Abstract: The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.
Abstract: Spectrum underutilization has made cognitive
radio a promising technology both for current and future
telecommunications. This is due to the ability to exploit the unused
spectrum in the bands dedicated to other wireless communication
systems, and thus, increase their occupancy. The essential function,
which allows the cognitive radio device to perceive the occupancy
of the spectrum, is spectrum sensing. In this paper, the performance
of modern adaptations of the four most widely used spectrum
sensing techniques namely, energy detection (ED), cyclostationary
feature detection (CSFD), matched filter (MF) and eigenvalues-based
detection (EBD) is compared. The implementation has been
accomplished through the PlutoSDR hardware platform and the
GNU Radio software package in very low Signal-to-Noise Ratio
(SNR) conditions. The optimal detection performance of the
examined methods in a realistic implementation-oriented model is
found for the common relevant parameters (number of observed
samples, sensing time and required probability of false alarm).
Abstract: Classically, an energy detector is implemented in time domain (TD). However, frequency domain (FD) based energy detector has demonstrated an improved performance. This paper presents a comparison between the two approaches as to analyze their pros and cons. A detailed performance analysis of the classical TD energy-detector and the periodogram based detector is performed. Exact and approximate mathematical expressions for probability of false alarm (Pf) and probability of detection (Pd) are derived for both approaches. The derived expressions naturally lead to an analytical as well as intuitive reasoning for the improved performance of (Pf) and (Pd) in different scenarios. Our analysis suggests the dependence improvement on buffer sizes. Pf is improved in FD, whereas Pd is enhanced in TD based energy detectors. Finally, Monte Carlo simulations results demonstrate the analysis reached by the derived expressions.
Abstract: As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.
Abstract: Spectrum sensing is the main feature of cognitive
radio technology. Spectrum sensing gives an idea of detecting the
presence of the primary users in a licensed spectrum. In this paper we
compare the theoretical results of detection probability of different
fading environments like Rayleigh, Rician, Nakagami-m fading
channels with the simulation results using energy detection based
spectrum sensing. The numerical results are plotted as Pf Vs Pd for
different SNR values, fading parameters. It is observed that
Nakagami fading channel performance is better than other fading
channels by using energy detection in spectrum sensing. A MATLAB
simulation test bench has been implemented to know the performance
of energy detection in different fading channel environment.
Abstract: Prediction of fault-prone modules provides one way to
support software quality engineering. Clustering is used to determine
the intrinsic grouping in a set of unlabeled data. Among various
clustering techniques available in literature K-Means clustering
approach is most widely being used. This paper introduces K-Means
based Clustering approach for software finding the fault proneness of
the Object-Oriented systems. The contribution of this paper is that it
has used Metric values of JEdit open source software for generation
of the rules for the categorization of software modules in the
categories of Faulty and non faulty modules and thereafter
empirically validation is performed. The results are measured in
terms of accuracy of prediction, probability of Detection and
Probability of False Alarms.
Abstract: A challenging problem in radar signal processing is to
achieve reliable target detection in the presence of interferences. In
this paper, we propose a novel algorithm for automatic censoring of
radar interfering targets in log-normal clutter. The proposed
algorithm, termed the forward automatic censored cell averaging
detector (F-ACCAD), consists of two steps: removing the corrupted
reference cells (censoring) and the actual detection. Both steps are
performed dynamically by using a suitable set of ranked cells to
estimate the unknown background level and set the adaptive
thresholds accordingly. The F-ACCAD algorithm does not require
any prior information about the clutter parameters nor does it require
the number of interfering targets. The effectiveness of the F-ACCAD
algorithm is assessed by computing, using Monte Carlo simulations,
the probability of censoring and the probability of detection in
different background environments.
Abstract: A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.