| Solving Haplotype Assembly Problem Using Harmony Search |
| Pages: 110-118 (9) | [Full Text] PDF (1.89 MB) |
| Saman Poursiah Navi, Ehsan Asgarian |
| Department of Computer Engineering, Islamic Azad University, Quchan Branch, Quchan, IranDepartment of Computer Engineering, Sharif University of Technology, Tehran, Iran |
|
Abstract - Single Nucleotide Polymorphisms (SNPs), a single DNA base varying from one individual to another, are believed to be the most frequent form responsible for genetic differences. Haplotypes have more information for disease-associating than individual SNPs or genotypes; it is substantially more difficult to determine haplotypes through experiments. Hence, computational methods that can reduce the cost of determining haplotypes become attractive alternatives. MEC, as a standard model for haplotype reconstruction, is fed by fragments input to infer the best pair of haplotypes with minimum errors needing correction. It is proved that haplotype reconstruction in the MEC model is a NP-Hard problem. Thus, researchers’ desire reduced running time and obtaining acceptable results. Heuristic algorithms and different clustering methods are employed to achieve these goals. In this paper, Harmony Search (HS) is considered a clustering approach. Extensive computational experiments indicate that the designed HS algorithm achieves a higher accuracy than the genetic algorithm (GA) or particle swarm optimization (PSO) to the MEC model in most cases. |
| Index Terms - Clustering, Bioinformatics, Evolutionary Optimization, Reconstruction Rate |
| Linear Switching State Space (LS3) Model for Task Scheduling: An Analytical Approach |
| Pages: 119-131 (13) | [Full Text] PDF (334 KB) |
| H. Tabatabaee, M.-R.Akbarzadeh-T and N. Pariz |
| Department of Computer Engineering, Islamic Azad University, Quchan Branch, Quchan, IranCenter of Excellence on Soft Computing and Intelligent Information ProcessingDept. of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran |
|
Abstract - Task Scheduling (TS) poses a challenging problem in distributed systems such as multiprocessor systems, flow-shop scheduling, and project management problems in which there are multiple tasks and processors (resources), and the problem is to efficiently assign tasks to processors. The importance of this problem includes several aspects such as heterogeneity of processors, computational complexity of reaching a solution as well as theoretical performance analysis. In this paper, control theory is used to construct a modeling paradigm. The approach which is basically a switching state space model opens a possibility of using the extensive theoretical developments that have taken place in this field within the past several decades. In this study, first we show the role of nonlinear state space equations in modeling the standard TS, a suitable transformation is then devised to convert this model to linear switching state space (LS3) equations with nonlinear constraints, and then the most important aspects of a control-based system – the stability, controllability, observability and stabilizability – for the proposed method is analytically proven. Finally we inspected the robustness of the proposed model in a similar way at the presence of the changes in processing power and link failure. |
| Index Terms - Task Scheduling, Distributed Systems, Linear Switching State Space Model, Stabilizability, Controllability, Observability |
| Contourlet Transformation for Text Hiding in HSV Color Image |
| Pages: 132-139 (8) | [Full Text] PDF (1.26 MB) |
| KHALIL IBRAHIM ALSAIF and MEAAD M. SALIH |
| Dept of Computer Science, College of Computer & Mathematic Science-Mosul Univ./ IRAQ |
|
Abstract - Texts hiding in digital image have recently received quite a bit of attention because it is very important in invisible communication. This paper presents a new data hiding technique for embedding text data in images represented in HSV color space using (NSCT). The text data is first converted to ASCII format and then represented in binary form and then, added to contourlet coefficients. A high frequency directional pass band from of the contourlet transform is selected for data embedding. Image features like Peak Signal to Noise Ratio (PSNR). The proposed method show that we could successfully embed data in cover_images and extract it with the average embedding capacity of (1/16 ) bits per pixel without any error . High capacity can be achieved using this method according to block size. |
| Index Terms - HSV_color image ,steganography , contourlet transform ,stego_image |
| Contourlet Transform and Histogram Equalization for Brightness Enhancement of Color Image |
| Pages: 140-143 (4) | [Full Text] PDF (722 KB) |
| Khalil Ibraheem AlSaif and Ahmed S. Abdullah |
| Dept. of Computer Science College of Computer & Mathematic Science-Mosul Univ./ IRAQ |
|
Abstract - For decades, several image enhancement techniques have been proposed. Although most techniques require profuse amount of advance and critical steps, the result for the perceive image are not as satisfied, In this paper a new approach for enhancing brightness of color image based on contourlet transform and histogram equalization proposes. The color image is converted to HSI (hue, saturation, intensity) values. The i, which represent the luminance value of color image, decomposed to its coefficients by non-sampling contourlet transform, then applying grey-level contrast enhancement technique on some of the coefficients. Then, inverse contourlet transform is performed to reconstruct the enhanced S compoment. The S component is enhanced by histogram equalization while the H component does not change to avoid degradation color balance between the HSI components. Finally the enhanced S and I together with H are converted back to its original color system. The new approach gives Brightness enhancement more than 20% when was applied on different type of images and tested the performance. |
| Index Terms - Image Processing, Image Enhancement, Brightness Enhancement, Contourlet Transform , HSI Color Space |
| Assessment of Offline Digital Signature Recognition Classification Techniques |
| Pages: 143-151 (9) | [Full Text] PDF (761 KB) |
| DINA DARWISH |
| Assisstant professor, International Academy for engineeering and Media Science, 6th October, Egypt |
|
Abstract - The digital signature verification has become an interesting domain, which is widely needed. The usage of online and offline digital signatures has been spreaded worldwide due to the increase of use of bank transactions and user authentication and other similar activities. This requires the creation and the diversification of new online and offline signature verification methods. The signature verification methods contain both online (or dynamic) and offline (or static) signature verification methods. In this paper, an offline digital signature verification technique is proposed, that depends on extracting several features from the signatures to be used during simulation. Some signatures were used for training and others were used for testing only. Different methods such as, vectors manipulation, ensemble classification using boosted trees, and bagged trees, were used in this paper during simulation to obtain results. |
| Index Terms - Signature Verification, Offline Digital Signature, Vectors Manipulation, Ensemble Classification, Bagged Trees |
| An Advance Security Technique Challenges to Government in Wireless Sensor Network for Health |
| Pages: 152-164 (13) | [Full Text] PDF (1.13 MB) |
| S.Mohapatra, G.S. Rout, S.S.Behera, A.K.Mohanty |
| School of Electronics, Campus-12, KIIT University |
|
Abstract - Changes in the Internet, World Wide Web technologies and services lead to new developments in the way of E-Government efforts to provide better services to citizens and businesses due to governments handles their internal operations. One of the revolutionary developments comes from adoption of wireless technologies in government related activities. E-Governance is an influential tool for bringing challenges to the government process in the developing world. Mainly, E-Governance operates at the cross roads between information and communication Technology (ICT) and Government Processes (GP). An effective E-Governance model is that systematically applied to a specific healthcare industry sector. As E-Governance is involved in global technology transfers data from the original project context into a different socio-cultural environment. The Health Services to the public is a collaborative program between the clinical medical programs and the Department of Health Systems; Management & Policy at the Public Health System and Health Educational System are an interdisciplinary program that evaluates organization, delivery and reimbursement in health care to public. It is response to the Government access the information from all sectors and will give them valuable suggestions. The need to collect data about people’s physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from urban and rural area. In this paper we present the the recent availability of the technologies that enable this data collection, storing, retrieving and security system for the information through wireless sensor networks for healthcare. In this paper, we outline prototype systems spanning application domains from physiological and activity monitoring the urban and rural hospitals and behavioral works and emphasize ongoing treatment challenges to the patient day to day and that information will be available in centrally. Then any moments the higher authorities can able to verify. |
| Index Terms - Healthcare monitoring; medical information systems; wireless sensor network, wavelet technology |
| Land Cover Classification Using Hidden Markov Models |
| Pages: 165-172 (8) | [Full Text] PDF (500 KB) |
| Dr. GHAYDA A. AL-TALIB and EKHLAS Z. AHMED |
| Computer Science Department, College of Computer Science and Mathematics, Mosul University, Mosul, Iraq |
|
Abstract - This paper, proposed a classification approach that utilizes the high recognition ability of Hidden Markov Models (HMM s) to perform high accuracy of classification by exploiting the spatial inter pixels dependencies ( i.e. the context ) as well as the spectral information. Applying unsupervised classification to remote sensing images can provide benefits in converting the raw image data into useful information which achieves high classification accuracy. It is known that other clustering schemes as traditional k-means does not take into account the spatial inter-pixels dependencies. Experiments work has been conducted on a set of 10 multispectral satellite images. Proposed algorithm is verified to simulate images and applied to a selected satellite image processing in the MATLAB environment. |
| Index Terms - Hidden Markov Models (HMM), Land cover, Multispectral Satellite Images, Clustering, Unsupervised classification |