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

Current Issue Vol. 11 Issue 2

Most Cited

Volume 8 Issue 1 January - June 2020

Research Paper

Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits

R. Gurunadha* , K. Babulu **

* Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India.

** Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra Pradesh, India.

Gurunadha, R., and Babulu, K. (2020). Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits. i-manager's Journal on Digital Signal Processing, 8(1), 1-6. https://doi.org/10.26634/jdp.8.1.17781

Abstract

The connections between circuit components in VLSI design are made by using a liquid crystal polymer substrate. Due to mistakes made by manufacturing machines and design engineers, there is a possibility of getting faulty connections in very large integrated circuits (VLSI). The faults in printed circuit boards (PCB) can be identified by using advanced technologies like computer vision and deep convolutional neural networks. In this paper, a model based on neural networks to detect faultiness of design has been proposed. The 3-4 Convolutional Neural Networks (CNN) model in deep learning can analyze the patterns in images and gives accurate results when the model is tested against new data. In our proposed model, an auto encoder based neural networks has been used for the detection of faults in printed circuit boards (PCB). An Artificial Intelligence (AI) camera to scan circuit design has also been used. The AI camera scan images at a higher resolution rate and at a higher dynamic range. The images taken by the camera are tested by the model to detect whether the image is faulty or not. The model is trained on a different set of circuit images and tested against validation data. Accuracy of 98.6% has been obtained by this approach.

References

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[3]. Costilla-Reyes, O., Scully, P., & Ozanyan, K. B. (2017). Deep neural networks for learning spatio-temporal features from tomography sensors. IEEE Transactions on Industrial Electronics, 65(1), 645-653. https://doi.org/10.1109/TIE.201 7.2716907

[4]. Ghamisi, P., Höfle, B., & Zhu, X. X. (2016). Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(6), 3011-3024. https://doi.org/10.1109/JSTARS.2016.2 634863

[5]. He, M., & He, D. (2017). Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 53(3), 3057-3065.

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[7]. Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Deen, M. J. (2017). Deep convolutional computation model for feature learning on big data in internet of things. IEEE Transactions on Industrial Informatics, 14(2), 790-798.

[8]. Liu, B., Yu, X., Yu, A., & Wan, G. (2018). Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification. Journal of Applied Remote Sensing, 12(2). https://doi.org/10.1117/1.JRS.12. 026028

[9]. Loghmani, M. R., Rovetta, S., & Venture, G. (2017, May). Emotional intelligence in robots: Recognizing human emotions from daily-life gestures. In 2017 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1677- 1684). IEEE.

[10]. Luo, B., Wang, H., Liu, H., Li, B., & Peng, F. (2018). Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Transactions on Industrial Electronics, 66(1), 509-518. https://doi.org/10.1109/TIE.2 018.2807414

[11]. Pan, J., Zi, Y., Chen, J., Zhou, Z., & Wang, B. (2017). Lifting Net: A novel deep learning network with layer wise feature learning from noisy mechanical data for fault classification. IEEE Transactions on Industrial Electronics, 65(6), 4973-4982. https://doi.org/10.1109/TIE.2017.276 7540

[12]. Shao, H., Jiang, H., Zhang, H., & Liang, T. (2017). Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network. IEEE Transactions on Industrial Electronics, 65(3), 2727-2736. https://doi.org/ 10.1109/TIE.2017.2745473

[13]. Shi, C., Panoutsos, G., Luo, B., Liu, H., Li, B., & Lin, X. (2018). Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultra precision manufacturing. IEEE Transactions on Industrial Electronics, 66(5), 3794-3803. https://doi.org/10.1109/TIE.2018.28 56193

[14]. Sun, W., Zhao, R., Yan, R., Shao, S., & Chen, X. (2017). Convolutional discriminative feature learning for induction motor fault diagnosis. IEEE Transactions on Industrial Informatics, 13(3), 1350-1359.

[15]. Vafeiadis, A., Kalatzis, D., Votis, K., Giakoumis, D., Tzovaras, D., Chen, L., & Hamzaoui, R. (2017, November). Acoustic scene classification: From a hybrid classifier to deep learning. In Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE2017).

[16]. Wen, L., Li, X., Gao, L., & Zhang, Y. (2017). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990-5998. https://doi.org/10.11 09/TIE.2017.2774777

[17]. Yan, W., Tang, D., & Lin, Y. (2016). A data-driven soft sensor modeling method based on deep learning and its application. IEEE Transactions on Industrial Electronics, 64(5), 4237-4245. https://doi.org/10.1109/TIE.2016.26 22668

[18]. Yao, L., & Ge, Z. (2017). Deep learning of semi supervised process data with hierarchical extreme learning machine and soft sensor application. IEEE Transactions on Industrial Electronics, 65(2), 1490-1498. https://doi.org/10.1 109/TIE.2017.2733448

[19]. Zhao, M., Kang, M., Tang, B., & Pecht, M. (2017). Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes. IEEE Transactions on Industrial Electronics, 65(5), 4290-4300. https://doi.org/10.1109/TIE.2017.2762639

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Research Paper

Smart Electrical Vehicle

B. Ram Vara Prasad * , Ch. Prasanthi , G. Jyothika Santhoshini , K. J. S. V. Kranti Kumar , K. Yernaidu

*-***** Department of Electrical and Electronics Engineering, Lendi Institute of Engineering & Technology, Vizianagaram, Andhra Pradesh, India.

Prasad, B. R. V., Prasanthi, Ch., Santhoshini, G. J., Kumar, K. J. S. V. K., and Yernaidu, K. (2020). Smart Electrical Vehicle. i-manager's Journal on Digital Signal Processing, 8(1), 7-14. https://doi.org/10.26634/jdp.8.1.17347

Abstract

The primary objective of this paper is to design a feasible yet highly adaptable E-BICYCLE, as number of motor vehicles on the roads all-over the world increases. The worlds’ car usage is booming. Cars are polluting the cities as they consume large quantity of Petroleum and the amounts of carbon dioxide released increases which in turn pollutes the green house gases causing climate change. Huge amount of money is being spent on the development and manufacturing of electrical vehicles. This paper presents the study of design of Electrical Bicycle. The aim of this paper is to design a simple, cost effective model of Electrical Bicycle with intelligent control system. The materials used are mostly environmental friendly and cost is much lower than the existing electric power bike. This can be implemented by connecting an electrical motor, a controller and battery pack, cabling and monitoring instruments.

References

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[8]. Prasad, B. R. V., Baba, C. H., Kumar, B. A., Vamsi, J., & Rajendra, G. (2019). Solar Powered BLDC Motor with HCC Fed Water Pumping System for Irrigation. International Journal for Research in Applied Science & Engineering Technology, 7(3), 788-796. https://doi.org/10.22214/ijrase t.2019.3137

[9]. Prasad, B. R. V., Babu, K. M., Sreekanth, K., Naveen, K., & Kumar, C. V. (2018). Minimization of torque ripple of brushless DC motor using HCC with DC-DC converter. International Journal of Research, 05(12), 110–117.

[10]. Prasad, B. R. V., Kiranmayee, S. S., Venkatesh, M., Sowrya, M. V., & Venugopal, S. (2020). Highway monitoring system and power saving. International Journal of Creative Research Thoughts (IJCRT), 8(4), 2270-2274.

[11]. Prasad, B. R. V., Poojitha, D. V. S. J., & Suneetha, K. (2017). Closed-loop control of bldc motor driven solar PV array using zeta converter fed water pumping system. International Journal of Research, 4(17), 2795-2803.

[12]. Salah, W. A., Ishak, D., & Hammadi, K. J. (2011). Minimization of torque ripples in BLDC motors due to phase commutation: A review. Przeglad Elektrotechniczny, 87(1), 182-188.

[13]. Su, N., Xu, D., Chen, M., & Tao, J. (2008, September). Study of bi-directional buck-boost converter with different control methods. In 2008 IEEE Vehicle Power and Propulsion Conference (pp. 1-5). IEEE.

[14]. Yedamale, P. (2003). Brushless DC (BLDC) motor fundamentals. Microchip Technology Inc. Retrieved http:// electrathonoftampabay.org/www/Documents/Motors/Brus hless%20DC%20(BLDC)%20Motor%20Fundamentals.pdf

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Research Paper

Development of Smart Electronic System to Implement Smart Home

Aparna M. Pawar * , J. D. Deshpande , S. N. Patil *

*-*** Department of Electronics, Tuljaram Chaturchand College of Arts, Science and Commerce, Baramati, Maharashtra, India.

Pawar, A. M., Deshpande, J. D., and Patil, S. N. (2020). Development of Smart Electronic System to Implement Smart Home. i-manager's Journal on Digital Signal Processing, 8(1), 15-26. https://doi.org/10.26634/jdp.8.1.17782

Abstract

Smart homes and home automation are analogue terms used in reference to a wide range of solutions for controlling, monitoring of internal environmental parameters and automating various functions of the home. Deploying novel embedded technology, the smart system is designed, with AVR ATmega 32 microcontroller. The research is specifically focused on the development of a precise and stable system for monitoring and controlling the temperature, light intensity, humidity, human interference, etc. for home automation. The system provides controlling of cooler fan and heaters, measurement of light intensity and controlling of both internal and external lights of garden or strait light, measurement of humidity and domestic gas leakage detection system. A highly precise humidity sensor SY-HS-220, temperature sensor LM 35, LDR, MQ-6, and PIR are employed for this purpose. The signal conditioning circuit is wired with single power supply operated CMOS operational amplifier TLV 274. The firmware is designed in embedded C, using CodeVisionAVR, the Integrated Development Environment (IDE). The designed system is implemented to monitor and control the parameters and the results are interpreted in this paper.

References

[1]. Bhimrao, P., Patil, L. S., & Tilekar, S. K. (2013). Development of Ni-Zn Ferrite based smart humidity sensor module by using mixed signal programmable System-onchip. Applied Mechanics and Materials, 310, 490-493.

[2]. Cook, D. J., Youngblood, M., Heierman, E. O., Gopalratnam, K., Rao, S., Litvin, A., & Khawaja, F. (2003, March). MavHome: An agent-based smart home. In Proceedings of the First IEEE International Conference on Pervasive Computing and Communication (pp. 521-524). IEEE.

[3]. Davidoff, S., Lee, M. K., Zimmerman, J., & Dey, A. K. (2006). Socially-aware requirements for a smart home. In Proceedings of the International Symposium on Intelligent Environments (pp. 41-44).

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[5]. Intille, S. S. (2006, June). The goal: smart people, not smart homes. In Proceedings of ICOST2006: The International Conference on Smart Homes and Health Telematics (pp. 3-6). Amsterdam: IOS Press.

[6]. Jahnke, J. H., D'entremont, M., & Stier, J. (2002). Facilitating the programming of the smart home. IEEE Wireless Communications, 9(6), 70-76.

[7]. Kidd, C. D., Orr, R., Abowd, G. D., Atkeson, C. G., Essa, I. A., MacIntyre, B., ..., & Newstetter, W. (1999, October). The aware home: A living laboratory for ubiquitous computing research. In Streitz N.A., Siegel J., Hartkopf V., Konomi S. (Eds) Cooperative Buildings. Integrating Information, Organizations, and Architecture. CoBuild 1999. Lecture Notes in Computer Science, vol 1670. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10705432_17

[8]. Ladgaonkar, B. P. (2011). Design and implementation of sensor node for wireless sensors network to monitor humidity of high-tech polyhouse environment. International Journal of Advances in Engineering and Technology, 1, 1-11.

[9]. Patil, S. N., Pawar, A. M., & Ladgaonkar, B. P. (2017a). Syntesis and deployment of namoferrites to design embedded system for monitoring ammonia gas. International Journal of Advances in Engineering & Technology (IJAET), 6, 27-31.

[10]. Patil, S. N., Pawar, A. M., Deshpande, J. D., & Ladgaonkar, B. P. (2017b). Comparative study of ferrite based humidity sensor for smart sensor module design. International Research Journal of Science and Engineering, 1, 203-209.

[11]. Patil, S. N., Pawar, A. M., Tilekar, S. K., & Ladgaonkar, B. P. (2016). Investigation of magnesium substituted nano particle zinc ferrites for relative humidity sensors. Sensors and Actuators A: Physical, 244, 35-43.

[12]. Pawar, A. M., Patil, S. N., & Ladgaonkar, B. P. (2014). Design and implementation of wireless sensor node for wsn for automatic meter reading. International Journal of Recent Research in Mathematics Computer Science and Information Technology, 1(1), 28-31.

[13]. Pawar, A. M., Patil, S. N., Powar, A. S., & Ladgaonkar, B. P. (2013). Wireless sensor network to monitor spatiotemporal thermal comfort of polyhouse environment. International Journal of Innovative Research in Science, Engineering and Technology, 2(10), 4866-4875.

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Research Paper

Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment

Vinay Kumar Jain*

Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Technical Campus, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Jain, V. K. (2020). Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment. i-manager's Journal on Digital Signal Processing, 8(1), 27-33. https://doi.org/10.26634/jdp.8.1.17838

Abstract

The Multilingual Speech Processing is the field of speech technology in which the speech signal of multiple languages of a speaker has been analyzed to observe the effect of the language on the speech features. On the basis of observation, a Multilingual Speaker Identification system can be designed for identification of the speaker in multilingual environments. For present study Multilingual Speech Processing database of different speakers has been recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The sentences consist of consonants, i.e., “Cha”, “Sha” and “Jha”. Total numbers of speakers involved are 30 including males and females. The basic features of the speech signal: Pitch and first three Formant F1, F2 and F3 are calculated through PRAAT software where as cepstral features like Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) has been extracted from MATLAB software. A model is proposed to identify the speaker by multi language speech signal of a speaker using MFCC, GFCC and combined features as acoustic features. For training and testing, it is performed using neural network function Resilient Back Propagation Algorithm and Radial Basis Functions and results are compared. In this experiment accuracy of multilingual speaker identification is 94.77% using BPA and 96.52% using RBF neural network.

References

[1]. Bashar, M. A., Ahmed, M. T., Syduzzaman, M., Ray, P. J., & Islam, A. T. (2014). Text-independent speaker identification system using average pitch and formant analysis. International Journal on Information Theory (IJIT), 3(3), 23-30.

[2]. Bhattacharjee, U., & Sarmah, K. (2012, March). A multilingual speech database for speaker recognition. In 2012 IEEE International Conference on Signal Processing, Computing and Control (pp. 1-5). IEEE.

[3]. Bourlard, H., Dines, J., Magimai-Doss, M., Garner, P. N., Imseng, D., Motlicek, P., ..., & Valente, F. (2011). Current trends in multilingual speech processing. Sadhana, 36(5), 885-915. https://doi.org//10.1007/s12046-011-0050-4

[4]. Burgos, W. (2014). Gammatone and MFCC features in speaker recognition (Doctoral dissertation). Florida Institute of Technology Melbourne, Florida.

[5]. Moinuddin, M., & Kanthi, A. N. (2014). Speaker Identification based on GFCC using GMM. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 1(8), 224-232.

[6]. Pahwa, A., & Aggarwal, G. (2016). Speech feature extraction for gender recognition. International Journal of Image, Graphics and Signal Processing, 8(9).

[7]. Rathore, P. S., & Tripathi, N. (2014). Multilingual person identification. International Journal of Engineering Trends and Technology (IJETT), 10(1), 1-3. https://doi.org/10.14445/ 22315381/IJETT-V10P201

[8]. Sarkar, S., Rao, K. S., Nandi, D., & Kumar, S. S. (2013, December). Multilingual speaker recognition on Indian languages. In 2013 Annual IEEE India Conference (INDICON) (pp. 1-5). IEEE.

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[10]. Tripathi, N. (2006). Study of face and speech parameters and identification of their relationship for emotional status recognition (Doctoral dissertation). National Institute of Technology, Raipur, India.

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Article

Photographing a Black Hole

Reproduced from OYLA DIGITAL Magazine*

OYLA DIGITAL Magazine. (2020). Photographing a Black Hole. i-manager's Journal on Digital Signal Processing, 8(1), 34-38.

Abstract

Photographing a black hole was a fiction for a long time. The Event Horizon Telescope (EHT) project achieved photographing the black home by the rational study on signal processing from different radio telescopes using technique called Very Long Baseline Interferometry (VLBI). The outcome as a photograph (the black hole) is the output of combinations of signals processed through several algorithms to sort and synchronize volumes of data from various radio telescopes.

References

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Research Paper

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

Current Issue Vol. 11 Issue 2

Most Read

Most Cited

Volume 8 Issue 1 January - June 2020

Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits

R. Gurunadha* , K. Babulu **

* Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India. ** Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra Pradesh, India.

Gurunadha, R., and Babulu, K. (2020). Auto Encoders based Neural Networks to Predict Faultiness of VLSI Circuits. i-manager's Journal on Digital Signal Processing, 8(1), 1-6. https://doi.org/10.26634/jdp.8.1.17781

Abstract

References

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Smart Electrical Vehicle

B. Ram Vara Prasad * , Ch. Prasanthi **, G. Jyothika Santhoshini ***, K. J. S. V. Kranti Kumar ****, K. Yernaidu *****

*-***** Department of Electrical and Electronics Engineering, Lendi Institute of Engineering & Technology, Vizianagaram, Andhra Pradesh, India.

Prasad, B. R. V., Prasanthi, Ch., Santhoshini, G. J., Kumar, K. J. S. V. K., and Yernaidu, K. (2020). Smart Electrical Vehicle. i-manager's Journal on Digital Signal Processing, 8(1), 7-14. https://doi.org/10.26634/jdp.8.1.17347

Abstract

References

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Development of Smart Electronic System to Implement Smart Home

Aparna M. Pawar * , J. D. Deshpande **, S. N. Patil ***

*-*** Department of Electronics, Tuljaram Chaturchand College of Arts, Science and Commerce, Baramati, Maharashtra, India.

Pawar, A. M., Deshpande, J. D., and Patil, S. N. (2020). Development of Smart Electronic System to Implement Smart Home. i-manager's Journal on Digital Signal Processing, 8(1), 15-26. https://doi.org/10.26634/jdp.8.1.17782

Abstract

References

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Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment

Vinay Kumar Jain*

Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Technical Campus, Shri Shankaracharya Group of Institutions, Bhilai, Chhattisgarh, India.

Jain, V. K. (2020). Multilingual Speaker Identification System through Multiple Features Analysis of Speech Signal in Multilingual Environment. i-manager's Journal on Digital Signal Processing, 8(1), 27-33. https://doi.org/10.26634/jdp.8.1.17838

Abstract

References

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Photographing a Black Hole

Reproduced from OYLA DIGITAL Magazine*

OYLA DIGITAL Magazine. (2020). Photographing a Black Hole. i-manager's Journal on Digital Signal Processing, 8(1), 34-38.

Abstract

References

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Development of an Intelligent Battery Charging System Based on PIC16F877A Microcontroller

This paper has been removed due to plagiarized work because of the dual submission by author.

* Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh, India.

** Department of Electronics and Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra Pradesh, India.

B. Ram Vara Prasad * , Ch. Prasanthi , G. Jyothika Santhoshini , K. J. S. V. Kranti Kumar , K. Yernaidu

Aparna M. Pawar * , J. D. Deshpande , S. N. Patil *