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Person Localization in an Indoor Environment with Artificial Intelligence 19

Person Localization in an Indoor Environment with Artificial...

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5th international conference on Computing and Artificial Intelligence (ICCAI)

作者： Acevedo, Elena Orozco, Ricardo Acevedo, Antonio Felipe, Federico Inst Politecn Nacl Av IPN S-N Lindavista 07738 Mexico

ISBN: (纸本)9781450361064

Associative models are Artificial Intelligence tools and have been used in many applications such as pattern recognition, classification, encryption, among others. In this paper we applied these models to trace a person in an indoor environment by the means of the power of a Wi-Fi signal. We deal with this problem as a classification task. We used a preprocessing for the data to improve the results. Our performance was 95.75%.

关键词： Artificial Intelligence Associative Models pattern recognition Classification User Localization Indoor Environment

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9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2020, held as parallel events of the 16th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2020

9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th ...

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9th Mining Humanistic Data Workshop, MHDW 2020, and the 5th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2020, held as parallel events of the 16th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2020

ISBN: (纸本)9783030491895

the proceedings contain 21 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: threat Landscape of Next Generation IoT-Enabled Smart Grids;towards a Smart Port: the Role of the Telecom Industry;a Graph-Based Extension for the Set-Based Model Implementing Algorithms Based on Important Nodes;a Sentiment-Based Hotel Review Summarization Using Machine Learning Techniques;an Advanced Deep Learning Model for Short-Term Forecasting U.S. Natural Gas Price and Movement;fake News Detection Regarding the Hong Kong Events from Tweets;improving Movie Recommendation Systems Filtering by Exploiting User-Based Reviews and Movie Synopses;the Converging Triangle of Cultural Content, Cognitive Science, and Behavioral Economics;application and Algorithm: Maximal Motif Discovery for Biological Data in a Sliding Window;A New Approach to 5G and MEC Integration;fingerprints recognition System-Based on Mobile Device Identification Using Circular String pattern Matching Techniques;mining and Analysis of Air Quality Data to Aid Climate Change;business Aspects of the Neutral Host Model: the Immersive Video Services Case;combined 5G-Based Video Production and Distribution in a Crowded Stadium Event;dynamic Network Slicing: Challenges and Opportunities;dynamic Resource Allocation and Computation Offloading for Edge Computing System;Intelligent Orchestration of End-to-End Network Slices for the Allocation of Mission Critical Services over NFV Architectures;on the Prediction of Future User Connections Based on Historical Records in Wireless Networks;Programmable Edge-to-Cloud Virtualization for 5G Media Industry: the 5G-MEDIA Approach.

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Age Invariant Face recognition using Frangi2D Binary pattern 19

Age Invariant Face Recognition using Frangi2D Binary Pattern

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2nd international conference on Image and Graphics Processing (ICIGP) / 5th international conference on Virtual Reality

作者： Afroze, Sabah Beham, M. Parisa Tamilselvi, R. Maraikkayar, S. M. Seeni Mohamed Aliar Rajakumar, K. Sethu Inst Technol Dept Elect & Commun Engn Pulloor 626115 Tamil Nadu India

ISBN: (纸本)9781450360920

the field of computer vision is devoted to discovering algorithms, data representations and computer architectures that embody the principles underlying visual capabilities. Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high level understanding from digital images or videos. While very promising result has been shown on face recognition related problems, age invariant face recognition still relics a challenge. Facial appearance of a human varies over time, which results in substantial intra-class variations. In order to address this problem, we propose Frangi2D method for normalization, Linear Binary pattern (LBP) for feature extraction and Sparse Representation Classifier (SRC). Extensive results on a well-known public domain face aging dataset: MORPH. the experimental results show the superiority of our proposed method in age invariant face recognition.

关键词： Age Invariant Face recognition Frangi 2D sparse representation classifier (SRC) Local binary pattern (LBP)

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Enhanced Segmentation-CNN based Finger-Vein recognition by Joint Training with Automatically Generated and Manual Labels 5

Enhanced Segmentation-CNN based Finger-Vein Recognition by J...

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5th IEEE international conference on Identity, Security, and Behavior Analysis (ISBA)

作者： Jalilian, Ehsaneddin Uhl, Andreas Univ Salzburg Dept Comp Sci Jakob Haringer Str 2 Salzburg Austria

ISBN: (纸本)9781728105321

Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems. For the first time, we utilize state-of-the-art semantic segmentation CNNs to extract vein patterns from near-infrared finger imagery and use them as the actual vein features in biometric finger-vein recognition. In this context, beside investigating the impact of training data volume, we propose a training model based on automatically generated labels, to improve the recognition performance of the resulting vein structures compared to (i) network training using manual labels only, and compared to (ii) well established classical recognition techniques relying on publicly available software. Proposing this model we also take a crucial step in reducing the amount of manually annotated labels required to train networks, whose generation is extremely time consuming and error-prone. As further contribution, we also release human annotated ground-truth vein pixel labels (required for training the networks) for a subset of a well known finger-vein database used in this work, and a corresponding tool for further annotations.

关键词： Veins Image segmentation Feature extraction Training pattern recognition Computer architecture Decoding

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A New U-Net Based License Plate Enhancement Model in Night and Day Images 5th

A New U-Net Based License Plate Enhancement Model in Night a...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Chowdhury, Pinaki Nath Shivakumara, Palaiahnakote Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Gjøvik Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. this paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. this process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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A Spatial Density and Phase Angle Based Correlation for Multi-type Family Photo Identification 5th

A Spatial Density and Phase Angle Based Correlation for Mult...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Grouver, Anaica Shivakumara, Palaiahnakote Kaljahi, Maryam Asadzadeh Chetty, Bhaarat Pal, Umapada Lu, Tong Hemantha Kumar, G. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Google Developers Group NASDAQ Bangalore India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Mysore MysoreKarnataka India

ISBN: (纸本)9783030412982

Due to change in mindset and living style of humans, the numbers of diversified marriages are increasing all around the world irrespective of race, color, religion and culture. As a result, it is challenging for research community to identify multi type family photos, namely, normal family (family of the same race, religion or culture), multi-culture family (family of different culture, religion or race) from the family and non-family photos (images with friends, colleagues, etc.). In this work, we present a new method that combines spatial density information with phase angle for multi-type family photo classification. the proposed method uses three facial key points, namely, left-eye, right-eye and nose, for the features which are based on color, roughness and wrinkleless of faces, these are prominent for extracting unique cues for classification. the correlations between features of Left & Right Eyes, Left Eye & Nose and Right Eye & Nose are computed for all the faces in an image. this results in feature vectors for respective spatial density and phase angle information. Furthermore, the proposed method fuses the feature vectors and feeds them to the Convolutional Neural Network (CNN) for the classification of the above-three class problem. Experiments conducted on our database which contains three classes, namely, multi-cultural, normal and non-family images and the benchmark databases (due to Maryam et al. and Wang et al.) which contain two class-family and non-family images, show that the proposed method outperforms the existing methods in terms of classification rate for all the three databases. © 2020, Springer Nature Switzerland AG.

关键词： Database systems

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ForestNet – Automatic Design of Sparse Multilayer Perceptron Network Architectures Using Ensembles of Randomized Trees 5th

ForestNet – Automatic Design of Sparse Multilayer Perceptro...

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5th Asian conference on pattern recognition, ACPR 2019

作者： Rodríguez-Salas, Dalia Ravikumar, Nishant Seuret, Mathias Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr. 3 Erlangen91058 Germany School of Computing University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9783030414030

In this paper, we introduce a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet. Networks built using our approach are capable of handling high-dimensional data and learning representations of both visual and non-visual data. the proposed approach first builds an ensemble of randomized trees in order to gather information on the hierarchy of features and their separability among the classes. Subsequently, such information is used to design the architecture of a sparse network, for a specific data set and application. the number of neurons is automatically adapted to the dataset. the proposed approach was evaluated using two non-visual and two visual datasets. For each dataset, 4 ensembles of randomized trees with different sizes were built. In turn, per ensemble, a sparse network architecture was designed using our approach and a fully connected network with same architecture was also constructed. the sparse networks defined using our approach consistently outperformed their respective tree ensembles, achieving statistically significant improvements in classification accuracy. While we do not beat state-of-art results with our network size and the lack of data augmentation techniques, our method exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks. © Springer Nature Switzerland AG 2020.

关键词： Multilayer neural networks

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Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes 17th

Distributed Cloud Intelligence: Implementing an ETSI MANO-Co...

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17th international conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020

作者： Faticanti, Francescomaria Zormpas, Jason Drozdov, Sergey Rausch, Kewin García, Orlando Avila Sardis, Fragkiskos Cretti, Silvio Amiribesheli, Mohsen Siracusa, Domenico Fondazione Bruno Kessler Trento Italy University of Trento Trento Italy Konica Minolta Global R&D London United Kingdom Atos Madrid Spain

ISBN: (纸本)9783030630577

While solutions for cloud bursting already exist and are commercially available, they often rely on a limited set of metrics that are monitored and acted upon when user-defined thresholds are exceeded. In this paper, we present an ETSI MANO compliant approach that performs proactive bursting of applications based on infrastructure and application metrics. the proposed solution implements Machine Learning (ML) techniques to realise a proactive offloading of tasks in anticipation of peak utilisation that is based on pattern recognition from historical data. Experimental results comparing several forecasting algorithms show that the proposed approach can improve upon reactive cloud bursting solutions by responding quicker to system load changes. this approach is applicable to both traditional datacentres and applications as well as 5G telco infrastructures that run Virtual Network Functions (VNF) at the edge. © 2020, Springer Nature Switzerland AG.

关键词： pattern recognition

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5th IAPR TC 9 Workshop on pattern recognition of Social Signals in Human-Computer Interaction, MPRSS 2018

5th IAPR TC 9 Workshop on Pattern Recognition of Social Sign...

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5th IAPR TC 9 Workshop on pattern recognition of Social Signals in Human-Computer Interaction, MPRSS 2018

ISBN: (纸本)9783030209834

the proceedings contain 9 papers. the special focus in this conference is on pattern recognition of Social Signals in Human-Computer Interaction. the topics include: A first-person vision dataset of office activities;perceptual judgments to detect computer generated forged faces in social media;combining deep and hand-crafted features for audio-based pain intensity classification;deep learning algorithms for emotion recognition on low power single board computers;improving audio-visual speech recognition using gabor recurrent neural networks;evolutionary algorithms for the design of neural network classifiers for the classification of pain intensity;visualizing facial expression features of pain and emotion data.

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Rock burst risk prediction method based on multi-factor pattern recognition and its application in coal mine 5

Rock burst risk prediction method based on multi-factor patt...

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5th international conference on Advances in Energy, Environment and Chemical Engineering (AEECE)

作者： Zhao, Weiguo Lan, Tianwei Wang, Jiren Sun, Jiuzheng Qiang, Li Liaoning Tech Univ Coll Min Fuxin 123000 Liaoning Peoples R China Heilongjiang Min Grp Shuangyashan Branch Shuangyashan 155100 Liaoning Peoples R China

Rockburst is one of the typical dynamic disasters in coal mine, and risk prediction of rockburst is the primary task of implementing comprehensive prevention and control measures. In order to predict the risk of rockburst in different areas of No. 9 coal seam in Jixian coal mine, the geo-dynamic division method is adopted to determine the characteristics of fault developments and mine field structures. Geological structure model is established and the influencing factors of rockburst such as fracture structure, roof lithology, stress and mining depth are analyzed in this paper. On these basics, the multi-factor pattern recognition method for predicting rockburst risk is put forward, and the risk of rockburst in Jixian coal mine is divided into four grades. the prediction results of the comprehensive index method for the 4th panel in second mining area are compared with the prediction outcomes of the multi-factor pattern recognition method. Results show that the multi-factor pattern recognition method have good performance in predicting the rockburst risk, thus providing theoretical and technical support for the management of rockburst.

关键词： Rock bursts

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