scholarly journals Next-Generation Electronics and Sensing Technology

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7958
Author(s):  
Trong-Yen Lee ◽  
Yen-Lin Chen ◽  
Yu-Cheng Fan
Keyword(s):  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Special Issue ◽  
Sensing Systems ◽  
Sensing Technology ◽  
Art Methods

This Special Issue is dedicated to several aspects of next-generation electronics and sensing technology and contains eight papers that focus on advanced sensing devices, sensing systems, and sensing circuits that focus on the state-of-the-art methods for sensing technologies [...]

scholarly journals Introduction

1990 ◽  
Vol 30 (4) ◽  
pp. 487-492 ◽  
Author(s):  
Carl F. Kaestle
Keyword(s):  
State Of The Art ◽  
History Of Education ◽  
The State ◽  
Special Issue ◽  
Research Papers ◽  
History Of

The History of Education Quarterly has done it again. Despite many scholars' previous attempts to summarize the state of the art in historical studies of literacy, this special issue will now be the best, up-to-date place for a novice to start. It should be required reading for everyone interested in this subfield. The editors have enlisted an impressive roster of prominent scholars in the field, and these authors have provided us with an excellent array of synthetic reviews, methodological and theoretical discussions, and exemplary research papers.

scholarly journals Special Issue “Optical Fiber Interferometric Sensors: New Production Methodologies and Novel Applications”

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 389
Author(s):  
M. Fátima Domingues ◽  
Nélia Alberto ◽  
Paulo André
Keyword(s):  
Optical Fiber ◽  
State Of The Art ◽  
The State ◽  
Special Issue ◽  
New Production ◽  
Interferometric Sensors ◽  
Novel Applications

The collection of papers presented in this Special Issue (SI) portraits the state-of-the-art of photonic-based interferometric sensors, where new application areas were explored (such as spirometry) and novel sensitivity limits were achieved, using innovative sensing techniques for the monitoring of parameters, such as displacement, temperature or salinity.

scholarly journals Smoothing vs. sharpening of colour images: Together or separated

2017 ◽  
Vol 2 (1) ◽  
pp. 299-316 ◽  
Author(s):  
Cristina Pérez-Benito ◽  
Samuel Morillas ◽  
Cristina Jordán ◽  
J. Alberto Conejero
Keyword(s):  
Image Denoising ◽  
Gaussian Distribution ◽  
State Of The Art ◽  
Visual Quality ◽  
The State ◽  
Art Methods ◽  
Efficiency And Effectiveness

AbstractIt is still a challenge to improve the efficiency and effectiveness of image denoising and enhancement methods. There exists denoising and enhancement methods that are able to improve visual quality of images. This is usually obtained by removing noise while sharpening details and improving edges contrast. Smoothing refers to the case of denoising when noise follows a Gaussian distribution.Both operations, smoothing noise and sharpening, have an opposite nature. Therefore, there are few approaches that simultaneously respond to both goals. We will review these methods and we will also provide a detailed study of the state-of-the-art methods that attack both problems in colour images, separately.

scholarly journals Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features

2017 ◽  
Vol 108 (1) ◽  
pp. 307-318 ◽  
Author(s):  
Eleftherios Avramidis
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Translation ◽  
State Of The Art ◽  
Linear Method ◽  
The State ◽  
Quality Estimation ◽  
Art Methods ◽  
Improved Performance

AbstractA deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics.Notable conclusions are reached through the examination of the contribution of the features in the models, whereas it is possible to identify common MT errors that are captured by the features. Many grammatical/fluency features have a good contribution, few adequacy features have some contribution, whereas source complexity features are of no use. The importance of many fluency and adequacy features is language-specific.

Towards Zero Defect Manufacturing paradigm: A review of the state-of-the-art methods and open challenges

2022 ◽  
Vol 134 ◽  
pp. 103548
Author(s):  
Bianca Caiazzo ◽  
Mario Di Nardo ◽  
Teresa Murino ◽  
Alberto Petrillo ◽  
Gianluca Piccirillo ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Art Methods

scholarly journals Uniformity Attentive Learning-Based Siamese Network for Person Re-Identification

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3603
Author(s):  
Dasol Jeong ◽  
Hasil Park ◽  
Joongchol Shin ◽  
Donggoo Kang ◽  
Joonki Paik
Keyword(s):  
Large Scale ◽  
Body Shape ◽  
State Of The Art ◽  
The State ◽  
Whole Body ◽  
Distinctive Features ◽  
Common Features ◽  
Siamese Network ◽  
Art Methods ◽  
Triplet Loss

Person re-identification (Re-ID) has a problem that makes learning difficult such as misalignment and occlusion. To solve these problems, it is important to focus on robust features in intra-class variation. Existing attention-based Re-ID methods focus only on common features without considering distinctive features. In this paper, we present a novel attentive learning-based Siamese network for person Re-ID. Unlike existing methods, we designed an attention module and attention loss using the properties of the Siamese network to concentrate attention on common and distinctive features. The attention module consists of channel attention to select important channels and encoder-decoder attention to observe the whole body shape. We modified the triplet loss into an attention loss, called uniformity loss. The uniformity loss generates a unique attention map, which focuses on both common and discriminative features. Extensive experiments show that the proposed network compares favorably to the state-of-the-art methods on three large-scale benchmarks including Market-1501, CUHK03 and DukeMTMC-ReID datasets.

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