106,247 research outputs found
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Segmentation of axon and myelin from microscopy images of the nervous system
provides useful quantitative information about the tissue microstructure, such
as axon density and myelin thickness. This could be used for instance to
document cell morphometry across species, or to validate novel non-invasive
quantitative magnetic resonance imaging techniques. Most currently-available
segmentation algorithms are based on standard image processing and usually
require multiple processing steps and/or parameter tuning by the user to adapt
to different modalities. Moreover, only few methods are publicly available. We
introduce AxonDeepSeg, an open-source software that performs axon and myelin
segmentation of microscopic images using deep learning. AxonDeepSeg features:
(i) a convolutional neural network architecture; (ii) an easy training
procedure to generate new models based on manually-labelled data and (iii) two
ready-to-use models trained from scanning electron microscopy (SEM) and
transmission electron microscopy (TEM). Results show high pixel-wise accuracy
across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and
84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed
and morphological metrics are extracted and compared against the literature.
AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepsegComment: 14 pages, 7 figure
Measuring Software Process: A Systematic Mapping Study
Context: Measurement is essential to reach predictable performance and high capability processes. It provides
support for better understanding, evaluation, management, and control of the development process
and project, as well as the resulting product. It also enables organizations to improve and predict its process’s
performance, which places organizations in better positions to make appropriate decisions. Objective:
This study aims to understand the measurement of the software development process, to identify studies,
create a classification scheme based on the identified studies, and then to map such studies into the scheme
to answer the research questions. Method: Systematic mapping is the selected research methodology for this
study. Results: A total of 462 studies are included and classified into four topics with respect to their focus
and into three groups based on the publishing date. Five abstractions and 64 attributes were identified,
25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the
most measured process attributes, while effort and performance were the most measured project attributes.
Goal Question Metric and Capability Maturity Model Integration were the main methods and models used
in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently
identified research contexts.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2016-76956-C3-2- RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Modeling batch annealing process using data mining techniques for cold rolled steel sheets
The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained
- …