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Design and Optimization of Camera HAL Layer Based on Android

  • Yu GanEmail author
  • Wei Hu
  • Yonghao Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)

Abstract.

Nowadays, in the field of mobile and communication, with the rapid popularization and application of Internet and network, and the continuous expansion to the family field, the trend of integration of consumer electronics, computer and communication (3C) is becoming increasingly obvious, and embedded system naturally becomes a research hotspot. The embedded operating system Android has been widely used in mobile devices, such as smart phones, smart watches and so on. Our design is based on the Android platform to optimize and improve Camera HAL. This design is applied to the development of the Android system, the design of the Camera subsystem, the description of the Camera subsystem from the structure, function and data flow, and then proceed to structural optimization. This design uses TI AM335x as the hardware platform, based on Android system, designed and implemented the Android Camera hardware abstraction layer and Camera subsystem. At the end of the development, after the actual experimental test, the development of the system achieved the expected function and worked well.

Keywords

Android Camera HAL Embedded system System architecture 

References

  1. 1.
    Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar
  2. 2.
    Arora, A., Peddoju, S.K.: Minimizing network traffic features for android mobile malware detection. In: Proceedings of the 18th International Conference on Distributed Computing and Networking, ICDCN 2017, pp. 32:1–32:10. ACM (2017)Google Scholar
  3. 3.
    Cimpanu, C.: CopyCat Adware Infects Zygote Android Core Process. BleepingComputerGoogle Scholar
  4. 4.
    Grace, M., Zhou, Y., Zhang, Q., Zou, S., Jiang, X.: Riskranker. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys 2012, pp. 281–294 (2012)Google Scholar
  5. 5.
    Kapratwar, A., Troia, F.D., Stamp, M.: Static and dynamic analysis of android malware. In: Proceedings of the 3rd International Conference on Information Systems Security and Privacy, pp. 653–662 (2017)Google Scholar
  6. 6.
    Liu, B., Nath, S., Govindan, R., Liu, J.: DECAF: detecting and characterizing ad fraud in mobile apps. In: Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation, NSDI 2014, pp. 57–70. USENIX Association (2014)Google Scholar
  7. 7.
    Moonsamy, V., Rong, J., Liu, S.: Mining permission patterns for contrasting clean and malicious Android applications. Future Gener. Comput. Syst. 36, 122–132 (2014)CrossRefGoogle Scholar
  8. 8.
    Sharma, D.: Android malware detection using decision trees and network traffic. Int. J. Comput. Sci. Inf. Technol. 7(4), 1970–1974 (2016)Google Scholar
  9. 9.
    Stamp, M.: Introduction to Machine Learning with Applications in Information Security. Chapman and Hall/CRC, Boca Raton (2017)CrossRefGoogle Scholar
  10. 10.
    Yan, L.-K., Yin, H.: DroidScope: seamlessly reconstructing the OS and Dalvik semantic views for dynamic android malware analysis. In: USENIX Security Symposium, pp. 569–584 (2012)Google Scholar
  11. 11.
    Zhang, L., Guan, Y.: Detecting click fraud in pay-per-click streams of online advertising networks. In: The 28th International Conference on Distributed Computing Systems, ICDCS 2008, pp. 77–84. IEEE (2008)Google Scholar
  12. 12.
    Aafer, Y., Du, W., Yin, H.: DroidAPIMiner: mining API-level features for robust malware detection in android. In: Proceedings of International Conference on Security and Privacy in Communication Systems, pp. 86–103 (2013)Google Scholar
  13. 13.
    Arp, D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K.: DREBIN: effective and explainable detection of android malware in your pocket. In: 21st Annual Network and Distributed System Security Symposium, NDSS 2014, vol. 14, pp. 23–26. The Internet Society (2014)Google Scholar
  14. 14.
    Crussell, J., Stevens, R., Chen, H.: MAdFraud: investigating ad fraud in Android applications. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 123–134. ACM (2014)Google Scholar
  15. 15.
    Daswani, N., Stoppelman, M.: The anatomy of Clickbot.A. In: Proceedings of the First Conference on Hot Topics in Understanding Botnets, HotBots 2007, pp. 11–31. USENIX Association, Berkeley (2007)Google Scholar
  16. 16.
    Enck, W., et al.: TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Trans. Comput. Syst. 32(2), 5 (2014)CrossRefGoogle Scholar
  17. 17.
    Miller, B., Pearce, P., Grier, C., Kreibich, C., Paxson, V.: What’s clicking what? Techniques and innovations of today’s clickbots. In: Holz, T., Bos, H. (eds.) DIMVA 2011. LNCS, vol. 6739, pp. 164–183. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22424-9_10CrossRefGoogle Scholar
  18. 18.
    Qiu, H., Noura, H., Qiu, M.: A user-centric data protection method for cloud storage based on invertible DWT. IEEE Trans. Cloud Comput. (Early Access), 1 (2017)Google Scholar
  19. 19.
    Qiu, H., Memmi, G.: Fast selective encryption method for bitmaps based on GPU acceleration. In: IEEE International Symposium on Multimedia, pp. 155–158. IEEE (2014)Google Scholar
  20. 20.
    Qiu, H., Memmi, G., Kapusta, K., Lu, Z., Qiu, M.: All-or-nothing data protection for ubiquitous communication: challenges and perspectives. Inf. Sci. 502, 434–445 (2019)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industria SystemWuhanChina
  3. 3.Digital Media LabBirmingham City UniversityBirminghamUK

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