Navneet Kaur ¹, Nitish Mahajan ¹

Affiliations
1 Chandigarh Univarsity, Gharuan, Mohali - 140413, Punjab, IN

Abstract

Objectives: The SAR images are highly noisy so a technique is applied that can de-noise the input image for batter analysis of the features. Methods: RADARSAT1 imagery data is utilized by Synthetic Aperture Radar to detect the ice at different regions of the seas. Automated algorithm gives better consequence of target utilizing R1 imagery data. To de-noise the input satellite images, gobar filter is applied. The gobar filter is the patch based filter in which patches are created of the whole image and patch which has dissimilar properties are removed from the image. Finding: The proposed and existing algorithms are implemented in MATLAB by considering the SAR dataset. It is analyzed that after de-noising the image, properties of the image like thickness, concentration and velocity is analyzed efficiently. The results of the image such as ice thickness, ice velocity and ice concentration is increased up to 10 %, 12 % and 18 %, respectively.

Keywords

Dual Polarization, Floe, Gabor Filtering, Raster Scan, SAR, Sea Ice.

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Navneet Kaur ¹, Nitish Mahajan ¹

Affiliations
1 Chandigarh University, Gharuan - 140413, Punjab, IN

Abstract

Objectives: The image forgery detection is the technique in which pixels are marked in the image, which are not similar to other pixels of the images. The Principal Component Analysis (PCA) is the classification of neural networks which will analyze each pixel of the image and classify pixels according to pixel type. Method: The PCA algorithm takes training and trained dataset as input and drive new values according to input image. In the paper improvement in the PCA algorithm with usage of Scale-Invariant-Feature-Transform algorithm (SIFT Algorithm), is proposed for image-forgery. The SIFT algorithm is the algorithm which analyze each pixel of the image and define type of pixels in the image. The output of the SIFT algorithm is given as input to PCA algorithm for data classification. The PCA algorithm will classify the data according to SIFT algorithm output. Findings: The results demonstrate that the proposed algorithm executes well in terms of “Peak Signal-to-Noise Ratio” (PSNR), “Mean-Square-Error” (MSE), fault detection rate and accuracy value.

Keywords

Forgery Detection, MSE, PCA, PSNR, SIFT.

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Navneet Kaur ¹, Rajan Kumar ¹

Affiliations
1 Department of Computer Science and Engineering, Chandigarh University, Mohali - 140413, Punjab, IN

Abstract

Objectives: The main objective of this work is to lessen energy utilization of Wireless Sensor Network. The Radio Frequency Identification is the protocol of Wireless Sensor Network which provides channel access using TDMA protocol. Due to weak clock synchronization of sensor nodes TDMA protocols do not work efficiently. The improvement will be proposed in Radio Frequency Identification protocol to synchronize clocks of sensor nodes for efficient working of TDMA protocol. Methods: To reduce energy consumption of sensor networks various operation modes are used and these modes are sleep, active and ready mode. The TDMA protocol is used to assign time to sensor nodes for efficient execution of these modes. In the sensor network clocks of the sensor nodes are weakly synchronized due to which packet loss may occur due to which network lifetime get reduced. The proposed improvement and existing Radio Frequency Identification protocol is simulated in NS2 with finite number of sensor nodes on area of 800*800 meter. The LEACH protocol is used for clustering and for Cluster Head selection. Findings: In this paper, improvement is being proposed in Radio Frequency Identification protocol for clock synchronization and proposed improvement in based on time lay technique. In time lay technique base station takes initiative to synchronize clocks of the sensor nodes. It is been analyzed that proposed algorithm performs well in terms of energy consumption, throughput and delay.

Keywords

Cluster Head, Diffusion, Leach Protocol, RFID (Radio Frequency Identification) Protocol, Time Lay Protocol.

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Navneet Kaur ¹, Arvinder Pal Singh Kalsi ¹

Affiliations
1 School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara - 144411, Punjab, IN

Abstract

Objectives: Forward Error Correction (FEC) is a mechanism that has been used for years to improve the reliability of digital communications over wireless channels as they are subjected to interference, fading and noise. The goal of FEC is to add some redundant data to the input stream which facilitates the receiver to recognize and correct the errors with no need of sender to transmit the data again. In this paper we have computed bit error rate using polar codes in an Additive White Gaussian Noise Channel (AWGN) and Binary Symmetric Channel (BSC) at different block lengths. Methods/Analysis: Shannon limit states that it is the maximum amount of information that any given channel can carry. Latterly, there have been inventions in Forward Error Correction (FEC) area that allows today's systems to approach the Shannon limit. One such advancement in FEC is Polar Codes which has mathematically proved to attain the Shannon capacity for Binary Discrete Memoryless Channels using the phenomenon of channel polarization which states that by recursively combining and splitting N individual channels, some channels will become completely noiseless and some become noisy. These noiseless channels are then chosen to send information bits. Findings: We have implemented polar codes over BIAWGN and BSC and have calculated their bit error rate's at different channel ranges. The decoder used for decoding polar codes is successive cancellation decoder and it's seen that the performance is not that good at small block lengths but as the block length increases the performance improves when compared with the un-coded data stream. Applications: As polar codes are the first codes that achieve the Shannon capacity, so they seem to be the best solution for discrete memoryless channels which encounter packet failures and other noisy disturbances. Also, apart from channel encoding, polar codes found its applications in lossy and lossless source coding, coding for secrecy, communication over multiple access channels.

Keywords

Additive White Gaussian Noise (AWGN), Binary Discrete Memoryless Channel (B-DMC), Binary Erasure Channel (BEC), Binary Symmetric Channel (BSC), Bit Error Rate (BER), Forward Error Correction (FEC), Log Likelihood Ratio (LLR).

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