| |
Records |
Links |
|
Author |
Cristina L. Abad; Yi Lu; Roy H. Campbell |
|
| |
Title  |
DARE: Adaptive Data Replication for Efficient Cluster Scheduling |
Type |
Conference Article |
| |
Year |
2011 |
Publication |
IEEE International Conference on Cluster Computing, 2011 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
159 - 168 |
|
| |
Keywords |
MapReduce, replication, scheduling, locality |
|
| |
Abstract |
Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
yes |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
21 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
|
| |
Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
IEEE International Conference on Image Processing (ICIP 2021) |
Abbreviated Journal |
|
|
| |
Volume |
2021-September |
Issue |
|
Pages |
2783-2787 |
|
| |
Keywords |
CyclicGAN, NDVI, near infrared spectra, instance normalization. |
|
| |
Abstract |
This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
164 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
N. Onkarappa; Cristhian A. Aguilera; B. X. Vintimilla; Angel D. Sappa |
|
| |
Title |
Cross-spectral Stereo Correspondence using Dense Flow Fields |
Type |
Conference Article |
| |
Year |
2014 |
Publication |
Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 |
Abbreviated Journal |
|
|
| |
Volume |
3 |
Issue |
|
Pages |
613 - 617 |
|
| |
Keywords |
Cross-spectral Stereo Correspondence, Dense Optical Flow, Infrared and Visible Spectrum |
|
| |
Abstract |
This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
2014 International Conference on Computer Vision Theory and Applications (VISAPP) |
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
27 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
64 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mildred Cruz; Cristhian A. Aguilera; Boris X. Vintimilla; Ricardo Toledo; Ángel D. Sappa |
|
| |
Title |
Cross-spectral image registration and fusion: an evaluation study |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
Abbreviated Journal |
|
|
| |
Volume |
331 |
Issue |
|
Pages |
|
|
| |
Keywords |
multispectral imaging; image registration; data fusion; infrared and visible spectra |
|
| |
Abstract |
This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Computer Vision Center |
Place of Publication |
Barcelona, Spain |
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
35 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
|
| |
Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1-5 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
57 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
|
| |
Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach. |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
358-364 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
92 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jacome-Galarza L.-R |
|
| |
Title |
Crop yield prediction utilizing multimodal deep learning |
Type |
Conference Article |
| |
Year |
2021 |
Publication |
16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
|
| |
Abstract |
La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
Español |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
150 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
|
| |
Title |
Corn Kernel Classification From Few Training Samples |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
In journal Artificial Intelligence in Agriculture |
Abbreviated Journal |
|
|
| |
Volume |
Vol. 9 |
Issue |
|
Pages |
pp. 89-99 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
25897217 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
223 |
|
| Permanent link to this record |
| |
|
| |
|
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
Hamburg, Germany |
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
41 |
|
| Permanent link to this record |
