Browsing by Author "Mohanty M."

Now showing 1 - 8 of 8

Automatic modulation classification using S-transform based features
(2015) Satija U.; Mohanty M.; Ramkumar B.
Automatic Modulation Classification plays a significant role in Cognitive Radio to identify the modulation format of the primary user. In this paper, we present the Stockwell transform (S-transform) based features extraction for classification of different digital modulation schemes using different classifiers such as Neural Network (NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes (NB), k-Nearest Neighbor (k-NN). The S - transform provides time-frequency or spatial-frequency localization of a signal. This property of S-transform gives good discriminant features for different modulation schemes. Two simple features i.e., energy and entropy are used for classification. Different modulation schemes i.e., BPSK, QPSK, FSK and MSK are used for classification. The results are compared with wavelet transform based features using probability of correct classification, performance matrix including classification accuracy and computational complexity (time) for SNR range varying from 0 to 20 dB. Based upon the results, we found that S-transform based features outperform wavelet transform based features with better classification accuracy and less computational complexity. � 2015 IEEE.
Cyclostationary Features Based Modulation Classification in Presence of Non Gaussian Noise Using Sparse Signal Decomposition
(2017) Satija U.; Mohanty M.; Ramkumar B.
Automatic modulation classification�(AMC) is a salient component in the area of cognitive radio, signal detection, interference identification, electronic warfare, spectrum management and surveillance. The majority of the existing signals detection and classification methods presume that the received signal is corrupted by additive white Gaussian noise. The performance of the modulation classification algorithms degrades severely under the non-Gaussian impulsive noise. Hence, in this paper, we introduce a robust algorithm to identify the modulation type of digital signal�contaminated with non-Gaussian impulse noise and additive white Gaussian noise (AWGN) using a sparse signal decomposition on hybrid dictionary. The algorithm first detects and removes the impulse noise using sparse signal decomposition thereafter it classifies the modulation schemes using cyclostationary feature extraction algorithm. Simulation results demonstrate the superiority of the proposed method under different non-Gaussian impulse noise and AWGN conditions. The performance of the proposed classifier is evaluated using well known classifiers available in the literature. � 2017, Springer Science+Business Media New York.
Digital modulation classification under non-Gaussian noise using sparse signal decomposition and maximum likelihood
(2015) Mohanty M.; Satija U.; Ramkumar B.; Manikandan M.S.
In recent years, automatic signal detection and modulation classification play a vital role in the field of cognitive radio applications. The majority of the existing signals detection and classification methods assume that the received signal is contaminated by additive white Gaussian noise. Under impulsive noise condition, the performance of the traditional modulation classification methods may be degraded. Therefore, in this paper, we investigate the application of sparse signal decomposition using an overcomplete dictionary for detection and classification of digital modulation signals. The overcomplete hybrid dictionary consists of impulse waveform and sine and cosine waveform for effectively capturing morphological components of the impulse noise and deterministic modulated signals. The proposed modulation classification method includes the following steps: sparse signal decomposition (SSD) on hybrid dictionaries, modulated signal extraction, matched filtering, and maximum likelihood (ML) classification. The performance of the direct ML and SSD-based ML classification methods are tested and validated using different modulation techniques under different Gaussian and impulse noise conditions. The proposed system achieves a classification accuracy of 89 percent at 0 dB SNR and hence outperforms the direct ML method. � 2015 IEEE.
Major Stream Delay under Limited Priority Conditions
(2019) Mohanty M.; Dey P.P.
Under a limited priority situation, U-turning vehicles attempt to accept smaller gaps, which compels approaching through vehicles to slow down and experience delays. Delay within the median opening area is termed median delay, which is studied in two parts: Median delay 1 (from start to center of median opening), and Median delay 2 (center to end of median opening). The significant difference between these two delays is established by a t-test. The effect of U-turning volume on Median delay 1, Median delay 2, and total median delay is studied. Considering the lateral movements of approaching through vehicles within the median opening area, the effect of the zone on median delay is studied. Furthermore, the effect of the category of U-turning vehicle on median delay is examined. Finally, three mathematical models are proposed considering the effect of (1) U-turning volume, (2) zone through which the vehicle is moving within the median opening area, and (3) category of U-turning vehicle. The proposed models are validated by comparing the model values with the field data. � 2018 American Society of Civil Engineers.
Modelling the area occupancy of major stream traffic
(2018) Mohanty M.; Dey P.P.
The U-turns at median openings interrupt the movement of approaching through vehicles. This results an overall reduction in speed and flow along with increase in density. It is difficult to measure density from field observations and it doesn't consider any heterogeneous characteristics of traffic stream. Alternatively, area occupancy takes into account the flow, speed, and the dimension of each vehicle. Therefore area occupancy is a much better measure to study the performance of a road as compared to traffic density. This concept assumes that the vehicles must move at a uniform speed while entering and exiting the test section which may be practically impossible to maintain for all the vehicles. Therefore, to overcome this assumption, a modified technique has been proposed in this study. Besides, the change in area occupancy at various segments of slow down section across different U-turning traffic volumes has been assessed. Finally, a regression equation has been modelled to estimate area occupancy at every segment of road section corresponding to any U-turning volume. This model has been validated by comparing the estimated values with the values obtained from field data and the MAPE is found to be less than 10%. � 2018 Institute for Transport Studies in the European Economic Integration. All right reserved.
Modelling the major stream delay due to U-turns
(2019) Mohanty M.; Dey P.P.
This study assesses the delay faced by approaching through vehicles at uncontrolled median openings due to U-turning movement. Under limited priority situation, the U-turning vehicles accept smaller gaps which compel the through vehicles to reduce their speeds and experience delays. First, the slowdown section is identified and the delay faced by through vehicles from the starting of slowdown section to start of median opening is estimated. This is termed as approach delay. The approaching vehicles shift laterally as they approach toward the median opening. The concept of zone has been introduced to study this lateral movement. This study shows that the vehicles moving through the zone adjacent to curb face more delay as compared to the vehicles moving through the zone adjacent to median. Finally, a regression model is developed for estimating approach delay faced by any vehicle category traveling through a particular zone at a given U-turning traffic volume. � 2017, � 2017 Informa UK Limited, trading as Taylor & Francis Group.
Quantification of LOS at Uncontrolled Median Openings Using Area Occupancy Through Cluster Analysis
(2019) Mohanty M.; Dey P.P.
This study quantifies LOS ranges for traffic movement at uncontrolled median openings using �area occupancy� as a measure of effectiveness. The Highway Capacity Manual is silent about LOS ranges at uncontrolled median openings. Traditionally, traffic density is considered as an important parameter for quantifying the traffic flow. However, it does not consider the heterogeneous characteristics of the traffic stream. Further, occupancy also does not explain the heterogeneous traffic and the absence of lane discipline which is predominant in developing countries. Therefore, area occupancy is used in this study to assess the performance of traffic conditions at median openings. The established method to measure area occupancy has been modified to overcome the assumption used in earlier techniques. Area occupancy has been estimated for both major and minor traffic streams. K-mean clustering has been employed to classify area occupancy ranges for various LOS categories. This methodology could be beneficial for practitioner engineers to monitor the vehicular movement at median opening. � 2018, King Fahd University of Petroleum & Minerals.
Sparse decomposition framework for maximum likelihood classification under alpha-stable noise
(2016) Mohanty M.; Satija U.; Ramkumar B.
Recently, automatic modulation classification has gained a lot of attention in the area of cognitive radio (CR), signal detection, electronic warfare and surveillance etc. Most of the existing modulation classification algorithms are developed based on the assumption that the received signal to be identified is corrupted by only additive white Gaussian noise. The performances of these conventional algorithms degrade significantly by addition of impulse noise. In this paper, we propose a robust algorithm using sparse signal decomposition which comprises of an overcomplete dictionary for detection and classification of modulated signals. In this work, an overcomplete dictionary is constructed using the identity basis, cosine and sine elementary waveforms to capture morphological components of the impulse noise and deterministic modulated signals effectively. The proposed method of modulation classification consists of the three major steps: sparse signal decomposition (SSD) on hybrid dictionaries, modulated signal extraction, and maximum likelihood (ML) based classification. The testing and validation of both direct ML and SSD-based ML classification methods are carried out under different Gaussian and impulse noise conditions for modulation classification. Our proposed method achieves a classification accuracy of 85% at 5 dB SNR and outperforms the conventional classification methods. � 2015 IEEE.