Abstract
This research work concentrates on developing new methodologies for weak signal detection, which are further used in the watermark detection application. The proposed detectors perform comparable to or better than most of the state-of-the-art techniques.
Stochastic resonance plays a significant role in weak signal detection. Injection of precalculated noise is a big concern for us. Noises i.e., symmetric and asymmetric have been utilized for getting an improved version of the weak signal detector. With equality and non-equality constraints, we use the particle swarm optimization method to optimize the objective function. After that, we investigate a new detector based on the fractional operator. The noisy signal is convolved with the coefficients of fractional order filter. The proposed method has been tested with different standard performance parameters. The results of the proposed detector have been compared with state-of-the-art detectors such as a threshold detector (TD), neural network-based detector etc. The robustness of the proposed detector with respect to the parameters of signal and noise has also been explored.
The problem of weak DC signal detection present in non-Gaussian noise is always a troublesome because of the non-linearity of the test statistic. We have used the concept of stochastic resonance in a conventional neural network-based detector. A predefined noise is injected during the back-propagation algorithm in order to minimize the error. The reduction in errors (in terms of enhancement in the probability of detection, \(P_D\) at a constant value of the probability of false alarm, \(P_{FA}\)) and faster convergence give a right justification to use the concept of stochastic resonance in the neural network-based detector.
References
Al-Hussaini E, Al-Bassiouni A (1985) Performance of MRC diversity systems for the detection of signals with Nakagami fading. IEEE Trans Commun 33(12):1315–1319
Asdrubali F, Baldinelli G, Bianchi F, Costarelli D, Rotili A, Seracini M, Vinti G (2018) Detection of thermal bridges from thermographic images by means of image processing approximation algorithms. Appl Math Comput 317:160–171
Bekkerman I, Tabrikian J (2006) Target detection and localization using mimo radars and sonars. IEEE Trans Signal Process 54(10):3873–3883
Capon J (1970) Applications of detection and estimation theory to large array seismology. Proc IEEE 58(5):760–770
Chen H, Varshney PK (2008) Theory of the stochastic resonance effect in signal detection-part II: variable detectors. IEEE Trans Signal Process 56(10):5031–5041
Chen H, Varshney PK, Kay SM, Michels JH (2007) Theory of the stochastic resonance effect in signal detection: part I-fixed detectors. IEEE Trans Signal Process 55(7):3172–3184
Chouksey M, Jha RK, Sharma R.: A fast technique for image segmentation based on two meta heuristic algorithms. In: Multimedia tools and applications, pp 1–53 (2020)
Fortmann T, Bar-Shalom Y, Scheffe M (1983) Sonar tracking of multiple targets using joint probabilistic data association. IEEE J Ocean Eng 8(3):173–184
Foschini GJ, Gans MJ (1998) On limits of wireless communications in a fading environment when using multiple antennas. Wirel Pers Commun 6(3):311–335
Gabbiani F, Cox SJ (2017) Mathematics for neuroscientists. Academic Press, Cambridge
Gandhi PP, Ramamurti V (1997) Neural networks for signal detection in non-Gaussian noise. IEEE Trans Signal Process 45(11):2846–2851
Garth LM, Poor HV (1994) Detection of non-Gaussian signals: a paradigm for modern statistical signal processing. Proc IEEE 82(7):1061–1095
Giakoumaki A, Pavlopoulos S, Koutsouris D (2006) Multiple image watermarking applied to health information management. IEEE Trans Inf Technol Biomed 10(4):722–732
Guo G, Mandal M, Jing Y (2012) A robust detector of known signal in non-gaussian noise using threshold systems. Signal Process 92(11):2676–2688
Halay N, Todros K, Hero AO (2016) Binary hypothesis testing via measure transformed quasi-likelihood ratio test. IEEE Trans Signal Process 65(24):6381–6396
Heidari-Bateni G, McGillem CD (1994) A chaotic direct-sequence spread-spectrum communication system. IEEE Trans Commun 42(234):1524–1527
Kaplan E (1955) Signal-detection studies, with applications. Bell Syst Tech J 34(2):403–437
Kay SM (2002) Fundamentals of statistical signal processing: detection theory prentice hall. Signal Proces Ser 2:20–24
Key S (1993) Fundamentals of statistical signal processing, volume ii: Detection theory
Kumar S, Chauhan N, Jha RK (2019) Suprathreshold stochastic resonance characterization for gamma noise with watermarking application. In: 25th international conference on noise and fluctuations (ICNF 2019), CONF
Kumar S, Gupta A, Jha R.K (2019) Analysis, diagnosis and correction of rain streaks. In: TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pp. 2700–2704. IEEE (2019)
Kumar S, Jha AKRK (2017) Performance analysis of segmentation using ssr under different noise conditions. In: 2017 international conference on noise and fluctuations (ICNF), IEEE, pp 1–4
Kumar S, Jha RK (2016) Enhancement of high dynamic range images using variational calculus regularizer with stochastic resonance. In: Proceedings of the tenth Indian conference on computer vision, graphics and image processing, pp 1–8
Kumar S, Jha RK (2018) A fractional integrator based novel detector for weak signal detection with watermark application. In: 2018 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC). IEEE, pp 1791–1795
Kumar S, Jha RK (2019) Fd-based detector for medical image watermarking. IET Image Process 13(10):1773–1782
Kumar S, Jha RK (2019) Noise-induced resonance and particle swarm optimization-based weak signal detection. Circ Syst Sig Process 38(6):2677–2702
Kumar S, Jha RK (2020) An fpga-based design for a real-time image denoising using approximated fractional integrator. In: Multidimensional systems and signal processing, pp 1–23
Kumar S, Jha RK, Sharma R, Verma A, Singh Y (2018) A robust sharing based encryption method in singular value decomposition domain using fractional Fourier transform. In: 2018 8th international symposium on embedded computing and system design (ISED). IEEE, pp 135–140
Kumar S, Jha RK et al (2017) Characterization of supra-threshold stochastic resonance for uniform distributed signal with laplacian and gaussian noise. In: 2017 international conference on noise and fluctuations (ICNF), IEEE, pp 1–4
Kumar S, Kumar A (2019) Jha RK (2019) A novel noise-enhanced back-propagation technique for weak signal detection in neyman–pearson framework. Neural Process Lett 50(3):2389–2406
Kumar S, Kumar A, Jha RK (2020) Noise-induced training for weak signal detection in neyman–pearson framework. In: Advances in VLSI, communication, and signal processing. Springer, pp 295–305
Kumar S, Panna B, Jha RK (2019) Medical image encryption using fractional discrete cosine transform with chaotic function. Med Biol Eng Comput 57(11):2517–2533
Kumar S, Singh T, Jha R, Rahman MA.: Randomness assists in wireless connectivity. In: 25th international conference on noise and fluctuations (ICNF 2019), CONF (2019)
Kumar Jha R, Soni B, Kumar S, Verma VS.: Radon transform and dynamic stochastic resonance based technique for line detection from noisy image. In: 25th international conference on noise and fluctuations (ICNF 2019), CONF (2019)
Li J, Park JH, Ye D (2017) Simultaneous fault detection and control design for switched systems with two quantized signals. ISA Trans 66:296–309
Li YG, Winters JH, Sollenberger NR (2002) Mimo-ofdm for wireless communications: signal detection with enhanced channel estimation. IEEE Trans Commun 50(9):1471–1477
López C, Zhong W, Lu S, Cong F, Cortese I (2017) Stochastic resonance in an underdamped system with Fitzhug-Nagumo potential for weak signal detection. J Sound Vib 411:34–46
Luo FL, Unbehauen R (1998) Applied neural networks for signal processing. Cambridge University Press, Cambridge
Ma TF, Chen YP, Guo JS, Wang W (2018) Cellular analysis and detection using surface plasmon resonance imaging. TrAC Trends in Analytical Chemistry
Makaju S, Prasad P, Alsadoon A, Singh A, Elchouemi A (2018) Lung cancer detection using CT scan images. Proc Comput Sci 125:107–114
Makki I, Younes R, Francis C, Bianchi T, Zucchetti M (2017) A survey of landmine detection using hyperspectral imaging. ISPRS J Photogramm Remote Sens 124:40–53
Miller ML, Doërr GJ, Cox IJ (2004) Applying informed coding and embedding to design a robust high-capacity watermark. IEEE Trans Image Process 13(6):792–807
Murty KSR, Yegnanarayana B (2008) Epoch extraction from speech signals. IEEE Trans Audio Speech Lang Process 16(8):1602–1613
Naderpour M, Ghobadzadeh A, Tadaion A, Gazor S (2015) Generalized wald test for binary composite hypothesis test. IEEE Signal Process Lett 22(12):2239–2243
Panna B, Kumar S, Jha RK (2019) Image encryption based on block-wise fractional fourier transform with wavelet transform. IETE Tech Rev 36(6):600–613
Papadopoulos CK, Ioannidis GC, Psomopoulos CS (2018) Detection of transient signals based on the tricepstrum. Digit Signal Process 78:232–249
Patel A, Kosko B (2009) Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection. IEEE Trans Signal Process 57(5):1655–1669
Patel A, Kosko B (2011) Noise benefits in quantizer-array correlation detection and watermark decoding. IEEE Trans Signal Process 59(2):488–505
Poor HV (2013) An introduction to signal detection and estimation. Springer, Berlin
Proakis JG, Salehi M, Zhou N, Li X (1994) Communication systems engineering, vol 2. Prentice Hall, New Jersey
Rahman MA, Jha RK, Gupta AK (2019) Gabor phase response based scheme for accurate pectoral muscle boundary detection. IET Image Process 13(5):771–778
Rajeswari J, Jagannath M (2017) Advances in biomedical signal and image processing—a systematic review. Inf Med Unlocked 8:13–19
Rajib J, Tiwari PK, Krishna O, Singh J, Pandey SK (2019) Dynamic stochastic resonance based blocking artifacts removal from compressed in dct domain. In: 25th international conference on noise and fluctuations (ICNF 2019), CONF
Ramamurti V, Rao SS, Gandhi PP (1993) Neural detectors for signals in non-Gaussian noise. In: IEEE international conference on acoustics, speech, and signal processing, 1993. ICASSP-93, vol 1. IEEE, pp 481–484
Rotello CM (2017) Signal detection theories of recognition memory
Shourya S, Kumar S, Jha RK (2016) Adaptive fractional differential approach to enhance underwater images. In: 2016 sixth international symposium on embedded computing and system design (ISED). IEEE, pp 56–60
Skolnik MI (1970) Radar handbook
Soualmi A, Alti A, Laouamer L (2018) A new blind medical image watermarking based on Weber descriptors and Arnold chaotic map. Arab J Sci Eng 43:7893–7905
Suman S, De S (2017) Solar-enabled green base stations: Cost versus utility. In: 2017 449 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), pp. 1–8. IEEE
Suman S, Kumar S, De S (2018) Path loss model for uav-assisted rfet. IEEE Communi Lett 22(10):2048–2051
Tsihrintzis GA, Nikias CL (1995) Performance of optimum and suboptimum receivers in the presence of impulsive noise modeled as an alpha-stable process. IEEE Trans Commun 43(234):904–914
Umaamaheshvari A, Thanushkodi K (2012) High performance and effective watermarking scheme for medical images. Eur J Sci Res 67(2):283–293
Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531
Verma VS, Jha RK (2015) Improved watermarking technique based on significant difference of lifting wavelet coefficients. Signal Image Video Process 9(6):1443–1450
Wax M, Kailath T (1985) Detection of signals by information theoretic criteria. IEEE Trans Acoust Speech Signal Process 33(2):387–392
Acknowledgements
This publication is an outcome of the R & D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation (Formerly Media Lab Asia) (Grant No. U72900MH2001NPL133410).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jha, R.K., Kumar, S. Research work under Visvesvaraya YFRF. CSIT 8, 271–284 (2020). https://doi.org/10.1007/s40012-020-00307-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40012-020-00307-2