Real-Time Noise Canceller Using Modified Sigmoid Function RLS Algorithm

2015 ◽  
pp. 63-70 ◽  
Author(s):  
V. K. Gupta ◽  
D. K. Gupta ◽  
Mahesh Chandra
Keyword(s):  
Real Time ◽  
Sigmoid Function ◽  
Rls Algorithm

scholarly journals An Effective Approach for Real-Time Parameters Estimation of Doubly-Fed Induction Machine Using Forgetting Factor RLS Algorithm

2020 ◽  
Vol 22 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Seif-El Islam Chelli ◽  
Ahmed Nemmour ◽  
Mourad Ahmed ◽  
Abdelfettah Boussaid ◽  
Abdelmalek Khezzar
Keyword(s):  
Real Time ◽  
Induction Machine ◽  
Parameters Estimation ◽  
Forgetting Factor ◽  
Rls Algorithm ◽  
Doubly Fed Induction Machine ◽  
Time Parameters ◽  
Doubly Fed

scholarly journals FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7399
Author(s):  
Ming-Hwa Sheu ◽  
S M Salahuddin Morsalin ◽  
Jia-Xiang Zheng ◽  
Shih-Chang Hsia ◽  
Cheng-Jian Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Sigmoid Function ◽  
Real Time Processing ◽  
Scale Choice ◽  
Training Stage

The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation. We created the passing detection lines and class counting windows and connected them with the proposed FGSC-SSD deep neural network model. The 'FGSC' blocks in the convolution layer emphasize essential features and find out unnecessary features by using the scale choice method at the training stage and eliminate that significant speedup of the model. In addition, FGSC blocks avoided many unusable parameters in the saturation interval and improved the performance efficiency. In addition, the Fuzzy Sigmoid Function (FSF) increases the activation interval through fuzzy logic. While performing operations, the FGSC-SSD model reduces the computational complexity of convolutional layers and their parameters. As a result, the model tested Frames Per Second (FPS) on edge artificial intelligence (AI) and reached a real-time processing speed of 38.4 and an accuracy rate of more than 94%. Therefore, this work might be considered an improvement to the traffic monitoring approach by using edge AI applications.

Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species Using TMS320C6713 DSK

2020 ◽  
pp. 800-821
Author(s):  
Amira Boulmaiz ◽  
Djemil Messadeg ◽  
Noureddine Doghmane ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Support Vector ◽  
Sigmoid Function ◽  
Noisy Environments ◽  
Mel Frequency Cepstral Coefficients ◽  
Design And Implementation ◽  
Waterbird Species ◽  
The Mean ◽  
Analysis Models

In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.

Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK

2017 ◽  
Vol 8 (1) ◽  
pp. 98-118 ◽  
Author(s):  
Amira Boulmaiz ◽  
Djemil Messadeg ◽  
Noureddine Doghmane ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Real Time ◽  
Support Vector Machine Classifier ◽  
Recognition System ◽  
Support Vector ◽  
Sigmoid Function ◽  
Noisy Environments ◽  
Mel Frequency Cepstral Coefficients ◽  
Waterbird Species ◽  
The Mean ◽  
Analysis Models

In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.

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