C. Mala

Comparative Study of Image Forgery and Copy-Move Techniques

Image forgery means manipulation of the digital image to conceal some meaningful or useful information of the image. There are cases when it is difficult to identify the edited region from the original image. The detection of a forged image is driven by the need of authenticity and to maintain integrity of the image. This paper surveys different types of image forgeries. The survey has been done on existing techniques for forged image and it also highlights various copy – move detection methods based on their robustness and computational complexity.

Multilevel threshold selection for image segmentation using soft computing techniques

Multilevel thresholding is the method applied to segment the given image into unique sub-regions when the gray value distribution of the pixels is not distinct. The segmentation results are affected by factors such as number of threshold and threshold values. Hence, this paper proposes different methods for determining optimal thresholds using optimization techniques namely GA, PSO and hybrid model. Parallel algorithms are also proposed and implemented for these methods to reduce the execution time. From the experimental results, it is inferred that proposed methods take less time for determining the optimal thresholds when compared with existing methods such as Otsu and Kapur methods.

Optimization of Dynamic Channel Allocation Scheme for Cellular Networks Using Genetic Algorithim

The spectrum used for wireless transmissions today is becoming overcrowded, due to the increase in the number of users and applications and the demand for the available frequency bands by the various service providers. The major factor that prevents reuse of these bands is the interference caused by neighboring cells. An efficient method to reduce interference needs to be devised. This paper presents work based on the optimization of dynamic channel allocation using genetic algorithm (GA). This attempts to allocate the channel to users such that overall congestion in the network is minimized by reusing already allocated frequencies. The working of Genetic Algorithm which is used in the optimization procedure is also explained. The optimized channel is then compared with a non-optimized channel to check the efficiency of the genetic algorithm.

Optimization of QoS Parameters for Channel Allocation in Cellular Networks Using Soft Computing Techniques

The usage of mobile communications systems has grown exponentially. But the bandwidth available for mobile communications is finite. Hence there is a desperate attempt to optimize the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls and priority are considered. Genetic Algorithm and Artificial Neural Networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that Genetic Algorithm performs better than Heuristic Method. But application of Artificial Neural Networks outperforms Genetic Algorithm and Heuristic method by a considerable margin. Channel allocation can be optimized using these soft computing techniques resulting in better throughput.

An efficient and dynamic backoff algorithm for IEEE 802.11 networks

Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of backoff algorithms, a new variant called History based Probabilistic Backoff Algorithm is proposed. Mathematical analysis and simulation results using NS-2 with parameters like retransmission counts, saturated throughput, transmission delay, different data sizes and different traffic patterns was carried out. It is seen that proposed History based Probabilistic Backoff algorithm outperforms the other backoff algorithms.

Analysis and Comparative Study of Different Backoff Algorithms with Probability Based Backoff Algorithm

Data Link Layer is the most important Layer in any type of Local Area Network. The main functions of the Data Link Layer is access control and flow control. The efficient implementation of access control protocol, decides the optimal usage of network resources. The backoff algorithm is a very important aspect in access control protocol implementation. Backoff algorithm is used to reduce the probability of frequent collisions when stations try to access the medium simultaneously. The basic Binary Exponential Backoff algorithm, Modified Binary Exponential Backoff algorithm and their drawbacks are analyzed and a new variation called Probability Based Backoff algorithm is proposed, which takes network traffic also into consideration.

A Novel Cross Layer Approach to Enhance QoS Performance in Multihop Adhoc Networks

To enhance the QoS performance in saturated multihop adhoc networks, it is essential to have cooperative algorithms for MAC layer contention resolution and TCP layer congestion control. This paper proposes a cross layer approach that modifies the back off mechanism and congestion control technique using Fibonacci sequence. Due to the golden section property of Fibonacci sequence, contention and congestion can be effectively controlled. The NS2 simulation results show that the proposed algorithm drastically enhances the QoS performance of 802.11 multihop adhoc networks.

Dynamic Sliding Contention Window Adjustment in Saturated Wireless Networks

Most of the existing back off algorithms adjust the Contention Window (CW) only after the occurrences of collision in wireless networks. To prevent/ reduce further collisions, this paper proposes a sliding CW approach, to be done, after each successful transmission. Based on the current channel status measured by channel utilization and contention ratio, sliding factors are determined to adjust both lower and upper bounds of CW. The NS2 simulation results show that the proposed algorithm significantly outperforms the conventional Binary Exponential Back off (BEB) and Double Increment Double Decrement (DIDD) algorithms in terms of throughput, delay and packet loss.

Parallel Algorithms for Edge Detection in an Image

Edge detection is an important process in image segmentation, object recognition, template matching, etc. It computes gradients in both horizontal and vertical directions of the image at each pixel position to find the image boundaries. The conventional edge detectors take significant time to detect the edges in the image. To reduce the computational time, this paper proposes parallel algorithms for edge detection with Sobel, Prewitt and Robert first order derivatives using a Shared Memory - Single Instruction Multiple Data (SM - SIMD) parallel architecture. From the experimental results, it is inferred that the proposed parallel algorithms for edge detection are faster than the conventional methods.

Tree Based Group Key Agreement – A Survey for Cognitive Radio Mobile Ad Hoc Networks

Cognitive radio networks solve the spectrum scarcity problem by dynamically utilizing the unused spectrums. To ensure secure and reliable communication, cognitive radio mobile ad hoc networks require more stringent and secure protocols due to their intrinsic nature. Tree based topology for cognitive radio network is widely used as it takes less time for join and leave operations for the users within the channel of the spectrum compared to other topologies. This paper presents a survey of tree based group key agreement schemes applicable to cognitive radio networks.