Informatics Publishing Limited

Amanpreet Kaur Bhogal ¹, Neeru Singla ¹, Maninder Kaur ²

Affiliations
1 Department of Electronics and Communication, R.I.E.I.T, Railmajra, Punjab, IN
2 Department of Electronics and Communication, R.B.C.E.B.W., Sahora, Mohali, IN

Abstract

Color image segmentation can be seen as an extension of the grayscale image segmentation. In order to provide basic data for image recognition and image segmentation, this paper studies the image under consideration based on the threshold selection method, Otsu (named after Nobuyuki Otsu). Firstly, pre-processing operation is carried out on the unprocessed original image. Then, we select The L*a*b* color space as the optimum color space for image segmentation, the color image of RGB color space is transformed to L*a*b* color space. Next, the channels of color space are separated and then a single channel is selected after which two-dimension Otsu segmentation is carried out based on the selected channel. This proposed method automatically performs histogram-shape based image thresholding. Experiments show that Otsu method will lead to a correct threshold value and yield an ideal result in segmentation with small computation time.

Keywords

Color Image Segmentation, Color Space, Grayscale Image Segmentation, Otsu, Threshold Selection.

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Rishi Sharma ¹, Neeru Singla ², Manish Dev Sharma ³

Affiliations
1 Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, IN
2 Department of Electronics and Communication Engineering at Rayat Institute of Engineering & Information Technology, SBS Nagar, IN
3 Department of Physics, Panjab University, Chandigarh, IN

Abstract

In this paper we optimize the wide narrow band antenna switching for 5G using feedforward neural network to measure the suitability for accuracy. As we know 5G terminals will have software defined radios and modulation scheme as well as new error-control schemes can be downloaded from the Internet on the run. Here we done the switching with the help of neural network taking some parameters. Each network will be responsible for handling user-mobility, while the terminal will make the final choice among different wireless/mobile access network providers for a given service. Here we have investigated and analyzed this system to optimize the neural networks as to what numbers of layers are most suitable for optimization. Here accuracy of above 81% is reported.

Keywords

Optimization, 5G, Neural Network, Software Defined Radios, Noise Threshold, Transmission Antenna Power, Channel Noise.

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Onkar Singh ¹, Neeru Singla ¹, Manish Dev Sharma ²

Affiliations
1 Department of Electronics and Communication Engineering, Rayat Institute of Engineering and Information Technology, SBS Nagar, Punjab-144533, IN
2 Department of Physics, Punjab University, Chandigarh-160014, IN

Abstract

Classification of audio signals according to their content has been a major concern in recent years. There have been many studies on audio content analysis, using different features and different methods. It is a well-known fact that audio signals are baseband, one-dimensional signals. General audio consists of a wide range of sound phenomena such as music, sound effects, environmental sounds, speech and nonspeech signals. In this paper we classified the audio systems using feedforward neural network to measure the suitability for accuracy in classification and time taken to classify. Here we have investigated and analyzed this system to optimize the neural networks as to what layers our system is most suitable to classify audio wave files. Here accuracy of above 99% is reported.

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