abstract network inventory representation, called Logical Network Inventory (LNI). We also provide a formal definition of the network model and problem statement along with the development of three algorithms to efficiently build an LNI. Results show the potential benefits of using an LNI to streamline service management and placement: (i) the relationship between compute nodes and links (i.e., density) in an LNI is reduced between 1.8-2.7x compared to a full network inventory topology; and (ii) up to 50% of time can be saved for service placement after abstracting around 20% of the compute nodes.

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.

A new form of digital images, light field images contain considerably more information than conventional 2D digital images. However, light field images require significantly more storage, transmission and processing resources than conventional 2D images, calling for efficient compression schemes. In this work, light field images are interpreted as multi-view sequences that are compressed using MV-HEVC. A novel scan order based on blocks of sub-aperture images and a two dimensional prediction scheme are proposed for improved random access performance, as well as to provide a scalable representation of the light field data. Our proposed compression scheme achieves PSNR gains as high as 7.8 dB over reference coding schemes based on JPEG.

This work discusses some characteristics of iterative harmonic analysis using interfaced electromagnetic transients and harmonic power flow programs. This approach allows combining the advantages of both program types. However, some problems arise, such as harmonic estimation inaccuracy and convergence issues, the first being originated by mis-synchronization, spectral leakage and picket-fences. These problems are highlighted in the present paper, and an application example is presented, in which two types of Flexible AC Transmission controllers, Thyristor Controlled Reactors and Static VAR Compensator, are included into the IEEE 14 bus system. The developed FFT-based algorithm, complying with IEC 61000, is evaluated, and it proves being adequate to avoid spectral leakage and picket-fences. Some alternatives to overcome convergence problems are presented.

This work investigates the applicability of available Internet of Things (IoT) technologies to 5G use cases in Brazil. We are particularly interested in two priority verticals: smart cities and agribusiness. It has been selected one use case for each of these two verticals, considering the particularities of the Brazilian scenario. For the smart cities vertical, the application of surveillance cameras in public lighting was chosen due to its high impact in terms of cost savings and improvement in public safety. In the agribusiness vertical, the application of cattle monitoring was chosen since Brazil is one of the largest beef exporter in the world, representing more than 6% of its GDP (Gross Domestic Product). For the chosen use cases an analysis of the limitations of the available IoT technologies such as LPWA (Low Power Wide Area) has been presented to guide future deployments.

This paper is concerned with the automatic evaluation of selected tasks performed by people with intellectual Disabilities. According to the International Classification of Functioning, Disability and Health (ICF) system, subjects must be divided into two groups: group with no difficulty (N) and group with difficulty (D) being this classification based on performances obtained in a conventional table (CT) soccer. Three tasks, with different levels of difficulty, were proposed for performance evaluation. Experimental results were obtained on the basis of the task execution in both a CT and a robotics table (RT) soccer. All participants were able to perform tasks with the joystick on the RT soccer and the automatic evaluation system identified differences in reaction times with and without red color flag in the participants, on RT soccer. One of the tasks was completely performed by all the participants by using the RT soccer.

Orchestration is crucial to address the growing complexity of IT, cloud and telecom services. Orchestrators enable service deployment, but continuous operation is given second-class status. Most orchestrators use static workflows, which may fail, misbehave or be insufficient, leaving services in an undesirable state. We propose the Reorchestration architecture for orchestration without workflows, which marries functional-inspired state management with an infrastructure-as-code approach. It enables changes to be applied to existing services, while providing a high-level framework for expressing how these changes should happen. We also share our first prototype, in the hope that it can foster discussions on new software architectures for orchestrators and closed-loop automation.

Orchestration is crucial to address the growing complexity of IT, cloud and telecom services. Orchestrators enable service deployment, but continuous operation is given second-class status. Most orchestrators use static workflows, which may fail, misbehave or be insufficient, leaving services in an undesirable state. We present Reorchestration, a prototype showing orchestration without workflows, combining functional-inspired state management with an infrastructure-as-code approach. The prototype demonstrates the viability of our ideas in some scenarios, and we expect it will contribute to discussions on software architectures for closed-loop automation.

Information and Communication Technologies appear one of the most critical areas to the success of tourism in the future and the way it will promote the tourist destinations. This paper aims to analyze the relevance given by the various Portuguese institutions of higher education to Information and Communication Technologies in their degrees. The analysis carried out was done in degree courses operating in this academic year, 2017/2018, in Portuguese universities and polytechnics, public and private. A comparison was also made with the reality of 2012/2013, since as undergraduate degrees have been evaluated by the Agency for Assessment and Accreditation of Higher Education (A3ES), often making changes, resulting from the evaluation.

Contrary to outdoor positioning and navigation systems, there isn’t a counterpart global solution for indoor environments. Usually, the deployment of an indoor positioning system must be adapted case by case, according to the infrastructure and the objective of the localization. A particularly delicate case is related with persons who are blind or visually impaired. A robust and easy to use indoor navigation solution would be extremely useful, but this would also be particularly difficult to develop, given the special requirements of the system that would have to be more accurate and user friendly than a general solution. This paper presents a contribute to this subject, by proposing a hybrid indoor positioning system adaptable to the surrounding indoor structure, and dealing with different types of signals to increase accuracy. This would permit lower the deployment costs, since it could be done gradually, beginning with the likely existing Wi-Fi infrastructure to get a fairy accuracy up to a high accuracy using visual tags and NFC tags when necessary and possible. info:eu-repo/semantics/publishedVersion

This work presents a hybrid simulation tool that combines fast analysis of quasi-static time series or transient stability programs and electromagnetic transients ones to evaluate the dynamic behaviour of electric power systems with distributed generation sources. The interaction between the two programs is performed by means of controllable current and voltage sources, which are used to interface external and detailed systems. The double second order generalized integrator (DSOGI) is used to extract the positive-sequence phasor from the detailed system to the external one. A local network server controls the data communication between the two simulation environments by means of the TCP/IP protocol. In the present paper, the proposed tool is used to simulate the integration of a wind power plant, based on a doubly-fed induction generator (DFIG), into a 29-bus electrical network. Results and computational timings are then compared with the ones obtained with an electromagnetic transients program, which demonstrate the accuracy and speed of the proposed strategy.

This work presents a hybrid simulation tool that combines quasi-static and electromagnetic transients models for faster and accurate results. The electrical network is splited in two different areas and they are solved by OpenDSS and Sim-PowerSys/Matlab programs, respectively. During the simulation, an Intranet Server controls the data transaction between the two simulation environments by using a TCP/IP protocol. The proposed methodology is demonstrated by integrating three PV power plant into different points of a distribution feeder based on 13-bus IEEE benchmark. Simulation results are used to validate the accuracy of the proposed method and evaluate the time reduction of the hybrid simulator when it is compared to the time when the same electrical system is fully simulated in the transient electromagnetic program.

This paper presents a hybrid simulation tool that combine fast analysis of quasi-static and electromagnetic transients electric models. The interaction between the two programs are carried out by controllable current and voltage sources which are used to represent the external and the detailed systems, respectively. A server controls the data communication between the two simulation environments. The proposed tool will be used here to simulate the integration of three distinct photovoltaic plants into a 13-bus electric distribution network. The results obtained with the proposed simulation strategy are compared with the ones carried out with detailed models, fully implemented and simulated in an electromagnetic transients program. Results demonstrate the accuracy of the proposed simulation strategy as well as the the lower simulation timings.

Heart sounds are difficult to interpret because a) they are composed by several different sounds, all contained in very tight time windows; b) they vary from physiognomy even if the show similar characteristics; c) human ears are not naturally trained to recognize heart sounds. Computer assisted decision systems may help but they require robust signal processing algorithms. In this paper, we use a real life dataset in order to compare the performance of a hidden Markov model and several hidden semi Markov models that used the Poisson, Gaussian, Gamma distributions, as well as a non-parametric probability mass function to model the sojourn time. Using a subject dependent approach, a model that uses the Poisson distribution as an approximation for the sojourn time is shown to outperform all other models. This model was able to recreate the “true” state sequence with a positive predictability per state of 96%. Finally, we used a conditional distribution in order to compute the confidence of our classifications. By using the proposed confidence metric, we were able to identify wrong classifications and boost our system (in average) from an ≈ 83% up to ≈90% of positive predictability per sample.

The world population growth poses several challenges, including the increase need of food supply. To overcome this issue, large-scale agricultural production plays an essential role. Besides, agriculture has become a strategic area in the economy of several countries. In this scenario, a common practice is the production management with the use of plant protection products in order to protect the crop from the harmful action of agricultural pests. During this activity, the applied products commonly drifts out of the target region, contaminating other environments or causing excessive deposition (due to product overlapping). The possibility of an spraying system that adapts to the weather conditions can reduce the off drift, increasing the accuracy of the deposition and, consequently, providing a more suitable environment for the growing crop. For such, the spraying system should perform the prediction of the deposition at run time, which is hard to achieve due to the high computational cost of current approaches. This article proposes and experimentally evaluates a new approach based on Machine Learning that predicts the amount of pulverized products with lower computational cost and high accuracy. Experimental results show that the proposed approach represents well the actual deposition. For the prediction of product deposition, it was able to obtain mean absolute error of 0.096 µL/cm² when employing Artificial Neural Networks and 0.089 µL/cm² when employing Regression Trees. These results stimulate new studies to compare the proposed approach with existing solutions.

Business continuity planning (BCP) requires a holistic approach that considers several technological and organizational aspects. Currently, there is a lack of scientifically validated solutions to support continuity planning that are based on standards and best practice frameworks, capable of addressing the complexity and specific needs of large public organizations. In this work, we propose to use Enterprise Architecture (EA) artifacts, based on the ArchiMate modelling language, to assist continuity planning. We demonstrated the proposal in a large public organization, using an ex post scenario. We evaluated the proposal using the demonstration, evaluation questionnaires, as well as group sessions, to measure the goal efficacy, the environment consistency and the structural quality. The main scientific contributions of this paper are the validation of EA's usefulness for assisting BCP, as well as a set of EA viewpoints for assisting BCP related initiatives.

International audience; Recent wireless medium access control techniques, such as the Timeslotted Synchronized Channel Hopping (TSCH) and Deterministic & Synchronous Multi-channel Extension (DSME) modes in the IEEE802.15.4-2015 standard, use frequency diversity to cope with external interference and multipath fading. The result is wire-like reliability in a network built from unreliable wireless links. Yet, the impact of using multiple frequencies on the medium access control layer is still not perfectly understood, and virtually all channel hopping solutions use "blind" channel hopping, i.e., hopping over all frequencies equivalently. The goal of this work is to improve our understanding of the behavior of the wireless medium when using multiple frequencies, which will enable the design of more efficient protocols in the future. We collect a large dense connectivity dataset over the USC Tutornet Internet of Things Testbed, with dozens of low-power wireless nodes deployed in an office building. This publicly-available dataset offers complete traces of link quality across frequency, time and space. We analyze the data and extract meaningful and practical insights on the wireless medium when using multiple frequencies.

Heart auscultation is one of the basic exams performed during a patient physical examination, but it is also one that has a high skill ceiling. Decision support systems can provide physicians with a tool that can help to reduce the demanding skill requirements of this exam. Nevertheless, this second opinion needs to be delivered in a timely interval in order to be truly useful for a physician. To do this we need not only optimized algorithms, but also a well designed system. In this paper, we have studied how two different data exchange protocols, that define how data should be transferred from an acquisition to a process module, can impact the celerity of delivering second opinion to a physician. With data collected from real exams, acquired in a field hospital initiative in Brazil, we recreated two use cases that allowed us to measure performance in the form of time and resources spent, as well as power consumption. Results have shown that different data exchange protocols can have a significant impact on a decision support system response time.

The onset of muscle contraction has been an important element in the understanding of human motor control system as well as in the development of medical devices. This task is problematic in the study of spasticity using surface Electromyography (sEMG). In fact, spasticity is characterized by involuntary muscle contractions that can be seen as both, a non-stationary background if they are weak or a severe non-stationary EMG signal if they are strong. In other hand, these sEMG signals present a very low signal to noise ratio, beyond the added noise that contaminates this signal. The double threshold protocol presumes non-stationary muscle activation under a stationary environment which does not accommodate non-stationary background conditions. Apart from that the Shewhart protocol which makes part of the Double Threshold Protocol (DTP) presumes an initial segment containing only noise which can't be guaranteed under spastic conditions. These are the main causes of failures of state of the art approaches when applied to sEMG spastic muscles. This paper proposes dealing with these constraints by adapting the first threshold to the noise conditions via Signal to Noise Ratio (SNR) estimation, which depends on the severity of the disease. The main idea is tuning the first threshold to low SNR conditions since it is where the DTP most degrades. This tuning is done in sEMG artificially contaminated at different SNRs where the multiple of standard deviation is heuristically determined based on experimentation. Noise is estimated in low energy segments instead of in an initial segment that can be contaminated by involuntary muscle contractions. The proposed algorithm was tested in sEMG signals from the Biceps Braquialis of 13 healthy individuals and in 9315 signals recorded in 23 subjects with spasticity. Improvements of more than 23% were obtained when compared with the classical DTP in moderate to severe spasticity.

Smart homes provide automatic services through a combination of wireless sensor networks (WSNs) and wireless devices that allow monitoring operations in the home. These smart homes have been increasingly studied not only by the academic community but also by industry on account of the great potential they have, especially in applications that can help people with some type of physical disability such as visual or hearing impairment. This kind of application can give them independence and make their lives safer and more comfortable. Thus, in this paper we propose a Health Smart Home system for disabled people that can warn them about possible accidents that can occur in a particular location of their house. In other words, the system anticipates possible accidents that may occur somewhere in the house. We implemented a preliminary prototype that monitors rooms and issues a warning whenever it detects a potential hazard. Our proposal relies on low-cost cutting-edge technology and includes the following: Raspberry Pi boards, motion sensors, cameras and a central server. The server will handle the data that has been captured by sensors and send warnings through smartphones to people with physical impairments.

Group work is an essential activity during both graduate and undergraduate formation. Students develop a set of skills, and employ criticism which helps them to better handle future interpersonal situations. There is a vast theoretical literature and numerous case studies about group work, but we haven't yet seen much development concerning the assessment of individual group participants. It is not always easy to have the perception of each student contribution to the whole work. Nevertheless, more than frequently, the assessment of the group is transposed to each group participant, which in turn results in each student having the same final mark. We propose and describe a tool to manage and assess individual group work taking into account the amount of work, interaction, quality, and the temporal evolution of each group participant. The module features the possibility to create two types of activities: collaborative or cooperative group work. We describe the conceptual design of our tool and present the two operating modes of the module, which is based on events, alerts and conditions. We then describe the methodology for the assessment in the two operating modes and how these two major approaches can be deployed through our module into pedagogical situations.

We present the design and implementation of Multi-Channel Collection (MCC) protocol , a high-rate multi-channel time-scheduled protocol for fair, real-time data collection in Wireless Sensor Networks (WSN). MCC incorporates sophisticated mechanisms for balanced routing tree formation, multiple frequency channel allocation and globally synchronized TDMA scheduling. Through systematic experiments with real WSN hardware (Tmote Sky), we identify the maximum possible throughput for many-to-one (convergecast) data collection as a function of key communication parameters such as packet size, use of acknowledgements, and network topology. Then, we demonstrate that the maximum achievable network throughput can in fact be attained in practice using a carefully designed mix of routing, frequency allocation and time scheduling. Compared to state of the art collection protocols for WSN, we show that MCC offers 33-155% improvement in throughput. We also show how to exploit the time-scheduled nature of this approach for reducing the number of required frequency channels. MCC presents an algorithmic approach for time-frequency scheduling and routing that could be adapted and used in conjunction with relevant emerging standards such as WirelessHART, ISA 100.11a and IEEE 802.15.4e TSCH.

Parking is a major problem of car transportation, with important implications in traffic congestion and urban landscape. Reducing the space needed to park cars has led to the development of fully automated and mechanical parking systems. These systems are, however, limitedly deployed because of their construction and maintenance costs. Leveraging on semi and fully-autonomous vehicular technology, as well as on the electric propulsion paradigm and in vehicular ad hoc networking, we propose a new parking concept where the mobility of parked vehicles is managed by a parking lot controller to create space for cars entering or exiting the parking lot, in a collaborative manner. We show that the space needed to park such vehicles can be reduced to half the space needed with conventional parking lot designs. We also show that the total travelled distance of vehicles in this new parking lot paradigm can be 30% less than in conventional parking lots. Our proposal can have important consequences in parking costs and in urban landscape.

This paper makes suggestions, based on the current grid codes, for different requirements that may be adopted by the transmission system regarding the connection of large wind and photovoltaic power plants to medium and high voltage networks. The main purpose is to present a proposal which harmonizes the requirements for renewable energy sources with electronic interface. Taking into consideration the characteristics of the point of connection, based on the stiffness and other relevant issues as background distortion and hosting capacity, in order to facilitate the integration of RES without compromising the integrity of the transmission and distribution grids.

The confined space of a car and the configuration of its controls and displays towards the driver, offer significant advantages for Augmented Reality (AR) systems in terms of the immersion level provided to the user. In addition, the inherent mobility and virtually unlimited power autonomy transform cars into perfect mobile computing platforms. However, the limited network connectivity that is currently available in automobiles leads to the design of Advanced Driver Assistance Systems (ADAS) that create AR objects based only on the information generated by on-board sensors, stored maps and databases, and eventually high-latency online content for Internet-enabled vehicles. By combining the new paradigm of Vehicular Ad Hoc Networking (VANET) with AR human machine interfaces, we show that it is possible to design novel cooperative ADAS, that base the creation of AR content on the information collected from neighbouring vehicles or roadside infrastructures. We provide a prototype implementation of a visual AR system that can significantly improve the driving experience.

Stress is a major factor for the degradation of cardiac health in first responder professionals such as firefighters. Monitoring stress during real events might be the key for controlling this problem. In this paper we inspect how standard heart rate variability (HRV) measures are associated with the self-perception of stress of firefighters in action, supported by an advanced technological solution to acquire this data. Results obtained from more than 94 hours of annotated ECG recordings of firefighters in action are promising, showing positive association with various standard HRV measures. Given the richness of the gathered data, we have also measured the association of the HRV measures with the stage of a firefighting event (pre, during, post), obtaining some interesting results that hint that the psychological impact of the post-event may be one of the most concerning situations for a firefighter, motivating further studies on this in the future.

Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.

This paper presents a video-based early fire detection system. Focus is given to the challenges related to the actual deployment of the vision system. Most importantly, background subtraction is performed in a windowed way for improved accuracy, an attentive mechanism is employed to focus a computationally expensive frequency analysis of potential fire regions, interaction with a people detection and tracking system is included so as to enable model-based false alarms rejection, a new colour-based model of fire's appearance as well as a new Wavelet-based model of fire's frequency signature are proposed, and the camera-world mapping is approximated according to a GPS-based learning calibration process. An average success rate of 92.7% at a processing rate of 10 Hz shows the applicability of the model to real-life applications.

This paper is concerned with the automatic control of drug administration in patients suffering from Brugada Syndrome (BS). Drugs such as flecainide, procainamide, ajmaline and pilsicainide should be administrated under carefully controlled electrocardiogram (ECG) monitoring given that the treatment must be stopped if some ECG disturbing conditions appear. These conditions are, among others the development of premature ventricular contraction (PVC), atrial fibrillation (AF) and the widening of the QRS wave. The proposed system can detect these abnormalities by using a pattern recognition approach based on Hidden Markov Models (HMM) with features extracted from three scales of the Wavelet Transform (WT). Performances higher than 98% were reached regarding the classification of normal and abnormal pulses. The system was trained and tested mainly in data from the standard MIT-BIH arrhythmia database.

This article presents a radio resource optimization model for Radio-over-Fiber (RoF) access networks. The proposed model arranges cells in a multi-tier fashion with increasing coverage radius. Considering the available structure of antennas the optimizer performs dynamic cell merging and cell splitting according to mobile users' demands for efficiently utilizing radio resources. It is proposed an integer programming model with a bi-criteria objective function that tries to minimize the use of network resources as well as to maximize network revenue. The computational demand for obtaining integer solutions increases proportionally to the number of mobile users and also to the number of tiers of antennas. In addition to the integer solutions, an algorithm based on linear relaxation technique is presented, which implies on significant computational time reduction when solving large instances of the problem. Moreover, results are very close to those given by the integer programming formulation.

Peripheral diabetic neuropathy is the major cause of chronic disability in diabetic patients. The early diagnosis and accurate assessment of peripheral neuropathy are important to define the higher risk patients, decrease patient morbidity and assess the performance of new therapies. However, the peripheral neuropathy diagnosis often fails or occurs only when patients became symptomatic due to the non-availability of a simple non-invasive method for early diagnosis. Corneal confocal microscopy is a non-invasive imaging modality that can document corneal nerves morphology. In this project, we will develop a technique for early diagnosis of diabetic neuropathy based on automatic analysis of corneal nerves images. This project comprises the development of automatic algorithms for nerve segmentation and morphometric parameters extraction, the evaluation of the best parameters for early diagnosis and follow-up of diabetic neuropathy and the development of a corneal confocal imaging module to be used as an add-on to a standard slit-lamp.

The constant evolution of access technologies are turning Internet access more ubiquitous, faster, better and cheaper. In connection with the proliferation of Internet access, Cloud Computing is changing the way users look at data, moving from local applications and installations to remote services, accessible from any device. This new paradigm presents numerous opportunities that even traditional businesses like telecoms cannot ignore, in particular, enabling new and more cost effective solutions to old problems. The work presented in this paper provides a detailed description of how a telecom application can be migrated to a NoSQL database. Particularly, by pointing out the necessary change of how we reason about data as well as the data structures that support it, in order to take full advantage of Cloud Computing. In addition, we also present a preliminary evaluation of different data persistency paradigms based on a fully tunable simulation platform that mimics the operation of a telecom business.

In this paper, we introduce a radio resource optimization model for Radio-over-Fiber (RoF) access networks. We propose a mixed integer programming model to minimize the network cost while providing service to all mobile users. The proposed architecture arranges cells in a multi-layer fashion, with cells in each layer providing different coverage radius. The optimization algorithm performs dynamic cell splitting to improve network capacity in congested areas and cell merging in areas with low demand to save resources. The computational demand of the proposed model increases proportionally to the number of layers of RAUs in the infrastructure. Results indicate that two layers of RAUs give the best trade-off between network cost reduction and computational demand.

The use of wireless technology based on Vehicular Ad hoc Networks (VANETs) for information exchange can influence the drivers' behavior towards improving driving performance and reducing road accidents. This information can even be more relevant if it is presented as a video stream. In this paper we propose a system that relies on VANET and video-streaming technology: the See-Through System (STS). The system enhances driver's visibility and supports the driver's overtaking decision in challenging situations, such as overtaking a vision-obstructing vehicle. The use of the See-Through System provides the driver with an additional tool for determining if traffic conditions permit starting an overtaking maneuver thus reducing the risk of overtaking. We tested the See-Through System on an experimental vehicle on the road as well as in the context of a driving simulator for real world environment. Results are promising, since the use of the 802.11p standard wireless communication protocol allows a vehicle-to-vehicle connection without significant delay and the totality of the participants regarded the information provided by the STS as useful.

The purpose of this work is to present an off-line technique for characterization of time-varying power line communications channels in the presence of additive impulsive noise when they are used for broadband data transmission. We propose and analyze a channel estimation technique based on Support Vector Machine (SVM). Simulation results reveal that the proposed method is more robust to impulsive noise and has better convergence rate for time-varying channels when compared to classic techniques like least squares (LS).

In this paper, we introduce a novel queue management technique for the buffer space at the base station of infrastructure 802.11 networks that considers mobile user's receiving characteristics. The maximum amount of the base station buffer that can be used by a given flow is updated proportionally to RTT, measured at the mobile nodes and sent to the base station my the mean of link layer acknowledgements. In this way, the proposed scheme remains transparent to high-level protocols. The proposed approach makes possible the implementation of algorithms able to provide RTT-fairness in wired-cum-wireless networks. Results show the advantage of the proposed queue management scheme when compared to that of traditional drop-tail queue management.

This paper reports a comparative study of feature extraction methods regarding cardiac arrhythmia classification, using state of the art Hidden Markov Models. The types of beat being selected are normal (N), premature ventricular contraction (V) which is often precursor of ventricular arrhythmia, two of the most common class of supra-ventricular arrhythmia (S), named atrial fibrillation (AF), atrial flutter (AFL), and normal rhythm (N). The considered feature extraction methods are the standard linear segmentation and wavelet based feature extraction. The followed approach regarding wavelets was to observe simultaneously the signal at different scales, which means with different level of focus. Experimental results are obtained in real data from MIT-BIH Arrhythmia Database and show that wavelet transform outperforms the conventional standard linear segmentation.

A very high speed FSO (free space optics) link has been successfully implemented for purposes of high requirement applications, at the University Campus. An experimental performance evaluation of this link has been carried out at various OSI levels (1, 4, and 7). Several results are presented and discussed.

Wi-Fi, using 2.4 GHz microwaves and IEEE 802.11b/g standards, is mainly applied to the development of outdoor wireless links. In addition, a FSO link is implemented for data and voice communications. Link quality has been tested. Several results are presented and discussed.

A MATLAB/SIMULINK-based simulator was employed for studies concerning the control of baker's yeast fed-batch fermentation. Four control algorithms were implemented and compared: the classical PID control, two discrete versions-modified velocity and position algorithms, and a fuzzy law. The simulation package was seen to be an efficient tool for the simulation and tests of control strategies of the nonlinear process.