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  1. Article ; Online: ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network.

    Hou, Ning / Shi, Jianhua / Ding, Xiaoxuan / Nie, Chuan / Wang, Cuicui / Wan, Jiafu

    Physics in medicine and biology

    2023  Volume 68, Issue 20

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Humans ; Infant, Newborn ; Retinopathy of Prematurity/diagnostic imaging ; Algorithms ; Sample Size ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-10-06
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/acf3c9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Vehicle Destination Prediction Using Bidirectional LSTM with Attention Mechanism.

    Casabianca, Pietro / Zhang, Yu / Martínez-García, Miguel / Wan, Jiafu

    Sensors (Basel, Switzerland)

    2021  Volume 21, Issue 24

    Abstract: Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle's position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, route ... ...

    Abstract Satellite navigation has become ubiquitous to plan and track travelling. Having access to a vehicle's position enables the prediction of its destination. This opens the possibility to various benefits, such as early warnings of potential hazards, route diversions to pass traffic congestion, and optimizing fuel consumption for hybrid vehicles. Thus, reliably predicting destinations can bring benefits to the transportation industry. This paper investigates using deep learning methods for predicting a vehicle's destination based on its journey history. With this aim, Dense Neural Networks (DNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BiLSTM), and networks with and without attention mechanisms are tested. Especially, LSTM and BiLSTM models with attention mechanism are commonly used for natural language processing and text-classification-related applications. On the other hand, this paper demonstrates the viability of these techniques in the automotive and associated industrial domain, aimed at generating industrial impact. The results of using satellite navigation data show that the BiLSTM with an attention mechanism exhibits better prediction performance destination, achieving an average accuracy of 96% against the test set (4% higher than the average accuracy of the standard BiLSTM) and consistently outperforming the other models by maintaining robustness and stability during forecasting.
    MeSH term(s) Algorithms ; Forecasting ; Natural Language Processing ; Neural Networks, Computer
    Language English
    Publishing date 2021-12-17
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s21248443
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: RGB-D Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System.

    Li, Shipeng / Li, Di / Zhang, Chunhua / Wan, Jiafu / Xie, Mingyou

    Sensors (Basel, Switzerland)

    2020  Volume 20, Issue 2

    Abstract: This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen ...

    Abstract This paper studies the control performance of visual servoing system under the planar camera and RGB-D cameras, the contribution of this paper is through rapid identification of target RGB-D images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGB-D camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, real-time under position-based visual servoing system. The results demonstrate and validate that the RGB-D image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.
    Language English
    Publishing date 2020-01-12
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s20020430
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory.

    Wang, Shiyong / Wan, Jiafu / Li, Di / Liu, Chengliang

    Sensors (Basel, Switzerland)

    2018  Volume 18, Issue 2

    Abstract: The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the ... ...

    Abstract The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the manufacturing system. However, the currently existing architectures are mainly for offline data analysis, not suitable for real-time data processing. In this paper, we first define the smart factory as a cloud-assisted and self-organized manufacturing system in which physical entities such as machines, conveyors, and products organize production through intelligent negotiation and the cloud supervises this self-organized process for fault detection and troubleshooting based on data analysis. Then, we propose a scheme to integrate knowledge reasoning and semantic data where the reasoning engine processes the ontology model with real time semantic data coming from the production process. Based on these ideas, we build a benchmarking system for smart candy packing application that supports direct consumer customization and flexible hybrid production, and the data are collected and processed in real time for fault diagnosis and statistical analysis.
    Language English
    Publishing date 2018-02-06
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s18020471
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

    Celesti, Fabrizio / Celesti, Antonio / Wan, Jiafu / Villari, Massimo

    IEEE reviews in biomedical engineering

    2018  Volume 11, Page(s) 68–76

    Abstract: Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this ... ...

    Abstract Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.
    MeSH term(s) Algorithms ; Animals ; Big Data ; Deep Learning/trends ; Genomics/trends ; High-Throughput Nucleotide Sequencing/trends ; Humans ; Internet ; Sequence Analysis, DNA/trends ; Software
    Language English
    Publishing date 2018-04-12
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ISSN 1941-1189
    ISSN (online) 1941-1189
    DOI 10.1109/RBME.2018.2825987
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: Artificial Intelligence-Driven Customized Manufacturing Factory

    Wan, Jiafu / Li, Xiaomin / Dai, Hong-Ning / Kusiak, Andrew / Martínez-García, Miguel / Li, Di

    Key Technologies, Applications, and Challenges

    2021  

    Abstract: The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized ... ...

    Abstract The traditional production paradigm of large batch production does not offer flexibility towards satisfying the requirements of individual customers. A new generation of smart factories is expected to support new multi-variety and small-batch customized production modes. For that, Artificial Intelligence (AI) is enabling higher value-added manufacturing by accelerating the integration of manufacturing and information communication technologies, including computing, communication, and control. The characteristics of a customized smart factory are to include self-perception, operations optimization, dynamic reconfiguration, and intelligent decision-making. The AI technologies will allow manufacturing systems to perceive the environment, adapt to external needs, and extract the processed knowledge, including business models, such as intelligent production, networked collaboration, and extended service models. This paper focuses on the implementation of AI in customized manufacturing (CM). The architecture of an AI-driven customized smart factory is presented. Details of intelligent manufacturing devices, intelligent information interaction, and the construction of a flexible manufacturing line are showcased. The state-of-the-art AI technologies of potential use in CM, i.e., machine learning, multi-agent systems, Internet of Things, big data, and cloud-edge computing are surveyed. The AI-enabled technologies in a customized smart factory are validated with a case study of customized packaging. The experimental results have demonstrated that the AI-assisted CM offers the possibility of higher production flexibility and efficiency. Challenges and solutions related to AI in CM are also discussed.

    Comment: 21 pages, 12 figures
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Multiagent Systems ; Computer Science - Robotics ; 68T40 ; 68T42 ; 68T05 ; I.2.1 ; I.2.11 ; I.2.9
    Subject code 670
    Publishing date 2021-08-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics

    Afrin, Mahbuba / Jin, Jiong / Rahman, Akhlaqur / Rahman, Ashfaqur / Wan, Jiafu / Hossain, Ekram

    A Comprehensive Survey

    2021  

    Abstract: Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of ... ...

    Abstract Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.
    Keywords Computer Science - Robotics ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 629
    Publishing date 2021-04-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Accelerated Dynamic MRI Using Kernel-Based Low Rank Constraint.

    Arif, Omar / Afzal, Hammad / Abbas, Haider / Amjad, Muhammad Faisal / Wan, Jiafu / Nawaz, Raheel

    Journal of medical systems

    2019  Volume 43, Issue 8, Page(s) 271

    Abstract: We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to ... ...

    Abstract We present a novel reconstruction method for dynamic MR images from highly under-sampled k-space measurements. The reconstruction problem is posed as spectrally regularized matrix recovery problem, where kernel-based low rank constraint is employed to effectively utilize the non-linear correlations between the images in the dynamic sequence. Unlike other kernel-based methods, we use a single-step regularized reconstruction approach to simultaneously learn the kernel basis functions and the weights. The objective function is optimized using variable splitting and alternating direction method of multipliers. The framework can seamlessly handle additional sparsity constraints such as spatio-temporal total variation. The algorithm performance is evaluated on a numerical phantom and in vivo data sets and it shows significant improvement over the comparison methods.
    MeSH term(s) Algorithms ; Humans ; Image Processing, Computer-Assisted/methods ; Liver/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Myocardial Perfusion Imaging
    Language English
    Publishing date 2019-07-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-019-1399-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1426: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  9. Book ; Online: Overhead Control with Reliable Transmission of Popular Packets in Ad-Hoc Social Networks

    Xia, Feng / Liaqat, Hannan Bin / Deng, Jing / Wan, Jiafu / Das, Sajal K.

    2020  

    Abstract: Reliable social connectivity and transmission of data for popular nodes is vital in multihop Ad-hoc Social Networks (ASNETs). In this networking paradigm, transmission unreliability could be caused by multiple social applications running on a single node. ...

    Abstract Reliable social connectivity and transmission of data for popular nodes is vital in multihop Ad-hoc Social Networks (ASNETs). In this networking paradigm, transmission unreliability could be caused by multiple social applications running on a single node. This leads to contentions among nodes and connection paths. In addition, congestions can be the result of multiple senders transmitting data to a single receiver and every sender waiting for a positive acknowledgment to move on. Therefore, traditional Transmission Control Protocol (TCP) performs poorly in ASNETs, due to the fact that the available bandwidth is shared among nodes using round trip time and the acknowledgment is provided individually to every data packet. To solve these issues, we propose a technique, called Overhead Control with Reliable Transmission of Popular Packets in Ad-Hoc Social Networks (RTPS), which improves transmission reliability by assigning bandwidth to users based on their popularity levels: extra bandwidth is assigned to the nodes with higher popularity and their acknowledgments are sent with higher priority. In addition, RTPS further reduces contentions and packet losses by delaying acknowledgment packet transmissions. Our detailed investigations demonstrate the excellent performance of RTPS in terms of throughput latency and overhead with different hop-distances and different numbers of concurrent TCP flows.

    Comment: 14 pages, 8 figures
    Keywords Computer Science - Networking and Internet Architecture ; Computer Science - Social and Information Networks
    Subject code 303
    Publishing date 2020-08-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: IoT sensing framework with inter-cloud computing capability in vehicular networking

    Li, Di / Lu, Rongshuang / Wan, Jiafu / Zhou, Keliang / Zou, Caifeng

    Electronic commerce research Vol. 14, No. 3 , p. 389-416

    2014  Volume 14, Issue 3, Page(s) 389–416

    Author's details Jiafu Wan; Caifeng Zou; Keliang Zhou; Rongshuang Lu; Di Li
    Keywords Wireless sensor networks ; Internet of things ; Vehicular networking ; Inter-cloud computing ; Semantic modeling ; Event processing flow
    Language English
    Size graph. Darst.
    Publisher Springer Science + Business Media Inc.
    Publishing place Dordrecht
    Document type Article
    ZDB-ID 2106016-2 ; 2038488-9
    ISSN 1572-9362 ; 1389-5753
    ISSN (online) 1572-9362
    ISSN 1389-5753
    Database ECONomics Information System

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