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| Author | Carlos Monsalve; Alain April and Alain Abran | ||||
| Title | Measuring software functional size from business process models | Type | Journal Article | ||
| Year | 2011 | Publication  |
International Journal of Software Engineering and Knowledge Engineering | Abbreviated Journal | |
| Volume | Vol. 21 | Issue | Pages | pp. 311–338 | |
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| Abstract | ISO 14143-1 specifies that a functional size measurement (FSM) method must provide measurement procedures to quantify the functional user requirements (FURs) of software. Such quantitative information, functional size, is typically used, for instance, in software estimation. One of the international standards for FSM is the COSMIC FSM method — ISO 19761 — which was designed to be applied both to the business application (BA) software domain and to the real-time software domain. A recurrent problem in FSM is the availability and quality of the inputs required for measurement purposes; that is, well documented FURs. Business process (BP) models, as they are commonly used to gather requirements from the early stages of a project, could be a valuable source of information for FSM. In a previous article, the feasibility of such an approach for the BA domain was analyzed using the Qualigram BP modeling notation. This paper complements that work by: (1) analyzing the use of BPMN for FSM in the BA domain; (2) presenting notation-independent guidelines for the BA domain; and (3) analyzing the possibility of using BP models to perform FSM in the real-time domain. The measurement results obtained from BP models are compared with those of previous FSM case studies. | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 19 | ||
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| Author | Marta Diaz; Dennys Paillacho; Cecilio Angulo | ||||
| Title | Evaluating Group-Robot Interaction in Crowded Public Spaces: A Week-Long Exploratory Study in the Wild with a Humanoid Robot Guiding Visitors Through a Science Museum. | Type | Journal Article | ||
| Year | 2015 | Publication |
International Journal of Humanoid Robotics | Abbreviated Journal | |
| Volume | Vol. 12 | Issue | Pages | ||
| Keywords | Group-robot interaction; robotic-guide; social navigation; space management; spatial formations; group walking behavior; crowd behavior | ||||
| Abstract | This paper describes an exploratory study on group interaction with a robot-guide in an open large-scale busy environment. For an entire week a humanoid robot was deployed in the popular Cosmocaixa Science Museum in Barcelona and guided hundreds of people through the museum facilities. The main goal of this experience is to study in the wild the episodes of the robot guiding visitors to a requested destination focusing on the group behavior during displacement. The walking behavior follow-me and the face to face communication in a populated environment are analyzed in terms of guide- visitors interaction, grouping patterns and spatial formations. Results from observational data show that the space configurations spontaneously formed by the robot guide and visitors walking together did not always meet the robot communicative and navigational requirements for successful guidance. Therefore additional verbal and nonverbal prompts must be considered to regulate effectively the walking together and follow-me behaviors. Finally, we discuss lessons learned and recommendations for robot’s spatial behavior in dense crowded scenarios. | ||||
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| Publisher | International Journal of Humanoid Robotics | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 34 | ||
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| Author | Juan C. Basurto, Patricia Chávez and Hernán Córdova | ||||
| Title | A Proximity-Aware Transparent Handoff Mobility Scheme for VoIP Communication over Infrastructure Mesh Networks | Type | Conference Article | ||
| Year | 2011 | Publication |
International Congress of Electronic, Electrical and Systems Engineering-INTERCON 2011 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Wireless Mesh Networks; Quality of Service; Mobility Management; Voice over IP. | ||||
| Abstract | 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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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 20 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images | Type | Conference Article | ||
| Year | 2018 | Publication |
International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing | Abbreviated Journal | |
| Volume | 721 | Issue | Pages | ||
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| Abstract | This paper proposes a novel approach to use cross-spectral images to achieve a better performance with the proposed Adaptive Harris corner detector comparing its obtained results with those achieved with images of the visible spectra. The images of urban, field, old-building and country category were used for the experiments, given the variety of the textures present in these images, with which the complexity of the proposal is much more challenging for its verification. It is a new scope, which means improving the detection of characteristic points using crossspectral images (NIR, G, B) and applying pruning techniques, the combination of channels for this fusion is the one that generates the largest variance based on the intensity of the merged pixels, therefore, it is that which maximizes the entropy in the resulting Cross-spectral images. Harris is one of the most widely used corner detection algorithm, so any improvement in its efficiency is an important contribution in the field of computer vision. The experiments conclude that the inclusion of a (NIR) channel in the image as a result of the combination of the spectra, greatly improves the corner detection due to better entropy of the resulting image after the fusion, Therefore the fusion process applied to the images improves the results obtained in subsequent processes such as identification of objects or patterns, classification and/or segmentation. |
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| Notes | 1 | Approved | no | ||
| Call Number | gtsi @ user @ | Serial | 84 | ||
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| Author | Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon | ||||
| Title | Open Source system for identification of corn leaf chlorophyll contents based on multispectral images | Type | Conference Article | ||
| Year | 2019 | Publication |
International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador | Abbreviated Journal | |
| Volume | Issue | Pages | 572-581 | ||
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| Abstract | It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images. Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%. It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests. However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1]. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 116 | ||
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| Author | Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco | ||||
| Title | Computer vision for image understanding. A comprehensive review | Type | Conference Article | ||
| Year | 2019 | Publication |
International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador | Abbreviated Journal | |
| Volume | Issue | Pages | 248-259 | ||
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| Abstract | Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 97 | ||
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| Author | Angel D. Sappa. | ||||
| Title | ICT Applications for Smart Cities | Type | Book Chapter | ||
| Year | 2022 | Publication |
Intelligent Systems Reference Library | Abbreviated Journal | BOOK |
| Volume | 224 | Issue | Pages | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 198 | ||
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| Author | M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé | ||||
| Title | Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains | Type | Conference Article | ||
| Year | 2015 | Publication |
Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 2488 - 2495 | ||
| Keywords | Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model | ||||
| Abstract | In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline. | ||||
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| Publisher | IEEE | Place of Publication | Hamburg, Germany | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 41 | ||
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| Author | Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication |
In Sensors Journal | Abbreviated Journal | |
| Volume | Vol. 17 | Issue | Pages | pp. 873 | |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 64 | ||
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| Author | Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa | ||||
| Title | Melamine faced panels defect classification beyond the visible spectrum. | Type | Journal Article | ||
| Year | 2018 | Publication |
In Sensors 2018 | Abbreviated Journal | |
| Volume | Vol. 11 | Issue | Issue 11 | Pages | |
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| Abstract | In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 89 | ||
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