2022/07/02 更新

写真a

チイ シン
斉 欣
所属
理工学術院 理工学術院総合研究所
職名
次席研究員(研究院講師)

兼担

  • 理工学術院   国際理工学センター(理工学術院)

  • 政治経済学術院   政治経済学部

 

研究分野

  • 情報ネットワーク

研究キーワード

  • IoT

  • 情報指向ネットワーク

論文

  • Optimizing Packet Transmission for Ledger-Based Points Transfer System in LPWAN: Solutions, Evaluation and Standardization

    Xin Qi, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2021 ITU Kaleidoscope: Connecting Physical and Virtual Worlds (ITU K)    2021年12月

    DOI

  • Ledger-based Points Transfer System in LPWAN: From Disaster Management Aspect

    Xin Qi, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)    2021年12月

    DOI

  • Deep Learning Based Concealed Object Recognition in Active Millimeter Wave Imaging

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Kazuhiko Tamesue, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    2021 IEEE Asia-Pacific Microwave Conference (APMC)    2021年11月

    DOI

  • Content-oriented Multicamera Trajectory Forecasting Surveillance Network System

    Xin Qi, Toshio Sato, Keping Yu, San Hlaing Myint, Yutaka Katsuyama, Kazuhiko Tamesue, Kiyohito Tokuda, Zheng Wen, Takuro Sato

    2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)    2021年08月

    DOI

  • Image super-resolution reconstruction for secure data transmission in Internet of Things environment.

    Hongan Li, Qiaoxue Zheng, Wenjing Yan, Ruolin Tao, Xin Qi, Zheng Wen

    Mathematical biosciences and engineering : MBE   18 ( 5 ) 6652 - 6671  2021年08月  [国際誌]

     概要を見る

    The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

    DOI PubMed

  • Position Estimation of Pedestrians in Surveillance Video Using Face Detection and Simple Camera Calibration

    Toshio Sato, Xin Qi, Keping Yu, Zheng Wen, Yutaka Katsuyama, Takuro Sato

    2021 17th International Conference on Machine Vision and Applications (MVA)    2021年07月

    DOI

  • Deep-learning-empowered 3D reconstruction for dehazed images in IoT-Enhanced smart cities

    Jing Zhang, Xin Qi, San Hlaing Myint, Zheng Wen

    Computers, Materials and Continua   68 ( 2 ) 2807 - 2824  2021年04月

     概要を見る

    With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities' information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional regression network (FT-DCRN) dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image. The DCRN is used to obtain the coarse transmission image, which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network. The fine transmission image is obtained by refining the coarse transmission image using a guided filter. The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image. Second, we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction. An advanced relaxed iterative fine matching based on the structure from motion (ARI-SFM) algorithm is proposed. The ARISFM algorithm, which obtains the fine matching corner pairs and reduces the number of iterations, establishes an accurate one-to-one matching corner relationship. The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms. In addition, the ARI-SFM algorithm guarantees the precision and improves the efficiency.

    DOI

  • Ledger-based Points Transfer System in LPWAN: From Disaster Management Aspect.

    Xin Qi 0002, Keping Yu, Toshio Sato, Kouichi Shibata, Eric Brigham, Takanori Tokutake, Rikiya Eguchi, Yusuke Maruyama, Zheng Wen, Kazuhiko Tamesue, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    ICT-DM     150 - 155  2021年

    DOI

  • Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.

    Hong-An Li, Qiaoxue Zheng, Xin Qi, Wenjing Yan, Zheng Wen, Na Li, Chu Tang

    Computational intelligence and neuroscience   2021   8387382 - 8387382  2021年  [国際誌]

     概要を見る

    Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

    DOI PubMed

  • Image super-resolution reconstruction for secure data transmission in Internet of Things environment

    Hongan Li, Qiaoxue Zheng, Wenjing Yan, Ruolin Tao, Xin Qi, Zheng Wen

    Mathematical Biosciences and Engineering   18 ( 5 ) 6652 - 6671  2021年

     概要を見る

    The image super-resolution reconstruction method can improve the image quality in the Internet of Things (IoT). It improves the data transmission efficiency, and is of great significance to data transmission encryption. Aiming at the problem of low image quality in image super-resolution using neural networks, a self-attention-based image reconstruction method is proposed for secure data transmission in IoT environment. The network model is improved, and the residual network structure and sub-pixel convolution are used to extract the feature of the image. The self-attention module is used extract detailed information in the image. Using generative confrontation method and image feature perception method to improve the image reconstruction effect. The experimental results on the public data set show that the improved network model improves the quality of the reconstructed image and can effectively restore the details of the image.

    DOI PubMed

  • Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems

    Hong An Li, Qiaoxue Zheng, Xin Qi, Wenjing Yan, Zheng Wen, Na Li, Chu Tang

    Computational Intelligence and Neuroscience   2021  2021年

     概要を見る

    Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.

    DOI PubMed

  • A Displacement Estimated Method for Real Time Tissue Ultrasound Elastography

    Hong an Li, Min Zhang, Keping Yu, Xin Qi, Jianfeng Tong

    Mobile Networks and Applications    2021年

     概要を見る

    As an important means of medical imaging, elastic imaging is an indispensable part of mobile telemedicine. Ultrasound elastography has become a research hotspot because it can accurately measure soft tissue lesions. Displacement estimation is the most important step in ultrasound elastography. At present, the phase zero search method is an accurate and fast displacement estimation method. However, when the displacement exceeds 1/4 wavelength, it is invalid. The accuracy of block matching method is not high, but it is suitable for large displacement, so it can overcome this shortcoming. It is worth noting that the quality-guided block matching method has good robustness under complex mutation conditions. It can provide prior knowledge to increase the robustness of the phase-zero search under large displacement conditions. So we propose a novel displacement estimation method for real time tissue ultrasound elastography, which combines the quality-guided block matching method and the phase-zero search method. The experimental results show that this method is more accurate, faster and robust than other displacement estimation methods.

    DOI

  • Congestion-Aware Suspicious Object Detection System Using Information-Centric Networking.

    Xin Qi 0002, Toshio Sato, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Kiyohito Tokuda, Takuro Sato

        1 - 6  2021年

    DOI

  • 3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

    Jing Zhang, Dan Li, Qiaozhi Hua, Xin Qi, Zheng Wen, San Hlaing Myint

    IEEE Access   9   15170 - 15180  2021年

     概要を見る

    Edge computing can provide many key functions without connecting to centralized servers, which enables remote areas to obtain real-time medical diagnoses. The combination of edge computing and Internet of things (IoT) devices can send remote patient data to the hospital, which will help to more effectively address long-term or chronic diseases. CT images are widely used in the diagnosis of clinical diseases, and their characteristics are an important basis for pathological diagnosis. In the CT imaging process, speckle noise is caused by the interference of ultrasound on human tissues, and its component information is complex. To solve these problems, we propose a 3D reconstruction method for noisy CT images in the IoT using edge computing. First, we propose a multi-stage feature extraction generative adversarial network (MF-GAN) denoising algorithm. The generator of MF-GAN adopts the multi-stage feature extraction, which can ensure the reconstruction of the image texture and edges. Second, we apply the denoised images generated from the MF-GAN method to perform the 3D reconstruction. A marching cube (MC) algorithm based on regional growth and trilinear interpolation (RGT-MC) is proposed. With the idea of regional growth, all voxels containing iso-surfaces are selected and calculated, which accelerates the reconstruction efficiency. The intersection point of the voxel and iso-surface is calculated by the trilinear interpolation algorithm, which effectively improves the reconstruction accuracy. The experimental results show that MF-GAN has a better denoising effect than other algorithms. Compared to other representative 3D algorithms, the RGT-MC algorithm greatly improves the efficiency and precision.

    DOI

  • Communication-Based Book Recommendation in Computational Social Systems

    Long Zuo, Shuo Xiong, Xin Qi, Zheng Wen, Yiwen Tang

    Complexity   2021  2021年

     概要を見る

    This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users' neighbors more accurately. Second, computational social system strategies can be adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm.

    DOI

  • 3D Reconstruction for Motion Blurred Images Using Deep Learning-based Intelligent Systems

    Jing Zhang, Keping Yu, Zheng Wen, Xin Qi, Anup Kumar Paul

    Computers, Materials & Continua   66 ( 2 ) 2087 - 2104  2021年

    DOI

  • A Lightweight Ledger-Based Points Transfer System for Application-Oriented LPWAN

    Keping Yu, Kouichi Shibata, Takanori Tokutake, Rikiya Eguchi, Taiki Kondo, Yusuke Maruyama, Xin Qi, Zheng Wen, Toshio Sato, Yutaka Katsuyama, Kazue Sako, Takuro Sato

    2020 IEEE 6th International Conference on Computer and Communications, ICCC 2020     1972 - 1978  2020年12月

     概要を見る

    Along with the rapid development of IoT technology, Low power wide area network (LPWAN) has become the primary technology for IoT access today due to its characteristics of low cost, low power consumption, long-distance, and mass connections. At the same time, the points transfer system, as a typical third-party payment application, is attracting more and more extensive attention from academia and industry. Therefore, the research and development of a points transfer system for LPWAN are of great practical importance. However, the current points transfer systems often face problems such as centralization, high requirements on node computing power, and low robustness, which are difficult to adapt to the development of IoT. To address these problems, we propose a lightweight ledger-based points transfer system for application-oriented LPWAN. The system enables the points transfer between tags in LPWAN, where both tags and nodes are limited. Furthermore, it can be utilized even in disaster situations. Experimental results show that our proposed system is intensely robust and has a lower processing time than Bitcoin-like systems to cope with LPWAN's requirement.

    DOI

  • AI-Based W-Band Suspicious Object Detection System for Moving Persons Using GAN: Solutions, Performance Evaluation and Standardization Activities

    Yutaka Katsuyama, Keping Yu, San Hlaing Myint, Toshio Sato, Zheng Wen, Xin Qi

    2020 ITU Kaleidoscope: Industry-Driven Digital Transformation, ITU K 2020    2020年12月

     概要を見る

    With the intensification of conflicts in different regions, the W-band suspicious object detection system is an essential security means to prevent terrorist attacks and is widely used in many crucial places such as airports. Because artificial intelligence can perform highly reliable and accurate services in the field of image recognition, it is used in suspicious object detection systems to increase the recognition rate for suspicious objects. However, it is challenging to establish a complete suspicious object database, and obtaining sufficient millimeter-wave images of suspicious objects from experiments for AI training is not realistic. To address this issue, this paper verifies the feasibility to generate a large number of millimeter-wave images for AI training by generative adversarial networks. Moreover, we also evaluate the factors that affect the AI recognition rate when the original images used for CNN training are insufficient and how to increase the service quality of AI-based W-band suspicious object detection systems for moving persons. In parallel, all the international standardization organizations have been collectively advancing the novel technologies of AI. We update the reader with information about AI research and standardization related activities in this paper.

    DOI

  • A blockchain-empowered AAA scheme in the large-scale HetNet

    Na Shi, Liang Tan, Wenjuan Li, Xin Qi, Keping Yu

    Digital Communications and Networks    2020年10月

    DOI

  • Radiometric Passive Imaging for Robust Concealed Object Identification

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    IEEE National Radar Conference - Proceedings   2020-September  2020年09月

     概要を見る

    Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

    DOI

  • PDKSAP : Perfected Double-Key Stealth Address Protocol without Temporary Key Leakage in Blockchain

    Cong Feng, Liang Tan, Huan Xiao, Keping Yu, Xin Qi, Zheng Wen, You Jiang

    2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020     151 - 155  2020年08月

     概要を見る

    The stealth address protocol is to create a one-time temporary output address of a transaction, hide its real output address, destroy the association between the input address and the real output address to achieve privacy protection for user identities in the transaction. However, the widely used double-key stealth address protocol (DKSAP) requires the sender to transmit the temporary public key along with the transaction, which enables attackers to easily identify stealth and non-stealth transactions and can lead to the loss of some private information. We propose a double-key stealth address protocol without temporary key leakage - PDKSAP by which senders and receivers maintain local transaction record databases to record the number of transactions with other users. Senders and receivers generate a temporary key pair for a transaction based on the number of transactions between them, which prevent leaking the transaction temporary key. Finally, we verify the protocol through experiments.

    DOI

  • Empirical Analysis of Bitcoin network (2016-2020)

    Ajay Kumar, Abhishek Kumar, Pranav Nerurkar, Muhammad Rukunuddin Ghalib, Achyut Shankar, Zheng Wen, Xin Qi

    2020 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2020     96 - 101  2020年08月

     概要を見る

    Bitcoin system (or Bitcoin) is a peer-to-peer and decentralized payment system that uses cryptocurrency named bitcoins (BTCs) and was released as open-source software in 2009. Bitcoin platform has attracted both social and anti-social elements. On the one hand, it is social as it ensures the exchange of value, maintaining trust in a cooperative, community-driven manner without the need for a trusted third party. At the same time, it is anti-social as it creates hurdles for law enforcement to trace suspicious transactions due to anonymity and privacy. To understand how the social and anti-social tendencies in the user base of Bitcoin affect its evolution, there is a need to analyze the Bitcoin system as a network. The current paper aims to explore the local topology and geometry of the Bitcoin network during its first decade of existence. Bitcoin transaction data from 01 Jan 2016 00:00:00 GMT to 08 May 2020 13:21:33 GMT was processed for this purpose to build a Bitcoin user graph.

    DOI

  • Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems

    Qingfang Liu, Baosheng Kang, Keping Yu, Xin Qi, Jing Li, Shoujin Wang, Hong An Li

    IEEE Access   8   12644 - 12654  2020年

     概要を見る

    Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.

    DOI

  • R3MR: Region Growing Based 3D Mesh Reconstruction for Big Data Platform

    Hong An Li, Min Zhang, Keping Yu, Xin Qi, Qiaozhi Hua, Yu Zhu

    IEEE Access   8   91740 - 91750  2020年

     概要を見る

    Visualization is one of the most intuitive and perceptible ways for information representation in the big data era. As an essential part of the visualization, 3D mesh reconstruction is facing great challenges due to its characteristics of quantity, non-structure, and low-accuracy. The traditional 3D mesh reconstruction method has strict theoretical proof and can be used to reconstruct the surface of the complex topological structure for computer rendering and display. However, it is not suitable to handle a large number of point cloud and noise point cloud in a big data platform because the process is inefficient, low-automation and requires massive calculations. To address this issue, we propose a region growing based 3D mesh reconstruction (R3MR) in the big data platform. Firstly, we divide the large data points into three categories: flat point set, high curvature point set, and boundary point set. The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points, so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction. Moreover, the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same. Hence, the division of the flat point set is beneficial to solve the problem of quantity and massive calculations. Secondly, our proposal is to start the mesh reconstruction from the flat point set progressively, because it can obtain the outline of the 3D model. In many scenarios, such as autonomous driving, only the overall outline of the model is required. Finally, during the 3D mesh reconstruction, the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system. Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform, especially effective for the scattered point cloud.

    DOI

  • Pedestrian Positioning in Surveillance Video using Anthropometric Properties for Effective Communication.

    Toshio Sato, Xin Qi 0002, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Kiyohito Tokuda, Takuro Sato

        1 - 6  2020年

    DOI

  • Blockchain-based Content-oriented Surveillance Network.

    Xin Qi 0002, Keping Yu, Zheng Wen, San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Kiyohito Tokuda, Takuro Sato

        1 - 6  2020年

    DOI

  • Blockchain-Empowered Contact Tracing for COVID-19 Using Crypto-Spatiotemporal Information.

    Zheng Wen, Keping Yu, Xin Qi 0002, Toshio Sato, Yutaka Katsuyama, Takuro Sato, Wataru Kameyama, Fumiyuki Kato, Yang Cao 0011, Masatoshi Yoshikawa, Min Luo, Jun Hashimoto

        1 - 6  2020年

    DOI

  • Radiometric Passive Imaging for Robust Concealed Object Identification

    San Hlaing Myint, Yutaka Katsuyama, Toshio Sato, Xin Qi, Zheng Wen, Keping Yu, Kiyohito Tokuda, Takuro Sato

    2020 IEEE RADAR CONFERENCE (RADARCONF20)    2020年

     概要を見る

    Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

  • Medical Image Coloring Based on Gabor Filtering for Internet of Medical Things

    Hong An Li, Jiangwen Fan, Keping Yu, Xin Qi, Zheng Wen, Qiaozhi Hua, Min Zhang, Qiaoxue Zheng

    IEEE Access   8   104016 - 104025  2020年

     概要を見る

    Color medical images better reflect a patient's lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can't be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.

    DOI

  • Block Chain Based Internet of Medical Things for Uninterrupted, Ubiquitous, User-Friendly, Unflappable, Unblemished, Unlimited Health Care Services (BC IoMT U6HCS)

    J. Indumathi, Achyut Shankar, Muhammad Rukunuddin Ghalib, J. Gitanjali, Qiaozhi Hua, Zheng Wen, Xin Qi

    IEEE Access   8   216856 - 216872  2020年

     概要を見る

    The most burning topic of today, calls for a holistic solution that is reliable, secure, privacy preserved, cost effective Cloud storage that can tide over the turbulent conditions of the rapidly budding digital storage technologies. This send an outcry for a devoted solution, in the form of an individualized, patient-centric care - IoMT that augments precise disease identifications, decrease in errors, reduction in costs of care through the support of technology, allows patients to direct health information data to doctors,manage drugs, keep Personal Health Records, caters to remote medical supports Care, provides proactive approach to preserving Good Health, improves and Accelerates Clinician Workflows, empowers extreme connectivity due to better automation and perceptions in the DNA of IoMT functions. But IoMT adoption is like a rose with thorns like constraints of increased administrative costs, deficiency of universal data access, present-day electronic medical records. The BCT is used in the framework to overcome the security issues of IoMT through the use of latest encryptions. Furthermore, this framework harnesses the benefits of Block Chain like reduced cost, speed, automation, immutability, near-impossible loss of data, permanence, removal of intermediaries, decentralization of consensus, legitimate access to health data, data safekeeping, accrual-based imbursement mechanisms, and medical supply chain efficacy. The outcomes in this paper are (i)A systematic investigation of the current IoMT, Block Chain and Cloud Storage in Health Care;(ii) Explore the challenges and necessities for the confluence of Block Chain (BC), Internet of Medical Things (IoMT), Cloud Computing (CC);(iii)Formulate the requirements necessary for the real-time remote Health Care of one-to-one care structure, which, supports the vital functions that are critical to the Patient Centric Health Care;(iv) Design and develop a novel BC IoMT U6 HCS (Block Chain based Internet of Medical Things for Uninterrupted, Ubiquitous, User-friendly, Unflappable, Unblemished, Unlimited Health Care Services) Layered Architecture, to support the vital functions critical for Patient Centric Health Care and (v) Implement and test with the previous established and proven techniques. The integrity of the Layered Architecture is validated with the already existing ones in terms of audit performances. The results from the Layered Architecture are validated and are proven to be competent in achieving safe auditing and surpass the former ones. The technology is in the sprouting phases, it is perilous that affiliates of the Health Care community realize the rudimentary ideas behind Block Chain, and detect its feasible impact on the future of patient centric medical care. Finally, and most importantly, this paper also gives a panoramic view on the current research status, and imminent directions of Secure Internet of Medical Things Using Block Chain.

    DOI

  • 3D Reconstruction for Super-Resolution CT Images in the Internet of Health Things Using Deep Learning

    Jing Zhang, Ling-Rui Gong, Keping Yu, Xin Qi, Zheng Wen, Qiaozhi Hua, San Hlaing Myint

    IEEE Access   8   121513 - 121525  2020年

    DOI

  • Design and Performance Evaluation of an AI-Based W-Band Suspicious Object Detection System for Moving Persons in the IoT Paradigm

    Keping Yu, Xin Qi, Toshio Sato, San Hlaing Myint, Zheng Wen, Yutaka Katsuyama, Kiyohito Tokuda, Wataru Kameyama, Takuro Sato

    IEEE Access   8   81378 - 81393  2020年

    DOI

  • Content-Oriented Common IoT Platform for Emergency Management Scenarios

    Zheng Wen, Xin Qi, Keping Yu, Jairo Eduardo Lopez, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2019-November  2019年11月

     概要を見る

    With the explosive growth of Internet of Things (IoT) devices, the demand for network systems is increasing rapidly. In terms of bandwidth, security, management, and more, there are many bottlenecks in current network systems. To address the above issues, future network technology, such as information-centric networking, is proposed to provide a good solution for IoT applications. However, new communications technologies need to be implemented with new communications applications. The traditional client/server-based network applications will become one of the bottlenecks in information-centric networking (ICN). In this paper, the work of the Ministry of Internal Affairs and Communications project at Waseda University over the past three years is summarized, and a highly efficient content-oriented common IoT platform for emergency management scenarios is proposed.

    DOI

  • A Context-Aware Framework for Reducing Bandwidth Usage of Mobile Video Chats

    Xin Qi, Qing Yang, David T. Nguyen, Ge Peng, Gang Zhou, Bo Dai, Daqing Zhang, Yantao Li

    IEEE TRANSACTIONS ON MULTIMEDIA   18 ( 8 ) 1640 - 1649  2016年08月  [査読有り]

     概要を見る

    Mobile video chat apps offer users an approachable way to communicate with others. As high-speed 4G networks are being deployed worldwide, the number of mobile video chat app users increases. However, video chatting on mobile devices brings users financial concerns, since streaming video demands high bandwidth and can use up a large amount of data in dozens of minutes. Lowering the bandwidth usage of mobile video chats is challenging since video quality may be compromised. In this paper, we attempt to tame this challenge. Technically, we propose a context-aware frame rate adaption framework, named low-bandwidth video chat (LBVC). It follows a sender-receiver cooperative principle that smartly handles the tradeoff between lowering bandwidth usage and maintaining video quality. We implement LBVC by modifying an open-source app-Linphone-and evaluate it with both objective experiments and subjective studies.

    DOI

  • Mining Personal Frequent Routes via Road Corner Detection

    Tianben Wang, Daqing Zhang, Xingshe Zhou, Xin Qi, Hongbo Ni, Haipeng Wang, Gang Zhou

    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS   46 ( 4 ) 445 - 458  2016年04月  [査読有り]

     概要を見る

    Frequent route is an important individual outdoor behavior pattern that many trajectory-based applications rely on. In this paper, we propose a novel framework for extracting frequent routes from personal GPS trajectories. The key idea of our design is to accurately detect road corners and utilize these new metaphors to tackle the problem of frequent route extraction. Concretely, our framework contains three phases: 1) characteristic point (CP) extraction; 2) corner detection; and 3) trajectory mapping. In the first phase, we present a linear fitting-based algorithm to extract CPs. In the second phase, we develop a multiple density level DBSCAN (density-based spatial clustering of applications with noise) algorithm to locate road corners by clustering CPs. In the third phase, we convert each trajectory into an ordered sequence of road corners and obtain all routes that have been traversed by an individual for at least F (frequency threshold) times. We evaluate the framework using real-world trajectory datasets of individuals for one year and the experimental results demonstrate that our framework outperforms the baseline approach by 7.8% on average in terms of precision and 21.9% in terms of recall.

    DOI

  • Parallel content-based sub-image retrieval using hierarchical searching

    Lin Yang, Xin Qi, Fuyong Xing, Tahsin Kurc, Joel Saltz, David J. Foran

    BIOINFORMATICS   30 ( 7 ) 996 - 1002  2014年04月  [査読有り]

     概要を見る

    Motivation: The capacity to systematically search through large image collections and ensembles and detect regions exhibiting similar morphological characteristics is central to pathology diagnosis. Unfortunately, the primary methods used to search digitized, wholeslide histopathology specimens are slow and prone to inter- and intra-observer variability. The central objective of this research was to design, develop, and evaluate a content-based image retrieval system to assist doctors for quick and reliable content-based comparative search of similar prostate image patches.
    Method: Given a representative image patch (sub-image), the algorithm will return a ranked ensemble of image patches throughout the entire whole-slide histology section which exhibits the most similar morphologic characteristics. This is accomplished by first performing hierarchical searching based on a newly developed hierarchical annular histogram (HAH). The set of candidates is then further refined in the second stage of processing by computing a color histogram from eight equally divided segments within each square annular bin defined in the original HAH. A demand-driven master-worker parallelization approach is employed to speed up the searching procedure. Using this strategy, the query patch is broadcasted to all worker processes. Each worker process is dynamically assigned an image by the master process to search for and return a ranked list of similar patches in the image.
    Results: The algorithm was tested using digitized hematoxylin and eosin (H&E) stained prostate cancer specimens. We have achieved an excellent image retrieval performance. The recall rate within the first 40 rank retrieved image patches is similar to 90%.

    DOI

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Misc

  • Content-oriented disaster network utilizing named node routing and field experiment evaluation

    Xin Qi, Zheng Wen, Keping Yu, Kazunori Murata, Kouichi Shibata, Takuro Sato

    IEICE Transactions on Information and Systems   E102D ( 5 ) 988 - 997  2019年05月

     概要を見る

    Copyright © 2019 The Institute of Electronics, Information and Communication Engineers. Low Power Wide Area Network (LPWAN) is designed for low-bandwidth, low-power, long-distance, large-scale connected IoT applications and realistic for networking in an emergency or restricted situation, so it has been proposed as an attractive communication technology to handle unexpected situations that occur during and/or after a disaster. However, the traditional LPWAN with its default protocol will reduce the communication efficiency in disaster situation because a large number of users will send and receive emergency information result in communication jams and soaring error rates. In this paper, we proposed a LPWAN based decentralized network structure as an extension of our previous Disaster Information Sharing System (DISS). Our network structure is powered by Named Node Networking (3N) which is based on the Information-Centric Networking (ICN). This network structure optimizes the excessive useless packet forwarding and path optimization problems with node name routing (NNR). To verify our proposal, we conduct a field experiment to evaluate the efficiency of packet path forwarding between 3N+LPWA structure and ICN+LPWA structure. Experimental results confirm that the load of the entire data transmission network is significantly reduced after NNR optimized the transmission path.

    DOI

  • Design and performance evaluation of content-oriented communication system for iot network: A case study of named node networking for real-Time video streaming system

    Xin Qi, Yuwei Su, Keping Yu, Jingsong Li, Qiaozhi Hua, Zheng Wen, Jairo Lopez, Takuro Sato

    IEEE Access   7   88138 - 88149  2019年01月

     概要を見る

    © 2013 IEEE. Information-Centric Networking (ICN) was born in an era that more and more users are shifting their interests to the content itself rather than the location where contents are stored. This paradigm shift in the usage patterns of the Internet, along with the urgent needs for content naming, pervasive caching, better security, and mobility support, has prompted researchers to consider a radical change to the Internet architecture. However, ICN is still in its infancy and development stage, and many issues still exist and need to be addressed. The common ICN architecture is lacking the host-centric communication ability and difficult to provide seamless mobility in current solutions. To solve this problem, our team had proposed the Named-Node Networking (3N) concept, which not only naming the content but also naming the node and it proved to have better performance of providing seamless mobility in the simulation. However, the previous contributions were limited in the 3N namespace for seamless mobility support in both producer and consumer. In this paper, we have proposed a 3N system which includes 3N naming, data delivery, mobility support, and data security. Moreover, we have created a 3N-based real-Time video streaming system to evaluate data delivery performance and mobility handoff performance. The evaluation result proofs that our system performs better than TCP video streaming in a multi-client situation and a WiFi-based handoff was demonstrated.

    DOI

  • Content-Oriented Surveillance System Based on ICN in Disaster Scenarios

    Koki Okamoto, Toru Mochida, Daichi Nozaki, Zheng Wen, Xin Qi, Takuro Sato

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2018-November   484 - 489  2018年07月

     概要を見る

    © 2018 IEEE. This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.

    DOI

  • AI Management System to Prevent Accidents in Construction Zones Using 4K Cameras Based on 5G Network

    Daichi Nozaki, Koki Okamoto, Toru Mochida, Xin Qi, Zheng Wen, San Hlaing Myint, Kiyohito Tokuda, Takuro Sato, Kazuhiko Tamesue

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2018-November   462 - 466  2018年07月

     概要を見る

    © 2018 IEEE. Accident prevention for trucks, cranes and work vehicles at construction sites is important. Here, we use high-precision surveillance cameras with 4K cameras as IoT terminals to implement safe workplace environments. In this research, the system transmits photographic images from trucks, cranes, and other construction equipment fitted with 4K cameras at the work site to a database via 5G wireless networks, and uses AI to assess the interactions and movements of workers in the database. We introduce a system that makes it possible to avoid crashes by informing truck and crane drivers, and notifying automatic driving trucks and crane cars of that information. It is conceivable that uplink traffic could become congested due to many vehicles equipped with 4K cameras simultaneously transmitting images over the 5G uplink. Here, as a basic 5G characteristic evaluation, 4K images from trucks or cranes moving at a low speed are transmitted to the database according to moving speed and distance to the 5G terminal connecting the base station and IoT device. Based on the error environment, we report the relation with the error of the system that judges and identifies the surrounding environment using AI when video quality is deteriorated.

    DOI

  • Naming scheme using NLP machine learning method for network weather monitoring system based on ICN

    Toru Mochida, Daichi Nozaki, Koki Okamoto, Xin Qi, Zheng Wen, Takuro Sato, Keping Yu

    International Symposium on Wireless Personal Multimedia Communications, WPMC   2017-   428 - 434  2018年02月

     概要を見る

    The market for IoT devices has been expanding rapidly for several years, and many fields are anticipating further demand in the future. However, together with this expansion, increased communication failures are expected, and solutions to prevent them are needed. Here, we aim to solve communication problems using a new network, called the CCN x(Contents Centric Networking), which is one of the leading ICN (Information Centric Networking) solutions. Our system was constructed by assuming the case of a weather monitoring IoT application in which large data communications were very likely to occur. In this paper, we propose a technique for distributing meteorological data gathered with a weather observation device and 4K camera by ICN, and caching content with a new Naming scheme using machine learning.

    DOI

  • A Novel ICN & Drone Based Emergency Information System for Disaster Area (回路とシステム)

    Wen Zheng, Zhang Di, Qi Xin, Yu Keping, Sato Takuro

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 421 ) 47 - 52  2017年01月

    CiNii

  • A Novel ICN & Drone Based Emergency Information System for Disaster Area (安全・安心な生活とICT)

    Wen Zheng, Zhang Di, Qi Xin, Yu Keping, Sato Takuro

    電子情報通信学会技術研究報告 = IEICE technical report : 信学技報   116 ( 422 ) 47 - 52  2017年01月

    CiNii

  • B-6-120 A Data Center Architecture Based on CCN

    Zhang Lu, Qi Xin, Zhang Di, Sato Takuro

    電子情報通信学会総合大会講演論文集   2016 ( 2 ) 120 - 120  2016年03月

    CiNii

  • BS-3-7 Analysis of Transmission Efficiency Based on Social Big Data(BS-3. Advanced Networking Technologies for Innovative Information Networks)

    Hua Qiaozhi, Zhang Di, Qi Xin, Sato Takuro

    電子情報通信学会総合大会講演論文集   2016 ( 2 ) "S - 25"-"S-26"  2016年03月

     概要を見る

    The information revolution process is boosting the development of social big data. Other than the use of traditional sampling of statistical methods to analysis data, people nowadays shifts to use the real-time data. And the transmission of massive data puts forward new demands. Here in this paper, to cater this trend, with the proposed new way of packets transmission management with Information-Centric Network (ICN), the transmission is compared with traditional IP based transmission, which simulation results demonstrated that both the network transmission speed and efficiency improved significantly by our method.

    CiNii

  • B-6-121 3N-SIMULATOR PACKAGE TRANSMISSION ON PHYSICAL NETWORK

    Qi Xin, Zhang Lu, Wen Zheng, Sato Takuro

    電子情報通信学会総合大会講演論文集   2016 ( 2 )  2016年03月

    CiNii

  • Content oriented surveillance system based on information-centric network

    Xin Qi, Zheng Wen, Toshitaka Tsuda, Wataru Kameyama, Jiro Katto, Takuro Sato, Kouichi Shibata

    2016 IEEE Globecom Workshops, GC Wkshps 2016 - Proceedings    2016年01月

     概要を見る

    © 2016 IEEE. Urban surveillance systems are being applied in a rapid pace with mature but inefficient solutions. The inefficiency is revealed with two aspects, too concentrated bandwidth and processing requirement. To solve this problem, we proposed a content oriented surveillance system based on Information-Centric Network. However, we can't simply replace TCP/IP streaming structure with named contents streaming structure because it can't improve the surveillance system's efficiency enough. In this paper, we took the ICN network's profits even further with the named contents. Instead of streaming live video to the central data center and processing multiple data stream in the same time, we have designed the nodes to process the captured raw data and produce objective contents for the central data center. With the extremely size difference in raw data and actual valued contents from it, we could apply the method in investigating area people traffic conditions and even in disaster and anti-terrorism scenarios. There was a field experiment performed to evaluate tourists' densities and dressing habits during winter season of March. The experiment expressed the benefits of our system and compared our method with traditional surveillance systems in saving network bandwidth and functionalities.

    DOI

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