Volume-3 Issue-3, July 2013 – International Journal of Soft Computing and Engineering (IJSCE)

C1592073313

IJSCE Journal — Mon, 11 Oct 2021 04:14:10 +0000

The International Journal of Soft Computing and Engineering (IJSCE) has ISSN: 2231-2307 (online), an open-access, peer-reviewed, periodical bi-monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, March, May, July, September and November.The journal aims to publish high-quality peer–reviewed original articles in the area of Soft Computing and Engineering that covers Data Communication and Networking, Artificial Intelligence and Neural Networking, Image Processing and Recognition, Software Engineering and Project Management (SEPM), Computer Graphics and Multimedia (CGM), Data Mining, Cryptography and Network Security, Smart Computing and Information Processing, Internet of Things (IoT), Computation Intelligence and Analytics, Machine Learning (ML) and Knowledge Mining (KM), Operating System, Data Base Management System (DBMS), Data Structure and Algorithm (DSA), Object Oriented Analysis and Design (OOAD), and all interdisciplinary streams of Soft Computing and Engineering. #Addressing, Routing, Switching and Location Management #Broadband, Mobile, Wired, Wireless Internet #Networks: Adhoc and Sensor Networks, High Speed Networks, Mobile and #Wireless Networks #Network Architectures #Network Based Applications #Network Protocols #Network Operations & Management #Network Security Trust & Privacy #Cross Layer Design and Optimization #Internet and Web Applications #Network Measurement & Performance Analysis #Next Generation Internet & Web Architectures #Peer to Peer and Overlay Networks #QOS And Resource Management #Recent Trends & Developments in Computer Networks #Self-Organizing Networks and Networked Systems #Ubiquitous Networks #Virtual & Overlay Networks #Wireless Mesh Networks and Protocols #Wireless Multimedia Systems #Personal and Wearable Networks #Information-Centric Networking #Embedded Networks #Opportunistic Networking #Delay-Tolerant Networks #Cognitive Radio Networks #Vehicular Networks #Smart Grid Communications #Underwater Sensor Networks #Cyber-Physical Systems #Social Networks #Green Networking #Overlay and Peer-To-Peer Networks #Local-Area and Metropolitan-Area Networks #Storage-Area Networks #Routing and Transport Protocols #Big Data Networking #Cloud Computing and Networking #Software Defined Networking and Network Function Virtualization #Internet of Things #Link Technologies #Adaptive Networking Applications #Authentication, Authorization, Accounting #Security and Privacy #Cross-Layer Optimization #Multimedia and Real-Time Communication #Machine-To-Machine Communications for Smart Environments #Smart Cities #Network Traffic Characterization and Measurements #Network Management, Reliability and QoS #Performance Evaluation of Networks #Testbeds for Network Experiments #Network Coding #Optical and High-Speed Access Networks #High Performance Computing #Digital System and Logic Design #Networked-Driven Multicourse Chips #Information-Centric Networking #Opportunistic Networking #Mobile and Ubiquitous Networking #Big Data Networking #Cloud Computing and Networking #Software Defined Networking and Network Function Virtualization #Adaptive Networking Applications #Antenna and Communications Devices #Microstrip Antenna #Microwave #Radar and Satellite #Smart Antenna #MIMO Antenna #Wireless Communication #RFID Network and Applications #5G Communication #6G Communication #Neural Networks #Fuzzy Logic #Simulated Biological Evolution Algorithms (Like Genetic Algorithm) #Ant Colony Optimization(etc) #Reasoning and Evolution #Intelligence Applications #Computer Vision and Speech Understanding #Multimedia and Cognitive Informatics #Data Mining and Machine Learning Tools #Heuristic and Ai Planning Strategies and Tools #Computational Theories of Learning #Technology and Computing (Like Particle Swarm Optimization) #Intelligent System Architectures #Knowledge Representation #Bioinformatics #Natural Language Processing #Multiagent Systems #Supervised Learning #Unsupervised Learning #Deep Learning #Big Data and Ai Approaches #Reinforcement Learning #And Learning with Generative Adversarial Networks #Regression and Prediction #Problem Solving and Planning #Clustering #Classification #Neural Information Processing #Vision and Speech Perception #Heterogeneous and Streaming Data #Probabilistic Models and Methods #Reasoning and Inference #Marketing and Social Sciences #Knowledge Discovery #Web Mining #Information Retrieval #Design and Diagnosis #Game Playing #Streaming Data #Music Modelling and Analysis #Robotics and Control #Multi-Agent Systems #Active Vision and Robotic Systems #Architecture of Imaging and Vision Systems #Autonomous Vehicles #Character and Handwritten Text Recognition #Chemical and Spectral Sensitization #Chemistry of Photosensitive Materials #Coating Technologies #Coding and Transmission #Cognitive Aspects of Image Understanding #Color Imaging #Communication of Visual Data #Data Fusion from Multiple Sensor Inputs #Display and Printing #Document Image Understanding # Generation and Display #Holography #Image Analysis and Interpretation #Image Capturing #Image Databases #Image Generation #Image Manipulation #Image Permanence #Image Processing #Image Processing Applications #Image Processing: Coding Analysis and Recognition #Image Representation #Image Sensing #Imaging Systems and Image Scanning #Implementation and Architectures #Latent Image #Materials for Electro-Photography #Monitoring and Surveillance #Network Architecture for Real-Time Video Transport #New Visual Services Over ATM/Packet Network #Non-Impact Printing Technologies #Object Modeling and Knowledge Acquisition #Photoconductors #Photographic Emulsions #Photopolymers #Prepress and Printing Technologies #Processing and Analysis #Protocols for Packet Video #Remote Image Sensing #Retrieval and Multimedia #Scene Modeling #Signal-Processing Aspects of Image Processing #Storage and Transmission #Video Coding Algorithms and Technologies for ATM/P #Ethics of Software Engineering and Management #Software Life Cycles Development Models #Project Planning and Management #Software Requirements Analysis #Software Design Analysis #Software Testing and Integration #Software Implementation and Maintenance #Software Maintenance and Maintainability #Software Metrics or Attributes #Software Risk Assessment and Containment #Reliability #Software Quality and Quality Assurance #Software Cost Estimation #Object Oriented Developments #Object Oriented Metrics #Object Oriented Testing #Formal Notations #3D Reconstruction #Computer Animation #Computational Fabrication #Computational Geometry #Computational Photography #Computer Vision for Computer Graphics #Data Compression for Graphics #Geometric Modelling #Geometric Processing #HCI and Computer Graphics #Human Modelling #Image Analysis #Image Based Rendering #Image Processing #Machine Learning for Graphics #Medical Imaging #Pattern Recognition #Physically Based Modelling #Illumination and Rendering ethods #Robotics and Vision #Saliency Methods #Scientific Visualization #Shape and Surface Modelling #Shape Analysis and Image Retrieval #Shape Matching #Sketch-based Modelling #Solid Modelling #Stylized rendering #Textures #Virtual and Augmented Reality #Visual Analytics #Volume Rendering #Data Mining #Data Science #Big Data #Data Warehouse #Visualization #Security #Privacy #Big DaaS #Scalable Computing #Cloud Computing #Knowledge Discovery #Integration #Transformation #Information Retrieval #Social Data and Semantics #Mining Functions #Data Regression #Data Classification #Anomaly Detection #Data Clustering #Data Association #Data Regression #Data Cleaning #Feature Selection and Extraction #Data Mining Algorithms #Apriori #Decision Tree #Generalized Linear Models #K-Means #Minimum Description Length #Naive Bayes #Non-Negative Matrix Factorization #0-Cluster #Support Vector Machines #Data Preparation #Mining Unstructured Data #Artificial Intelligence #Future Directions and Challenges in Data Mining #Industrial Challenges in Data Mining #Cryptographic Protocols #Cryptography and Coding #Untrace-Ability #Privacy and Authentication #Key Management #Authentication #Trust Management #Quantum Cryptography #Computational Intelligence in Security #Artificial Immune Systems #Biological & Evolutionary Computation #Intelligent Agents and Systems #Reinforcement & Unsupervised Learning #Autonomy-Oriented Computing #Coevolutionary Algorithms #Fuzzy Systems #Biometric Security #Trust Models and Metrics #Regulation and Trust Mechanisms #Data Integrity #Models for Authentication #Trust and Authorization #Wireless Network Security #Information Hiding #Data & System Integrity #Access Control and Intrusion Detection #Intrusion Detection and Vulnerability Assessment #Authentication and Non-Repudiation #Identification and Authentication #Insider Threats and Countermeasures #Intrusion Detection & Prevention #Secure Cloud Computing #Security Information Systems Architecture and Design and Security Patterns #Security Management #Security Requirements (Threats, Vulnerabilities, Risk, Formal Methods, etc.) #Sensor and Mobile ad hoc Network Security #Service and Systems Design and QOS Network Security #System Security #Intrusion Detection #Secure end Systems #Secure Operating Systems #Data Base Security #Security Infrastructures #Security Evaluation #Software Security #Security and Privacy in Mobile Systems#Security and Privacy in Pervasive/Ubiquitous Computing #Security and Privacy in Web Services #Security and Privacy Policies #Security Area Control #Security Deployment #Security Engineering #Security for Grid Computing #Security in Distributed Systems #Network Security #Internet Security #Firewalls #Mobile Security #Security Agents #Protocols #Anti-Virus and Anti-Hacker Measures #Content Protection #Software Protection #Tamper Resistant Software #Electronic Commerce #Electronic Government #Health #Telecommunications #Mobility #Foundations #Privacy #Access Control #Authentication #Identification #Applied Cryptography #Formal Methods in Information Security #IoT & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Blockchain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for Smart City #Logic, Functional programming and Microcontrollers for IoT #Sensor Networks, Actuators for Internet of Things #Data Visualization using IoT #IoT Application and Communication Protocol #Big Data Analytics for Social Networking using IoT #IoT Applications for Smart Cities #Emulation and Simulation Methodologies for IoT #IoT Applied for Digital Contents #Soft Computing #Advance Ubiquitous Computing #Parallel Computing #Distributed Computing #Machine Learning #Information Retrieval #Expert Systems #Data Mining #Text Mining #Data Warehousing #Predictive 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Agent-Based Systems #Autonomic Computing #Adaptive Systems #Agent Architectures, Ontologies, Languages and Protocols #Multi-Agent Systems #Agent-Based Learning and Knowledge Discovery #Interface Agents #Agent-Based Auctions and Marketplaces #Secure Mobile and Multi-Agent Systems #Mobile agents #SOA and Service-Oriented Systems #Service-Centric Software Engineering #Service Oriented Requirements Engineering #Service Oriented Architectures #Middleware for Service Based Systems #Service Discovery and Composition #Service Level Agreements (Drafting, Negotiation, Monitoring and Management) #Runtime Service Management #Semantic web #Data Modelling, Mining and Data Analytics #Aggregation, Integration, and Transformation #Web and Text Mining #Data Mining Methods, Techniques, and Tools #Data Analytics Modelling and Algorithms #Patterns and Frameworks #Data Visualization #Knowledge Systems and Engineering #Knowledge Acquisition #Knowledge-based and Expert Systems #Knowledge Representation and Retrievals #Knowledge Engineering Tools and Techniques #Time and Knowledge Management Tools #Knowledge Modelling, Integration, Transformation, and Management #Uncertainty Knowledge Management #Knowledge Visualization #Mobile Computing and Mobile System Engineering #Mobile App Design and Development #Innovative Mobile Applications #Pervasive Computing #Mobile System Validation and Test Automation #Software & System Quality of Service #Soft Computing #Software and System Testing Methods #Quality Assurance Process, Standards, and Systems #Software Safety Systems #Software Test Automation and Tools #Software Dependability, Reliability, Scalability #Software & System Security #Software and System Security and Privacy #Mobile App Security and Privacy #Encryption Methods and Tools #Security Service Systems #Cloud, Sensor Cloud and Mobile Cloud Security #Mobile System Integrity, Security, And Fault Tolerance #Emergent Topics #Cloud Computing and Innovative Cloud-Based Application Systems 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Mobile Robot Floor Navigation using RGB+D and Stereo Cameras
K. Al-Mutib¹, E. Mattar², M. AlSulaiman³, H. Ramdane⁴, M. Emaduddin⁵
¹K. Al-Mutib, Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia.
²E. Mattar, Electrical and Electronics Engineering Department, College of Engineering, University of Bahrain, Kingdom of Bahrain.
³M. AlSulaiman, Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia.
⁴M. AlSulaiman, Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia.
⁵M. AlSulaiman, Department of Computer Engineering, King Saud University, Riyadh, Saudi Arabia.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 286-292 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1592073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Real world indoor environments are rich in planar surfaces. Floor detection or ground-plane detection is a crucial requirement for a robotic navigation task. Despite frequent successes in this area, problems with detection of navigable floor with multiple planar and non-planar slopes at multiple heights still exist. For robust and safe navigation, such small variations such as floor joins, carpet deformities, raised steps and floor gradients need to be detected and robot path and kinodynamics plan must be adjusted accordingly. The authors suggest a recursive RANSAC segmentation based algorithm that estimates the dominant and sub-dominant plane models for all the navigable planes within a detected floor or a ground plane. The algorithm also divides the input point clouds intelligently into multiple regions of interest for both efficiency and accuracy enhancement. The recursive estimation approach for determining plane parameters helps to detect multiple planes within each region. Among other benefits of this approach, reduction of search space size for the estimation of plane parameters stands out to be the most striking result of this work. This region wise plane estimation approach also helps to reduce the computational load by selectively dropping less significant floor sections from estimation process. The floor estimation technique coupled with sensor response functions for two different point cloud generators further investigates into the robustness of the method when deployed on two distinct sensors i.e. RGB+D sensor and a stereo vision camera. In our experiments we segment navigable floor planes in real-time for a slowly moving sensor. The location and geometrical parameters of the floor planes are updated in a global coordinate system whenever a change their location is detected. The planes are associated to a grid map which serves as a path-planning reference to a mobile robot used in our experiments. The results of floor detection and the precision of floor anomaly detection are compared sensor-wise and with the ground truth defined by obstacle heights and configuration.
Keywords: Mobile Robotic System, Stereo Vision, Navigation, Grid-map, 3D terrain Maps.

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C1658073313

IJSCE Journal — Mon, 11 Oct 2021 04:04:13 +0000

Converting UML Description of Software Architecture to QNM and Performance Evaluation
Rahmat Zolfaghari
Rahmat Zolfaghari, Islamic Azad University, Hashtgerd Branch, Department of Computer Engineering, Tehran, Iran.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 28, 2013. | Manuscript published on July 05, 2013. | PP: 281-285 | Volume-3 Issue-3, July 2013. | Retrieval Number: B1442053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Converting UML Description of Software Architecture to QNM, provides a comparison between all kinds of designs with respect to performance indicators.Present study suggested a method for converting the UML description designing software to Queuing networks model (QN)model, which provides the application of using the UML in designing software with high performance; in other words it putting the performance in designing software and a high quality software is designed.In order tomodeling the parts of system we use deployment diagram for allocating software components to hardware resources and activity diagrams to model software behavior, and use case diagrams to model workloads with the performance profile and An algorithm is provided for automatic production of the QN performance model from the XML(Extensible Markup Language) documents .used diagrams with performance profiles(stereotype, label and limitation), using the ExportXMIsoftware rational rose. performace modelload to software tool for performance analysis, so as the designer can test thefulfill of performance goals of his design according to type of different performance parameters and changing in value and chooses the best option in designing.
Keywords: UML(Unified Modeling Language),Queuing networks model (QNA)and Performance evaluation.

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C1718073313

IJSCE Journal — Mon, 11 Oct 2021 04:00:19 +0000

ICI Cancellation using Zero-Padded Conjugate Transmission with Adaptive Receiver for OFDM Systems
Amandeep Kaur¹, Garima Saini²
¹Amandeep Kaur, M.E Student (E.C.E), National Institute of Technical Teachers Training and Research, Sector-26, Chandigarh, India.
²Mrs. Garima Saini, Assistant Professor (E.C.E) National Institute of Technical Teachers Training and Research, Sector-26, Chandigarh, India.
Manuscript received on June 04, 2013. | Revised Manuscript received on June 27, 2013. | Manuscript published on July 05, 2013. | PP: 278-281 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1718073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: To overcome the effect of Intercarrier Interference (ICI) in OFDM system, the Zero-padded Conjugate scheme with adaptive receiver is proposed. In the conjugate algorithm first path contains the regular OFDM signal and in second path conjugate of first path is transmitted using Time Division multiplexing (TDM) for Time variant channels. Zero padding is done between two consecutive symbols to mitigate the effect of intercarrier interference to provide better time and frequency synchronization. Adaptive receiver uses block least mean-squared algorithm (BLMS) to adaptively update the frequency offset error. The simulation results are carried out using BPSK, QPSK and 16-QAM modulation techniques. The proposed scheme provides better BER rate than regular OFDM system, Conjugate cancellation (CC) and previous work for AWGN channel. Keywords: Orthogonal frequency division multiplexing (OFDM), Adaptive receiver, Block least mean-squared (BLMS) algorithm, Intercarrier Interference (ICI), frequency offset.
Keywords: Orthogonal frequency division multiplexing (OFDM), Adaptive receiver, Block least mean-squared (BLMS) algorithm, Intercarrier Interference (ICI), frequency offset.

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C1725073313

IJSCE Journal — Mon, 11 Oct 2021 03:55:30 +0000

Advanced Signal Processing of Radar Wind Profiler using Wavelet Transform Techniques
M. Krupa Swaroopa Rani¹, P. Jagadamba², G. Kiran Kumar³
¹M. Krupa Swroopa Rani, P.G. Student in the Department of ECE, SriKalahastheeswara Institute of Technology, Srikalahasthi, India.
²P. Jagadamba, Sr. Asst. Prof. in the Department of ECE, Sri Kalahastheeswara Institute of Technology, Srikalahasthi, India.
³G. Kiran Kumar, Research Scholar in Department of Physics,Sri Venkateshwara University, Tirupati, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 273-277 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1725073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Atmospheric Signal processing has been one field of signal processing where there is a lot of scope for development of new and efficient tools for cleaning of the spectrum, detection and estimation of the desired parameters. Atmospheric signal processing deals with the processing of the signals received from the atmosphere when manually stimulated using atmospheric Radar. Removal of clutter and noise in the radar wind profiler is the utmost important consideration in radar. In this paper, we implement wavelet thresholding for removing clutter and noise from radar wind profiler data. By applying the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which identifies the coefficients relevant for clutter and noise and suppresses them and increases the accuracy of wind vector reconstruction.
Keywords: Atmospheric Signal Processing, Spectrum, Detection, Clutter, Wind Profiler.

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C1723073313

IJSCE Journal — Mon, 11 Oct 2021 03:49:31 +0000

Implementation of Data Encryption Standard using Reversible Gate Logic
Nuthan.A.C¹, Nagaraj.C², Havyas.V.B³
¹Nuthan.A.C, Assistant Professor, Department of Electronics and Communication Engineering, G. Madegowda Institute of Technology, Bharathinagara, Mandya, Karnataka, India.
²Nagaraj.C, Assistant Professor, Department of Electronics and Communication Engineering, B.G.S. Institute of Technology, B.G. Nagar, Mandya, Karnataka, India.
³Havyas.V.B, PG Student, VLSI Design and Embedded System, B.G.S. Institute of Technology, B.G. Nagar, Mandya, Karnataka, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 28, 2013. | Manuscript published on July 05, 2013. | PP: 270-272 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1723073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cryptography is quite an old art where user can encrypt and decrypt the information using the strongest algorithm which is required for the specific task. But, the scope of this unique information hiding technology has been limited only to the field of software application. Data Encryption Standard (DES) is one of widely used standard for data encryption. Implementation of DES has been done in many ways. But in this proposed implementation, DES is implemented using Reversible logic. Till today not many inventions and growth is observed in reversible logic because of the high complexity in designing them. Reversible logic solutions will be explored to achieve increased levels of power efficiency and area efficiency for digital communication based applications. The modular and flexible nature of the design enables easy incorporation of future updates.
Keywords: Reversible logic, Cryptography, Data Encryption Standard (DES).

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C1713073313

IJSCE Journal — Mon, 11 Oct 2021 03:43:05 +0000

Use of Fuzzy C-Mean and Fuzzy Min-Max Neural Network in Lung Cancer Detection
Kiran S. Darne¹, Suja S. Panicker²
¹Kiran S. Darne, Computer Engineering, Pune University/ MIT College, Pune, India.
²Suja S. Panicker, Computer Engineering, Pune University/ MIT College, Pune, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 265-269 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1713073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Lung cancer is a disease characterized by uncontrolled cell growth in tissues of the lung and is the most common fatal malignancy in both men and women. Early detection and treatment of lung cancer can greatly improve the survival rate of patient. Artificial Neural Network (ANN), Fuzzy C-Mean (FcM) and Fuzzy Min-Max Neural network (FMNN) are useful in medical diagnosis because of several advantages. Like ANN has fault tolerance, flexibility, non linearity, while FcM gives best result for overlapped data set, data point may belong to more then one cluster center and always converges .and , also, FMNN has advantages like online adaptation, non-linear separability, less training time, soft and hard decision. In this work, we propose to use FcM and FMNN on standard datasets, to detect lung cancer.
Keywords: Classification, Clustering, Fuzzy System, FCM, FMNN.

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C1705073313

IJSCE Journal — Mon, 11 Oct 2021 03:38:37 +0000

Segmentation of Touching Conjunct Consonants in Telugu using Minimum Area Bounding Boxes
J. Bharathi¹, P. Chandrasekar Reddy²
¹J. Bharathi, Department of Electronics and Communication Engineering, Deccan College of Engineering and Technology, Hyderabad, India.
²Dr. P. Chandrasekhar Reddy, Department of Electronics and Communication Engineering, JNTU College of Engineering, Hyderabad, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 260-264 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1705073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper addresses the problem of segmenting touching characters which are written or printed in the bottom zone. In the segmentation of machine printed Telugu document image, conjunct consonants are more prone to touching due to shape of the characters. It is important to segment them properly to improve the accuracy of the Telugu OCR as otherwise the reconstruction and mapping to editable electronic document is incomplete and often needs lot of tedious manual intervention. It is based on the script level characteristic that the secondary form of consonants are written in smaller size and its bounding box is smaller compared to the primary character. The structural feature of sharp peaks in both left and right side profiles at the touching location of the combined character is used for determining the correct segmentation location. The algorithm is tested on a dataset created from large set of documents. The success rate of 96.39% is achieved.
Keywords: Minimum area bounding box, segmentation, side profile peaks, touching conjunct consonants.

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C1728073313

IJSCE Journal — Mon, 11 Oct 2021 03:28:30 +0000

Performance Analysis of Agent based IPSM for Windows based Operating Systems
Deepshikha Bhargava1¹, Madhavi Sinha²
¹Deepshikha Bhargava, Amity Institute of Information Technology, Amity University Rajasthan, Jaipur, Rajasthan, India.
²Dr. Madhavi Sinha, Associate Professor, BIT-Mesra (Ranchi), Jaipur, Rajasthan, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 255-259 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1728073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: All processes prevent casual exchange of data. However, occasionally two processes might need to communicate with each other. One method that enables processes to communicate is called Inter Process Synchronization (IPS). In an Operating System on which several threads run concurrently, it is important to be able to synchronize the activities of various threads. Windows provides several synchronization objects that enable to synchronize a thread’s actions with those of another thread. These objects include critical sections, mutexes, events, and semaphores. The different solutions for Inter Process Synchronization problem are suggested [1] where some of these solutions have their own limitations or performance related issues. The agent based approach used in this paper has suggested a new algorithm for agent IPSM which is an attempt to propose an optimal solution to the problem. IPSM stands for Inter Process Synchronization Manager which is an agent used for solving the problem of inter process synchronization. In the present paper agent based Inter Process Synchronization Manager (IPSM) is described and its performance is compared with agent based IPSM on different Windows based operating systems.
Keywords: Inter Process Synchronization, Inter process Synchronization Manager, agent, IPSM.

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C1717073313

IJSCE Journal — Mon, 11 Oct 2021 03:19:39 +0000

Multicast and Multipath Based on Batch Signature Scheme on Adhoc Networks
Satish K¹, Anand R², Jitendranath Mungara³
¹Mr. Satish K, Pursuing MTech (CNE) Final Year in CMR Institute of Technology, Bangalore, Visveswaraiah Technological University, Belgaum, India
²Mr. R.Anand, Associate Professor, CMR Institute of Technology, Bangalore, India.
³Dr. M. Jitendranath, working as Professor & Dean of Research in Computer Science Engineering department in CMRIT, Bangalore, India.
Manuscript received on June 05, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 250-254 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1717073313/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Adhoc Networks are becoming a very important tool for many nonconventional civil and military applications. In this adverse environment it is very difficult for the source to send the information to the destination in multicasting style. The main requirement of the network is to provide the security and promising the message integrity. To solve the various problems in the multicasting style for very large adhoc networks a Modified Tiered Authentication Multicast scheme is developed. This scheme has two steps, the first step includes generation of keys and these keys are used to form the Message Authentication Code for every packet sent to the receiver. The second step includes the Message Digest Algorithm 5 is used for message integrity. The movement of nodes in the mobile adhoc network quickly changes the topology resulting in the increase of the overhead message in topology maintenance. There are many clustering schemes are proposed for many adhoc networks. This paper presents on the performance of the Modified TAM protocol in different clustering schemes such as Distance Based Clustering and Weighted Based Clustering. Comparing the generated results from various clustering schemes by using Modified TAM protocol and decide which clustering scheme is better for implementing the Modified TAM protocol.
Keywords: Authentication, Clustering, Mobile Adhoc Networks.

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C171207331

IJSCE Journal — Mon, 11 Oct 2021 03:11:40 +0000

Comparing and Evaluating Open Source E-learning Platforms
Fakhreldeen Abbas SaeedFakhreldeen, Information Technology, King AbdulAziz University, Jeddah ,Saudi Arabia.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 244-249 | Volume-3 Issue-3, July 2013. | Retrieval Number: C171207331/2013©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Because of the huge development in E-learning and the spread of its open and close source platforms, and the necessity to have the benefit of it in universities and graduate institutes as basic education or supportive of the traditional education, we have to know how to choose the suitable E-learning Platform from available platforms or to adopt it to suit us. In this paper we explained how to evaluate Open source E-learning Platforms and tried to integrate some of them to produce new platform with great capabilities, more flexible and efficient, Also we used metrics like security, performance, support, interoperability, flexibility, easy of using, management, communication tool, administration tools, course delivery tools and content development to evaluate this E-learning Platform. We obtain that there are differences between E-learning Platforms for each metric so we selected the best four (Moodle 1.9, Claroline 1.8.1, Mambo 4.6.1and Atutor 1.5.4) to integrate it to make a new platform with 97.72 average weights of the metrics while the best Open Source E-learning Platform is 89.4.
Keywords: Comparison, Evaluation, E-learning, Open Sources.

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