Abstract
In recent years, the significance of millimeter wave sensors has achieved a paramount role, especially in the non-invasive and ubiquitous analysis of various materials and objects. This paper introduces a novel IoT-based fake currency detection using millimeter wave (mmWave) that leverages machine and deep learning algorithms for the detection of fake and genuine currency based on their distinct sensor reflections. To gather these reflections or signatures from different currency notes, we utilize multiple receiving (RX) antennae of the radar sensor module. Our proposed framework encompasses three different approaches for genuine and fake currency detection, Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Transfer Learning Technique (TLT). After extensive experiments, the proposed framework exhibits impressive accuracy and obtained classification accuracy of 96%, 94%, and 98% for CNN, k-NN, and TLT in distinguishing 10 different currency notes using radar signals.
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References
World-currency: Know about 180 countries currency. https://www.eurochange.co.uk/travel/tips/world-currency-abbreviations-symbols-and-codes. Accessed: April (2024)
Currency-security: Chinese currency security features. https://blog.remitforex.com/how-to-identify-counterfeit-rmb/. Accessed: April (2024)
Nasser N, Emad-ul-Haq Q, Imran M, Ali A, Razzak I, Al-Helali A (2021) A smart healthcare framework for detection and monitoring of covid-19 using iot and cloud computing. Neural Comput Appl 35:1–15
Chouat H, Abbassi I, Graiet M, Südholt M (2023) Adaptive configuration of iot applications in the fog infrastructure. Computing 105(12):2747–2772
Ghiri RE, Entesari K (2019) A miniaturized UWB microwave dual-comb dielectric spectroscopy system. IEEE Trans Microw Theory Tech 67(12):5218–5227
Vakili I, Ohlsson L, Wernersson L-E, Gustafsson M (2015) Time-domain system for millimeter-wave material characterization. IEEE Trans Microw Theory Tech 63(9):2915–2922
Tu W, Yang Y, Du B, Yang W, Zhang X, Zheng J (2020) Rnn-based signal classification for hybrid audio data compression. Computing 102:813–827
Björklund S, Johansson T, Petersson H (2012) Evaluation of a micro-doppler classification method on mm-wave data. In: 2012 IEEE radar conference, pp 0934–0939. IEEE
Jamali B, Zhou J, Babakhani A (2019) Broadband spectroscopy of materials with an integrated comb-based millimeter-wave detector. In: 2019 44th international conference on infrared, millimeter, and terahertz waves (IRMMW-THz), pp 1–2. IEEE
Hazra S, Santra A (2018) Robust gesture recognition using millimetric-wave radar system. IEEE Sens Lett 2(4):1–4
Saluja J, Casanova J, Lin J (2019) A supervised machine learning algorithm for heart-rate detection using doppler motion-sensing radar. IEEE J Electromagn RF Microw Med Biol 4(1):45–51
Zhang R, Cao S (2018) Real-time human motion behavior detection via CNN using mmwave radar. IEEE Sens Lett 3(2):1–4
Sarkar A, Ghosh D (2019) Detection of multiple humans equidistant from IR-UWB SISO radar using machine learning. IEEE Sens Lett 4(1):1–4
Weiß J, Santra A (2018) One-shot learning for robust material classification using millimeter-wave radar system. IEEE Sens Lett 2(4):1–4
Lien J, Gillian N, Karagozler ME, Amihood P, Schwesig C, Olson E, Raja H, Poupyrev I (2016) Soli: ubiquitous gesture sensing with millimeter wave radar. ACM Trans Gr (TOG) 35(4):1–19
Ens B, Quigley A, Yeo H-S, Irani P, Piumsomboon T, Billinghurst M (2018) Counterpoint: exploring mixed-scale gesture interaction for ar applications. In: Extended abstracts of the 2018 CHI conference on human factors in computing systems, pp 1–6
Wang S, Song J, Lien J, Poupyrev I, Hilliges O (2016) Interacting with soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. In: Proceedings of the 29th annual symposium on user interface software and technology, pp 851–860
Eckhardt H (1971) Simple model of corner reflector phenomena. Appl Opt 10(7):1559–1566
Yang X, Zhang Y (2021) Cubesense: Wireless, battery-free interactivity through low-cost corner reflector mechanisms. In: Extended abstracts of the 2021 CHI conference on human factors in computing systems, pp 1–6
Yeo H-S, Flamich G, Schrempf P, Harris-Birtill D, Quigley A (2016) Radarcat: radar categorization for input & interaction. In: Proceedings of the 29th annual symposium on user interface software and technology, pp 833–841
McIntosh J, Fraser M, Worgan P, Marzo A (2017) Deskwave: desktop interactions using low-cost microwave doppler arrays. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems, pp 1885–1892
Arakawa R, Zhang Y (2021) Low-cost millimeter-wave interactive sensing through origami reflectors. In: CHIIoT@ EWSN/EICS
Zhao P, Lu CX, Wang J, Chen C, Wang W, Trigoni N, Markham A (2019) Mid: tracking and identifying people with millimeter wave radar. In: 2019 15th international conference on distributed computing in sensor systems (DCOSS), pp 33–40. IEEE
Hsu C-Y, Hristov R, Lee G-H, Zhao M, Katabi D (2019) Enabling identification and behavioral sensing in homes using radio reflections. In: Proceedings of the 2019 CHI conference on human factors in computing systems, pp 1–13
Yeo H-S, Quigley A (2017) Radar sensing in human-computer interaction. Interactions 25(1):70–73
Yue S, Katabi D (2019) Liquid testing with your smartphone. In: Proceedings of the 17th annual international conference on mobile systems, applications, and services, pp 275–286
Dhekne A, Gowda M, Zhao Y, Hassanieh H, Choudhury RR (2018) Liquid: a wireless liquid identifier. In: Proceedings of the 16th annual international conference on mobile systems, applications, and services, pp 442–454
Xie B, Xiong J, Chen X, Chai E, Li L, Tang Z, Fang D (2019) Tagtag: material sensing with commodity rfid. In: Proceedings of the 17th conference on embedded networked sensor systems, pp 338–350
Ha U, Leng J, Khaddaj A, Adib F (2020) Food and liquid sensing in practical environments using \(\{\)RFIDs\(\}\). In: 17th USENIX symposium on networked systems design and implementation (NSDI 20), pp 1083–1100
Guo J, Wang T, He Y, Jin M, Jiang C, Liu Y (2019) Twinleak: Rfid-based liquid leakage detection in industrial environments. In: IEEE INFOCOM 2019-IEEE conference on computer communications, pp 883–891. IEEE
Feng C, Xiong J, Chang L, Wang J, Chen X, Fang D, Tang Z (2019) Wimi: target material identification with commodity wi-fi devices. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), pp 700–710. IEEE
Wang C, Liu J, Chen Y, Liu H, Wang Y (2018) Towards in-baggage suspicious object detection using commodity wifi. In: 2018 IEEE conference on communications and network security (CNS), pp 1–9 . IEEE
Corradini F, Fedeli A, Fornari F, Polini A, Re B, Ruschioni L (2023) X-iot: a model-driven approach to support iot application portability across iot platforms. Computing 105:1–25
Qayyum A, Mazher M, Nuhu A, Benzinou A, Malik AS, Razzak I (2022) Assessment of physiological states from contactless face video: a sparse representation approach. Computing 105:1–21
Zhu Y, Zhu Y, Zhao BY, Zheng H (2015) Reusing 60ghz radios for mobile radar imaging. In: Proceedings of the 21st annual international conference on mobile computing and networking, pp 103–116
Kwon S, Park S, Cho H, Park Y, Kim D, Yim K (2021) Towards 5g-based iot security analysis against vo5g eavesdropping. Computing 103:425–447
IWR1443 single-chip 76- to 81-GHz mmWave sensor evaluation module. Available at: https://www.ti.com/tool/IWR1443BOOST (2021)
Liu L, Xiao W, Wu J, Xiao S (2020) Wavelet analysis based noncontact vital signal measurements using mm-wave radar. In: Green, pervasive, and cloud computing: 15th international conference, GPC 2020, Xi’an, China, November 13–15, 2020, Proceedings 15, pp 3–14. Springer
Łuczak D (2023) Mechanical vibrations analysis in direct drive using cwt with complex Morlet wavelet. Power Electron Drives 8(1):65–73
Xia Y (2020) Research on statistical machine translation model based on deep neural network. Computing 102:643–661
Theodoridis S (2015) Machine learning: a Bayesian and optimization perspective. Academic press
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Lobanova V, Slizov V, Anishchenko L (2022) Contactless fall detection by means of multiple bioradars and transfer learning. Sensors 22(16):6285
Balanis CA (2011) Modern antenna handbook. Wiley, Hoboken
Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer, Berlin
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Fahim Niaz and Jian Zhang emerged as the primary driving forces, spearheading the conceptualization, design, and execution of the study. Fahim Niaz played a pivotal role in data collection and analysis, while Author Jian Zhang was instrumental in developing computational models and interpreting results. Muhammad Khalid and Kashif Naseer Qureshi were actively engaged in the revision process, providing critical feedback and ensuring methodological rigor. They also contributed significantly to refining the manuscript drafts. Yang Zheng, Muhammad Younas, and Naveed Imran played crucial roles in the presentation and clarity of the work. Yang Zheng focused on designing figures, while Muhammad Younas concentrated on the related work, and Naveed Imran contributed to the writing process, including the discussion and conclusion sections. Collectively, the diverse skills and expertise of the team harmonized to produce a comprehensive and well-rounded research paper.
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Niaz, F., Zhang, J., Khalid, M. et al. AI enabled: a novel IoT-based fake currency detection using millimeter wave (mmWave) sensor. Computing 106, 2851–2873 (2024). https://doi.org/10.1007/s00607-024-01300-2
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DOI: https://doi.org/10.1007/s00607-024-01300-2