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Applied Sciences
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Mobile Computing and Intelligent Sensing
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Mobile Computing and Intelligent Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 7688

Special Issue Editors

Dr. Han Ding

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: AIOT; smart sensing; human-centric applications
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Xi

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: Internet of Things; artificial intelligence; network security

Special Issue Information

Dear Colleagues,

Recent advances in mobile computing and intelligent sensing technologies pave the way for opportunities to extend a wide variety of applications, for example, location estimation, context sensing, virtual or augmented reality, healthcare, human–mobile interactions, vehicular and mobile robotic systems, and so on. This Special Issue aims to collect high-quality and innovative research on all aspects of mobile computing, applications, and services.

Topics of interest include, but are not limited to, the following:

  • Mobile / Pervasive / Ubiquitous / Wearable computing
  • New platforms and communication paradigms for networked sensor systems
  • Communication media (6G, millimeter wave, UWB, ultrasound, RFID, NFC)
  • IoT systems and applications in Smart Cities
  • Systems for location estimation and context sensing
  • Learning algorithms and models for perception, understanding, and adaptation
  • Novel mobile applications using machine learning
  • Security and privacy in mobile applications and systems

Dr. Han Ding
Dr. Wei Xi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pervasive computing
  • signal processing for mobile systems
  • communication paradigm and hardware design
  • intelligence systems with machine/deep learning
  • security and privacy in mobile systems
  • IoT systems and applications

Published Papers (7 papers)

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Research

17 pages, 3763 KiB  
Article
Multi-Task Mean Teacher Medical Image Segmentation Based on Swin Transformer
by Jie Zhang, Fan Li, Xin Zhang, Yue Cheng and Xinhong Hei
Appl. Sci. 2024, 14(7), 2986; https://doi.org/10.3390/app14072986 - 02 Apr 2024
Viewed by 477
Abstract
As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduce a transformer-based multi-task framework that concurrently leverages both labeled and unlabeled [...] Read more.
As a crucial task for disease diagnosis, existing semi-supervised segmentation approaches process labeled and unlabeled data separately, ignoring the relationships between them, thereby limiting further performance improvements. In this work, we introduce a transformer-based multi-task framework that concurrently leverages both labeled and unlabeled volumes by encoding shared representation patterns. We first integrate transformers into YOLOv5 to enhance segmentation capabilities and adopt a multi-task approach spanning shadow region detection and boundary localization. Subsequently, we leverage the mean teacher model to simultaneously learn from labeled and unlabeled inputs alongside orthogonal view representations, enabling our approach to harness all available annotations. Our network can improve the learning ability and attain superior performance. Extensive experiments demonstrate that the transformer-powered architecture encodes robust inter-sample relationships, unlocking substantial performance gains by capturing shared information between labeled and unlabeled data. By treating both data types concurrently and encoding their shared patterns, our framework addresses the limitations of existing semi-supervised approaches, leading to improved segmentation accuracy and robustness. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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19 pages, 2943 KiB  
Article
Automatic Medical Image Segmentation with Vision Transformer
by Jie Zhang, Fan Li, Xin Zhang, Huaijun Wang and Xinhong Hei
Appl. Sci. 2024, 14(7), 2741; https://doi.org/10.3390/app14072741 - 25 Mar 2024
Viewed by 517
Abstract
Automatic image segmentation is vital for the computer-aided determination of treatment directions, particularly in terms of labelling lesions or infected areas. However, the manual labelling of disease regions is inconsistent and a time-consuming assignment. Meanwhile, radiologists’ comments are exceedingly subjective, regularly impacted by [...] Read more.
Automatic image segmentation is vital for the computer-aided determination of treatment directions, particularly in terms of labelling lesions or infected areas. However, the manual labelling of disease regions is inconsistent and a time-consuming assignment. Meanwhile, radiologists’ comments are exceedingly subjective, regularly impacted by personal clinical encounters. To address these issues, we proposed a transformer learning strategy to automatically recognize infected areas in medical images. We firstly utilize a parallel partial decoder to aggregate high-level features and then generate a global feature map. Explicit edge attention and implicit reverse attention are applied to demonstrate boundaries and enhance their expression. Additionally, to alleviate the need for extensive labeled data, we propose a segmentation network combining propagation and transformer architectures that requires only a small amount of labeled data while leveraging fundamentally unlabeled images. The attention mechanisms are integrated within convolutional networks, keeping their global structures intact. Standalone transformers connected straightforwardly and receiving image patches can also achieve impressive segmentation performance. Our network enhanced the learning ability and attained a higher quality execution. We conducted a variety of ablation studies to demonstrate the adequacy of each modelling component. Experiments conducted across various medical imaging modalities illustrate that our model beats the most popular segmentation models. The comprehensive results also show that our transformer architecture surpasses established frameworks in accuracy while better preserving the natural variations in anatomy. Both quantitatively and qualitatively, our model achieves a higher overlap with ground truth segmentations and improved boundary adhesion. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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15 pages, 2034 KiB  
Article
Complex Background Reconstruction for Novelty Detection
by Kun Zhao, Man Su, Ran An, Hui He and Zhi Wang
Appl. Sci. 2023, 13(19), 10702; https://doi.org/10.3390/app131910702 - 26 Sep 2023
Viewed by 613
Abstract
Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. [...] Read more.
Novelty detection aims to detect samples from classes different from the training samples (i.e., the normal class). Existing approaches predominantly make the target reconstruction better and choose the appropriate reconstruction error measurement method but ignore the influence of background information on this process. This paper proposes a novel reconstruction network and mutual information Siamese network. The reconstructed network aims to make the distribution of reconstructed samples consistent with that of original samples, intending to reduce background interference in the reconstruction process. After this, we measure the distance between the original and generated images based on a mutual information Siamese network, which extracts more discriminative features to calculate the similarity between the original images and their reconstructed ones. This part of the network uses global context information to improve the detection accuracy. We conduct extreme experiments to evaluate the proposed solution on two challenging public datasets. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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21 pages, 2058 KiB  
Article
Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
by Yingying Liang, Peng Zhao and Yimeng Wang
Appl. Sci. 2023, 13(18), 10458; https://doi.org/10.3390/app131810458 - 19 Sep 2023
Viewed by 722
Abstract
Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a challenge. One feasible approach is to leverage [...] Read more.
Deep learning has undergone significant progress for machinery fault diagnosis in the Industrial Internet of Things; however, it requires a substantial amount of labeled data. The lack of sufficient fault samples in practical applications remains a challenge. One feasible approach is to leverage prior knowledge from similar source domains to enhance fault diagnosis with limited samples in the target domain. Nevertheless, complex operating conditions and fault types can give rise to domain shift issues between different domains, therefore hindering direct data-sharing due to data privacy concerns. To address these challenges, this article introduces a novel federated few-shot fault-diagnosis method called FedCDAE-MN. FedCDAE-MN employs a convolutional denoising auto-encoder and feature-space metric learning to enhance the model’s generalization across domains for improving the adaptability to varying working conditions, new fault types, and noisy data. Moreover, our approach ensures privacy preservation by avoiding the need to share sensitive data with other participants. Through extensive experiments on real-world datasets, FedCDAE-MN surpasses existing methods and significantly improves the accuracy of fault diagnosis. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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31 pages, 3525 KiB  
Article
Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier
by Qaisar Abbas, Talal Saad Albalawi, Ganeshkumar Perumal and M. Emre Celebi
Appl. Sci. 2023, 13(17), 9880; https://doi.org/10.3390/app13179880 - 31 Aug 2023
Cited by 2 | Viewed by 1800
Abstract
In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task [...] Read more.
In recent years, advances in deep learning (DL) techniques for video analysis have developed to solve the problem of real-time processing. Automated face recognition in the runtime environment has become necessary in video surveillance systems for urban security. This is a difficult task due to face occlusion, which makes it hard to capture effective features. Existing work focuses on improving performance while ignoring issues like a small dataset, high computational complexity, and a lack of lightweight and efficient feature descriptors. In this paper, face recognition (FR) using a Convolutional mixer (AFR-Conv) algorithm is developed to handle face occlusion problems. A novel AFR-Conv architecture is designed by assigning priority-based weight to the different face patches along with residual connections and an AdaBoost classifier for automatically recognizing human faces. The AFR-Conv also leverages the strengths of pre-trained CNNs by extracting features using ResNet-50, Inception-v3, and DenseNet-161. The AdaBoost classifier combines these features’ weighted votes to predict labels for testing images. To develop this system, we use the data augmentation method to enhance the number of datasets using human face images. The AFR-Conv method is then used to extract robust features from images. Finally, to recognize human identity, an AdaBoost classifier is utilized. For the training and evaluation of the AFR-Conv model, a set of face images is collected from online data sources. The experimental results of the AFR-Conv approach are presented in terms of precision (PR), recall (RE), detection accuracy (DA), and F1-score metrics. Particularly, the proposed approach attains 95.5% PR, 97.6% RE, 97.5% DA, and 98.5% of F1-score on 8500 face images. The experimental results show that our proposed scheme outperforms advanced methods for face classification. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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17 pages, 971 KiB  
Article
Efficient and Secure Federated Learning for Financial Applications
by Tao Liu, Zhi Wang, Hui He, Wei Shi, Liangliang Lin, Ran An and Chenhao Li
Appl. Sci. 2023, 13(10), 5877; https://doi.org/10.3390/app13105877 - 10 May 2023
Cited by 1 | Viewed by 1510
Abstract
Conventional machine learning (ML) and deep learning approaches require sharing customers’ sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated [...] Read more.
Conventional machine learning (ML) and deep learning approaches require sharing customers’ sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated learning has emerged as a promising approach to protect data privacy. However, the high communication costs associated with federated systems, particularly for large neural networks, can be a bottleneck. To mitigate this issue, it is necessary to limit the number and size of communications for practical training of large neural structures. Gradient sparsification is a technique that has gained increasing attention as a method to reduce communication costs, as it updates only significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly employ gradient sparsification. To overcome this limitation, this article proposes two sparsification methods for reducing the communication costs of federated learning. The first method is a time-varying hierarchical sparsification method for model parameter updates, which addresses the challenge of maintaining model accuracy after a high sparsity ratio. This method can significantly reduce the cost of a single communication. The second method is to apply sparsification to the secure aggregation framework. Specifically, the encryption mask matrix is sparsified to reduce communication costs while protecting privacy. Experiments demonstrate that our method can reduce the upload communication costs to approximately 2.9% to 18.9% of the conventional federated learning algorithm under different non-IID experiment settings when the sparsity rate is 0.01. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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14 pages, 683 KiB  
Article
CRC-Based Reliable WiFi Backscatter Communiation for Supply Chain Management
by Yun-Hao Liu, Tao Liu, Yimeng Huang, Han Ding, Wei Xi and Wei Gong
Appl. Sci. 2023, 13(9), 5471; https://doi.org/10.3390/app13095471 - 27 Apr 2023
Viewed by 1379
Abstract
Supply chain management aims to achieve both efficiency and low cost. Backscatter technology provides a low-energy consumption approach for critical links in the supply chain, such as warehouse management and cargo identification. Traditional backscatter systems achieve tag data transmission through dedicated hardware or [...] Read more.
Supply chain management aims to achieve both efficiency and low cost. Backscatter technology provides a low-energy consumption approach for critical links in the supply chain, such as warehouse management and cargo identification. Traditional backscatter systems achieve tag data transmission through dedicated hardware or controlled transmission sources. An additional access point (AP) can be used to ensure that the original data are always known in tag data decoding. These requirements increase the deployment costs and are not suitable for large-scale applications. To address these challenges, we introduce CRCScatter, a backscatter system based on a cyclic redundancy check (CRC) reverse algorithm, with an uncontrolled source and a single-AP receiver. The CRCScatter decoder at the receiver uses the constraints within 802.11b WiFi packets to recover the original packet and decode tag data from the backscatter packet. Our Matlab simulation results show that CRCScatter is effective in the low signal-to-noise ratio (SNR) regime, and its average decoding time is independent of the length of tag data. By appending redundant bits in tag data, the decoding accuracy of CRCScatter can be improved. In summary, CRCScatter presents a backscatter communication mode based on ambient WiFi signals with fewer hardware requirements and low deployment costs. Furthermore, the decoding idea of calculating unknown data based on the packet constraints has the potential to expand to different types of excitation packages. Full article
(This article belongs to the Special Issue Mobile Computing and Intelligent Sensing)
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