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  • 1
    Online Resource
    Online Resource
    Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-11-2), p. 1-9
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-11-2), p. 1-9
    Abstract: The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/4482578
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
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  • 2
    Online Resource
    Online Resource
    Broadband Circularly Polarized Crossed Bowtie Dipole Antenna Loaded With Parasitic Elements
    Institute of Electrical and Electronics Engineers (IEEE) ; 2018
    In:  IEEE Antennas and Wireless Propagation Letters Vol. 17, No. 1 ( 2018-1), p. 114-117
    Details
    In: IEEE Antennas and Wireless Propagation Letters, Institute of Electrical and Electronics Engineers (IEEE), Vol. 17, No. 1 ( 2018-1), p. 114-117
    Type of Medium: Online Resource
    ISSN: 1536-1225 , 1548-5757
    URL: Article
    DOI: 10.1109/LAWP.2017.2777442
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018
    detail.hit.zdb_id: 2084816-X
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  • 3
    Online Resource
    Online Resource
    Ontology alignment based on instance using NSGA-II
    SAGE Publications ; 2015
    In:  Journal of Information Science Vol. 41, No. 1 ( 2015-02), p. 58-70
    Details
    In: Journal of Information Science, SAGE Publications, Vol. 41, No. 1 ( 2015-02), p. 58-70
    Abstract: Nowadays, ontologies are widely used to solve data heterogeneity problems on the Semantic Web. However, simple use of these ontologies may raise the heterogeneity problem to a higher level. Addressing this problem requires identification of correspondences between the entities of various ontologies. Since the real semantics of a concept is often better defined by the actual instances assigned to it, instance, as an important element of ontology, contains a great quantity of knowledge that should be utilized to obtain the ontology alignment. To this end, in this paper, we propose a novel instance-based aligning approach using NSGA-II to determine the optimal instance correspondences and a similarity propagation algorithm that makes use of various semantic relations to propagate the similarity values to other entities of ontologies. The experiment of comparing our approach with the participants of OAEI 2012 has demonstrated that our method is an effective approach that can obtain the alignment with high precision value.
    Type of Medium: Online Resource
    ISSN: 0165-5515 , 1741-6485
    URL: Article
    DOI: 10.1177/0165551514550142
    RVK:
    AN 10100
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 439125-1
    detail.hit.zdb_id: 2025062-9
    SSG: 24,1
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  • 4
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    Online Resource
    Improved Joint Optimization Design for Wireless Sensor and Actuator Networks with Time Delay
    Hindawi Limited ; 2021
    In:  Wireless Communications and Mobile Computing Vol. 2021 ( 2021-12-30), p. 1-10
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2021 ( 2021-12-30), p. 1-10
    Abstract: With the rapid development of wireless communication technology, the newest development of wireless sensor and actuator networks (WSANs) provides significant potential applications for various real-time scenarios. Currently, extensive research activities have been carried out in the field of efficient resource management and control design. However, the stability of the controlled plant and the efficiency of network resources are rarely considered collaboratively in existing works. In this paper, in order to enhance the control stability and improve the power consumption efficiency for the WSAN, a novel three-step optimization algorithm jointly designing the control strategy and transmission path routing is proposed when the time delay is considered. First, the minimum hop routing algorithm is used to obtain the set of candidate transmission paths. Then, the optimal control signals for each candidate transmission path can be iteratively derived with a backward recursion method. Finally, the best transmission path is determined under the optimal control strategy to achieve the joint optimization design. The effectiveness of the proposed joint optimization algorithm is verified by the simulations of the application in the power grid system.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2021/3927584
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2045240-8
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  • 5
    Online Resource
    Online Resource
    Variational Autoencoder for Zero-Shot Recognition of Bai Characters
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-7-4), p. 1-8
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-7-4), p. 1-8
    Abstract: When talking about Bai nationality, people are impressed by its long history and the language it has created. However, since fewer people of the young generation learn the traditional language, the glorious Bai culture becomes less known, making understanding Bai characters difficult. Based on the highly precise character recognition model for Bai characters, the paper is aimed at helping people read books written in Bai characters so as to popularize the culture. To begin with, a data set is built with the support of Bai culture fans and experts. However, the data set is not large enough as knowledge in this respect is limited. This makes the deep learning model less accurate since it lacks sufficient data. The popular zero-shot learning (ZSL) is adopted to overcome the insufficiency of data sets. We use Chinese characters as the seen class, Bai characters as the unseen class, and the number of strokes as the attribute to construct the ZSL format data set. However, the existing ZSL methods ignore the character structure information, so a generation method based on variational autoencoder (VAE) is put forward, which can automatically capture the character structure information. Experimental results show that the method facilitates the recognition of Bai characters and makes it more precise.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/2717322
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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  • 6
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    Online Resource
    Label Propagation Clustering Algorithm Based on Adaptive Angle
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-8-25), p. 1-11
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-8-25), p. 1-11
    Abstract: The direction-based label propagation clustering (DBC) algorithm needs to set the number of neighbors ( k ) and the angle value (degree), which are highly sensitive. Moreover, DBC algorithm is not suitable for datasets with uneven neighbor density distribution. To overcome above problems, we propose an improved DBC algorithm based on adaptive angle and label redistribution (ALR-DBC). The ALR-DBC algorithm no longer input parameter degree, but dynamically adjusts the deviation angle through the concept of high-low density region to determine the receiving range. This flexible receiving range is no longer affected by the uneven distribution of neighbor density. Finally, those points that do not meet the expectations of the main direction are redistributed. Experiments show that the ALR-DBC algorithm performs better than DBC algorithm in most artificial datasets and real datasets. It is also superior to the classical algorithms listed. It also has good experimental results when applied to wireless sensor data annotation.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/7535575
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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  • 7
    Online Resource
    Online Resource
    Sensor Ontology Metamatching with Heterogeneity Measures
    Hindawi Limited ; 2020
    In:  Wireless Communications and Mobile Computing Vol. 2020 ( 2020-11-25), p. 1-10
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2020 ( 2020-11-25), p. 1-10
    Abstract: The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2020/6666228
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2045240-8
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  • 8
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    Online Resource
    An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals
    Hindawi Limited ; 2021
    In:  Wireless Communications and Mobile Computing Vol. 2021 ( 2021-2-27), p. 1-13
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2021 ( 2021-2-27), p. 1-13
    Abstract: As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2021/6655125
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2045240-8
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  • 9
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    Online Resource
    IOT Automation with Segmentation Techniques for Detection of Plant Seedlings in Agriculture
    Hindawi Limited ; 2022
    In:  Wireless Communications and Mobile Computing Vol. 2022 ( 2022-4-25), p. 1-10
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2022 ( 2022-4-25), p. 1-10
    Abstract: The present work proposes to evaluate, compare, and determine software alternatives that present good detection performance and low computational cost for the plant segmentation operation in computer vision systems. In practical aspects, it aims to enable low-cost and accessible hardware to be used efficiently in real-time embedded systems for detecting seedlings in the agricultural environment. The analyses carried out in the study show that the process of separating and classifying plant seedlings is complex and depends on the capture scene, which becomes a real challenge when exposed to unstable conditions of the external environment without the use of light control or more specific hardware. These restrictions are driven by functionality and market perspective, aimed at low-cost and access to technology, resulting in limitations in processing, hardware, operating practices, and consequently possible solutions. Despite the difficulties and precautions, the experiments showed the most promising solutions for separation, even in situations such as noise and lack of visibility.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2022/6466555
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045240-8
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  • 10
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    Online Resource
    Intelligent Recognition System Based on Contour Accentuation for Navigation Marks
    Hindawi Limited ; 2021
    In:  Wireless Communications and Mobile Computing Vol. 2021 ( 2021-3-3), p. 1-11
    Details
    In: Wireless Communications and Mobile Computing, Hindawi Limited, Vol. 2021 ( 2021-3-3), p. 1-11
    Abstract: Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.
    Type of Medium: Online Resource
    ISSN: 1530-8677 , 1530-8669
    URL: Article
    DOI: 10.1155/2021/6631074
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2045240-8
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