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Author |
Xavier Soria; Angel D. Sappa; Arash Akbarinia |
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Multispectral Single-Sensor RGB-NIR Imaging: New Challenges an Oppotunities |
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Conference Article |
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2017 |
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The 7th International Conference on Image Processing Theory, Tools and Application |
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1-6 |
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gtsi @ user @ |
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72 |
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Author |
Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero |
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Title |
Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. |
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Conference Article |
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2018 |
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IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador |
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1-6 |
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The identification of shrimp organs in biology using
histological images is a complex task. Shrimp histological images
poses a big challenge due to their texture and similarity among
classes. Image classification by using feature engineering and
convolutional neural networks (CNN) are suitable methods to
assist biologists when performing organ detection. This work
evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof-
Words (PBOW) models for image classification leveraging big
data techniques; and transfer learning for the same classification
task by using a pre-trained CNN. A comparative analysis
of these two different techniques is performed, highlighting
the characteristics of both approaches on the shrimp organs
identification problem. |
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gtsi @ user @ |
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87 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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Conference Article |
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Year |
2018 |
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14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA |
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This paper proposes a novel approach to remove haze
degradations in RGB images using a stacked conditional
Generative Adversarial Network (GAN). It employs a triplet
of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a
conditional probabilistic model, is proposed. The proposed
GAN architecture learns to remove the haze, using as conditioned
entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast
model training convergence and a homogeneous model generalization.
Experiments showed that the proposed method
generates high-quality clear images. |
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gtsi @ user @ |
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83 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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Year |
2018 |
Publication |
25 th IEEE International Conference on Image Processing, ICIP 2018 |
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2237-2241 |
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This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization. |
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gtsi @ user @ |
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81 |
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Author |
Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez |
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Title |
Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell |
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Conference Article |
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2018 |
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7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. |
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1221-1226 |
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This work describes an approximate reasoning
technique to deal with the non-linearity that occurs in the
stabilization of the pressure of anodic and cathodic gases of a
proton exchange membrane fuel cell (PEM). The implementation
of a supervisory element in the stabilization of the pressure of the
PEM cell is described. The fuzzy supervisor is a reference
control, it varies the value of the reference given to the classic
low-level controller, Proportional – Integral – Derivative (PID),
according to the speed of change of the measured pressure and
the change in the error of the pressure. The objective of the fuzzy
supervisor is to achieve a rapid response over time of the variable
pressure, avoiding unwanted overruns with respect to the
reference value. A comparative analysis is detailed with the
classic PID control to evaluate the operation of the “fuzzy
supervisor”, with different flow values and different sizes of
active area of the PEM cell (electric power generated). |
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gtsi @ user @ |
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88 |
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Author |
Gomer Rubio; Wilton Agila |
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Title |
Dynamic Modeling of Fuel Cells in a Strategic Context |
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Conference Article |
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2018 |
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7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. |
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86 |
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Author |
Carlos Monsalve; Alain April; Alain Abran |
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Title |
Requirements Elicitation Using BPM Notations: Focusing on the Strategic Level Representation |
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Conference Article |
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2011 |
Publication |
10th WSEAS international conference on Applied computer and applied computational science |
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235-241 |
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Business process modeling, levels of abstraction, requirements elicitation, case study, action research |
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Business process models (BPM) can be useful for requirements elicitation, among other uses. Since the active participation of all stakeholders is a key factor for successful requirements engineering, it is important that BPM be shared by all stakeholders. Unfortunately, organizations may end up with inconsistent BPM not covering all stakeholders’ needs and constraints. The use of multiple levels of abstraction (MLA), such as at the strategic, tactical and operational levels, is often used in various process-oriented initiatives to facilitate the consolidation of various stakeholders’ needs and constraints. This article surveys the use of MLA in recent BPM research publications and reports on a BPM action-research case study conducted in a Canadian organization, with the aim of exploring the usefulness of the strategic level. |
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CIDIS – Electrical and Computer Engineering Department Escuela Superior Politécnica del Litoral Km. 30.5 vía Perimetral |
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1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3 CANADA |
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English |
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cidis @ cidis @ |
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16 |
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Author |
Juan C. Basurto, Patricia Chávez and Hernán Córdova |
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Title |
A Proximity-Aware Transparent Handoff Mobility Scheme for VoIP Communication over Infrastructure Mesh Networks |
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2011 |
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International Congress of Electronic, Electrical and Systems Engineering-INTERCON 2011 |
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Wireless Mesh Networks; Quality of Service; Mobility Management; Voice over IP. |
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Mobility Management plays a key role in Voice-over- IP (VoIP) communications over Wireless Mesh Networks (WMN) as clients should maintain adequate levels of Quality of Service (QoS) as they move across the network. This paper presents PATH, a Proximity-Aware Transparent Handoff mobility scheme for real time voice communications over wireless mesh networks. Our study focuses on Medium Access Control (MAC) layer procedures and relies on gratuitous ARP unicasting in order to provide fast-handoffs. An experimental evaluation has been conducted and its results are shown in this paper. |
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cidis @ cidis @ |
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20 |
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Author |
Xavier Soria; Angel D. Sappa |
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Title |
Improving Edge Detection in RGB Images by Adding NIR Channel. |
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Conference Article |
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2018 |
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14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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266-273 |
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gtsi @ user @ |
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95 |
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Author |
Xavier Soria; Angel D. Sappa; Riad Hammoud |
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Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. |
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Journal Article |
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2018 |
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Vol. 18 |
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Issue 7 |
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Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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gtsi @ user @ |
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96 |
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