i-manager Publications

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

Most Cited

Volume 10 Issue 1 January - June 2023

Research Paper

Development of Anomaly Detector for Motor Bearing Condition Monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Bashir Sulaimon Adebayo* , Oladebo Suliat Jimoh, Idris Mohammed Kolo*, Enesi Femi Aminu****

*-**** Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria.

Bashir, S. A., Jimoh, O. S., Kolo, I. M., and Aminu, E. F. (2023). Development of Anomaly Detector for Motor Bearing Condition Monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder. i-manager’s Journal on Pattern Recognition, 10(1), 1-15. https://doi.org/10.26634/jpr.10.1.19762

Abstract

Anomaly detection in motor bearings is a critical task for preventing downtime and ensuring efficient operation. This paper proposes a novel approach for anomaly detection using Fast Fourier Transform (FFT) and Long Short-Term Memory (LSTM)-Autoencoder (AE). A data processing approach based on FFT was developed to pre-process the raw sensor data. This helped to reduce noise and improve the Signal-to-Noise Ratio (SNR). Additionally, an anomaly detection model based on LSTM-Autoencoder was developed and trained on the pre-processed data. The proposed approach was able to detect anomalies at a low threshold and achieved a high accuracy score.

References

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[2]. Abouelanouar, B., Elamrani, M., Elkihel, B., & Delaunois, F. (2018). Application of wavelet analysis and its interpretation in rotating machines monitoring and fault diagnosis - A review. International Journal of Engineering & Technology, 7(4), 3465-3471.

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[4]. Ahmad, S., Styp-Rekowski, K., Nedelkoski, S., & Kao, O. (2020, December). Autoencoder-based condition monitoring and anomaly detection method for rotating machines. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 4093-4102). IEEE.

[5]. Asakura, T., Yashima, W., Suzuki, K., & Shimotou, M. (2020). Anomaly detection in a logistic operating system using the Mahalanobis–Taguchi method. Applied Sciences, 10(12), 4376.

[6]. Barot, A., & Kulkarni, P. (2021). Technological evolution in the fault diagnosis of rotating machinery: A review. IOSR Journal of Mechanical and Civil Engineering, 18(2), 9-26.

[7]. Boniol, P., Linardi, M., Roncallo, F., & Palpanas, T. (2020, April). Automated anomaly detection in large sequences. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1834-1837). IEEE.

[8]. Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.

[9]. Cooper, C., Zhang, J., Gao, R. X., Wang, P., & Ragai, I. (2020). Anomaly detection in milling tools using acoustic signals and generative adversarial networks. Procedia Manufacturing, 48, 372-378.

[10]. Egaji, O. A., Ekwevugbe, T., & Griffiths, M. (2020, July). A data mining based approach for electric motor anomaly detection applied on vibration data. In 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) (pp. 330-334). IEEE.

[11]. Glowacz, A. (2019). Fault diagnosis of single-phase induction motor based on acoustic signals. Mechanical Systems and Signal Processing, 117, 65-80.

[12]. Gong, C. S. A., Lee, H. C., Chuang, Y. C., Li, T. H., Su, C. H. S., Huang, L. H., ... & Chang, C. H. (2018). Design and implementation of acoustic sensing system for online early fault detection in industrial fans. Journal of Sensors, 1-15.

[13]. Hruntovich, N. V., Kapanski, A. A., Petrov, I. V., & Kostyleva, E. E. (2019). Vibration diagnostic of electric motor roller bearings. In E3S Web of Conferences, 124, 02008. EDP Sciences.

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[15]. Jiang, W., Hong, Y., Zhou, B., He, X., & Cheng, C. (2019). A GAN-based anomaly detection approach for imbalanced industrial time series. IEEE Access, 7, 143608-143619.

[16]. Jin, X., Que, Z., Sun, Y., Guo, Y., & Qiao, W. (2019). A data-driven approach for bearing fault prognostics. IEEE Transactions on Industry Applications, 55(4), 3394-3401.

[17]. Kanwal, N., Girdhar, A., Kaur, L., & Bhullar, J. S. (2019, April). Detection of digital image forgery using fast Fourier transform and local features. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 262-267). IEEE.

[18]. Khadersab, A., & Shivakumar, S. (2018). Vibration analysis techniques for rotating machinery and its effect on bearing faults. Procedia Manufacturing, 20, 247-252.

[19]. Kwon, D., Kim, H., Kim, J., Suh, S. C., Kim, I., & Kim, K. J. (2019). A survey of deep learning-based network anomaly detection. Cluster Computing, 22, 949-961.

[20]. Lee, G. Y., Kim, M., Quan, Y. J., Kim, M. S., Kim, T. J. Y., Yoon, H. S., ... & Ahn, S. H. (2018a). Machine health management in smart factory: A review. Journal of Mechanical Science and Technology, 32, 987-1009.

[21]. Lee, H., Li, G., Rai, A., & Chattopadhyay, A. (2020). Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft. Advanced Engineering Informatics, 44, 101071.

[22]. Lee, K., Kim, J. K., Kim, J., Hur, K., & Kim, H. (2018b, July). CNN and GRU combination scheme for bearing anomaly detection in rotating machinery health monitoring. In 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII) (pp. 102-105). IEEE.

[23]. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A. Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

[24]. Liu, C., & Gryllias, K. (2020). A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis. Mechanical Systems and Signal Processing, 140, 106682.

[25]. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.

[26]. Nath, S. (2020). Low Latency Anomaly Detection with Imperfect Models (Doctoral dissertation, University of Arkansas).

[27]. Pandarakone, S. E., Masuko, M., Mizuno, Y., & Nakamura, H. (2018, September). Deep neural network based bearing fault diagnosis of induction motor using fast Fourier transform analysis. In 2018 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 3214-3221). IEEE.

[28]. Pittino, F., Puggl, M., Moldaschl, T., & Hirschl, C. (2020). Automatic anomaly detection on in-production manufacturing machines using statistical learning methods. Sensors, 20(8), 2344.

[29]. Saeki, M., Ogata, J., Murakawa, M., & Ogawa, T. (2019, June). Visual explanation of neural network based rotation machinery anomaly detection system. In 2019 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 1-4). IEEE.

[30]. Schmidt, S., & Heyns, P. S. (2019). Localised gear anomaly detection without historical data for reference density estimation. Mechanical Systems and Signal Processing, 121, 615-635.

[31]. Sohaib, M., & Kim, J. M. (2018). Reliable fault diagnosis of rotary machine bearings using a stacked sparse autoencoder-based deep neural network. Shock and Vibration, 2018, 1-11.

[32]. Sohaib, M., & Kim, J. M. (2019). Fault diagnosis of rotary machine bearings under inconsistent working conditions. IEEE Transactions on Instrumentation and Measurement, 69(6), 3334-3347.

[33]. Sulka, P., Sapietova, A., Dekys, V., & Stalmach, O. (2019). Vibration analysis and comparison of the damaged and undamaged rolling ball bearing. Transportation Research Procedia, 40, 511-518.

[34]. Vo, Q., Fang, F., Zhang, X., & Gao, H. (2017). Surface recovery algorithm in white light interferometry based on combined white light phase shifting and fast Fourier transform algorithms. Applied Optics, 56(29), 8174-8185.

[35]. Wang, Z., Zhou, Y., & Li, G. (2019, November). Anomaly detection for machinery by using Big Data RealTime processing and clustering technique. In Proceedings of the 3rd International Conference on Big Data Research (pp. 30-36).

[36]. Wei, W., Wu, H., & Ma, H. (2019). An autoencoder and LSTM-based traffic flow prediction method. Sensors, 19(13), 2946.

[37]. Zhang, S., Ye, F., Wang, B., & Habetler, T. G. (2019). Semi-supervised learning of bearing anomaly detection via deep variational autoencoders. arXiv preprint arXiv:1912.01096.

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Research Paper

Navigation of Empty Parking Slots using Image Processing

Srujan Panthula* , Yaramala John Wesley, Alle Saiteja Wesley, Sudha Mishra, Agnivesh Pandey

*-***** Department of Information Technology, School of Engineering & Technology, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur, India.

Panthula, S., Wesley, Y. J., Wesley, A. S., Mishra, S., and Pandey, A. (2023). Navigation of Empty Parking Slots using Image Processing. i-manager’s Journal on Pattern Recognition, 10(1), 16-24. https://doi.org/10.26634/jpr.10.1.19328

Abstract

Navigation of empty parking slots has been proposed to solve the major problem of traffic congestion at parking places, such as malls and theatres. In other words, it can also reduce time consumption and costs. When there is no information about free parking slots, drivers face the issue of spending time to find a suitable parking spot. In multi-story buildings or malls, it is even more difficult to find an available parking. To solve this issue, the navigation of empty parking slots is used. In many parking areas, there is only an automated entrance to enter the vehicle into the parking area, but there is no software that can display live video to consumers to show whether there is an available space. Additionally, there is no website or application that can show live footage of the parking area, indicating whether it is empty or occupied. In this research live information is displayed before drivers enter the parking area. With the help of the display and website, drivers can save a significant amount of time finding an empty parking slot to park their vehicle. The system is based on camera sensors and software, where image processing is used to process the image.

References

[1]. Abel, A., Aulia, S., Ramadhan, D. N., & Hadiyoso, S. (2021). A prototype of parking space information system based on image processing. Jurnal Rekayasa Elektrika, 17(3), 185-190.

[2]. Alsafery, W., Alturki, B., Reiff-Marganiec, S., & Jambi, K. (2018, April). Smart car parking system solution for the internet of things in smart cities. In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS) (pp. 1-5). IEEE.

[3]. Farooqi, N., Alshehri, S., Nollily, S., Najmi, L., Alqurashi, G., & Alrashedi, A. (2019, November). UParking: Developing a smart parking management system using the internet of things. In 2019 Sixth HCT Information Technology Trends (ITT) (pp. 214-218). IEEE.

[4]. Gupta, R., Budhiraja, N., Mago, S., & Mathur, S. (2020). An IoT-based smart parking framework for smart cities. In Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, 1, 19-32. Springer Singapore.

[5]. Jeffrey, J., Patil, R. G., Narahari, S. K. K., Didagi, Y., Bapat, J., & Das, D. (2012, August). Smart parking system using wireless sensor networks. In The Sixth International Conference on Sensor Technologies and Applications (pp. 306-311).

[6]. Kaarthik, K., Sridevi, A., & Vivek, C. (2017, April). Image processing based intelligent parking system. In 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE) (pp. 1-4). IEEE.

[7]. Pham, T. N., Tsai, M. F., Nguyen, D. B., Dow, C. R., & Deng, D. J. (2015). A cloud-based smart-parking system based on Internet-of-Things technologies. IEEE Access, 3, 1581-1591.

[8]. Rane, S., Dubey, A., & Parida, T. (2017, July). Design of IoT based intelligent parking system using image processing algorithms. In 2017 International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1049-1053). IEEE.

[9]. Suhr, J. K., & Jung, H. G. (2016). Automatic parking space detection and tracking for underground and indoor environments. IEEE Transactions on Industrial Electronics, 63(9), 5687-5698.

[10]. Thangam, E. C., Mohan, M., Ganesh, J., Sukesh, C. V., & Prof, A. (2018). Internet of Things (IoT) based smart parking reservation system using raspberry-pi. International Journal of Applied Engineering Research, 13(8), 5759-5765.

[11]. Yusnita, R., Norbaya, F., & Basharuddin, N. (2012). Intelligent parking space detection system based on image processing. International Journal of Innovation, Management and Technology, 3(3), 232-235.

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Research Paper

Tracking Missing Persons using Facial Recognition

Bhavika Gupta* , Palak Agarwal, Disha Devalia*

*-*** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India.

Gupta, B., Agarwal, P., and Devalia, D. (2023). Tracking Missing Persons using Facial Recognition. i-manager’s Journal on Pattern Recognition, 10(1), 25-33. https://doi.org/10.26634/jpr.10.1.19435

Abstract

Finding missing people is a time-critical and labor-intensive task and the longer it takes to locate the person, the lower the likelihood of a successful outcome. To address this challenge, an integrated and centralized database of missing persons using Aadhar card details was developed. The approach incorporates facial recognition technology, specifically the deep face algorithm, which has shown high accuracy in identifying individuals. While facial recognition has been in use for several years, recent advancements have made it easier to identify individuals accurately. By leveraging Artificial Intelligence (AI) powered facial recognition technology, officials can enhance and streamline the process of finding, tracking, and retrieving missing persons. The system matches facial features with the data stored in Aadhar cards, providing a reliable means of identification. This research presents a system that centralizes data, improving the efficiency of locating missing individuals. By utilizing facial recognition and centralizing data, the system offers an efficient approach to find missing people. The integration of technology and data allows quick and more accurate identification, increasing the chances of locating missing persons promptly.

References

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[2]. Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4690-4699).

[3]. Gholape, N., Gour, A., & Mourya, S. (2021). Finding missing person using ML, AI. International Research Journal of Modernization in Engineering Technology and Science, 3, 1517-1520.

[4]. Goyal, K., Agarwal, K., & Kumar, R. (2017, April). Face detection and tracking: Using OpenCV. In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), 1, 474-478. IEEE.

[5]. King, D. E. (2009). Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10, 1755- 1758.

[6]. Mehta, J., Ramnani, E., & Singh, S. (2018, February). Face detection and tagging using deep learning. In 2018 International Conference on Computer, Communication, and Signal Processing (ICCCSP) (pp. 1-6). IEEE.

[7]. Patil, S., Gaikar, P., Kare, D., & Pawar, S. (2021). Finding missing person using AI. International Journal of Progressive Research in Science and Engineering, 2(6), 101-104.

[8]. Ren, X., Ding, J., Sun, J., & Sui, Q. (2017, October). Face modeling process based on Dlib. In 2017 Chinese Automation Congress (CAC) (pp. 1969-1972). IEEE.

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[10]. Shahsavarani, S., Analoui, M., & Ghiass, R. S. (2020). M^ 2 Deep-ID: A novel model for multi-view face identification using convolutional deep neural networks. arXiv preprint arXiv:2001.07871.

[11]. Sharma, S., Shanmugasundaram, K., & Ramasamy, S. K. (2016, May). FAREC-CNN based efficient face recognition technique using Dlib. In 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) (pp. 192-195). IEEE.

[12]. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1701-1708).

[13]. Wang, M., & Deng, W. (2021). Deep face recognition: A survey. Neurocomputing, 429, 215-244.

[14]. Zhao, F., Li, J., Zhang, L., Li, Z., & Na, S. G. (2020). Multi-view face recognition using deep neural networks. Future Generation Computer Systems, 111, 375-380.

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Research Paper

Performance Analysis of Photonics-Based RF Transceiver for High-Speed Data Transmission

Mayank Kaushik* , J. S. Sidana, Shivendra Kumar*, P. M. Menghal****

*-*** Military College of Electronics and Mechanical Engineering, Secunderabad, India.

Kaushik, M., Sidana, J. S., Kumar, S., and Menghal, P. M. (2023). Performance Analysis of Photonics-Based RF Transceiver for High-Speed Data Transmission. i-manager’s Journal on Pattern Recognition, 10(1), 34-41. https://doi.org/10.26634/jpr.10.1.19783

Abstract

Current digital radar systems have limitations at higher frequencies, and a new approach is needed to be able to operate in different environments and at higher frequencies. Photonics offers a solution to these limitations. It has UltraWide Bandwidth (UWB) and high precision, which allows for the flexible generation of highly consistent Radio Frequencies (RF) signals and the accurate direct digitization of signals without down-conversion. This research proposes a novel Wavelength-Division Multiplexing (WDM)-based photonics link for a radar demonstrator. The proposed system uses a single pulsed laser as a source to design a transceiver for high-speed data transmission and reception. The system can generate tunable radar signals and their echoes, avoiding the up/down conversion of radio frequency and ensuring both high resolution and simulation-based operation. The proposed system has the potential to revolutionize radar technology by enabling high resolution that can operate at higher frequencies. The system is also scalable and can be easily adapted to different radar applications.

References

[1]. Brandão, T. H., Scotti, F., Filgueiras, H. R. D., Alves, A. A. D. C., Onori, D., Melo, S., ... & Sodré Jr, A. C. (2019). Coherent dual band radar system based on a unique antenna and a photonics based transceiver. IET Radar, Sonar & Navigation, 13(4), 505-511.

[2]. Cenkeramaddi, L. R., Bhatia, J., Jha, A., Vishkarma, S. K., & Soumya, J. (2020, November). A survey on sensors for autonomous systems. In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1182- 1187). IEEE.

[3]. Cenkeramaddi, L. R., Rai, P. K., Dayal, A., Bhatia, J., Pandya, A., Soumya, J., ... & Jha, A. (2021). A novel angle estimation for mmWave FMCW radars using machine learning. IEEE Sensors Journal, 21(8), 9833-9843.

[4]. De, S., & Raj, A. A. B. (2023). A survey on photonics technologies for radar applications. Journal of Optics, 52(1), 90-119.

[5]. Ghatak, A. (2010). Optics. McGraw-Hill, New Delhi.

[6]. Gupta, S., Rai, P. K., Kumar, A., Yalavarthy, P. K., & Cenkeramaddi, L. R. (2021). Target classification by mmWave FMCW radars using machine learning on range-angle images. IEEE Sensors Journal, 21(18), 19993-20001.

[7]. Keiser, G. (1983). Optical Fiber Communications. McGraw-Hill, New York.

[8]. Laghezza, F., Scotti, F., Onori, D., & Bogoni, A. (2016, May). ISAR imaging of non-cooperative targets via dual band photonics-based radar system. In 2016 17th International Radar Symposium (IRS) (pp. 1-4). IEEE.

[9]. Liu, Y., Zhang, Z., Burla, M., & Eggleton, B. J. (2022). 11 GHz Bandwidth photonic radar using MHz electronics. Laser & Photonics Reviews, 16(4), 2100549.

[10]. Pan, S., & Zhang, Y. (2020). Microwave photonic radars. Journal of Lightwave Technology, 38(19), 5450-5484.

[11]. Ragheb, A., Esmail, M. A., Seleem, H., Sethi, W. T., Ashraf, M. A., Fathallah, H., & Alshebeili, S. A. (2020). Photonics-based multi-band/multi-mode radar signal generation. Photonic Network Communications, 39, 91-101.

[12]. Rai, P. K., Idsøe, H., Yakkati, R. R., Kumar, A., Khan, M. Z. A., Yalavarthy, P. K., & Cenkeramaddi, L. R. (2021). Localization and activity classification of unmanned aerial vehicle using mmWave FMCW radars. IEEE Sensors Journal, 21(14), 16043-16053.

[13]. Serafino, G., Scotti, F., Lembo, L., Hussain, B., Porzi, C., Malacarne, A., ... & Bogoni, A. (2019). Toward a new generation of radar systems based on microwave photonic technologies. Journal of Lightwave Technology, 37(2), 643-650.

[14]. Skolnik, M. I. (2017). Introduction to Radar System. McGraw-Hill, New York.

[15]. Tang, Z., Li, Y., Yao, J., & Pan, S. (2020). Photonics based microwave frequency mixing: Methodology and applications. Laser & Photonics Reviews, 14(1), 1800350.

[16]. Urick, V. J., Williams, K. J., & McKinney, J. D. (2015). Fundamentals of Microwave Photonics. John Wiley & Sons.

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Research Paper

Music Recommender System using Facial Feature Recognition

Harsh Mishra* , Deep Kothari, Prajwal Jaiswal*, Sheetal Rathi****

*-**** Department of Computer Engineering, Thakur College of Engineering & Technology, Mumbai, India.

Mishra, H., Kothari, D., Jaiswal, P., and Rathi, S. (2023). Music Recommender System using Facial Feature Recognition. i-manager’s Journal on Pattern Recognition, 10(1), 42-51. https://doi.org/10.26634/jpr.10.1.19473

Abstract

Facial expressions convey a lot of information about the emotional state of a person and even a small change in it can help to detect changes in an individual's mood. Researchers have conducted numerous studies in the fields of machine learning and computer vision, training ML models to detect various human emotions or moods based on captured facial features. In this research, a system was proposed which is capable of identifying the user's emotions and moods and suggesting a list of appropriate songs to improve their mood. Incorporating mood detection offers significant benefits to customers' mental health and personal satisfaction. The objective of this system is to capture the user's image, detect their mood and create a music player system that recommends music based on the user's real-time mood, utilizing a web camera and deep learning algorithms

References

[1]. Donald, B. (2014). Music Recommendation System: “Sound Tree”.

[2]. Fung-Lung, L., Nycander-Barúa, M., & ShiguiharaJuárez, P. (2019, August). An image acquisition method for face recognition and implementation of an automatic attendance system for events. In 2019 IEEE XXVI International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1-4). IEEE.

[3]. Gilda, S., Zafar, H., Soni, C., & Waghurdekar, K. (2017, March). Smart music player integrating facial emotion recognition and music mood recommendation. In 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 154-158). IEEE.

[4]. Giri, S., Singh, G., Kumar, B., Singh, M., Vashisht, D., Sharma, S., & Jain, P. (2022, April). Emotion detection with facial feature recognition using CNN & OpenCV. In 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 230-232). IEEE.

[5]. Hemanth, P., Adarsh, Aswani, C. B., Ajith, P., & Kumar. V. A. (2018). EMO PLAYER: Emotion based music player. International Research Journal of Engineering and Technology, 5(4), 4822-4827.

[6]. James, H. I., Arnold, J. J. A., Ruban, J. M. M., Tamilarasan, M., & Saranya, R. (2019). Emotion based music recommendation system. Emotion, 6(3), 2096- 2101.

[7]. Li, K., Jin, Y., Akram, M. W., Han, R., & Chen, J. (2020). Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. The Visual Computer, 36, 391-404.

[8]. Ma, S., Zhang, X., Wang, S., Zhang, X., Jia, C., & Wang, S. (2018). Joint feature and texture coding: Toward smart video representation via front-end intelligence. IEEE Transactions on Circuits and Systems for Video Technology, 29(10), 3095-3105.

[9]. Mahadik, A., Milgir, S., Patel, J., Jagan, V. B., & Kavathekar, V. (2021). Mood based music recommendation system. International Journal of Engineering Research & Technology, 10(6), 553-559.

[10]. Meena, D., & Sharan, R. (2016, December). An approach to face detection and recognition. In 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (pp. 1-6). IEEE.

[11]. Raut, N. (2018). Facial Emotion Recognition using Machine Learning (Master's thesis, San José State University).

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i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

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Volume 10 Issue 1 January - June 2023

Development of Anomaly Detector for Motor Bearing Condition Monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Bashir Sulaimon Adebayo* , Oladebo Suliat Jimoh**, Idris Mohammed Kolo***, Enesi Femi Aminu****

*-**** Department of Computer Science, School of Information and Communication Technology, Federal University of Technology, Minna, Nigeria.

Abstract

References

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Navigation of Empty Parking Slots using Image Processing

Srujan Panthula* , Yaramala John Wesley**, Alle Saiteja Wesley***, Sudha Mishra****, Agnivesh Pandey*****

*-***** Department of Information Technology, School of Engineering & Technology, Guru Ghasidas Vishwavidyalaya (Central University), Bilaspur, India.

Panthula, S., Wesley, Y. J., Wesley, A. S., Mishra, S., and Pandey, A. (2023). Navigation of Empty Parking Slots using Image Processing. i-manager’s Journal on Pattern Recognition, 10(1), 16-24. https://doi.org/10.26634/jpr.10.1.19328

Abstract

References

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Tracking Missing Persons using Facial Recognition

Bhavika Gupta* , Palak Agarwal**, Disha Devalia***

*-*** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India.

Gupta, B., Agarwal, P., and Devalia, D. (2023). Tracking Missing Persons using Facial Recognition. i-manager’s Journal on Pattern Recognition, 10(1), 25-33. https://doi.org/10.26634/jpr.10.1.19435

Abstract

References

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Performance Analysis of Photonics-Based RF Transceiver for High-Speed Data Transmission

Mayank Kaushik* , J. S. Sidana**, Shivendra Kumar***, P. M. Menghal****

*-*** Military College of Electronics and Mechanical Engineering, Secunderabad, India.

Kaushik, M., Sidana, J. S., Kumar, S., and Menghal, P. M. (2023). Performance Analysis of Photonics-Based RF Transceiver for High-Speed Data Transmission. i-manager’s Journal on Pattern Recognition, 10(1), 34-41. https://doi.org/10.26634/jpr.10.1.19783

Abstract

References

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Music Recommender System using Facial Feature Recognition

Harsh Mishra* , Deep Kothari**, Prajwal Jaiswal***, Sheetal Rathi****

*-**** Department of Computer Engineering, Thakur College of Engineering & Technology, Mumbai, India.

Mishra, H., Kothari, D., Jaiswal, P., and Rathi, S. (2023). Music Recommender System using Facial Feature Recognition. i-manager’s Journal on Pattern Recognition, 10(1), 42-51. https://doi.org/10.26634/jpr.10.1.19473

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Harsh Mishra* , Deep Kothari, Prajwal Jaiswal*, Sheetal Rathi****