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i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Cited

Volume 10 Issue 2 July - December 2022

Review Paper

Performance Analysis of Various Copy-Move Forgery Detection Methods

Deependra Kumar Shukla* , Abhishek Bansal, Pawan Singh*

* Department of Computer Science and Engineering, A. K. S. University, Satna, Madhya Pradesh, India.

Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India.

* Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India.

Shukla, D. K., Bansal, A., and Singh, P. (2022). Performance Analysis of Various Copy-Move Forgery Detection Methods. i-manager’s Journal on Digital Signal Processing, 10(2), 1-6. https://doi.org/10.26634/jdp.10.2.19181

Abstract

Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, including comparisons of the strategies, advantages, disadvantages, and experimental findings.

References

[1]. Ashraf, R., Mehmood, M. S., Mahmood, T., Rashid, J., Nisar, M. W., & Shah, M. (2020, October). An efficient forensic approach for copy-move forgery detection via discrete wavelet transform. In 2020 International Conference on Cyber Warfare and Security (ICCWS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICCWS48432.2020.9292372

[2]. Chen, C. C., Lu, W. Y., & Chou, C. H. (2019). Rotational copy-move forgery detection using SIFT and region growing strategies. Multimedia Tools and Applications, 78(13), 18293-18308. https://doi.org/10.1007/s11042-019-7165-8

[3]. Dhanaraj, R. S., & Sridevi, M. (2021, February). A study on detection of copy-move forgery in digital images. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 900-905). IEEE. https://doi.org/10.1109/ICICV50876.2021.9388576

[4]. Ferreira, W. D., Ferreira, C. B., da Cruz Júnior, G., & Soares, F. (2020). A review of digital image forensics. Computers & Electrical Engineering, 85, 106685. https://doi.org/10.1016/j.compeleceng.2020.106685

[5]. Hegazi, A., Taha, A., & Selim, M. M. (2021). An improved copy-move forgery detection based on density-based clustering and guaranteed outlier removal. Journal of King Saud University-Computer and Information Sciences, 33(9), 1055-1063. https://doi.org/10.1016/j.jksuci.2019.07.007

[6]. Jana, M., Jana, B., & Joardar, S. (2022). Local feature based self-embedding fragile watermarking scheme for tampered detection and recovery utilizing AMBTC with fuzzy logic. Journal of King Saud University-Computer and Information Sciences, 33(10), 9822-9835. https://doi.org/10.1016/j.jksuci.2021.12.011

[7]. Jeyalakshmi, A. (2019). Improved copy-move forgery detection using sift with adaptive over kernel principal component analysis. International Journal of Scientific Research in Computing, 1(1), 21-27.

[8]. Mahfoudi, G., Morain-Nicollier, F., Retraint, F., & Marc, P. I. C. (2019, November). Copy and move forgery detection using sift and local color dissimilarity maps. In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 1-5). IEEE. https://doi.org/10.1109/GlobalSIP45357.2019.8969355

[9]. Meena, K. B., & Tyagi, V. (2020). A hybrid copy-move image forgery detection technique based on Fourier- Mellin and scale invariant feature transforms. Multimedia Tools and Applications, 79(11), 8197-8212. https://doi.org/10.1007/s11042-019-08343-0

[10]. Pourkashani, A., Shahbahrami, A., & Akoushideh, A. (2021). Copy-move forgery detection using convolutional neural network and K-mean clustering. International Journal of Electrical and Computer Engineering, 11(3), 2604–2612. https://doi.org/10.11591/ijece.v11i3.pp2604-2612

[11]. Rahma, F. I., Utami, E., & Al Fatta, H. (2020, November). The using of gaussian pyramid decomposition, compact watershed segmentation masking and DBSCAN in copy-move forgery detection with SIFT. In 2020 3rd International Conference on Information and Communications Technology (ICOIACT) (pp. 325-330). IEEE. https://doi.org/10.1109/ICOIACT50329.2020.9332081

[12]. Raja, K., Gupta, G., Venkatesh, S., Ramachandra, R., & Busch, C. (2022). Towards generalized morphing attack detection by learning residuals. Image and Vision Computing, 126, 104535. https://doi.org/10.1016/j.imavis.2022.104535

[13]. Rajput, A. (2018, February 15). Image Splicing | Set 1 (Introduction). Retrieved from https://www.geeksforgeeks.org/image-splicing-set-1-introduction/

[14]. Rani, A., Jain, A., & Kumar, M. (2021). Identification of copy-move and splicing based forgeries using advanced SURF and revised template matching. Multimedia Tools and Applications, 80(16), 23877-23898. https://doi.org/10.1007/s11042-021-10810-6

[15]. Singh, G. P., & Singh, K. (2020). An improved block based copy-move forger y detection technique. Multimedia Tools and Applications, 79(19), 13011-13035. https://doi.org/10.1007/s11042-019-08354-x

[16]. Uma, S., & Sathya, P. D. (2019). A detailed review of copy-move forgery detection in digital image. Global Journal of Engineering Science and Researches, 6(1), 38-49. https://doi.org/10.1007/978-3-642-10467-1_72

[17]. Yang, J., Liang, Z., Gan, Y., & Zhong, J. (2021). A novel copy-move forgery detection algorithm via twostage filtering. Digital Signal Processing, 113, 103032. https://doi.org/10.1016/j.dsp.2021.103032

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Review Paper

Systematic Digital Signal Processing Approach in Various Biometric Identification

K. P. Ajitha Gladis* , D. Sharmila**

* Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

** Department of Computer Applications, Government Arts & Science College, Kanyakumari, Tamil Nadu, India.

Gladis, K. P. A., and Sharmila, D. (2022). Systematic Digital Signal Processing Approach in Various Biometric Identification. i-manager’s Journal on Digital Signal Processing, 10(2), 7-15. https://doi.org/10.26634/jdp.10.2.19290

Abstract

Biometrics are unique physical characteristics, such as fingerprints, that can be used for automatic recognition. Biometric identifiers are often classified as physiological characteristics associated with body shape. The goal is to capture a piece of biometric data from that person. It could be a photograph of their face, a recording of their voice, or a picture of their fingerprints. While there are numerous types of biometrics for authentication, the six most common are facial, voice, iris, near-field communication, palm or finger vein patterns, and Quick Response (QR) code. Biometrics is a subset of the larger field of human identification science. This paper explores computational approaches to speaker recognition, face recognition, speech recognition, and fingerprint recognition to assess the overall state of digital signal processing in biometrics.

References

[1]. Bhairannawar, S. S., Sarkar, S., Raja, K. B., & Venugopal, K. R. (2018). Implementation of fingerprint based biometric system using optimized 5/3 DWT architecture and modified CORDIC based FFT. Circuits, Systems, and Signal Processing, 37, 342-366. https://doi.org/10.1007/s00034-017-0555-0

[2]. de Luis-Garcı́a, R., Alberola-Lopez, C., Aghzout, O., & Ruiz-Alzola, J. (2003). Biometric identification systems. Signal Processing, 83(12), 2539-2557. https://doi.org/10.1016/j.sigpro.2003.08.001

[3]. Dey, S., Barman, S., Bhukya, R. K., Das, R. K., Haris, B. C., Prasanna, S. M., & Sinha, R. (2014, February). Speech biometric based attendance system. In 2014 Twentieth National Conference on Communications (NCC) (pp. 1-6). IEEE. https://doi.org/10.1109/NCC.2014.6811345

[4]. Dominguez-Morales, J. P., Liu, Q., James, R., Gutierrez-Galan, D., Jimenez-Fernandez, A., Davidson, S., & Furber, S. (2018, July). Deep spiking neural network model for time-variant signals classification: a real-time speech recognition approach. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN.2018.8489381

[5]. Du, Y., Ives, R. W., Etter, D. M., & Welch, T. B. (2004, May). Biometric signal processing laboratory. In 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. 5, pp. V-1025). IEEE. https://doi.org/10.1109/ICASSP.2004.1327288

[6] Jain, A. K., Flynn, P., & Ross, A. A. (2008). Handbook of Biometrics. Springer.

[7]. Kariapper, R. K. A. R. (2021). Attendance system using RFID, IoT and Machine learning: A two factor verification approach. Systematic Reviews in Pharmacy, 12(3), 314-321.

[8]. Masalha, F., & Hirzallah, N. (2014). A students attendance system using QR code. International Journal of Advanced Computer Science and Applications, 5(3), 75-79.

[9]. Nasir, M. A. H. M, bin Asmuni, M. H., Salleh, N., & Misra, S. (2015). A review of student attendance system using near-field communication (NFC) technology. In Computational Science and Its Applications--ICCSA 2015: 15th International Conference (pp. 738-749). Springer International Publishing. https://doi.org/10.1007/978-3-319-21410-8_56

[10]. Okokpujie, K. O., Noma-Osaghae, E., Okesola, O. J., John, S. N., & Robert, O. (2017, December). Design and implementation of a student attendance system using iris biometric recognition. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 563-567). IEEE. https://doi.org/10.1109/CSCI.2017.96

[11]. Saundade, M., & Kurle, P. (2013). Speech recognition using digital signal processing. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE), 2(6), 31-34.

[12]. Taskiran, M., Kahraman, N., & Erdem, C. E. (2020). Face recognition: Past, present and future (a review). Digital Signal Processing, 106, 102809. https://doi.org/10.1016/j.dsp.2020.102809

[13]. Wayman, J. L. (2002, September). Digital signal processing in biometric identification: a review. In Proceedings. International Conference on Image Processing (Vol. 1, pp. I-I). IEEE. https://doi.org/10.1109/ICIP.2002.1037953

[14] Wayman, J. L., Jain, A. K., Maltoni, D., & Maio, D. (2005). Biometric Systems: Technology, Design and Performance Evaluation. Springer.

[15]. Yang, H., & Han, X. (2020). Face recognition attendance system based on real-time video processing. IEEE Access, 8, 159143-159150. https://doi.org/10.1109/ACCESS.2020.3007205

[16]. Yongqiang, Z., & Ji, L. (2006, November). The design of wireless fingerprint attendance system. In 2006 International Conference on Communication Technology (pp. 1-4). IEEE. https://doi.org/10.1109/ICCT. 2006.341990

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Review Paper

Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview

D. Suja Darling* , J. Hinduja**

* Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, Udaya School of Engineering, Ammandivilai, Tamil Nadu, India.

Darling, D. S., and Hinduja, J. (2022). Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview. i-manager’s Journal on Digital Signal Processing, 10(2), 16-21. https://doi.org/10.26634/jdp.10.2.19289

Abstract

Over the past years, advancements in speech processing have mostly been driven by DSP approaches. The speech interface was designed to convert speech input into a parametric form for further processing (Speech-to-Text) and the resulting text output to speech synthesis (Text-to-Speech). Feature extraction is done by changing the speech waveform into a parametric representation at a relatively low data rate so that it can be processed and analyzed later. There are numerous feature extraction techniques available. This paper presents the overview of Linear Predictive Coding (LPC).

References

[1]. Agrawal, S., Shruti, A. K., & Krishna, C. R. (2010). Prosodic feature based text dependent speaker recognition using machine learning algorithms. International Journal of Engineering Science and Technology, 2(10), 5150-5157.

[2]. Ainsworth, W. (1988). Speech Recognition by Machine. Peter Peregrinus.

[3]. Analog Devices. (n.d.). Beginner's Guide to Digital Signal Processing (DSP). Retrieved from https://www.analog.com/en/design-center/landing-pages/001/beginners-guide-to-dsp.html

[4]. Bou-Ghazale, S. E., & Hansen, J. H. (2000). A comparative study of traditional and newly proposed features for recognition of speech under stress. IEEE Transactions on Speech and Audio Processing, 8(4), 429-442. https://doi.org/10.1109/89.848224

[5]. Bradbury, J. (2000). Linear Predictive Coding. McGraw Hill.

[6]. Diniz, P. S., Da Silva, E. A., & Netto, S. L. (2002). Digital Signal Processing: System Analysis and Design. Cambridge University Press.

[7]. Kumar, R., Ranjan, R., Singh, S. K., Kala, R., Shukla, A., & Tiwari, R. (2009). Multilingual speaker recognition using neural network. Proceedings of the Frontiers of Research on Speech and Music, FRSM, 1-8.

[8]. Narang, S., & Gupta, M. D. (2015). Speech feature extraction techniques: a review. International Journal of Computer Science and Mobile Computing, 4(3), 107-114.

[9]. Nehe, N. S., & Holambe, R. S. (2012). DWT and LPC based feature extraction methods for isolated word recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2012(1), 1-7. https://doi.org/10.1186/1687-4722-2012-7

[10]. Rabiner, L., & Juang, B. (2003). Fundamentals of Speech Recognition. Pearson Education.

[11]. Rabiner, L., & Juang, B. H. (1993). Fundamentals of Speech Recognition. Prentice-Hall, Inc.

[12]. Shrawankar, U., & Mahajan, A. (2013). Speech: a challenge to digital signal processing technology for human-to-computer interaction. Proceedings National Conference on Recent Trends in Electronics & Information Technology (RTEIT), 206-212. https://doi.org/10.48550/arXiv.1305.1925

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Concept paper

Fire and Smoke Detection with Deep Learning: A Review

Vinay Kumar Jain* , Chitrangad Jain**

* Shri Shankaracharya Technical Campus, Bhilai, India.

** Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Jain, V. K., and Jain, C. (2022). Fire and Smoke Detection with Deep Learning: A Review. i-manager’s Journal on Digital Signal Processing, 10(2), 22-32. https://doi.org/10.26634/jdp.10.2.19262

Abstract

This paper outlines a state-of-the-art method for smoke and fire detection utilizing Convolutional Neural Networks (CNNs). The current smoke detectors installed in buildings pose a challenge for effective fire detection. The inefficiency of traditional methods in terms of speed and cost led to the exploration of using Artificial Intelligence (AI) to identify and alert from Closed Circuit Television (CCTV) footage. In this paper, an analytical overview of AI is conducted by using a selfcreated dataset of video frames containing flames and smoke. The data undergoes pre-processing before being used to train a CNN-based machine learning model. The goal of this review study is to understand the available literature in the field and propose a highly accurate, cost-effective, and simple system for fire detection in various scenarios.

References

[1]. Alkhatib, A. A. (2014). A review on forest fire detection techniques. International Journal of Distributed Sensor Networks, 10(3), 597368. https://doi.org/10.1155/2014/597368

[2]. Angayarkkani, K., & Radhakrishnan, N. (2009). Efficient forest fire detection system: a spatial data mining and image processing based approach. International Journal of Computer Science and Network Security, 9(3), 100-107.

[3]. Arrue, B. C., Ollero, A., & De Dios, J. M. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems and their Applications, 15(3), 64-73. http://doi.org/10.1109/5254.846287

[4]. Arul, A., Prakaash, R. H., Raja, R. G., Nandhalal, V., & Kumar, N. S. (2021, May). Fire detection system using machine learning. In Journal of Physics: Conference Series, 1916(1), 012209. IOP Publishing. http://doi.org/10.1088/1742-6596/1916/1/012209

[5]. Breejen, E., Breuers, M., Cremer, F., Kemp, R., Roos, M., Schutte, K., & De Vries, J. S. (1998). Autonomous forest fire detection. III International Conference on Forest Fire Research 14th Conference on Fire and Forest Meteorology, 2, 2003-2012.

[6]. Cheng, C., Sun, F., & Zhou, X. (2011). One fire detection method using neural networks. Tsinghua Science and Technology, 16(1), 31-35. http://doi.org/10.1016/S1007-0214(11)70005-0

[7]. Fernandes, A. M., Utkin, A. B., Lavrov, A. V., & Vilar, R. M. (2004). Development of neural network committee machines for automatic forest fire detection using lidar. Pattern Recognition, 37(10), 2039-2047. https://doi.org/10.1016/j.patcog.2004.04.002

[8]. Kim, B., & Lee, J. (2019). A video-based fire detection using deep learning models. Applied Sciences, 9(14), 2862. https://doi.org/10.3390/app9142862

[9]. Muhammad, K., Ahmad, J., Mehmood, I., Rho, S., & Baik, S. W. (2018). Convolutional neural networks based fire detection in surveillance videos. IEEE Access, 6, 18174-18183. https://doi.org/10.1109/ACCESS.2018.2812835

[10]. Pragati., Shambhuwani, S., & Umbrajkar, P. (2020). Forest fire detection using machine learning. International Journal of Advance Scientific Research and Engineering Trends, 4(12), 6-12.

[11]. Saponara, S., Elhanashi, A., & Gagliardi, A. (2021). Real-time video fire/smoke detection based on CNN in antifire surveillance systems. Journal of Real-Time Image Processing, 18, 889-900. https://doi.org/10.1007/s11554-020-01044-0

[12]. Wang, Y., Dang, L., & Ren, J. (2019). Forest fire image recognition based on convolutional neural network. Journal of Algorithms & Computational Technology, 13, 1-11. https://doi.org/10.1177/1748302619887689

[13]. Xu, R., Lin, H., Lu, K., Cao, L., & Liu, Y. (2021). A forest fire detection system based on ensemble learning. Forests, 12(2), 217. https://doi.org/10.3390/f12020217

[14]. Yu, L., Wang, N., & Meng, X. (2005, September). Real-time forest fire detection with wireless sensor networks. In Proceedings 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005. (Vol. 2, pp. 1214-1217). https://doi.org/10.1109/WCNM.2005.1544272

[15]. Zhang, Q., Xu, J., Xu, L., & Guo, H. (2016, January). Deep convolutional neural networks for forest fire detection. In 2016 International Forum on Management, Education and Information Technology Application (pp. 568-575). Atlantis Press. https://doi.org/10.2991/ifmeita-16.2016.105

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Artificial Intelligence Role in Identifying Paralysis with Paralysis Prediction and Monitoring Model

Vemuri Bharath Kumar* , Govardhana Giridhari Narayana Sri Ranga, Mary Sujatha*

*-*** Department of Computer Science, National Sanskrit University, Tirupati, Andhra Pradesh, India.

Kumar, V. B., Ranga, G. G. N. S. and Sujatha, M. (2022). Artificial Intelligence Role in Identifying Paralysis with Paralysis Prediction and Monitoring Model. i-manager’s Journal on Digital Signal Processing, 10(2), 33-39. https://doi.org/10.26634/jdp.10.2.19247

Abstract

Computer vision, autonomous driving, natural language processing, and speech recognition are just a few of the industrial and research disciplines that are being transformed by Artificial Intelligence (AI) and Machine Learning (ML) approaches. The availability of automated solutions may improve the precision and reproducibility of the execution of crucial activities in a variety of sectors, including radiology, diagnostics, and many others, where it is already having a significant impact. Paralysis occurs when people are unable to make voluntary muscle movements. Paralysis is caused by a problem with the nervous system. This paper focuses on the study of the contribution of Artificial Intelligence in identifying paralysis and explores the idea of building a Paralysis Prediction and Monitoring Model (PPAMM) with the help of AI and ML techniques. It is used to determine the frequency of nerve stimulation in the affected region as well as monitor the stimulus and paralysis-precipitating factors.

References

[1]. Ali, Z. H., Ali, H. A., & Badawy, M. M. (2015). Internet of Things (IoT): Definitions, challenges and recent research directions. International Journal of Computer Applications, 128(1), 37-47.

[2]. Chan, Y. K., Chen, Y. F., Pham, T., Chang, W., & Hsieh, M. Y. (2018). Artificial intelligence in medical applications. Journal of Healthcare Engineering, 2018, 1-2. https://doi.org/ 10.1155/2018/4827875.

[3]. Das, S. (2019). Intel Joins Researchers to Use AI, ML in Curing Paralysis from Spinal Injuries. Rertieved from https://www.news18.com/news/tech/intel-joins-researchers-to-use-ai-ml-in-curing-paralysis-from-spinalinjuries-2333003.html

[4]. Dhamanda, A., Ahmad, M. H., Imran, M., Khalid, M. A., & Bazyar, P. (2021). Artificial Intelligence Applications. Iksad Publications.

[5]. Farnsworth, B. (2022). EEG vs. MRI vs. fMRI – What are the Differences? Rertieved from https://imotions.com/blog/eeg-vs-mri-vs-fmri-differences/

[6]. Habeeb, A. (2017). Introduction to Artificial Intelligence (pp. 1-9). https://doi.org/10.13140/RG.2.2. 25350.88645/1

[7]. IBM. (2020a). What is Artificial Intelligence (AI)? Rertieved from https://www.ibm.com/cloud/learn/what-isartificial-intelligence

[8]. IBM. (2020b). What is Machine Learning? Rertieved from https://www.ibm.com /cloud/learn/machinelearning

[9]. IMotions. (2019). Top 3 Devices for Monitoring and Measuring Brain Activity. Rertieved from https://imotions.com/blog/learning/best-practice/top-3-devicesmeasuring-brain-activity/

[10]. Kumar, S., Tiwari, P., & Zymbler, M. (2019a). Internet of Things is a revolutionary approach for future technology enhancement: A review. Journal of Big Data, 6(1), 1-21. https://doi.org/10.1186/s40537-019-0268-2

[11]. Kumar, V. B., Vijayalakshmi, K., & Padmavathamma, M. (2019b). A hybrid data mining approach for diabetes prediction and classification. In 2019 World Congress on Engineering and Computer Science, WCECS (Vol. 22, pp. 298-303).

[12]. Olunu, E., Kimo, R., Onigbinde, E. O., Akpanobong, M. A. U., Enang, I. E., Osanakpo, M., & Fakoya, A. O. J. (2018). Sleep paralysis, a medical condition with a diverse cultural interpretation. International Journal of Applied and Basic Medical Research, 8(3), 137-142. https://doi.org/10.4103/ijabmr.IJABMR_19_18

[13]. Rabinovitch, A. (2022). Here's how a Nerve- Stimulation Device has Brought Movement Back to Patients Who Are Paralyzed. Rertieved from https://www.weforum.org/agenda/2022/02/nerve-stimulationdevice-paralyzed-artificial-intelligence-biotechnology/

[14]. Sidey-Gibbons, J. A., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19(1), 1-18. https://doi.org/10.1186/s12874-019-0681-4

[15]. Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., & Housheya, O. J. (2014). Artificial intelligence and its applications. Mathematical problems in Engineering, 2014, 1-10. http://doi.org/10.1155/2014/840491

i-manager's Journal on Digital Signal Processing (JDP)

Current Issue Vol. 12 Issue 1

Most Read

Most Cited

Volume 10 Issue 2 July - December 2022

Performance Analysis of Various Copy-Move Forgery Detection Methods

Deependra Kumar Shukla* , Abhishek Bansal**, Pawan Singh***

Shukla, D. K., Bansal, A., and Singh, P. (2022). Performance Analysis of Various Copy-Move Forgery Detection Methods. i-manager’s Journal on Digital Signal Processing, 10(2), 1-6. https://doi.org/10.26634/jdp.10.2.19181

Abstract

References

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Systematic Digital Signal Processing Approach in Various Biometric Identification

K. P. Ajitha Gladis* , D. Sharmila**

* Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India. ** Department of Computer Applications, Government Arts & Science College, Kanyakumari, Tamil Nadu, India.

Gladis, K. P. A., and Sharmila, D. (2022). Systematic Digital Signal Processing Approach in Various Biometric Identification. i-manager’s Journal on Digital Signal Processing, 10(2), 7-15. https://doi.org/10.26634/jdp.10.2.19290

Abstract

References

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Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview

D. Suja Darling* , J. Hinduja**

* Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India. ** Department of Electronics and Communication Engineering, Udaya School of Engineering, Ammandivilai, Tamil Nadu, India.

Darling, D. S., and Hinduja, J. (2022). Feature Extraction in Speech Recognition using Linear Predictive Coding: An Overview. i-manager’s Journal on Digital Signal Processing, 10(2), 16-21. https://doi.org/10.26634/jdp.10.2.19289

Abstract

References

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Fire and Smoke Detection with Deep Learning: A Review

Vinay Kumar Jain* , Chitrangad Jain**

* Shri Shankaracharya Technical Campus, Bhilai, India. ** Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Jain, V. K., and Jain, C. (2022). Fire and Smoke Detection with Deep Learning: A Review. i-manager’s Journal on Digital Signal Processing, 10(2), 22-32. https://doi.org/10.26634/jdp.10.2.19262

Abstract

References

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Artificial Intelligence Role in Identifying Paralysis with Paralysis Prediction and Monitoring Model

Vemuri Bharath Kumar* , Govardhana Giridhari Narayana Sri Ranga**, Mary Sujatha***

*-*** Department of Computer Science, National Sanskrit University, Tirupati, Andhra Pradesh, India.

Kumar, V. B., Ranga, G. G. N. S. and Sujatha, M. (2022). Artificial Intelligence Role in Identifying Paralysis with Paralysis Prediction and Monitoring Model. i-manager’s Journal on Digital Signal Processing, 10(2), 33-39. https://doi.org/10.26634/jdp.10.2.19247

Abstract

References

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Deependra Kumar Shukla* , Abhishek Bansal, Pawan Singh*

* Department of Information Technology, C.S.I Institute of Technology, Thovalai, Tamil Nadu, India.

** Department of Computer Applications, Government Arts & Science College, Kanyakumari, Tamil Nadu, India.

* Department of Electronics and Communication Engineering, C.S.I. Institute of Technology, Thovalai, Tamil Nadu, India.

** Department of Electronics and Communication Engineering, Udaya School of Engineering, Ammandivilai, Tamil Nadu, India.

* Shri Shankaracharya Technical Campus, Bhilai, India.

** Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.

Vemuri Bharath Kumar* , Govardhana Giridhari Narayana Sri Ranga, Mary Sujatha*