The paper is designed to develop distance measurement system using ultrasonic waves and interfaced with arduino. We know that human audible range is 20hz to 20khz. We can utilize these frequency range waves through ultrasonic sensor HC-SR04.The advantages of this sensor when interfaced with arduino which is a control and sensing system, a pro per distance measurement can be made with new techniques. As large amounts are spent for hundreds of inflexible circuit boards, the arduino will allow business to bring many more unique devices. This distance measurement system can be widely used as range meters and as proximity detectors in industries. The hardware part of ultrasonic sensor is interfaced with arduino. This method of measurement is efficient way to measure small distances precisely. The distance of an obstacle from the sensor is measured through ultrasonic sensor. After knowing the speed of sound the distance can be calculated. Designed to develop distance measurement system using ultrasonic waves and interfaced with arduino. We know that human audible range is 20hz to 20khz. We can utilize these frequency range waves through ultrasonic sensor HC-SR04.The advantages of this sensor when interfaced with arduino which is a control and sensing system, a pro per distance measurement can be made with new techniques. As large amounts are spent for hundreds of inflexible circuit boards, the arduino will allow business to bring many more unique devices. This distance measurement system can be widely used as range meters and as proximity detectors in industries. The hardware part of ultrasonic sensor is interfaced with arduino. This method of measurement is efficient way to measure small distances precisely. The distance of an obstacle from the sensor is measured through ultrasonic sensor. After knowing the speed of sound the distance can be calculated.

Keywords : Ultrasonic Waves,Arduino , Control And Sensing System.

Author : P.Ramesh, Sai Sudheera, D.Vinay Reddy

Title : Distance Measurement Using Ultrasonic Sensor and Arduino

Volume/Issue : 2021;03(2)

Page No : 1-5

Speaker Recognition is essential in the field of authentication, and surveillance to validate the user’s identity using extracted feature characteristics of audio speech signal. In this work, the speaker recognitions performed by deep neural network-Radial Basis Function (DNN-RBF). Initially, the available speech signals are preprocessed to remove the noise from the input signal. The noise removal in the input signal is performed by wiener filter. From this pre-processed signal Mel frequency cepstral coefficients (MFCC) features are extracted. The i-vector is estimated from the Gaussian Mixture Model (GMM) super vector in which the dimensionality of extracted features is reduced.Extracted i-vector features are then injected within classifier for recognizing the specific speaker. Based on these extracted features, the speakers are recognized by Adaptive Harris Hawk Optimization (AHHO) based DNN-RBFin an appropriate manner. The performance of this speaker recognition process is evaluated with TIMIT (Texas Instruments/Massachusetts Institute of Technology)dataset. Some of the performance metrics like precision, accuracy, and recall are evaluated to evaluate the effectiveness of this proposed technique.The proposed speaker recognition technique is evaluated with various performance measures such as EER, precision, recall, and accuracy. The accuracy, precision, and recall values attained by proposed AHHO based DNN-RBF is 94.92%, 89.87 and 94.67 respectively. The presence of adaptive optimization approach improves the performance of DNN-RBF in speaker recognition. The implementation process is performed in Mat lab platform.

Keywords : Speaker Recognition, Adaptive Harris Hawk optimization, DNN-RBF, MFCC.

Author : P S Subhashini Pedalanka, Dr M. SatyaSai Ram, Dr Duggirala Sreenivasa Rao

Title : DNN-RBF & AHHO for Speaker Recognition using MFCC

Volume/Issue : 2021;03(2)

Page No : 6-12