CTM - Indexed Articles in Journals

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    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.
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    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.
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    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.
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    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.
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    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.
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    Advanced delivery of sensitive multimedia content for better serving user expectations in Virtual Collaboration applications
    ( 2011) Hemantha Arachchi ; Safak Dogan ; Anna Carreras ; Vitor Barbosa ; Maria Teresa Andrade ; Ahmet M. Kondoz ; Jaime Delgado
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    Aesthetics in Breast Conserving Therapy: Do Objectively Measured Results Match Patients' Evaluations?
    ( 2011) G. Rauch ; J. Rom ; C. Domschke ; Maria João Cardoso ; C. Sohn ; J. Heil ; J. Dahlkamp ; M. Golatta
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    Analog circuits with high-gain topologies using a-GIZO TFTs on glass
    ( 2015) Bahubalindruni,PG ; Silva,B ; Vítor Grade Tavares ; Barquinha,P ; Cardoso,N ; Guedes De Oliveira,P ; Martins,R ; Fortunato,E
    This paper presents analog building blocks that find potential applications in display panels. A buffer (source-follower), subtractor, adder, and high-gain amplifier, employing only n-type enhancement amorphous gallium-indium-zinc-oxide thin-film transistors (a-GIZO TFTs), were designed, simulated, fabricated, and characterized. Circuit simulations were carried out using a neural model developed in-house from the measured characteristics of the transistors. The adder-subtractor circuit presents a power consumption of 0.26 mW, and the amplifier presents a gain of 34 dB and a power consumption of 0.576 mW, with a load of 10 MO16 pF. To the authors' knowledge, this is the highest gain reported so far for a single-stage amplifier with a-GIZO TFT technology. © 2015 IEEE.
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    Analysis of object description methods in a video object tracking environment
    ( 2013) Pedro Miguel Carvalho ; Oliveira,T ; Lucian Ciobanu ; Gaspar,F ; Luís Filipe Teixeira ; Bastos,R ; Jaime Cardoso ; Dias,MS ; Luís Corte Real
    A key issue in video object tracking is the representation of the objects and how effectively it discriminates between different objects. Several techniques have been proposed, but without a generally accepted method. While analysis and comparisons of these individual methods have been presented in the literature, their evaluation as part of a global solution has been overlooked. The appearance model for the objects is a component of a video object tracking framework, depending on previous processing stages and affecting those that succeed it. As a result, these interdependencies should be taken into account when analysing the performance of the object description techniques. We propose an integrated analysis of object descriptors and appearance models through their comparison in a common object tracking solution. The goal is to contribute to a better understanding of object description methods and their impact on the tracking process. Our contributions are threefold: propose a novel descriptor evaluation and characterisation paradigm; perform the first integrated analysis of state-of-the-art description methods in a scenario of people tracking; put forward some ideas for appearance models to use in this context. This work provides foundations for future tests and the proposed assessment approach contributes to the informed selection of techniques more adequately for a given tracking application context.
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    An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms
    ( 2014) Pinto,AR ; Montez,C ; Araujo,G ; Francisco Vasques ; Paulo Portugal
    Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks.
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    Assessing Cosmetic Results After Breast Conserving Surgery
    ( 2014) Maria João Cardoso ; Hélder Filipe Oliveira ; Jaime Cardoso
    "Taking less treating better" has been one of the major improvements of breast cancer surgery in the last four decades. The application of this principle translates into equivalent survival of breast cancer conserving treatment (BCT) when compared to mastectomy, with a better cosmetic outcome. While it is relatively easy to evaluate the oncological results of BCT, the cosmetic outcome is more difficult to measure due to the lack of an effective and consensual procedure. The assessment of cosmetic outcome has been mainly subjective, undertaken by a panel of expert observers or/and by patient self-assessment. Unfortunately, the reproducibility of these methods is low. Objective methods have higher values of reproducibility but still lack the inclusion of several features considered by specialists in BCT to be fundamental for cosmetic outcome. The recent addition of volume information obtained with 3D images seems promising. Until now, unfortunately, no method is considered to be the standard of care. This paper revises the history of cosmetic evaluation and guides us into the future aiming at a method that can easily be used and accepted by all, caregivers and caretakers, allowing not only the comparison of results but the improvement of performance. (C) 2014 Wiley Periodicals, Inc.
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    AutoMashUpper: Automatic creation of multi-song music mashups
    ( 2014) Matthew Davies ; Hamel,P ; Yoshii,K ; Goto,M
    In this paper we present a system, AutoMashUpper, for making multi-song music mashups. Central to our system is a measure of "mashability" calculated between phrase sections of an input song and songs in a music collection. We define mashability in terms of harmonic and rhythmic similarity and a measure of spectral balance. The principal novelty in our approach centres on the determination of how elements of songs can be made fit together using key transposition and tempomodification, rather than based on their unaltered properties. In this way, the properties of two songs used to model their mashability can be altered with respect to transformations performed to maximize their perceptual compatibility. AutoMashUpper has a user interface to allow users to control the parameterization of the mashability estimation. It allows users to define ranges for key shifts and tempo as well as adding, changing or removing elements from the created mashups. We evaluate AutoMashUpper by its ability to reliably segment music signals into phrase sections, and also via a listening test to examine the relationship between estimated mashability and user enjoyment. © 2014 IEEE.
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    Automatic Image Registration Through Image Segmentation and SIFT
    ( 2011) Hernâni Gonçalves ; Luís Corte Real ; José A. Gonçalves
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    Automatic TV Logo Identification for Advertisement Detection without Prior Data
    ( 2021) Pedro Miguel Carvalho ; Américo José Pereira ; Paula Viana ; 1107 ; 4358 ; 6078
    Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.
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    Availability Issues in Wireless Visual Sensor Networks
    ( 2014) Costa,DG ; Silva,I ; Guedes,LA ; Francisco Vasques ; Paulo Portugal
    Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks.
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    BacalhauNet: A tiny CNN for lightning-fast modulation classification
    ( 2022) Jose Rosa ; Daniel Granhao ; Guilherme Carvalho ; Tiago Gon?alves ; Monica Figueiredo ; Luis Conde Bento ; Nuno Miguel Paulino ; Luis M. Pessoa ; 5802
    Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI/ML in 5G Challenge 2021, achieving 61.73? compression over the challenge baseline and being over 2.6? better than the second best submission. The source code of this work is publicly available at github.com/ITU-AI- ML-in-5G-Challenge/ITU-ML5G-PS-007-BacalhauNet.
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    Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning
    ( 2011) André Marçal ; Janete Borges ; José Bioucas-Dias