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Management and Teaching Practice for In-Depth Interactive Lesson of Digital signal processing 2nd

Management and Teaching Practice for In-Depth Interactive Le...

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2nd international conference on Decision Science and Management, ICDSM 2019

作者： Wang, Shiqiang Gao, Caiyun Zhang, Qin Wang, Xin Zeng, Huiyong Bai, Juan Air and Missile-Defence College Air Force Engineering University Xi’an710051 China Department of Basic Sciences Air Force Engineering University Xi’an710051 China

ISBN: (纸本)9789811535871

Effectively carrying out classroom teaching management under the objective law of teaching activities can further enhance the teaching effect. The digital signal processing course attracts much attention because of its versatility, comprehensiveness, strong frontier, and practicality, but because of its abstract concept, cumbersome formulas, using mainstream education methods such as blackboard, and PPT, the teaching results are not ideal and the students have limited harvest. Taking information technology as the traction and intelligent interactive whiteboard system as the platform, through the effective classroom management, an in-depth interactive teaching method based on intelligent interactive whiteboard is constructed. Practice shows that in-depth interactive teaching methods can break the traditional one-way teaching and realize the two-way interaction between teachers and students. That is to say, through this teaching method, teachers can vividly expound the key teaching contents, clearly explain the arguments, and perform the image interpretation. Students become passive and active, actively participate, can actively think and feedback, and practice multidimensional model thinking through in-depth interactive teaching, and extract methods from it. In addition, under the effective classroom management constraints, in-depth interactive teaching enhances the fun of teaching and learning, turning the classroom into a real interactive classroom, and its results can promote the development of education and teaching of digital signal processing courses. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Teaching

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Wireless signal Service Type Identification Based on Convolutional Neural Network 2

Wireless Signal Service Type Identification Based on Convolu...

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IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Tian, Haomin Yin, Liang Yang, Xiaofeng Li, Shufang Yu, Haoyang Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Labratory Network Syst Architecture & Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781538692981

Nowadays, with the birth of 5G and the Internet of Things, more and more business signals have emerged. The identification of signal service types has become a hot research topic. Whether it is suitable for daily life in the military field, there is a wide application prospect. Using the power spectrum data of the wireless signal, the characteristics of the power spectrum waveform are captured to identify the type of traffic of the wireless signal. This paper proposes the use of convolutional neural networks to extract and classify the wireless signal power spectrum data. After hundreds of iterative training, it can achieve an ideal recognition effect.

关键词： signal recognition convolutional neural network deep learning feature extraction

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Design of a Mini Robot for the Automation of 3D Winding machines Axes and Self-correction by Artificial Vision Using Deep learning 2nd

Design of a Mini Robot for the Automation of 3D Winding Mach...

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

作者： El Wahabi, Abdelhamid Baraka, Ibrahim Hadj Hamdoune, Salaheddine El Mokhtari, Karim LIST UAE Faculty of Science and Technology Tangier Tangier Morocco Data Science Laboratory Ryerson University Toronto Canada

ISBN: (纸本)9783030366735

Object recognition is among the most important subjects in computer vision, it has undergone a huge evolution during these last decades, but in the last years artificial intelligence has seen the appearance of Deep learning, and through the efforts of researchers, Deep learning is having great success, its applications have touched on different fields, such as robotics, industry, automotive. In this context, in collaboration with an Automotive components manufacturer and FST faculty of sciences and technologies of tangier (UAE University) they have taken the initiative to develop an object recognition and self-correction system for a winding machine that requires a good accuracy of location of the needle using the Deep learning which is the purpose of this paper. This report summarizes the work done within this Company concerning the development and implementation of a system for automating and self-correcting the location of the needles of winding machines using artificial vision with Deep learning. The convolutional neural networks have become advanced methods for classification and detection of objects over the last five years. At present, they work better than conventional image processing method set, on many image classification data sets. Most of these datasets are based on the notion of concrete classes. In this paper, we present a new set of image classification data as well as object detection data, which should be easy for humans to solve, but its variations are difficult for CNN. The classification performance of popular CNN architectures is evaluated on this dataset and variations of this dataset may be of interest for future research. © 2020, Springer Nature Switzerland AG.

关键词： Computer vision

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Combining Quality Metrics for improved HDR Image Quality Assessment 2

Combining Quality Metrics for improved HDR Image Quality Ass...

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2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Choudhury, Anustup Daly, Scott Dolby Labs Inc Sunnyvale CA 94085 USA

ISBN: (纸本)9781728111988

We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework and non-linearly combine the scores from different quality metrics using support vector machine regression. To improve performance and reduce complexity, we use the Sequential Forward Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.

关键词： High dynamic range image quality assessment combination of quality metrics machine learning

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Lung image segmentation by generative adversarial networks 2

Lung image segmentation by generative adversarial networks

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

作者： Cai, Jiaxin Zhu, Hongfeng Xiamen Univ Technol Sch Appl Math Xiamen Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.

关键词： Image segmentation lung image analysis machine learning deep learning generative adversarial networks

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Validation Feedback based Image Transfer Network for Data Augmentation 20

Validation Feedback based Image Transfer Network for Data Au...

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

作者： Weili Chen Seiichiro Kamata Zitang Sun Waseda University Japan

ISBN: (纸本)9781450388931

Modern image classifiers are often suffering over-fitting problems because of the insufficient number of images in the dataset. Data augmentation is a strategy to increase the number of training samples. However, recent data augmentation methods are designed manually and cannot generate real-like images. Some neural network-based image generation methods such as GAN and VAE can also be used for data augmentation, but they are usually applied to unbalanced datasets. Since the generated images cannot be guaranteed to be from the same label, using them to extend a balanced dataset may lead to decreasing the accuracy of the classifier. In this paper, we propose an image transfer network to produce images that automatically adapt to a specific dataset and classifier. The image transfer network will search for the output images which can maximize the validation accuracy and help the classifier to overcome the over-fitting problems. Through the experiments, our method achieves high accuracy on CIFAR-10 and CIFAR-100 datasets. Moreover, since it could combine with other data augmentation methods, we show that using our method can push the state-of-the-art results furthermore.

关键词： Neural Network Data augmentation machine learning

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Literature reviews on applying artificial intelligence/machine learning to software engineering research problems: Preliminary 2

Literature reviews on applying artificial intelligence/machi...

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2nd Software Engineering Education Workshop, SEED 2019

作者： Muenchaisri, Pornsiri Department of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

This paper is aimed to explore the application of Artificial Intelligence/machine learning (AI/ML) to software engineering research problems. Which activities of software engineering use AI/ML the most for solving research problems? The scope of the paper is to preliminary review research papers published in Asia-Pacific Software Engineering conference 2018 (APSEC 2018) proceedings and researches conducted at the Department of Computer Engineering, Chulalongkorn University (CPCU). The author manually reviews papers with some keywords such as machine learning, neural network, and natural language processing. The result shows that machine learning is used in coding and software quality improvement activities more than other activities. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Application programs

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Security Issues, Vulnerabilities and Countermeasures in Information Communication Technology and signal processing 2

Security Issues, Vulnerabilities and Countermeasures in Info...

引用

2nd international conference on signal processing and Communication, ICSPC 2019

作者： Goel, Akshay Kumar Gaur, Jayesh Rose, Ajay HMR Institute of Engineering Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781728118499

This paper discusses the heavy usage of source software which is free and open was because of the fact that it had many advantages associated with it like it was a cost saving and fast method but we know that everything has some disadvantages associated with it, this was also the case with the heavy usage of above discussed source software, it brought many vulnerabilities with it and researchers are still struggling to find a method of dealing with these vulnerabilities. Vulnerabilities are not only present in such open source software's, it is also present in outsourcing of Information Communication Technology (ICT). The algorithm of signal processing is also discussed in the paper. Various applications of signal processing are also discussed in the paper. The last section of the paper throws light on the challenges and goals of sensor network security. Finally, the paper concludes discussing the security vulnerabilities in ICT and signal processing. © 2019 IEEE.

关键词： signal processing

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2019 2nd IEEE international conference on Information Communication and signal processing, ICICSP 2019

2019 2nd IEEE International Conference on Information Commun...

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2nd IEEE international conference on Information Communication and signal processing, ICICSP 2019

ISBN: (纸本)9781728151021

The proceedings contain 106 papers. The topics discussed include: FPGA implementation of real-time star centroid extraction algorithm;PIMD signal modeling based on FTDNN;Wireless Sensor Networks Congestion Control Protocol Based on intelligent power allocation;region adaptive mode selection algorithms for image super-resolution;computation offloading management in vehicular edge network under imperfect CSI;offloading decisionalgorithm using evolutionary game for mobile edge computing;seismic facies recognition based on prestack data using two-dimensional gabor transform and unsupervised clustering;and research on blind equalization algorithm of multipath interference PCM-FM signal based on CMA.

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Food Image Recognition for Price Calculation using Convolutional Neural Network 2019

Food Image Recognition for Price Calculation using Convoluti...

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3rd international conference on Digital signal processing (ICDSP) / 2nd international conference on Computer Graphics and Virtuality (ICCGV)

作者： Nordin, Md Jan Aziz, Norshakirah Xin, Ooi Wei Univ Kebangsaan Malaysia Fac Informat Sci & Technol CAIT Bangi Malaysia Univ Teknol PETRONAS High Performance Cloud Comp Ctr HPC3 Comp Informat Sci Dept Seri Iskandar Malaysia Univ Teknol PETRONAS Comp Informat Sci Dept Seri Iskandar Malaysia

ISBN: (纸本)9781450362047

This project is attempting to solve the issue of unfair and inconsistent food price being charged in economy rice or mixed rice that widely seen in the cafe of hawker stall in Malaysia. The main cause of the problem is the absence of standardized price list of the food which causes the pricing of the mixed rice remains unknown. Hence, the authors had decided to propose this project by utilizing convolutional neural network (CNN) algorithm and develop a web application to ease the vendor as well as to provide transparency to the buyer on the food price being charged. CNN model is trained to classify the different types of food. The food price will be stored in a database of the web application in order to calculate the food price with the recognized food in the machine learning model. The outcome of this project is a customized web application for Village 3 Cafe, UTP with a trained CNN classification model at the backend.

关键词： Food image recognition convolutional neural network (CNN) machine learning

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