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Hybrid Transform-Based Image Compression Using Adaptive Grid Scanning 1

引用

2nd International Conference on intelligent computing, Information and Control systems, ICICCS 2020

作者： Jetti, Venkatateja Karsh, Ram Kumar National Institute of Technology Silchar Assam788010 India

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

A lot of attention has been paid by the researchers for lossy color image compression. Due to the high uses of multimedia, a high image compression ratio while keeping good image quality is still a research issue. In this paper, an image compression is proposed using adaptive scanning, in which discrete wavelet transform (DWT) is performed to source image, discrete cosine transform (DCT) is performed to obtained DWT elements. Next, nonzero quantized discrete cosine transform (DCT) coefficients are represented in less number of bits. First, the source image is divided into 16 × 16 grids and then DWT transformed, later the obtained elements are divided into 8 × 8 grids and then DCT is performed, quantized, differential encoded, and an indexed vector is formed from the quantized matrix using adaptive scanning. Furthermore, the indexed vector is divided into two indexed vectors, namely nonzero vector and zerocount vector. The nonzero coefficients are stored in nonzero vector and the number of zeros preceding a nonzero coefficient is stored in zerocount vector. A new block is introduced to store the number of bits required to store the highest value of nonzero coefficient in nonzero vector so that all the nonzero elements can be represented in that many bits rather than a fixed number of bits. The experimental results show that the proposed method has achieved more compression ratio with slight reduction in image quality as compared to existing recent methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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Improved Electric Wheelchair Controlled by Head Motion 1

引用

International conference on Research in intelligent and computing in Engineering, RICE 2020

作者： Bui, He-Thong Nguyen, Le-Van Ngo, Thanh-Nghi Nguyen, Tuan-Sinh V. Ho, Anh-Ngoc T. Phan, Qui-Tra The University of Da Nang-University of Technology and Education Danang Viet Nam The University of Da Nang-University of Science and Technology Danang Viet Nam

ISBN: (数字)9789811575273

ISBN: (纸本)9789811575266

The electric wheelchairs are designed to assist disabled people. However, for people with disabilities who have a higher degree of impairment, such as quadriplegics, who cannot move any of the body parts except the head, medical devices for them are usually very complex, rare and expensive. In this paper, we improved an electric wheelchair model that is controlled by the motion of the head by using a microcontroller system. The system includes microelectromechanical components: tilt angle sensors and mechanical mechanisms. A new head-motion recognition technique based on processing angular acceleration data. The process control of wheelchair motion has been achieved by using 6050 tilt angle sensor and Arduino family UNO R3 microcontroller. The signal of head movement direction is sent to the microcontroller. Depending on the orientation of the sensor, microcontroller will control wheels, such as turn left, turn right, moving forward or backward with the help of DC motors. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Secure Communication in Internet of Things Based on Packet Analysis 1

引用

International Conference on Machine Intelligence and Soft computing, ICMISC 2020

作者： Narayana, V. Lakshman Gopi, A. Peda Department of IT Vignan’s Nirula Institute of Technology & Science for Women PedaPalakaluru Guntur AP India Department of CSE Vignan’s Nirula Institute of Technology & Science for Women PedaPalakaluru Guntur AP India

ISBN: (数字)9789811595165

ISBN: (纸本)9789811595158

Internet of Things (IoT) platform is projected to continue to expand to more than 80 billion connected gadgets by 2025. This flood of relevance opens up a wide range of roads to those with a noxious aim of going after the ignorant. With the anticipated development of IoT gadgets, it is inferred that a progressive way of life for your regular customer. As more gadgets coordinate our day-to-day lives, the safety of these gadgets appears to be progressively significant. In this paper, for the most part, RPL convening model is focused. Analyzing the route control messages in this protocol is performed. The proposed mechanism focuses mainly on the most problematic communicative attack called man in the middle attack. Proposed mechanism produces promising outcomes with respect to secure communication. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Unsupervised Deep Learning for Detection of Non-uniform Surface Defect Distributions in Flat Steel Production 1

引用

Workshop on Impact and Opportunities of Artificial Intelligence in the Steel Industry, 2020

作者： Camargo-Torres, Nicolas Brandenburger, Jens VDEH-Betriebsforschungsinstitut Sohnstr. 65 Düsseldorf40237 Germany

ISBN: (数字)9783030693671

ISBN: (纸本)9783030693664

Over the past few years, deep learning architectures had achieved astonishing success in solving problems in a wide array of fields. The main requirement for the use of deep learning techniques is the existence of a large enough dataset for training. Recent advances in digitalization and Industry 4.0 have made it possible to acquire, process, and store large amounts of data, allowing the use of deep learning in industrial applications. In the framework of a former research project a large database containing product and production data for the manufacture of flat steel was generated. This database contains, among other information, the in-coil positions of surface defects over multiple production stages. An analysis of these data suggested that the presence of an accumulation of surface defects at certain regions of the coil could be an indication of problems in the configuration of the production parameters. This prompted the need for a tool that can automatically identify production stages where a non-uniform distribution of surface defects is present in the coils. This work uses a deep learning model to automatically detect and cluster coils with non-uniform defect distributions. Since for this purpose generally no class labels are provided in the databases, only unsupervised algorithms were used. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Real-Time Numerical Gesture Recognition Using MPU9250 Motion Sensor 1

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2nd International Conference on intelligent computing, Information and Control systems, ICICCS 2020

作者： Bommannan, Sathish Raja Vineeth, Chennuru Uma Hema Sri, Mylavarapu Sri Vidya, Boyanapalli Vidhya, S. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

Human–computer interaction is one of the most exciting areas of research. Hand gesture recognition stands vital for developing better human–computer interaction systems. Most of the existing approaches using camera-based or 3D depth sensors for hand gesture recognition are rather expensive and sensitive to environmental changes. In this paper, we propose a low-cost data glove embedded with MPU9250 motion sensor which overcomes the drawbacks of existing systems. In our work, the primary focus is to develop a numerical gesture recognition system deployable in any real-time application. An extensive comparison of the performance of different machine learning and neural network models is presented. An optimal network model is chosen, and details of deploying the trained model in a real-time unity game application are presented. In our experiment, the highest accuracy achieved is 98.41% with an average real-time inference delay of 2 ms. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Human computer interaction

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Improving the Performance of an Ultrasonic Sensor Using Soft computing Techniques for 2D Localization 1

引用

2nd International Conference on intelligent computing, Information and Control systems, ICICCS 2020

作者： Vijay Sunder, R. Venkatachalam, S. Sree Yeshvathi, G. Venkat Hruday, Kaza Adarsh, S. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811584435

ISBN: (纸本)9789811584428

Ultrasonic sensors (HCSR04) are used for distance measurements whose range extends up to a distance of 4 m. These kinds of sensors are found being used in car reverse parking mechanisms and also helping blind people in navigation. But, there always exists a trade-off between distance and accuracy. This paper presents a method to improve the accuracy of an ultrasonic sensor using soft computing techniques for 2-D indoor localization. Advanced neuro fuzzy inference system (ANFIS) was used to minimize the error in distance measurement. Finally, the simulated FIS model was implemented in hardware for real-time evaluation. The error analysis showed that the distance estimation accuracy was improved by 77%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soft computing

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intelligent Assistive Algorithm for Detection of Osteoarthritis in Wrist X-Ray Images Based on JSW Measurement 1

引用

International Conference on Machine Intelligence and Soft computing, ICMISC 2020

作者： Bharodiya, Anil K. Gonsai, Atul M. BCA Department UCCC & SPBCBA & SDHG College of BCA and I.T. Surat Gujarat India Department of Computer Science Saurashtra University Rajkot Gujarat India

ISBN: (数字)9789811595165

ISBN: (纸本)9789811595158

Osteoarthritis is a common disease in human having age more than 40 years. It happens in bone joints such as knee, hips, hand, wrist. In osteoarthritis, joint cartilage is ruptured, and as a result, bone rubs against bones with a severe pain. Orthopedic doctors are advising patients to take an X-ray image of joints to diagnose osteoarthritis. After physical X-ray printing, a doctor or physician studies it physically to diagnose joint space width (JSW). But, it is not exactly possible to measure accurate JSW because of visual interpretation and JSW may be different from orthopedician to orthopedician. In this research paper, we have presented an intelligent algorithm to assist an orthopedician to measure JSW of wrist X-ray images automatically and to diagnose whether patient suffers from wrist osteoarthritis or not based on JSW measurement using digital image processing. We have given name to this algorithm as WODJSW (wrist osteoarthritis detection using JSW). The algorithm is divided into steps such as inputting X-ray images of human hand, ROI detection, edge detection, measurement of vector height, conversion of vector height into millimeter, comparison of JSW with standard wrist joint width and making decision. To diagnose osteoarthritis, we have conducted experiments on 75 X-ray images of human hand collected from openly available dataset and achieved an average accuracy of 96%. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Joints (anatomy)

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021) 1st ed. 2021

Proceedings of the International Conference on Artificial In...

引用

丛书名： advances in intelligent systems and computing

2021年

作者： Aboul Ella Hassanien Abdelkrim Haqiq Peter J. Tonellato Ladjel Bellatreche Sam Goundar Ahmad Taher Azar Essaid Sabir Driss Bouzidi

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Detection of DoS, DDoS Attacks in Software-Defined Networking 1

引用

International conference on Research in intelligent and computing in Engineering, RICE 2020

作者： Truong, Dinh-Tu Tran, Khanh-Dang Nguyen, Quoc-Binh Tran, Dac-Tot Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City University of Food Industry Ho Chi Minh City Viet Nam

ISBN: (数字)9789811575273

ISBN: (纸本)9789811575266

In the traditional network system, control plane (CP) and data plane (DP) are both located on the same network device. If that device fails, the whole system will stop working and the traffics cannot be managed by the administrator. In distributed denial of service (DDoS) or denial of service (DoS), the attackers usually use botnets to generate a large or medium size traffic flow toward the server where the service is active. Nowadays, many techniques have been proposed to detect DoS, DDoS attacks but they are not very effective. However, the separation of CP and DP in software-defined networking (SDN) provides a good foundation for attack detection and prevention no matter whether the attackers attack some parts of the SDN or all of them. In this research, we use entropy to calculate the randomness level of the size of the packet to CP, the system’s brain. With this method, the controller can handle faster and we can get more correct output compared with the machine learning method in the SDN. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Denial-of-service attack

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ACP: A Deep Learning Approach for Aspect-category Sentiment Polarity Detection 1

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International Conference on Machine Intelligence and Soft computing, ICMISC 2020

作者： Kumar, Ashish Dahiya, Vasundhra Sharan, Aditi School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi110067 India

ISBN: (数字)9789811595165

ISBN: (纸本)9789811595158

In recent years, sentiment analysis has gained popularity due to its wide range of application domains. With the fundamental aim of identifying the polarity of sentiment hidden in a piece of text, the ways to conduct sentiment analysis have improved with time too. Aspect-based sentiment analysis (ABSA) is one such approach. The main objective of ABSA is the extraction of sentiment polarity with respect to certain ‘aspects’ of the text. Semantically, an aspect itself is not a single entity but rather a representation of an aspect-category. Therefore, it makes more sense to find sentiment over an aspect-category. As the aspect-category concept is quite subjective, determining sentiment over aspect-category becomes challenging. In this paper, we developed a deep learning model that aims to perform ABSA by explicitly incorporating the aspect information into the model itself by assigning sentiment to every aspect-category. We evaluate our model on a benchmark SemEval’14 dataset and explore the state-of-the-art text-representations techniques. This paper tries to bring forward performances of these representations on the taken aspect-based model. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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