Shailesh Tiwari

Impact Analysis of Contributing Parameters in Audio Watermarking Using DWT and SVD

This paper proposes a non-blind audio watermarking algorithm for copyright protection of audio files which has proved to satisfy the minimum requirements of optimal audio watermarking standards set by International Federation of Photographic Industry (IFPI). The algorithm is set to meet the IFPI requirements as it includes the two powerful mathematical tools: Discrete wavelet transform (DWT) and singular value decomposition (SVD). In this paper, we have also analyzed the contribution of parameters like watermark size and the embedding intensity factor on the algorithm.

An efficient and provably secure time-limited key management scheme for outsourced data: An efficient and provably secure time-limited key management scheme for outsourced data

A time‐limited data access control scheme allows a users access to the data files only for a specified time period. A cryptographic solution to the time‐limited access control problem is by encrypting each data group associated with a time period with a distinct key. The data is encrypted by the data owner. The respective decryption keys are then distributed to authorized users by the data owner. A user requires one secret decryption key storage for each authorized time period. To reduce the secret key storage with each user, time‐limited hierarchical key management schemes are generally used. Many such schemes are proposed in the recent years. The objective of these schemes is system efficiency and data security. Construction of such schemes become more challenging when data is outsourced to an untrusted third party service provider. In current work, an efficient and secure time‐limited hierarchical key assignment scheme is proposed for key management suitable for data outsourcing scenario. We compare it with the other recent similar schemes. The scheme is formally proved against the modern stronger security notion called key indistinguishability.

A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem

Environmental Adaptation Method (EAM) and Improved Environmental Adaptation Method (IEAM) were proposed to solve optimization problems with the biological theory of adaptation in mind. Both of these algorithms work with binary encoding, and their performance is comparable with other state-of-art algorithms. To further improve the performance of these algorithms, some major changes are incorporated into the proposed algorithm. The proposed algorithm works with the real value parameter encoding, and, in order to maintain significant convergence rate and diversity, it maintains a balance between exploitation and exploration. The choice to explore or exploit a solution depends on the fitness of the individual. The performance of the proposed algorithm is compared with 17 state-of-art algorithms in 2-D, 3-D, 5-D, 10-D and 20-D dimensions using the COCO (COmparing Continuous Optimisers) framework with Black-Box Optimization Benchmarking (BBOB) functions. It outperforms all other algorithms in 3-D and 5-D, and its performance is comparable to other algorithms for other dimensions. In addition, IEAM-R has been applied to the real world problem of economic load dispatch, and its results demonstrate that it gives minimum fuel cost when compared to other algorithms in different cases.

Model Order Reduction Using Fuzzy C-Means Clustering and Particle Swarm Optimization

The hybrid method which combines the evolutionary programming technique, i.e., based on the swarm optimization algorithm and fuzzy c-means clustering method is used for reducing the model order of high-order linear time-invariant systems in the presented work. The process of clustering is used for finding the group of objects with similar nature that can be differentiated from the other dissimilar objects. The reduction of the numerator of original high-order model is done using the particle swarm optimization algorithm, and fuzzy c-means clustering technique is used for reducing the denominator of the higher-order model. The stability of the model is also verified using the pole zero stability analysis, and it was found that the obtained reduced-order model is stable. Further, the transient and steady state response of the obtained lower-order model as compared to the other existing techniques are better. The output of the obtained lower-order model is also compared with the other existing techniques in the literature in terms of ISE, ITSE, IAE, and ITAE.

A PSO-Based ANN Model for Short-Term Electricity Price Forecasting

In the last few decades, electricity markets around the world have gradually transformed from highly regulated to deregulated and competitive markets. Prior knowledge of electricity demand and price is needed by the generation companies and market operators for getting best return of investment and for maintaining the real-time balance between demand and supply, respectively. Although, the nonlinear and black box structure of the forecasting models based on artificial intelligence techniques have made them popular among the researchers, their inherent limitations posed due to their structure can be overcome by using evolutionary optimization techniques along with them for achieving better forecasting accuracy. The proposed work presents artificial neural network-based short-term electricity price forecasting model. In the presented work, dynamic particle swarm optimization technique is used to adjust the weights of the neural network model to the optimal values. The electricity price of New South Wales electricity market is forecasted using the proposed model in order to verify the performance of the proposed model.

Digital Audio Watermarking: A Survey

Counterfeiting and piracy of the intellectual property such as patents, copyrights, etc. is known to be a serious problem worldwide though it is more intense in some regions owing to the usage. Presently, millions of digital audio data is being copied over networks resulting into the loss of revenue to music industries at a big scale. Right owners and creators have been looking forward to find out ways to inhibit this process that ultimately prompted the research in digital watermarking to enable copyright protection in order to prevent illegal acts of forgery and pirate distribution of data. In this paper, we have surveyed the various existing methodologies of digital audio watermarking for preserving the copyright laws and highlighting the related issues.

A New Particle Acceleration-Based Particle Swarm Optimization Algorithm

Optimization of one or more objective function is a requirement for many real life problems. Due to their wide applicability in business, engineering and other areas, a number of algorithms have been proposed in literature to solve these problems to get optimal solutions in minimum possible time. Particle Swarm Optimization (PSO) is a very popular optimization algorithm, and was developed by Dr. James Kennedy and Dr. Russell Eberhart in 1995 which was inspired by social behavior of bird flocking or fish schooling. In order to improve the performance of PSO algorithm, number of its variants has been proposed in literature. Few variants such as PSO Bound have been designed differently, whereas others use various methods to tune the random parameters. PSO - Time Varying Inertia Weight (PSO-TVIW), PSO Random Inertia Weight (PSO-RANDIW), and PSO-Time Varying Acceleration Coefficients (PSO-TVAC), APSO-VI, LGSCPSOA and many more are based on parameter tuning. On similar principle, the proposed approach improves the performance of PSO algorithm by adding new parameter henceforth called as “acceleration to particle” in its velocity equation. Efficiency of the proposed algorithm is checked against other existing PSO, and results obtained are very encouraging.