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


Volume 9 Issue 1 January - June 2021

Research Paper

Design of Auto Adaptive IIR Filter Using Pth Optimization Algorithm and Artificial Neural Network Technique

Lokendra Pankaj* , Vikas Soni **
*-** Modi Institute of Technology, Kota (Rajasthan), India.
Pankaj, L., and Soni, V. (2021). Design of Auto Adaptive IIR Filter Using Pth Optimization Algorithm and Artificial Neural Network Technique. i-manager's Journal on Digital Signal Processing, 9(1), 1-14. https://doi.org/10.26634/jdp.9.1.18355

Abstract

As digitization is continuously going to be increased, the representation of signal, its communication, storage and processing have been rapidly shifted in to digital landscape instead of analog landscape. With the advent & rapid proliferation of Digital Signal Processing devices, it is highly desirable to process much of the signal in software in digital domain, so as to reduce the analog front end & component count. An important & widely used process on signals is filtering, which is required in numerous applications. Digital filters are widely classified as Finite Impulse Response (FIR) and IIR Infinite Impulse Response (IIR) Filters, where IIR Filters with some number of coefficients exhibit more advantages over FIR Filter, however IIR filters provide some disadvantages like complex design and multimodal noise. In the present work, an Auto Adoptive IIR Filter has been developed using hybrid techniques. This hybrid technique has been composed by integrating or combining the Pth Norm Optimization with Artificial Neural Network (ANN). These two techniques have been used in such a way that the filter is designed using Pth Norm Optimization algorithm and filter's coefficients have been auto adapted using Artificial Neural Network to achieve the desired results. The simulation work is obtained using MATLAB and realtime audio capture, filtering and playback are demonstrated.

Research Paper

Smart Covid Safety Measures Checker Using Embedded Systems and Machine Learning

A. Samydurai* , S. Sri Aparna **, M. Subhashri ***, V. Sudharsan ****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Kanchipuram, Tamil Nadu, India.
Samydurai, A., Aparna, S. S., Subhashri, M., and Sudharsan, V. (2021). Smart Covid Safety Measures Checker Using Embedded Systems and Machine Learning. i-manager's Journal on Digital Signal Processing, 9(1), 15-22. https://doi.org/10.26634/jdp.9.1.18147
World Health Organization : COVID-19 - Global literature on coronavirus disease
https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1527125
ProQuest Central | ID: covidwho-1527125

Abstract

In this pandemic situation, everyone are vulnerable to COVID-19. So the necessity for safety measures checking system has become inevitable. Every measure is currently being checked separately using man power, crowded places are more susceptible for the spread of disease. Using infrared sensor and setting up a threshold crowd management is accomplished. The proposed system delivers a non contact solution by integrating sensors in order to monitor the human body temperature and environmental conditions remotely, automatically and quickly. The system completely avoids direct contact with the residents during the process of screening. Along with temperature sensing, proper sanitization is also critical. As a result, the proposed work also employs a pump motor to spray sanitization automatically and without the need for human intervention. Many companies and organizations must adjust to and protect an infected person by spotting someone who does not wear a masked face; however, this is not always possible. The proposed system also investigates the performance of detecting people wearing a face mask in a real-time situation using Machine Learning. The results show the highest precision from all input images and a camera.

Research Paper

Analysing the Growth of Plants Using Hydroponic Techniques with Embedded System Controller

Pavithra R.* , Prabha A. G. **, Preethi J. ***, Preethi S. ****, Sasirekha D. *****
*-***** Department of Electronics and Communication Engineering, Rajalakshmi Engineering College (Anna University), Chennai, Tamil Nadu, India.
Pavithra, R., Prabha, A. G., Preethi, J., Preethi, S., and Sasirekha, D. (2021). Analysing the Growth of Plants Using Hydroponic Techniques with Embedded System Controller. i-manager's Journal on Digital Signal Processing, 9(1), 23-29. https://doi.org/10.26634/jdp.9.1.18226

Abstract

This paper aims to develop a monitoring tool for farming using Hydroponic techniques. The water with nutrient solution level is detected by the Ultrasonic sensor HC-SR04 and the temperature is detected by the temperature sensor W1209 digital temperature. The pH level of water is detected by pH sensor and PH4502C for transferring the voltage generated by the sensor to the Arduino uno board. Data from the sensor will be forwarded into Arduino Uno and displayed in Liquid Crystal Display (LCD) and then the wireless fidelity (WIFI) ESP8266 module will transmit the level of water with nutrient solution, pH value and the temperature to the Android phone through ThingView app.

Research Paper

Augmented Reality Based Doctor's Assistive System

S. Benila* , Mukil Nataraj Pandian A. **, Naveen N. ***, Praveen Kumar R.****
*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Chennai, Tamil Nadu, India.
Benila, S., Pandian, A. M. N., Naveen, N., and Kumar, R. P. (2021). Augmented Reality Based Doctor's Assistive System. i-manager's Journal on Digital Signal Processing, 9(1), 30-34. https://doi.org/10.26634/jdp.9.1.18140

Abstract

In this fast moving world, Technology plays a major role in the medical field too. Among Doctors, Surgeons are most importantly in need of new technologies that could make their job easier and efficient. They easily adopt to new technologies which would aid them in providing a better surgical and patient experience. Augmented Reality (AR) is a rapidly developing technology and is easily available, accessible and is also affordable. Hence they are used in healthcare systems to enhance them. We are developing a system that displays the vital information of a patient to the surgeon through an AR glass which is also automated, to give alert to the surgeon if anything is abnormal about the patient.

Research Paper

Crowd Monitoring Using Machine Learning

Arivoli K.* , Daniel Andrew B.**, Fraser Kagoo E. ***, Harshavardhan S. A. ****, Tamilarasi M. *****
*-***** Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
Arivoli, K., Andrew, B. D., Kagoo, E. F., Harshavardhan, S. A., and Tamilarasi, M. (2021). Crowd Monitoring Using Machine Learning. i-manager's Journal on Digital Signal Processing, 9(1), 35-38. https://doi.org/10.26634/jdp.9.1.18227
World Health Organization : COVID-19 - Global literature on coronavirus disease
https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/covidwho-1525391
ProQuest Central | ID: covidwho-1525391

Abstract

Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, public safety, crowd abnormalities, visual surveillance and urban planning. The purpose of crowd density analysis is to calculate the concentration of the crowd in the videos of observers. The job of detecting a face in the crowd is complicated due to the variability present in human faces including color, pose, expression, position, orientation, and illumination. This paper proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.