Browsing by Author "Kalam, L.M."

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Hardware implementation of dual-tree wavelet transform based image reconstruction
(Institute of Electrical and Electronics Engineers Inc., 2020) Sudhakar, H.; Kalam, L.M.; Muralitharan, S.; Deepu, S.P.; Sumam David, S.S.
Real-time implementations of image processing algorithms on embedded platforms are gaining importance. In this paper, we propose an Application Specific Integrated Circuit (ASIC) architecture for the perfect reconstruction of images using wavelets with a view to extending this to denoising and feature extraction of images. An architecture that implements the Dual-Tree Wavelet Transform is presented. The architecture features a 128x128 single-port block memory and its addressing schemes, a simple upsampling/downsampling method and a folding and adding mechanism. It is implemented using 180nm technology. The results show perfect reconstruction of 128x128 grayscale images with up to 1-bit error in pixel values when compared to the corresponding input images. Â© 2021 IEEE
Multistage clustering based automatic modulation classification
(2019) Kalam, L.M.; Theagarajan, L.N.
Automatic modulation classification (AMC) is the problem of identifying the modulation type of a given radio frequency (RF) signal. This operation is one of the key steps in a cognitive radio based spectrum sharing communication network. It is known that the optimal classification algorithms for AMC are computationally intensive which renders real-time implementation almost impossible. In this paper, we propose a practical AMC algorithm that employs multiple stages of clustering to identify the modulation type of the received RF signal. Here, we consider the communication signals to be modulated using the most common digital modulation types: phase shift keying (PSK) or quadrature amplitude modulation (QAM). First, we present a novel algorithm that performs multiple stages of clustering to identify the clusters present in the received data and classifies it to one of the several possible modulation types. Second, we validate our proposed algorithm through practical implementation using software defined radios (SDR). Our results show that the proposed multistage clustering based AMC algorithm works well in practical conditions. � 2019 IEEE.
Multistage clustering based automatic modulation classification
(Institute of Electrical and Electronics Engineers Inc., 2019) Kalam, L.M.; Narasimhan, L.N.
Automatic modulation classification (AMC) is the problem of identifying the modulation type of a given radio frequency (RF) signal. This operation is one of the key steps in a cognitive radio based spectrum sharing communication network. It is known that the optimal classification algorithms for AMC are computationally intensive which renders real-time implementation almost impossible. In this paper, we propose a practical AMC algorithm that employs multiple stages of clustering to identify the modulation type of the received RF signal. Here, we consider the communication signals to be modulated using the most common digital modulation types: phase shift keying (PSK) or quadrature amplitude modulation (QAM). First, we present a novel algorithm that performs multiple stages of clustering to identify the clusters present in the received data and classifies it to one of the several possible modulation types. Second, we validate our proposed algorithm through practical implementation using software defined radios (SDR). Our results show that the proposed multistage clustering based AMC algorithm works well in practical conditions. Â© 2019 IEEE.