CTM

Permanent URI for this community

http://localhost:4000/handle/123456789/37
This service operates in key areas within modern communications networks and services, especially in network architectures, telecommunications services, signal and image processing, microelectronics, digital TV and multimedia.

Browse

Browsing CTM by Title

  • Item
    2017 Wireless Days, Porto, Portugal, March 29-31, 2017
    ( 2017) Manuel Ricardo ; Campos,Rui ; Ruela,Jose ; Ricardo Morla ; Teixeira,Filipe ; Luís Manuel Pessoa ; Salgado,Henrique
  • Item
    A 3D low-cost solution for the aesthetic evaluation of breast cancer conservative treatment
    ( 2014) Hélder Filipe Oliveira ; Jaime Cardoso ; Magalhães,A ; Cardoso,MJ
    Breast cancer conservative treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as the software tool Breast Cancer Conservative Treatment.cosmetic results (BCCT.core). Although results are satisfying, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system a low-cost tool, this work studies the incorporation of the Microsoft Kinect sensor in BCCT evaluations. The aim is to enable the automatic joint detection of prominent points, both on depth and RGB images. Afterwards, using those prominent points, it is possible to obtain two-dimensional and volumetric features. Finally, the aesthetic result is achieved using machine learning techniques converted automatically from the set of measures defined. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. In addition, comparing with previous research, the procedure for detecting prominent points was automated. © 2013 © 2013 Taylor & Francis.
  • Item
    3D Model for Aesthetic Objective Evaluation after Breast Cancer Surgery using Infrared Laser Projector
    ( 2011) Jaime Cardoso ; André T. Magalhaes ; Hélder Filipe Oliveira ; João Soares ; Anténio José Moura ; Maria João Cardoso
  • Item
    3D Models for Leg Prothesis - The first step to measure the stub's fitting into a prosthetic device
    ( 2012) Pedro Costa ; Hélder Filipe Oliveira ; Filipe Magalhães ; Frederico Carpinteiro
  • Item
    3D Reconstruction of Body Parts Using RGB-D Sensors: Challenges from a Biomedical Perspective
    ( 2014) Costa,P ; Zolfagharnasab,H ; João Pedro Monteiro ; Cardoso,JS ; Hélder Filipe Oliveira
  • Item
    A 3D Simulation Framework for Safe Ambient-Assisted Home Care
    ( 2011) José Manuel Torres ; Ricardo Morla ; Rui Moreira ; Pedro Sobral ; Carlos Velasquez ; Christophe Soares
  • Item
    5.36 Gbit/s OFDM optical wireless communication link over the underwater channel
    ( 2020) João Henrique Araújo ; Luís Manuel Pessoa ; Henrique Salgado ; Pereira,F ; Joana Santos Tavares ; Kraemer,R ; 7658 ; 4760 ; 296 ; 5686
    An OFDM transmission system is reported based on a directly modulated blue LASER diode, for high bit rate under-water optical communication applications. The 256 subcarriers 16-QAM signal is transmitted over a total distance of 2.4 m underwater with an EVM lower than -28.5 dB for a 250 MHz bandwidth and -16.5 dB for a 2 GHz bandwidth, the BER being lower than the forward error corrector limit. At the maximum bandwidth of 2 GHz a transmission rate of 5.36 Gbit/s is achieved. © 2020 IEEE.
  • Item
    802.11 wireless simulation and anomaly detection using HMM and UBM
    ( 2020) Anisa Allahdadidastjerdi ; Ricardo Morla ; Jaime Cardoso ; 5587 ; 3645 ; 3889
    Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT /INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.
  • Item
    a-GIZO TFT neural modeling, circuit simulation and validation
    ( 2015) Bahubalindruni,PG ; Vítor Grade Tavares ; Barquinha,P ; Manuel Cândido Santos ; Cardoso,N ; de Oliveira,PG ; Martins,R ; Fortunato,E
    Development time and accuracy are measures that need to be taken into account when devising device models for a new technology. If complex circuits need to be designed immediately, then it is very important to reduce the time taken to realize the model. Solely based on data measurements, artificial neural networks (ANNs) modeling methodologies are capable of capturing small and large signal behavior of the transistor, with good accuracy, thus becoming excellent alternatives to more strenuous modeling approaches, such as physical and semi-empirical. This paper then addresses a static modeling methodology for amorphous Gallium-Indium-Zinc-Oxide - Thin Film Transistor (a-GIZO TFT), with different ANNs, namely: multilayer perceptron (MLP), radial basis functions (RBF) and least squares-support vector machine (LS-SVM). The modeling performance is validated by comparing the model outcome with measured data extracted from a real device. In case of a single transistor modeling and under the same training conditions, all the ANN approaches revealed a very good level of accuracy for large- and small-signal parameters (g(m) and g(d)), both in linear and saturation regions. However, in comparison to RBF and LS-SVM, the MLP achieves a very acceptable degree of accuracy with lesser complexity. The impact on simulation time is strongly related with model complexity, revealing that MLP is the most suitable approach for circuit simulations among the three ANNs. Accordingly, MLP is then extended for multiple TFTs with different aspect ratios and the network implemented in Verilog-A to be used with electric simulators. Further, a simple circuit (inverter) is simulated from the developed model and then the simulation outcome is validated with the fabricated circuit response.
  • Item
    Accelerating solitons in gas-filled hollow-core photonic crystal fibers
    ( 2013) Facao,M ; Maria Inês Carvalho ; Almeida,P
    We found the self-similar solitary solutions of a recently proposed model for the propagation of pulses in gas-filled hollow-core photonic crystal fibers that includes a plasma induced nonlinearity. As anticipated for a simpler model and using a perturbation analysis, there are indeed stationary solitary waves that accelerate and self-shift to higher frequencies. However, if the plasma nonlinearity strength is large or the pulse amplitudes are small, the solutions have distinguished long tails and decay as they propagate.
  • Item
  • Item
    An accurate and interpretable model for BCCT.core
    ( 2010) Hélder Filipe Oliveira ; Jaime Cardoso ; André Magalhães ; Maria João Cardoso
    Breast Cancer Conservative Treatment (BCCT) is considered nowadays to be the most widespread form of locorregional breast cancer treatment. However, aesthetic results are heterogeneous and difficult to evaluate in a standardized way. The limited reproducibility of subjective aesthetic evaluation in BCCT motivated the research towards objective methods. A recent computer system (BCCT.core) was developed to objectively and automatically evaluate the aesthetic result of BCCT. The system is centered on a support vector machine (SVM) classifier with a radial basis function (RBF) used to predict the overall cosmetic result from features computed on a digital photograph of the patient. However, this classifier is not ideal for the interpretation of the factors being used in the prediction. Therefore, an often suggested improvement is the interpretability of the model being used to assess the overall aesthetic result. In the current work we investigate the accuracy of different interpretabl
  • Item
  • Item
    Actas das VII Jornadas sobre Sistemas Reconfiguráveis
    ( 2011) Mário Véstias ; João Canas Ferreira
  • Item
    Active Learning from Video Streams in a Multi-Camera Scenario
    ( 2014) Samaneh Khoshrou ; Jaime Cardoso ; Luís Filipe Teixeira
    While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams generated in a multi-camera surveillance scenario. The framework exploits active learning strategies in order to interact wisely with operators to address various problems that exist in such non-stationary environments, such as concept drift and concept evolution. If we look at the problem as mining parallel streams, the framework can address learning from uneven parallel streams applying a class-based ensemble, a problem that has not been addressed before. Favourable results indicate the success of the framework.
  • Item
    Active Mining of Parallel Video Streams
    ( 2014) Samaneh Khoshrou ; Jaime Cardoso ; Luís Filipe Teixeira
  • Item
    An Adaptive Duty-Cycle Methodology for PV Power Maximization Using a Single Variable
    ( 2013) Vidal,AA ; Vítor Grade Tavares ; Principe,JC
    This paper presents a new methodology to maximize the power output of Photovoltaic panels (PV), based on an adaptive duty-cycle methodology. The approach embeds the DC/DC converter characteristic in the cost function, allowing an optimization based on a single measured variable. Two cost functions, and respective learning rules, are derived. The first, more complex and comprehensive, traces the ground for the second which is less computational intensive and solves stability issues and implementation difficulties. It is also demonstrated that the system is asymptotically stable around the optimum duty-cycle, in the Lyapunov sense. Both methods are compared through simulations and deviations from the optimal solution are assessed.
  • Item
    Adaptive Electrical Equalization of Optical Impairments in Coherent Optical Systems
    ( 2007) Luís Manuel Pessoa ; Henrique Salgado ; I. Darwazeh
  • Item
    Adaptive modeling of synthetic nonstationary sinusoids
    ( 2015) Marcelo Freitas Caetano ; Kafentzis,AG ; Mouchtaris,A
    Nonstationary oscillations are ubiquitous in music and speech, ranging from the fast transients in the attack of musical instruments and consonants to amplitude and frequency modulations in expressive variations present in vibrato and prosodic contours. Modeling nonstationary oscillations with sinusoids remains one of the most challenging problems in signal processing because the fit also depends on the nature of the underlying sinusoidal model. For example, frequency modulated sinusoids are more appropriate to model vibrato than fast transitions. In this paper, we propose to model nonstationary oscillations with adaptive sinusoids from the extended adaptive quasi-harmonic model (eaQHM).We generated synthetic nonstationary sinusoids with different amplitude and frequency modulations and compared the modeling performance of adaptive sinusoids estimated with eaQHM, exponentially damped sinusoids estimated with ESPRIT, and log-linear-amplitude quadratic-phase sinusoids estimated with frequency reassignment. The adaptive sinusoids from eaQHM outperformed frequency reassignment for all nonstationary sinusoids tested and presented performance comparable to exponentially damped sinusoids.
  • Item
    An Adaptive Signal Processing Framework for PV Power Maximization
    ( 2015) Vidal,AA ; Vítor Grade Tavares ; Principe,JC
    This paper discusses the possibility of using adaptive signal processing techniques for maximum power point tracking controllers, in order to extract peak power from individual photovoltaic modules. A new technique grounded on unsupervised Hebbian learning theory (maximum eigenvector of the output power) is presented, which works on-online and is capable of operating without a desired response. Important modifications were made to the generic Hebbian adaptation to accommodate the intrinsic feedback loop between the controller and the plant. Analytic derivation of the new update rule is presented, as well as stability analysis by means of Lyapunov theory. Simulation results showing its effectiveness are presented, as well as experimental results.