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A Literature Review of Steering Angle Prediction Algorithms for Self-driving Cars 1

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2nd international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2019

作者： Oussama, Aatiq Mohamed, Talea Information Processing Laboratory Ben M’Sik Faculty of Sciences Hassan II University of Casablanca Av. Idriss Elharti B.P. 7955 Casablanca Morocco

ISBN: (数字)9783030366742

ISBN: (纸本)9783030366735

A crucial requirement for intelligent, driverless cars is to maneuver without moving out of its drivable region of the road. It is well known that steering angle calculation plays an important role in maintaining the vehicle in the center of the road or within the boundary lanes to meet safety critical requirements. This paper presents a review of autonomous steering techniques for self driving cars which is a relatively unexplored task in the fields of computer vision, robotics and machine learning. Our principle aim is to find out the state-of-the-art models in traditional computer vision approach and end-to-end Deep learning approach. Subsequently we have analyzed and compared the performance of each model based on the reported experimental results. Our research investigations lead us to conclude that ResNet50 Deep network combined with event cameras can be assumed to give better prediction of the due wheel angle in comparison to the use of traditional cameras. An overview of future research direction and applications is also given. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

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PIMD signal Modeling Based on FTDNN 2

PIMD Signal Modeling Based on FTDNN

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2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Jang, Beomhee Im, Sungbin Lee, Donggeon Kim, Chonghoon Hong, Seungmo Song, Jinzi Soongsil Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781728151021

In this paper, modeling of the third-order passive intermodulation distortion (PIMD) caused by the downlink signal in the frequency division duplex (FDD) wireless communication system is developed with the Focused Time Delay Neural Network (FTDNN). PIMD is an interference signal generated due to the nonlinearity of a passive element in a wireless communication environment, and degrades the noise level of a receiver, resulting in a deterioration of communication quality. Offline learning and testing of the proposed approach are investigated with measured PIMD signals and transmitted downlink signals. As a result, it is confirmed that FTDNN structure is suitable for learning nonlinearity of the PIMD generation process.

关键词： passive intermodulation wireless communication system FDD nonlinear system FTDNN

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Target Speech signal Enhancement Based on Deep Neural Networks 2

Target Speech Signal Enhancement Based on Deep Neural Networ...

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2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Zhang, Xin Wang, MingJiang Xuan, XiaoGuang Sun, FengJiao Harbin Inst Technol Elect & Informat Engn Coll Shenzhen Peoples R China

ISBN: (纸本)9781728151021

This paper presents a speech enhancement system based on deep neural network (DNN) and covers the signal processing methods used in speech enhancement and the fundamentals of DNN. DNNs containing multiple hidden layers have the capability to suppress noise by learning the relationship between noisy speech and target clean speech. A series of experiments were carried out using the Chinese corpus to evaluate the performance of the speech enhancement model. Meanwhile, the model was identified as a result of a good generalization capability in mismatched noise types. In addition, the model is compared with OMLSA in terms of the quality of speech signals.

关键词： target speech signal enhancement speech signal processing deep neural networks

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Human Action Recognition using Pre-trained Convolutional Neural Networks 20

Human Action Recognition using Pre-trained Convolutional Neu...

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2020 2nd international conference on Video, signal and Image processing

作者： Cherry Aly Fazly Salleh Abas Hock Ann Goh Multimedia University Malaysia

ISBN: (纸本)9781450388931

Recognition of human action is one of the challenges in the field of artificial intelligence. Deep learning model has become a research issue in action recognition applications due to its ability to outperform traditional machine learning approaches. The Convolutional Neural Network is one of the architectures commonly used in most action recognition works. There are different models in the Convolutional Neural Network, but no study has been done to evaluate which model has the best performance in understanding human actions. Thus, in this paper, we compare the performance of two separate pre-trained models of deep Convolutional Neural Network in classifying the human actions to identify the different behaviours. GoogleNet and AlexNet are the used two models with fine-tuned parameters used for comparison, in addition, to use Long-Short Term Memory for the video's labels prediction. The paper's main contribution is that it offers a performance analysis of two separate fine-tuned deep CNN pre-trained models compared to the results of other recently proposed human action recognition methods applied on KTH, Weizmann, UCF11(YouTube actions) and UCF-Sports datasets.

关键词： Action recognition AlexNet Convolutional neural network (CNN) Deep learning GoogleNet Long-Short Term Memory (LSTM)

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Affective Interaction based Hybrid Approach for Emotion Detection using machine learning 2

Affective Interaction based Hybrid Approach for Emotion Dete...

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2nd international conference on Smart Systems and Inventive Technology, ICSSIT 2019

作者： Souza, Alice D. Souza, Rio D. St Raymond's PU College Vamanjoor Mangaluru India

ISBN: (纸本)9781728121192

Affective communication is a fresh region of research interest emerging in the interaction designers. This research will explore our hybrid approach's potential depends on both, Lexical and machine learning methods to detect the six emotional categories of Ekman in the text of the user. Initial results of the presentation evaluation of the suggested hybrid method is promoting and similar to relate the research. A demonstrative mobile application that uses the suggested strategy has been created to engage the users in a dialog that requests their views on multiple daily occurrences and offers appropriate affective answers. The novelty of this strategy is the selection of characteristics and the generation for classification of a single feature vector. Our primary objective is to improve the precision of voice classification of emotions by taking into account audio characteristics. Standard techniques like Natural Language processing, Support Vector machines, WordNet Affect and SentiWordNet are used in this job. © 2019 IEEE.

关键词： Emotion Recognition

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Application of manifold learning algorithms to improve the classification performance of an electronic nose

Application of manifold learning algorithms to improve the c...

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2020 international Instrumentation and Measurement Technology conference

作者： Leon-Medina, Jersson X. Anaya, Maribel Pozo, Francesc Tibaduiza, Diego A. Univ Nacl Colombia Dept Ingn Mecan & Mecatron Sede Bogota Bogota Colombia Univ Santo Tomas Fac Ingn Elect Bogota Colombia Univ Politecn Catalunya UPC Dept Matemat CoDAlab Barcelona Spain Univ Nacl Colombia Dept Ingn Elect & Elect Sede Bogota Bogota Colombia

ISBN: (纸本)9781728144610;9781728144603

The problem of classification and the use of robust methodologies in electronic nose sensor arrays applications are still open. Among the several steps in the developed methodologies, it is clear that feature extraction methods are one of the tools that allow improving the classification accuracy in this kind of sensors. As a contribution to solving this problem, in this work, a machine learning methodology is introduced to improve the signal processing and the development of methodologies in the classification task with electronic noses. The methodology considers a normalization to scale the data from the sensors, and four manifold learning algorithms were compared to reduce the dimensionality of the data at the input of the classification machine, these methods are Kernel PCA, Isomap, Laplacian Eigenmaps, and Locally Linear Embedding. The classifier used as a machine learning algorithm was K-NearestNeighbor (KNN) with Euclidean distance and one neighbor. To validate the methodology, a dataset of 3600 measurements that measured six volatile organic compounds is used. Holdout cross-validation approach was used to evaluate the proposed methodology, results showed the best classification accuracy of 98.33% was reached by Kernel PCA as a nonlinear feature extraction method. Finally, the influence in the number of dimensions used to reduce the data was determined to establish the correct number according to the best classification rate in the classification process.

关键词： electronic nose feature extraction manifold learning subspace learning odor recognition

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Developing Interactive Bible learning Model Based on Mobile for Children 2019

Developing Interactive Bible Learning Model Based on Mobile ...

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2nd international conference on Software Engineering and Information Management (ICSIM) / 2nd international conference on Big Data and Smart Computing (ICBDSC)

作者： Sutopo, Hadi Purnomo, Hindriyanto D. Maria, Silaen Sondang Lee, Swati Lobodally, Altobeli Prasida, Arie Setiawan Inst Teknol & Bisnis Kalbis Jl Pulomas Selatan Kav 22 Jakarta 13210 Indonesia Univ Kristen Satya Wacana Jl Dr O Notohamidjojo Blotongan 50714 Salatiga Indonesia Univ Persada Indonesia YAI Jl Diponegoro 74 Jakarta 10430 Indonesia Dr Lee Brain Acad Grand Slipi Tower Lt-23DJalan S Parman Kav 22-24 Jakarta 11480 Indonesia

ISBN: (纸本)9781450366427

Bible stories have been introduced to children at early ages and continuously learned. The learning process is conventionally conducted in classrooms with books as the main media and lately accompanied by video presentations. However, the current life style in the IT era may shift children learning style. For them, smartphones and other digital media are more interesting than books. This paper is intended to create a mobile-based Bible-learning for children and youth. In developing the application, the researchers conduct qualitative approaches combining Borg and Gall's Research and Development Method with Luther's Multimedia Development Life Cycle. It is the utmost hope that the new IT-based learning model could facilitate easier, more fun and more interesting learning for children and youth. This research is still on going and the researchers would like to present a smartphone-based Bible learning games. Collected data is processed using NVivo, a qualitative software data processing. Research findings show that mobile-based learning including games is the most feasible model.

关键词： mobile multimedia Bible learning game interactive

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WInd TURBINE PLANETARY GEAR FAULT IDENTIFICATION USING STATISTICAL COndITION IndICATORS And machine learning 38

WIND TURBINE PLANETARY GEAR FAULT IDENTIFICATION USING STATI...

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38th ASME international conference on Ocean, Offshore and Arctic Engineering (OMAE 2019)

作者： Peeters, Cedric Verstraeten, Timothy Nowe, Ann Helsen, Jan Vrije Univ Brussel Dept Appl Mech Acoust & Vibrat Res Grp Pl Laan 2 B-1050 Brussels Belgium Vrije Univ Brussel Artificial Intelligence Lab Pl Laan 2 B-1050 Brussels Belgium

ISBN: (纸本)9780791858899

This work describes an automated condition monitoring framework to process and analyze vibration data measured on wind turbine gearboxes. The current state-of-the-art in signal processing often leads to a large quantity in health indicators thanks to the multiple potential pre-processing steps. Such large quantities of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper proposes a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is investigated on an experimental wind turbine gearbox vibration data set. It is found that the combination of physics -based statistical indicators with machine learning is capable of detecting planetary gear stage damage and significantly simplifying the data analysis and inspection in the process.

关键词： Planetary gears Wind turbines machine learning Vibration signal processing Turbines Physics Mechanical drives Inspection Condition monitoring Damage

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Analysis on EEG signal with machine learning 2

Analysis on EEG signal with Machine Learning

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Cha, Jaehoon Kim, Kyeong Soo Zhang, Haolan Lee, Sanghyuk Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn SIP 111 Renai Rd Suzhou 215123 Jiangsu Peoples R China Zhejiang Univ Ningbo Inst Technol SCDM Ctr Hangzhou Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In this paper, research on electroencephalogram (EEG) is carried out through principal component analysis (PCA) and support vector machine (SVM). PCA is used to collect EEG data characteristics to discriminate the behaviors by SVM methodology. The actual EEG signals are obtained from 18 experimenters who raised hands with meditation and actual movement during the experiments. The 16-channel data from the experiments form one data set. In order to get principal component of EEG signal, 16 features are considered from each channel and normalized. Simulation results demonstrate that two behaviors - i.e., raising hands and meditation - can be clearly classified using SVM, which is also visualized by a 2-dimensional principal component plot. Our research shows that specific human actions and thinking can be efficiently classified based on EEG signals using machine learning techniques like PCA and SVM. The result can apply to make action only with thinking.

关键词： Electroencephalogram (EEG) principal component analysis (PCA) neural network decision making brain computer interface (BCI)

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Laser Scar Classification in Retinal Fundus Images Using Wavelet Transform and Local Variance 1st

Laser Scar Classification in Retinal Fundus Images Using Wav...

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1st international Symposium on Computer Vision and machine Intelligence in Medical Image Analysis, ISCMM 2019 in conjunction with the 2nd international conference on Advanced Computational and Communication Paradigms, ICACCP 2019

作者： Raut, Rashmi Sapate, Visharad Rokde, Abhay Pachade, Samiksha Porwal, Prasanna Kokare, Manesh Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

ISBN: (纸本)9789811387975

Diabetic retinopathy (DR) affects the vision of the person and may eventually lead to blindness. In the initial stage of the disease, patients are treated with a laser to restrict its progression. Such laser treatment leaves behind scars on the retina and patients are advised to undergo screening regularly to track further complications. This paper presents a novel retinal background characterization approach that explores the potential of discrete wavelet transform and rotational-invariant variance features for texture classification of retinal images with and without laser marks. For this experiment, different classifiers, namely, support vector machine, naive Bayes, neural network, and random forest classifiers are tested. We used two publicly available datasets, namely, LMD-DRS and LMD-BAPT. In all cases, the proposed approach obtained the sensitivity, specificity, and accuracy values higher than 68.9%, 70.2%, and 69.4%, respectively. It was found that all performance measures achieve over 87.5, 89.4, and 86.7% for the classification task using random forest classifier. These promising results suggest that the proposed technique can discriminate retinal images having laser marks and without laser marks, and has the potential to be an important constituent in computerized screening solution for retinal images. © Springer Nature Singapore Pte Ltd. 2020

关键词： Discrete wavelet transforms

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