scholarly journals AEMF: An Attention-Based Efficient and Multifeature Fast Text Detector

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wanqi Ma ◽  
Chaoyu Yang ◽  
Jie Yang ◽  
Jian Wu
Keyword(s):  
Semantic Segmentation ◽  
Detection Methods ◽  
Excellent Performance ◽  
Important Data ◽  
Original Algorithm ◽  
The Past ◽  
Related Information ◽  
Different Shapes ◽  
Public Datasets ◽  
F Measure

The label from industrial commodity packaging usually contains important data, such as production date, manufacturer, and other commodity-related information. As such, those labels are essential for consumers to purchase goods, help commodity supervision, and reveal potential product safety problems. Consequently, packaging label detection, as the prerequisite for product label identification, becomes a very useful application, which has achieved promising results in the past decades. Yet, in complex industrial scenarios, traditional detection methods are often unable to meet the requirements, which suffer from many problems of low accuracy and efficiency. In this paper, we propose a multifeature fast and attention-based algorithm using a combination of area suggestion and semantic segmentation. This algorithm is an attention-based efficient and multifeature fast text detector (termed AEMF). The proposed approach is formed by fusing segmentation branches and detection branches with each other. Based on the original algorithm that can only detect text in any direction, it is possible to detect different shapes with a better accuracy. Meanwhile, the algorithm also works better on long-text detection. The algorithm was evaluated using ICDAR2015, CTW1500, and MSRA-TD500 public datasets. The experimental results show that the proposed multifeature fusion with self-attention module makes the algorithm more accurate and efficient than existing algorithms. On the MSRA-TD500 dataset, the AEMF algorithm has an F-measure of 72.3% and a frame per second (FPS) of 8. On the CTW1500 dataset, the AEMF algorithm has an F-measure of 62.3% and an FPS of 23. In particular, the AEMF algorithm has achieved an F-measure of 79.3% and an FPS of 16 on the ICDAR2015 dataset, demonstrating the excellent performance in detecting label text on industrial packaging.

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

scholarly journals Toward a Coronavirus Knowledge Graph

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 998
Author(s):  
Peng Zhang ◽  
Yi Bu ◽  
Peng Jiang ◽  
Xiaowen Shi ◽  
Bing Lun ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Angiotensin Converting Enzyme ◽  
Information Sources ◽  
Knowledge Graph ◽  
Converting Enzyme ◽  
Related Information ◽  
Relationship Types ◽  
Entity Disambiguation ◽  
Public Datasets ◽  
Biological Entities

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG’s usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.

scholarly journals Multifunctional Optical Crystals for All-Solid-State Raman Lasers

Crystals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Hui Zhao ◽  
Shibo Dai ◽  
Siqi Zhu ◽  
Hao Yin ◽  
Zhen Li ◽  
...  
Keyword(s):  
Solid State ◽  
Continuous Wave ◽  
Frequency Doubling ◽  
Raman Laser ◽  
Rare Earth Ions ◽  
Excellent Performance ◽  
The Past ◽  
Raman Lasers ◽  
Optical Crystals ◽  
Laser Crystals

In the past few decades, the multifunctional optical crystals for all-solid-state Raman lasers have been widely studied by many scholars due to their compactness, convenience and excellent performance. In this review, we briefly show two kinds of multifunctional Raman crystals: self-Raman (laser and Raman effects) crystals and self-frequency-doubled Raman (frequency-doubling and Raman effects) crystals. We firstly introduce the properties of the self-Raman laser crystals, including vanadate, tungstate, molybdate and silicate doped with rare earth ions, as well as self-frequency-doubled Raman crystals, including KTiOAsO4 (KTA) and BaTeMo2O9 (BTM). Additionally, the domestic and international progress in research on multifunctional Raman crystals is summarized in the continuous wave, passively Q-switched, actively Q-switched and mode-locked regimes. Finally, we present the bottleneck in multifunctional Raman crystals and the outlook for future development. Through this review, we contribute to a general understanding of multifunctional Raman crystals.

scholarly journals Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 721
Author(s):  
Ao Feng ◽  
Hongxiang Li ◽  
Zixi Liu ◽  
Yuanjiang Luo ◽  
Haibo Pu ◽  
...  
Keyword(s):  
Grain Weight ◽  
Gaussian Kernel ◽  
Density Level ◽  
Thousand Grain Weight ◽  
Original Algorithm ◽  
Different Types ◽  
Different Shapes ◽  
Density Map ◽  
Important Basis ◽  
Adhesion Condition

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

Advances in reconstructive options of nerve injuries in the hand

2012 ◽  
Vol 153 (45) ◽  
pp. 1767-1778 ◽  
Author(s):  
Vilmos Bíró
Keyword(s):  
Peripheral Nerves ◽  
Common Property ◽  
Review Of Literature ◽  
Nerve Injuries ◽  
Important Data ◽  
The Past ◽  
Reconstructive Operations ◽  
Detailed List ◽  
High Level ◽  
Reconstructive Techniques

The author summarizes the most important data about the development of reconstructive techniques of nerve injuries in the hand based on literature references and the author’s own experience in the past decades. A new bulk of knowledge turned into a common property related to the micro- and macroanatomic structure of peripheral nerves, the process of nerve regeneration, and the technical conditions of nerve reconstructive operations. This knowledge is a prerequisite for hand surgeons to perform their nerve reconstructive operations on a contemporary high level with an optimal result. After a critical review of literature data, the author reports his own experience and sketches the coming possible roads. A detailed list of references is also provided for those who are interested in the field. Orv. Hetil., 2012, 153, 1767–1778.

scholarly journals EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1497-1505
Author(s):  
R. B. Andrade ◽  
G. A. O. P. Costa ◽  
G. L. A. Mota ◽  
M. X. Ortega ◽  
R. Q. Feitosa ◽  
...  
Keyword(s):  
Global Climate ◽  
Biodiversity Loss ◽  
Semantic Segmentation ◽  
Training Sample ◽  
Training Data ◽  
Detection Methods ◽  
Amazon Forest ◽  
Convolutional Network ◽  
Detection Approach ◽  
Segmentation Methods

Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.

scholarly journals OUTLIER DETECTION METHOD USE FOR THE NETWORK FLOW ANOMALY DETECTION / IŠSKIRČIŲ RADIMO METODŲ TAIKYMAS ANOMALIJOMS KOMPIUTERIŲ TINKLO PAKETŲ SRAUTUOSE APTIKTI

2016 ◽  
Vol 8 (3) ◽  
pp. 327-333 ◽  
Author(s):  
Rimas Ciplinskas ◽  
Nerijus Paulauskas
Keyword(s):  
Anomaly Detection ◽  
Network Flow ◽  
Detection Method ◽  
Attack Detection ◽  
Detection Methods ◽  
Cyber Attack ◽  
Normal Network ◽  
New Type ◽  
Flow Detection ◽  
F Measure

New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure. Kibernetinių atakų gausa ir įvairovė bei siekis nuo jų apsisaugoti verčia nuolat kurti naujus ir tobulinti jau esamus atakų aptikimo metodus. Kaip rodo praktika, dabartiniai atakų atpažinimo metodai iš esmės veikia pagal antivirusinių programų principą, t.y. sudaromi žinomų atakų šablonai, kuriais remiantis yra aptinkamos atakos, tačiau pagrindinis tokių metodų trūkumas – negalėjimas aptikti naujų, dar nežinomų atakų. Šiai problemai spręsti yra pasitelkiami anomalijų aptikimo metodai, kurie leidžia aptikti nukrypimus nuo normalios tinklo būsenos. Straipsnyje yra pateiktas naujas metodas, leidžiantis aptikti kompiuterių tinklo paketų srauto anomalijas taikant lokalių išskirčių faktorių algoritmą. Atliktas tyrimas leido surasti požymių grupes, kurias taikant anomalūs tinklo srautai yra atpažįstami geriausiai, t. y. pasiekiamos didžiausios tikslumo, atkuriamumo ir F-mato reikšmės.

scholarly journals Periodicity: Interpreting Waves of Information in Osundare’s Harvestcall

2017 ◽  
Vol 10 ◽  
pp. 42-70
Author(s):  
Taofeek Olaiwola Dalamu
Keyword(s):  
Time Frame ◽  
The Past ◽  
Systemic Analysis ◽  
Literary Device ◽  
Different Shapes ◽  
Analytical Tools ◽  
The Waves

The study investigated the waves of information of Osundara’s Harvestcall to show textual movements as layered by the poet. To achieve that objective, the poem of about 76 lines was collapsed into 44 clauses as a tradition that paves a way for systemic analysis of texts in different shapes, sizes and constructs. Halliday’s Theme and Rheme served as analytical tools that processed the text after which the investigation utilized tables and graphs as indicators of waves of information of Harvestcall. The analysis reveals three separate waves of the text, namely: (i) sectional organization – perceives Sections I, II and III are the Theme while Section IV is the Rheme; (ii) clause constructs – demonstrates multiple Themes that flow to rhematic structures; and (iii) time frame exposition – espouses the past farming commitment as Theme and its current neglect as Rheme. In addition, observation shows waves interference. That is, the switching of Theme 2 and Subject Theme in Sections I, II and III for Subject Theme and Theme 2 respectively in Section IV. As linguistic concepts can reveal so much meanings of a literary device, the study suggests their applications across the board of genres of literature.

Y-Net: Dual-branch Joint Network for Semantic Segmentation

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Yizhen Chen ◽  
Haifeng Hu
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Computational Cost ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Global Context ◽  
Multi Level ◽  
The One ◽  
Public Datasets ◽  
High Level

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.

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