Publications
Enhancement automatic speech recognition by deep neural networks
Nov 10, 2021Journal Periodicals of Engineering and Natural Sciences
Publisher International University of Sarajevo
DOI 10.21533/pen.v9i4.2450
Issue 4
Volume 9
The performance of speech recognition tasks utilizing systems based on deep learning has improved dramatically in recent years by utilizing different deep designs and learning methodologies. A popular way to boosting the number of training data is called Data Augmentation (DA), and research shows that using DA is effective in teaching neural network models how to make invariant predictions. furthermore, EM approaches have piqued machine-learning researchers' attention as a means of improving classifier performance. In this study, have been presented a unique deep neural network speech recognition that employs both EM and DA approaches to improve the system's prediction accuracy. firstly, reveal an approach based on vocal tract length disturbance that already exists and then propose a Feature perturbation is an alternative Data Augmentation approach. in order to make amendment training data sets. This is followed by an integration of the posterior probabilities obtained from several DNN acoustic models trained on diverse datasets. The study's findings reveal that the proposed system's recognition skills have improved.
Separately excited DC motor speed using ANN neural network
Oct 11, 2021Journal AIP Conference Proceedings
Publisher American Institute of Physics Inc.
DOI 10.21533/pen.v9i4.2450
Separately Excited Direct Current Motors (SEDCM) is termed by high efficiency in electrical traction manufacturing. SEDCMs are utilized by high power applications such as aircrafts and ships traction. Speed control is more likely required to maintain the performance of the motor in different load scenarios. Conventional methods of speed control such as proportional integral controller (PIC) are reported good performance in error tackling but it may consume longer time and computational cost. In this paper, computational speed controller is proposed which uses neural network to predict speed and then to produce the reference voltage in order to update the armature terminal voltage. In this study have been assumed a three layers neural network to implement the control on speed of motor and the model is outperformed over the conventional controller models.
Design of Multiband Slot Patch Antennas for Modern Wireless Applications
Nov 30, 2019Journal International Journal on Communications Antenna and Propagation (IRECAP)
Publisher PRAISE WORHTY PRIZ
DOI https://doi.org/10.15866/irecap.v10i5.19071
Issue 10
Volume 5
In the past few years, microstrip antenna have been used in many applications because of their thin planar profile that could be easily integrated into various surfaces of aircraft, rockets and other customer-beneficial products. They are easily integrated into the same board and they allow the addition of active devices. These antennas are used in different areas like satellite navigation, mobile communication, automobiles, internet services and radars. In this study, antennas of the Coplanar (CPW-Fed) system have been used. Multi-band microstrip antennas have been designed and simulated with the help of the HFSS program about the return loss (S11). The goal of this study is to obtain multiband for working on several frequencies. Microstrip patch slot antennas using Coplanar (CPW- Fed) and working on different frequencies have been designed. This design is used in modern wireless appliances.
The impact of relay node deployment in vehicle ad hoc network: Reachability enhancement approach
Oct 18, 2019Journal 2019 Global Conference for Advancement in Technology (GCAT)
Publisher IEEE
DOI 10.21533/pen.v9i4.2450
Speaker VANET or vehicular ad hoc network is intelligent means of transportation which is essentially ensuring the safety norms. This technology can be established in any road such as highway or inter-city urban roads; it is basically a set of mobile vehicles connected with static base station. VANET is adopted ad hoc nature where dynamic vehicles are capable to communicate with each other due to less infrastructure network strategy as no clear administration tasks are functional. However, VANET is reported efficient in low and medium speeds i.e. 10 through 40 km/h. This paper aims to enhance network reachability in random traffic conditions. Results shown that 15% enhancement after deploying single RSU and 25% enhancement after deploying two RSUs.