Technology & Management Review

Peer Reviewed Article

Vol. 5 (2020)

Emerging Trends in Compressive Sensing for Efficient Signal Acquisition and Reconstruction

Published
2020-02-15

Abstract

This paper explores new compressive sensing (CS) directions for effective signal reconstruction and acquisition to clarify this novel framework's benefits, drawbacks, and consequences. The study aims to investigate the latest advancements in computer science algorithms, the incorporation of machine learning methods, adaptive sampling approaches, and their applications in diverse fields. Using a secondary data-based review technique, the study methodically reviews the literature from various sources, including research articles, review papers, and conference proceedings. Key conclusions from the survey highlight the versatility of computer science (CS) in applications related to medical imaging, remote sensing, wireless communications, and the Internet of Things (IoT). Additionally, CS may be integrated with machine learning to improve reconstruction accuracy and computational efficiency. However, issues like hardware implementation difficulties, noise resilience, and computational complexity still need to be addressed. The significance of policy implications underscores the need to tackle these obstacles via technological advancements, legislative modifications, and stakeholder partnerships to fully actualize the promise of computer science in molding the course of signal processing and data analysis in the future.

References

  1. Ande, J. R. P. K. (2018). Performance-Based Seismic Design of High-Rise Buildings: Incorporating Nonlinear Soil-Structure Interaction Effects. Engineering International, 6(2), 187–200. https://doi.org/10.18034/ei.v6i2.691
  2. Ande, J. R. P. K., Varghese, A., Mallipeddi, S. R., Goda, D. R., & Yerram, S. R. (2017). Modeling and Simulation of Electromagnetic Interference in Power Distribution Networks: Implications for Grid Stability. Asia Pacific Journal of Energy and Environment, 4(2), 71-80. https://doi.org/10.18034/apjee.v4i2.720
  3. Baddam, P. R. (2019). Surgical Robotics Unveiled: The Robotic Surgeon’s Role in Modern Surgical Evolution. ABC Journal of Advanced Research, 8(2), 131-144. https://doi.org/10.18034/abcjar.v8i2.718
  4. Baddam, P. R., Vadiyala, V. R., & Thaduri, U. R. (2018). Unraveling Java’s Prowess and Adaptable Architecture in Modern Software Development. Global Disclosure of Economics and Business, 7(2), 97-108. https://doi.org/10.18034/gdeb.v7i2.710
  5. Colonnese, S., Biagi, M., Cattai, T., Cusani, R., Fallani, F. D. V. (2018). Green Compressive Sampling Reconstruction in IoT Networks. Sensors, 18(8). https://doi.org/10.3390/s18082735
  6. Deming, C., Baddam, P. R., & Vadiyala, V. R. (2018). Unlocking PHP’s Potential: An All-Inclusive Approach to Server-Side Scripting. Engineering International, 6(2), 169–186. https://doi.org/10.18034/ei.v6i2.683
  7. Fadziso, T., Vadiyala, V. R., & Baddam, P. R. (2019). Advanced Java Wizardry: Delving into Cutting-Edge Concepts for Scalable and Secure Coding. Engineering International, 7(2), 127–146. https://doi.org/10.18034/ei.v7i2.684
  8. Goda, D. R. (2016). A Fully Analytical Back-gate Model for N-channel Gallium Nitrate MESFET's with Back Channel Implant. California State University, Northridge. http://hdl.handle.net/10211.3/176151
  9. Goda, D. R., Yerram, S. R., & Mallipeddi, S. R. (2018). Stochastic Optimization Models for Supply Chain Management: Integrating Uncertainty into Decision-Making Processes. Global Disclosure of Economics and Business, 7(2), 123-136. https://doi.org/10.18034/gdeb.v7i2.725
  10. Hou, M., Sen, X., Wu, W., Lin, F. (2018). A Distributed Compressive Sensing Scheme for Event Capture in Wireless Visual Sensor Networks. Journal of Physics: Conference Series, 960(1). https://doi.org/10.1088/1742-6596/960/1/012007
  11. Kaluvakuri, S., & Vadiyala, V. R. (2016). Harnessing the Potential of CSS: An Exhaustive Reference for Web Styling. Engineering International, 4(2), 95–110. https://doi.org/10.18034/ei.v4i2.682
  12. Krzakala, F., Mézard, M., Sausset, F., Sun, Y. F., Zdeborová, L. (2012). Statistical-Physics-Based Reconstruction in Compressed Sensing. Physical Review. X, 2(2). https://doi.org/10.1103/PhysRevX.2.021005
  13. Kumar, G. E. P., Baskaran, K., Blessing, R. E., Lydia, M. (2016). A Comprehensive Review on the Impact of Compressed Sensing in Wireless Sensor Networks. International Journal on Smart Sensing and Intelligent Systems, 9(2), 818-844. https://doi.org/10.21307/ijssis-2017-897
  14. Li, X., Lan, X., Yang, M., Xue, J., Zheng, N. (2014). Efficient Lossy Compression for Compressive Sensing Acquisition of Images in Compressive Sensing Imaging Systems. Sensors, 14(12), 23398-23418. https://doi.org/10.3390/s141223398
  15. Liu, L., Yang, P., Zhang, J., Wei, G. (2015). Compressive Sensing by Colpitts Chaotic Oscillator for Image Sensors. International Journal on Smart Sensing and Intelligent Systems, 8(2), 1225-1243. https://doi.org/10.21307/ijssis-2017-804
  16. Mahadasa, R. (2016). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and Trust. Technology & Management Review, 1, 14-20. https://upright.pub/index.php/tmr/article/view/113
  17. Mahadasa, R. (2017). Decoding the Future: Artificial Intelligence in Healthcare. Malaysian Journal of Medical and Biological Research, 4(2), 167-174. https://mjmbr.my/index.php/mjmbr/article/view/683
  18. Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-88. https://doi.org/10.18034/apjee.v3i2.713
  19. Mahadasa, R., Goda, D. R., & Surarapu, P. (2019). Innovations in Energy Harvesting Technologies for Wireless Sensor Networks: Towards Self-Powered Systems. Asia Pacific Journal of Energy and Environment, 6(2), 101-112. https://doi.org/10.18034/apjee.v6i2.727
  20. Mallipeddi, S. R., & Goda, D. R. (2018). Solid-State Electrolytes for High-Energy-Density Lithium-Ion Batteries: Challenges and Opportunities. Asia Pacific Journal of Energy and Environment, 5(2), 103-112. https://doi.org/10.18034/apjee.v5i2.726
  21. Mallipeddi, S. R., Goda, D. R., Yerram, S. R., Varghese, A., & Ande, J. R. P. K. (2017). Telemedicine and Beyond: Navigating the Frontier of Medical Technology. Technology & Management Review, 2, 37-50. https://upright.pub/index.php/tmr/article/view/118
  22. Mallipeddi, S. R., Lushbough, C. M., & Gnimpieba, E. Z. (2014). Reference Integrator: a workflow for similarity driven multi-sources publication merging. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). https://www.proquest.com/docview/1648971371
  23. Mandapuram, M., Mahadasa, R., & Surarapu, P. (2019). Evolution of Smart Farming: Integrating IoT and AI in Agricultural Engineering. Global Disclosure of Economics and Business, 8(2), 165-178. https://doi.org/10.18034/gdeb.v8i2.714
  24. Sreeharitha, S., Sabarinath, G., Jose, B. R. (2018). Compressive Sensing Recovery Algorithms and Applications- A Survey. IOP Conference Series. Materials Science and Engineering, 396(1). https://doi.org/10.1088/1757-899X/396/1/012037
  25. Stankovic, S., Ioana, C., Li, X., Papic, V. (2016). Algorithms for Compressive Sensing Signal Reconstruction with Applications. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/8376531
  26. Sugimura, D., Tomabechi, M., Hosaka, T., Hamamoto, T. (2016). Compressive Multi-spectral Imaging Using Self-correlations of Images Based on Hierarchical Joint Sparsity Models. Machine Vision and Applications, 27(4), 499-510. https://doi.org/10.1007/s00138-016-0761-y
  27. Surarapu, P. (2016). Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 17-24. https://upright.pub/index.php/ijrstp/article/view/114
  28. Surarapu, P. (2017). Security Matters: Safeguarding Java Applications in an Era of Increasing Cyber Threats. Asian Journal of Applied Science and Engineering, 6(1), 169–176. https://doi.org/10.18034/ajase.v6i1.82
  29. Surarapu, P., & Mahadasa, R. (2017). Enhancing Web Development through the Utilization of Cutting-Edge HTML5. Technology & Management Review, 2, 25-36. https://upright.pub/index.php/tmr/article/view/115
  30. Surarapu, P., Mahadasa, R., & Dekkati, S. (2018). Examination of Nascent Technologies in E-Accounting: A Study on the Prospective Trajectory of Accounting. Asian Accounting and Auditing Advancement, 9(1), 89–100. https://4ajournal.com/article/view/83
  31. Tiwari, V., Bansod, P. P., Kumar, A. (2015). Designing Sparse Sensing Matrix for Compressive Sensing to Reconstruct High Resolution Medical Images. Cogent Engineering, 2(1). https://doi.org/10.1080/23311916.2015.1017244
  32. Tuli, F. A., Varghese, A., & Ande, J. R. P. K. (2018). Data-Driven Decision Making: A Framework for Integrating Workforce Analytics and Predictive HR Metrics in Digitalized Environments. Global Disclosure of Economics and Business, 7(2), 109-122. https://doi.org/10.18034/gdeb.v7i2.724
  33. Vadiyala, V. R. (2017). Essential Pillars of Software Engineering: A Comprehensive Exploration of Fundamental Concepts. ABC Research Alert, 5(3), 56–66. https://doi.org/10.18034/ra.v5i3.655
  34. Vadiyala, V. R. (2019). Innovative Frameworks for Next-Generation Cybersecurity: Enhancing Digital Protection Strategies. Technology & Management Review, 4, 8-22. https://upright.pub/index.php/tmr/article/view/117
  35. Vadiyala, V. R., Baddam, P. R., & Kaluvakuri, S. (2016). Demystifying Google Cloud: A Comprehensive Review of Cloud Computing Services. Asian Journal of Applied Science and Engineering, 5(1), 207–218. https://doi.org/10.18034/ajase.v5i1.80
  36. Yerram, S. R., & Varghese, A. (2018). Entrepreneurial Innovation and Export Diversification: Strategies for India’s Global Trade Expansion. American Journal of Trade and Policy, 5(3), 151–160. https://doi.org/10.18034/ajtp.v5i3.692

Most read articles by the same author(s)