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Editorial

Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living

by
Haoran Wei
1,
Zhendong Wang
1,*,
Yuchao Chang
2 and
Zhenghua Huang
3
1
Department of ECE, University of Texas at Dallas, Richardson, TX 75081, USA
2
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(20), 13631; https://doi.org/10.3390/su142013631
Submission received: 17 October 2022 / Accepted: 20 October 2022 / Published: 21 October 2022
(This article belongs to the Special Issue Artificial Intelligence Applications for Sustainable Urban Living)
Versions Notes

1. Introduction

All global cities are in the process of transforming from classical cities to sustainable smart cities. In this process, people face many urgent challenges in sustainable urban living, such as urban safety, urban living quality, urban energy usage, urban traffic management, urban information security, and so on. In dealing with these urgent challenges in sustainable urban living, artificial intelligence (AI)-based applications play an important role. State-of-the-art AI-based technologies in image processing [1,2,3], video processing [4,5], speech and audio processing [6,7,8,9], music processing [10], natural language processing [11], multimodality processing [12,13,14], Internet of Things [15], edge computing [16], autonomous driving [17], heterogeneous computing [18,19,20], wireless networks [21,22,23], social science [24] and smart healthcare [25,26,27,28] could be helpful in adding intelligence to urban living and will provide better solutions to address challenges in sustainable urban living.
The aim of this Special Issue is to present a multidisciplinary state-of-the-art reference regarding theoretical and real-world challenges, and innovative solutions by inviting high-quality research papers for AI applications in sustainable urban living.

2. Methods

To explore the AI applications for sustainable urban living, methods from these aspect are considered:
  • image classification, denoising, segmentation, object detection and tracking;
  • video surveillance, video object detection, video object tracking, and video denoising applications;
  • automatic speech recognition (ASR), text to speech (TTS), speech denoising, and speaker identification applications for urban living;
  • artificial intelligence-based music composition, analytics, recommendation, and instruction applications;
  • natural language processing (NLP) applications for urban living;
  • multimodality applications for urban living;
  • artificial-Intelligence-based content generation applications for sustainable urban living;
  • optical flow estimation for sustainable urban living;
  • autonomous driving techniques’ applications;
  • IoT applications in urban living;
  • Edge computing models and lite deep learning models for real-time applications;
  • heterogeneous computing for smart urban living;
  • human–computer Interaction applications for smart and sustainable urban living;
  • medical image processing and smart healthcare applications for urban living;
  • wireless artificial intelligence applications for sustainable urban living;
  • other artificial intelligence applications in the transformation of classical cities to smart cities.
Computer vision aims to improve the obtained image quality, detect its contents, and estimate the motion of its contents. The major parts include image enhancement, image fusion, object detection and tracking, optical flow estimation. Such computer vision technologies will highlight the objects and their behaviors in the images. Video related processing includes video surveillance, video object detection, video object tracking, and video denoising applications. These applications will be beneficial for intelligence systems to real-time monitor the real scene and provide strong evidence for possible unsafe behaviors in sustainable urban living.
Speech processing and music processing is another AI application areas, which utilizing audio as input of AI system. Audio related applications includes Automatic speech recognition (ASR), text to speech (TTS), speech denoising, and speaker identification applications. While NLP application utilize text as input of AI system. Audio and text is widely used in urban living, utilizing these information can help people achieve better communication and easily access to useful information.
Multimodality applications utilize information from more than two modalities. Combining different modalities of information can achieve better understanding of urban living environment and make more accurate decision. AI system can also generate content based on the specific input, these generated content can be used for advertisement and many other scenarios.
The Internet of things (IoT) application connected smart devices together and will provide convenience for people. To achieve IoT performance, edge computing focus to address the limitation of power and computation usage for small edge devices. While heterogeneous computing aims to get better computation efficiency and achieve faster computation. These applications will provide solutions for faster computation and more efficient energy usage. Wireless artificial also plays a important role in urban living, especially for autonomous driving, which is supposed to realized in urban living in the near future.
Human–computer interaction aims to find a more efficient way to communicate with computing devices. Smart healthcare solutions will provide better user experience for future urban living. Other AI applications will also be covered in this Special Issue. All these applications will provide good directions in the transformation of classical cities to smart cities.

3. Discussion and Conclusions

This issue contains several articles about artificial intelligence applications for sustainable urban living. These papers provide more AI application details in the transformation of classical cities to smart cities.
Thanks to all of the authors, reviewers and editors for your efforts and supports. By sharing your professional knowledge in various ways, the readers will be provided clear view of AI applications for sustainable urban living.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liu, Z.; Meng, L.; Tan, Y.; Zhang, J.; Zhang, H. Image compression based on octave convolution and semantic segmentation. Knowl.-Based Syst. 2021, 228, 107254. [Google Scholar] [CrossRef]
  2. He, Y.; Zeng, T.; Xiong, Y.; Li, J.; Wei, H. Deep Leaning Based Frequency-Aware Single Image Deraining by Extracting Knowledge from Rain and Background. Mach. Learn. Knowl. Extr. 2022, 4, 738–752. [Google Scholar] [CrossRef]
  3. Azarang, A.; Kehtarnavaz, N. A generative model method for unsupervised multispectral image fusion in remote sensing. Signal Image Video Process. 2022, 16, 63–71. [Google Scholar] [CrossRef]
  4. Zhu, H.; Wei, H.; Li, B.; Yuan, X.; Kehtarnavaz, N. A review of video object detection: Datasets, metrics and methods. Appl. Sci. 2020, 10, 7834. [Google Scholar] [CrossRef]
  5. Zhang, C.; Zhao, Y.; Huang, Y.; Zeng, M.; Ni, S.; Budagavi, M.; Guo, X. Facial: Synthesizing dynamic talking face with implicit attribute learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10–17 October 2021; pp. 3867–3876. [Google Scholar]
  6. Kim, M.; Cao, B.; Mau, T.; Wang, J. Speaker-independent silent speech recognition from flesh-point articulatory movements using an LSTM neural network. IEEE/ACM Trans. Audio Speech Lang. Process. 2017, 25, 2323–2336. [Google Scholar] [CrossRef] [PubMed]
  7. Cao, B.; Wisler, A.; Wang, J. Speaker Adaptation on Articulation and Acoustics for Articulation-to-Speech Synthesis. Sensors 2022, 22, 6056. [Google Scholar] [CrossRef] [PubMed]
  8. Wei, L.; Long, Y.; Wei, H.; Li, Y. New Acoustic Features for Synthetic and Replay Spoofing Attack Detection. Symmetry 2022, 14, 274. [Google Scholar] [CrossRef]
  9. Sang, M.; Li, H.; Liu, F.; Arnold, A.O.; Wan, L. Self-supervised speaker verification with simple siamese network and self-supervised regularization. In Proceedings of the ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, 23–27 May 2022; pp. 6127–6131. [Google Scholar]
  10. Gong, X.; Zhu, Y.; Zhu, H.; Wei, H. Chmusic: A traditional Chinese music dataset for evaluation of instrument recognition. In Proceedings of the 2021 4th International Conference on Big Data Technologies, Zibo, China, 24–26 September 2021; pp. 184–189. [Google Scholar]
  11. Sun, J.; Xue, F.; Li, J.; Zhu, L.; Zhang, H.; Zhang, J. TSINIT: A Two-Stage Inpainting Network for Incomplete Text. IEEE Trans. Multimed. 2022. [Google Scholar] [CrossRef]
  12. Tao, F.; Busso, C. Gating neural network for large vocabulary audiovisual speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 2018, 26, 1290–1302. [Google Scholar] [CrossRef]
  13. Wei, H.; Kehtarnavaz, N. Simultaneous utilization of inertial and video sensing for action detection and recognition in continuous action streams. IEEE Sens. J. 2020, 20, 6055–6063. [Google Scholar] [CrossRef]
  14. Tao, F.; Busso, C. End-to-end audiovisual speech recognition system with multitask learning. IEEE Trans. Multimed. 2020, 23, 1–11. [Google Scholar] [CrossRef]
  15. Zhu, H.; Huang, J.; Liu, H.; Zhou, Q.; Zhu, J.; Li, B. Deep-Learning-Enabled Automatic Optical Inspection for Module-Level Defects in LCD. IEEE Internet Things J. 2021, 9, 1122–1135. [Google Scholar] [CrossRef]
  16. Wang, Z.; Wang, Z.; Liu, C.; Hu, Y. Understanding and tackling the hidden memory latency for edge-based heterogeneous platform. In Proceedings of the 3rd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 20), Santa Clara, CA, USA, 25–26 June 2020. [Google Scholar]
  17. Wang, Z.; Wei, H.; Wang, J.; Zeng, X.; Chang, Y. Security Issues and Solutions for Connected and Autonomous Vehicles in a Sustainable City: A Survey. Sustainability 2022, 14, 12409. [Google Scholar] [CrossRef]
  18. Wang, Z.; Jiang, Z.; Wang, Z.; Tang, X.; Liu, C.; Yin, S.; Hu, Y. Enabling Latency-Aware Data Initialization for Integrated CPU/GPU Heterogeneous Platform. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 2020, 39, 3433–3444. [Google Scholar] [CrossRef]
  19. Zhao, M.; Jha, A.; Liu, Q.; Millis, B.A.; Mahadevan-Jansen, A.; Lu, L.; Landman, B.A.; Tyska, M.J.; Huo, Y. Faster Mean-shift: GPU-accelerated clustering for cosine embedding-based cell segmentation and tracking. Med. Image Anal. 2021, 71, 102048. [Google Scholar] [CrossRef]
  20. You, L.; Jiang, H.; Hu, J.; Chang, C.H.; Chen, L.; Cui, X.; Zhao, M. GPU-accelerated Faster Mean Shift with euclidean distance metrics. In Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Los Alamitos, CA, USA, 27 June–1 July 2022; pp. 211–216. [Google Scholar]
  21. Chang, Y.; Tang, H.; Li, B.; Yuan, X. Distributed joint optimization routing algorithm based on the analytic hierarchy process for wireless sensor networks. IEEE Commun. Lett. 2017, 21, 2718–2721. [Google Scholar] [CrossRef]
  22. Cai, T.; Zhang, J.; Yan, S.; Meng, L.; Sun, J.; Al-Dhahir, N. Reconfigurable Intelligent Surface Aided Non-Orthogonal Unicast-Multicast Secure Transmission. IEEE Wirel. Commun. Lett. 2021, 11, 578–582. [Google Scholar] [CrossRef]
  23. Zhang, J.; Zhang, J.; Zhou, Y.; Ji, H.; Sun, J.; Al-Dhahir, N. Energy and spectral efficiency tradeoff via rate splitting and common beamforming coordination in multicell networks. IEEE Trans. Commun. 2020, 68, 7719–7731. [Google Scholar] [CrossRef]
  24. Li, B.; Jiang, F.; Xia, H.; Pan, J. Under the Background of AI Application, Research on the Impact of Science and Technology Innovation and Industrial Structure Upgrading on the Sustainable and High-Quality Development of Regional Economies. Sustainability 2022, 14, 11331. [Google Scholar] [CrossRef]
  25. Qian, Y.; Wang, Z.; Chen, L.; Huang, Z. Vascular enhancement with structure preservation from noisy X-ray angiogram images by employing non-local Hessian-based filter. Optik 2021, 232, 166523. [Google Scholar] [CrossRef]
  26. Cao, B.; Teplansky, K.; Sebkhi, N.; Bhavsar, A.; Inan, O.T.; Samlan, R.; Mau, T.; Wang, J. Data Augmentation for End-to-end Silent Speech Recognition for Laryngectomees. In Proceedings of the Interspeech 2022, Incheon, Korea, 18–22 September 2022; pp. 3653–3657. [Google Scholar]
  27. Ni, A.; Azarang, A.; Kehtarnavaz, N. A review of deep learning-based contactless heart rate measurement methods. Sensors 2021, 21, 3719. [Google Scholar] [CrossRef] [PubMed]
  28. Zhao, M.; Cao, X.; Zhou, M.; Feng, J.; Xia, L.; Pogue, B.W.; Paulsen, K.D.; Jiang, S. MRI-guided near-infrared spectroscopic tomography (MRg-NIRST): System development for wearable, simultaneous NIRS and MRI imaging. In Proceedings of the Multimodal Biomedical Imaging XVII, San Francisco, CA, USA, 2 March 2022; pp. 87–92. [Google Scholar]
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MDPI and ACS Style

Wei, H.; Wang, Z.; Chang, Y.; Huang, Z. Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living. Sustainability 2022, 14, 13631. https://doi.org/10.3390/su142013631

AMA Style

Wei H, Wang Z, Chang Y, Huang Z. Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living. Sustainability. 2022; 14(20):13631. https://doi.org/10.3390/su142013631

Chicago/Turabian Style

Wei, Haoran, Zhendong Wang, Yuchao Chang, and Zhenghua Huang. 2022. "Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living" Sustainability 14, no. 20: 13631. https://doi.org/10.3390/su142013631

APA Style

Wei, H., Wang, Z., Chang, Y., & Huang, Z. (2022). Introducing the Special Issue on Artificial Intelligence Applications for Sustainable Urban Living. Sustainability, 14(20), 13631. https://doi.org/10.3390/su142013631

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