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Über dieses Buch

The book is about all aspects of computing, communication, general sciences and educational research covered at the Second International Conference on Computer & Communication Technologies held during 24-26 July 2015 at Hyderabad. It hosted by CMR Technical Campus in association with Division – V (Education & Research) CSI, India. After a rigorous review only quality papers are selected and included in this book. The entire book is divided into three volumes. Three volumes cover a variety of topics which include medical imaging, networks, data mining, intelligent computing, software design, image processing, mobile computing, digital signals and speech processing, video surveillance and processing, web mining, wireless sensor networks, circuit analysis, fuzzy systems, antenna and communication systems, biomedical signal processing and applications, cloud computing, embedded systems applications and cyber security and digital forensic. The readers of these volumes will be highly benefited from the technical contents of the topics.



Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule

Curvelet transform achieves a compact representation of edges and curved shapes in the image, which other techniques like wavelets and ridgelets are not able to represent. This property of curvelet transform facilitates the retrieval of complementary information from medical images for precise and efficient clinical diagnosis. This paper presents a combination of curvelet transform along with principal component analysis (PCA) and maximum selection rule as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using curvelet transform followed by application of PCA for dimensionality reduction and the selection of maximum matrix to select only the relevant information in the images. Fusion factor (FF) and structural similarity index (SSIM) are used for performance evaluation of the proposed approach. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics.

Himanshi, Vikrant Bhateja, Abhinav Krishn, Akanksha Sahu

Gigabit Network Intrusion Detection System Using Extended Bloom Filter in Reconfigurable Hardware

Network intrusion detection system collects information from network and identifies all the possible existing network security threats. Software based detection systems are common but are not good enough for the current network security requirements. Present day network intrusion detection needs wire-level data transfer to avoid the inefficiency in pattern matching process. Hardware based solutions like field programmable gate array which is known for its high processing capability can easily solve these issues. This paper implements a hardware based gigabit intrusion detection system using extended Bloom filter concepts. The paper presents a solution to reduce the high error rate of Bloom Filter by introducing a Reference Vector to the work and evaluates its performance. The reference vector verifies the Bloom filter output for any possible false positive results and reduces the error rate in the system.

Akshay Eldho Jose, T. Gireeshkumar

Hash-Based Rule Mining Algorithm in Data-Intensive Homogeneous Cloud Environment

Today Innovative Technology is used to analyze and manipulate huge amount of data in the cloud computing environment. It is very challenging task because the privacy and security are the main issue. Because the scenario of the cloud environment is given, then the distributed database comes in the picture as well as privacy. In this paper, we used the concept of pseudo random number, and for finding the strong Association rule in the database, we used the Inverted hashing and pruning as well as distributing the database into the different number of cloud nodes, and finding the global result, we used Distributed secure sum protocol in the homogenous cloud environments, where the number of attributes will be same, the number of transactions wearies from node to node.

Raghvendra Kumar, Prasant Kumar Pattnaik, Yogesh Sharma

Privacy Preservation in Distributed Environment Using RSA-CRT

Most data mining applications are based on information sharing and additional challenges, when we are dealing with data that are containing sensitive or private information. There is no common data mining technique available that deals with private information without any leakage. Therefore, the knowledge extracted from such data may disclose the pattern with sensitive/private information. This may put privacy on the individual/group of parties. In the past few years, privacy preserving data mining has attracted the research interest and potential for a wide area of applications. There are many techniques for privacy preservation like cryptography, anonymity, and randomization, etc., experimented for privacy preservation in data mining. In this paper, information system-based approach is considered, so some of the attributes required higher privacy compared to the other attributes. This paper explores the use of cryptography techniques, namely RSA with Chinese remainder theorem (CRT) to encrypt and decrypt the database, when all the parties are trying to find their global result in presence of trusted third party or absence of trusted third party, without disclosing their private information to each other.

Raghvendra Kumar, Prasant Kumar Pattnaik, Yogesh Sharma

Priority-Based Classification: An Automated Traffic Approach

In this paper, an advanced methodology is proposed to automate the traffic system by categorizing the incoming vehicles. Vehicles are classified as ‘Public’ and ‘Private’ transport. ‘Public’ transport is considered to carry large number of people. It is considered in this paper that avoidance of traffic congestion and man power wastage are achieved by releasing public vehicles with highest priority. Lanes are categorized as high priority lane (



), normal lane (



), and idle lane (



). Probability of waiting in the queue for an incoming vehicle is measured based on the Erlang distribution method. Avoidance of traffic congestion and manpower wastage due to indefinite waiting time in traffic is handled efficiently by our proposed approach. It is presumed that efficiency and productivity of human resource are increased by providing efficient and smooth congestion-free transport system. Minimum production time is expected from the human resources; hence usage time of electronic resource is minimized. Power and energy consumption are minimized as an indirect effect of efficient traffic system.

Somasree Bhadra, Anirban Kundu

A Taxonomy of Software’s Non-functional Requirements

Software requirements are divided into two parts, FRs and NFRs. FRs determine the functionality, while, NFRs determine how a system is supposed to be. In the literature, we have identified that most of the work is related to FRs. NFRs have received less attention by goal-oriented requirements engineering community. The aim of this paper is to present taxonomy of non-functional requirements so that the requirements analyst can easily identify different types of NFRs according to their needs in the early phase of requirements engineering.

Nida Afreen, Asma Khatoon, Mohd. Sadiq

Segmentation of the Human Corpus Callosum Variability from T1 Weighted MRI of Brain

Corpus Callosum is an important part of the brain which works as major neural pathway that connects homologous cortical areas of the two cerebral hemispheres. The size of Corpus Callosum is affected by age, sex, neurodegenerative diseases and various lateralized behaviour in people. Here T1 weighted Magnetic Resonance Imaging (MRI) of brain, usually the sagittal sections is taken which is then followed by the automated segmentation of the MRI slide. This segmentation has an important application in neurology as the shape as the thickness, size and orientation of Corpus Callosum depends on the various characteristics of the person. Lobar connectivity based percolations of the corpus callosum can be computed by our proposed method which is very accurate segmentation.

Shayak Sadhu, Sudipta Roy, Siddharth Sadhukhan, S K Bandyopadhyay

Analysis of Organic Molecular Single-Electron Transistor Using C4H6B2 with Different Metal Electrodes

An organic molecule-based single-electron transistor (SET) is analysed by ab initio method using Density Functional Theory (DFT). Initially, benzene molecule is taken; two carbon atoms from benzene are replaced by boron atoms, and the structure of the molecule is optimized. The optimized structure C






is kept above the gate dielectric in the island for weak coupling. The charge energies of device are calculated in both isolated and SET environment. We have done analysis by using different electrodes with gold (work function = 5.28 eV), osmium (work function = 5.93 eV) and caesium (work function = 2.14 eV) in SET environment. By charge stability diagrams, the conductance dependence of SET on gate voltage and bias potential are verified.

E. Meher Abhinav, M. Chandra Mohan, A. Suresh Reddy, Vemana Chary, Maragani Thirupathi

Analysis of Molecular Single-Electron Transistors Using Silicene, Graphene and Germanene

By using Ab initio approach, we have analysed Silicene-, Germanene- and Graphene-based molecular single-electron transistors. It is based on non-equilibrium greens function (NGEF) and density functional theory (DFT). Three different fullerene molecules are taken and optimization is done. In Coulomb blockade regime, silicene, germanene and graphene are kept above gate dielectric between drain and source for weak coupling. We have taken gold electrodes for SET environment. Gold is widely used as metal electrode in nanoscale devices. We have calculated the HOMO and LUMO values and total energy versus gate voltage. Charge stability diagrams are obtained by calculating charging energy as function of external gate potential. By these calculations, the analysis of three different molecular single-electron transistors is done. The total energies of these molecules are highly negative (very low) compared to other molecules.

E. Meher Abhinav, Sai Naveen Kavuri, Thota Sandeep Kumar, Maragani Thirupathi, M. Chandra Mohan, A. Suresh Reddy

Identification of the Plants Based on Leaf Shape Descriptors

Plants are living organisms belonging to the vegetal kingdom that can live on land and in water. Plants form the critical base of food chains in nearly all ecosystems. Plants are vitally important for environmental protection and contribute to maintain biodiversity. Plant taxonomy has attracted many researchers to study the bio-diversities based on plants. Automated identification of plant species using leaf shape descriptor addresses the automatic classification of plants and simplifies taxonomic classification process. In this research work, we used Zernike moments (ZM) and Histogram of Oriented Gradient (HOG) method as a shape descriptor resulting 84.66 and 92.67 % accuracy for ZM and HOG, respectively, on ‘


’ database.

Pradip Salve, Milind Sardesai, Ramesh Manza, Pravin Yannawar

Use of Global Positioning System to Track Movement of Mobile Node in Proxy Mobile Internet Protocol Version 6

In Proxy Mobile Internet Protocol version 6 (PMIPv6), while moving in networks, whenever mobile node (MN) gets connected to new mobile access gateway (MAG), every time MAG needs to verify authenticity of MN due to which, there is a significant increase in hand over delay. To reduce this, researchers have proposed that as soon as MN gets disconnected from current MAG, current MAG should send the authentication information of MN to new MAG to which MN next gets connected. To predict new MAG, movement of MN should be tracked. In this paper, a method is proposed to track the movement of MN using GPS.

Nitesh M. Tarbani, A. S. Alvi

Is Mobile Cloud Computing Efficient for E-Learning?

In the modern era, cloud computing is an emergent research area, spanning over multiple disciplines. It is a new service-centric technology that reduces the cost of resources by leveraging the efficient pooling of on-demand, self-management computer-generated infrastructure, consumed as a service through the Internet. Mobile network integrated with cloud computing to create new technology that is mobile cloud computing (MCC) to bring benefits for mobile users. Cloud computing popularity is rising by leaps and bounds in all domains like IT, business, electronic media, communication, academia, scientific research and education, etc. In present era; cloud computing has changed the traditional education systems to modern education systems like e-learning. E-Learning is the virtualized distance learning system; which is an electronic communication mechanism over the internet. In this paper, we will explore “is mobile cloud computing useful/efficient for E-Learning,” the most recent advancement of mobile cloud computing. We join the several new technologies to reach this goal. We are going to summarize present features and characteristics of E-learning and also look at the idea of E-learning through mobile cloud computing (MCC). We will also discuss the mobile cloud computing architecture (MCCA) and explain the design of mobile cloud computing stage by merging the feature of E-Learning.

Aftab Alam, Abdul Mateen Ansari, Mohammad Mujahid Barga, Ahmad Albhaishi

A Comparative Analysis of Different Social Network Parameters Derived from Facebook Profiles

In social network analysis (SNA), using online social media, it is possible to collect large open source information and to analyze those data for knowing the characteristics of these networks. The main objective of this work is to study online social network parameters commonly used to explain social structures. In this paper, we have extracted data from the three real-time facebook accounts using Netvizz application. Gephi, a open source free software, is used for analysis and evaluation of these network parameters. This analysis shows some well-known network parameters like calculating clustering coefficient (CC) of clusters, group formation, finding node degree distribution (NDD), identifying influential node etc., which can be used for further feature extraction.

Paramita Dey, Sarbani Roy

A Fast and Hardware-Efficient Visual Cryptography Scheme for Images

Shamir’s encryption method is to share a secret image by n number of shadow images and then r shadow images can be used to obtain the original secret image. In proposed method, the size of secret image is greater than the size of the shadow image used for the encryption. Such shadow image is beneficial to process in image hiding, transmission, or storage. For this purpose Shamir’s encryption technique is used in this proposed work. In Shamir’s encryption technique, author used the equation to encrypt the data into multiple parts. In the equation, time required for the multiplication and division of the input components of the data is more than addition and subtraction. Hence, the encryption time is little bit high. To reduce this encryption and decryption time, Shamir’s equation is modified by converting all the multiplication part into addition and division parts into subtraction in this proposed work.

Dipesh Vaya, Sarika Khandelwal

Energy-Efficient Modified Bellman Ford Algorithm for Grid and Random Network Topologies

Energy-efficient routing techniques are required for mobile ad hoc Networks (MANETs) to improve the lifetime of the network. The lifetime of the network depends on the battery capacity of nodes. The link failure due to the battery discharge of node can be avoided by considering the nodes having good residual energy (RE) with less change in their battery capacity. In this paper, the Bellman–Ford algorithm (BFA) is considered to find the shortest path for routing. Bellman–Ford algorithm is modified and the nodes whose change in battery capacity is less than a predefined threshold value are considered for routing to avoid the link failures and to enhance the lifetime of the network. In the proposed modified Bellman–Ford algorithm (MBFA), residual energy (RE) is considered as a metric to find the shortest path. IEEE 802.11 a/g standards using orthogonal frequency division multiplexing (OFDM) are considered for simulation. Energy consumed by the radio transceiver, processor, losses in the battery, and DC–DC converter are taken into consideration for energy calculation. The performance of BFA and MBFA for the grid and random network topologies is simulated by considering the network with multiple sources and destinations are compared with and without mobility by assuming various densities, i.e., 15, 30, 45, and 60. The mobility of the node increases the loss of orthogonality among the OFDM subcarriers and results inter carrier interference (ICI). The effect of mobility and network size on throughput, delay, jitters for the grid, and random network topologies using BFA and MBFA are compared. Simulation results show that the performance of proposed MBFA is better compared to BFA.

Rama Devi Boddu, K. Kishan Rao, M. Asha Rani

Text-Dependent Versus Text-Independent Speech Emotion Recognition

The communication between individual and equipment is through speech emotion recognition which plays a vital role and is very exigent to handle. Today, this filed has become an important area of research. It has wide range of applications. This paper analyzes the performance of emotion recognition for eight speakers. Indian Institute of Technology Kharagpur Simulated Hindi Emotional Speech Corpus (IITKGP-SEHSC) emotional speech corpora used for emotions recognition. The sentiments under surveillance for this study are anger, fear, happy, neutral, sarcastic, and surprise. The categorization is prepared using Gaussian mixture model (GMM). Mel-frequency cepstral coefficients (MFCCs) attributes have been used for defining the emotions. We have extracted the percentage of accuracy of emotion for both text-dependent data and text-independent data. We also observed that emotion recognition performance depends on text and speaker. We found that the percentage of accuracy of text-dependent data is more than the text-independent data.

Biswajit Nayak, Manoj Kumar Pradhan

A Novel Approach for Speaker Recognition by Using Wavelet Analysis and Support Vector Machines

Speaker recognition approach through wavelet analysis as well as support vector machines is presented in this paper. The wavelet-based approach is used to differentiate among regular and irregular voices. The wavelet filter banks were utilized to coincide by means of support vector machine for extraction of the feature and its classification. This approach creates utilization of wavelets as well as support vector machine to separate particular speech signal through multi-dialog settings. In this approach, first we apply the wavelets to calculate audio features those have sub-band power and calculated pitch values from the given data of the speech. Multi-speaker separation of speech data is carried out by the utilization of SVM more than these audio features as well as other values of the signal. This entire database was utilized to calculate the performance of the system and it represents over 95 % accuracy.

Kanaka Durga Returi, Vaka Murali Mohan, Y. Radhika

Real-Time Surveillance for Critical Activity Detection in ICUs

In today’s scenario, motion detection has gathered attention of researchers due to its promising applications in numerous areas. Such as video surveillance, patient monitoring, traffic management, security, video games, military armors, object classification, and sign language recognition. The review of this intelligent application demands gradation of technologies. The findings say that it is still in its early developmental stage and there is need to improve its robustness when applied to a complex and changing environment. Therefore, it is effective to improve the surveillance techniques. Upgradation can be obtained successfully using 3D camera, but it is expensive. The methodology mentioned in this paper follows the human eyes visualization concept by using pair of identical two-dimensional cameras to generate stereoscopic video. Growing number of cameras enables new signal processing applications. The amount of the data also increases which is to be processed to be supportive to design new modified algorithm to obtain accuracy. This can also track the movement using optimized Kalman filter. This real-time method is useful in monitoring and detecting every inch and second of information of interested areas. The technique mentioned in this paper is beneficial for online and offline applications. Project implemented using this paper trim down memory requirements for activity storage. It is efficient, sensitive, and absolutely useful for society welfare. The proposed method in this paper will activate a warning system, highlight the changes, and capture the live streaming video when minute movement of coma patient is detected, also it keeps track on mental stress of patients.

Dhakate Pankaj

High Speed Network Intrusion Detection System Using FPGA

Modern Network Intrusion detection needs a high-speed interface to analyze the incoming packet. Several network intrusion detection applications detect multiple strings in the payload of a packet by comparing it against predefined pattern set which requires more memory and computation power. To meet this, a dedicated hardware with high processing capacity can be placed at the port of incoming packets. Field Programmable Gate Array (FPGA) is the choice as it can be programmed easily and dynamically for parallel computing. Moreover, FPGA devices support at high-speed interface and are capable of providing better processing capability than other device; also it can be reprogrammed when it is needed. This paper proposes a new alternative approach to leaf attaching algorithm to improve the memory efficiency of algorithm.

S. Anuraj, P. Premalatha, T. Gireeshkumar

Hawk Eye: A Plagiarism Detection System

College students today use their smartphones or electronic media smartly to capture software code that are part of their curriculum and then circulate same code among the entire batch, leading to plagiarism. To avoid this kind of plagiarism an innovative plagiarism detection mobile system is needed. The proposed system will eventually help students to develop their coding skills and avoid cloning. The system uses Multi-Language OCR-Compiler Engine to convert the clicked snapshot into relevant text file and triggers appropriate compiler to compile the code. Then the system uses plagiarism detection algorithms that use the concept of tokenizing source code. Tokenizing makes difficult to replace the system by unnecessary comments, punctuations, or changing variables–methods names. Mobile applications like Viper, Plagiarisma.Net, Plagiarism CheckerX can be deployed to check the cloned code. Future extension includes applying modified concepts related to (cloned) flowcharts and also have desktop compatible version of this mobile system.

Karuna Puri, Preeti Mulay

A Novel Action Descriptor to Recognize Actions from Surveillance Videos

Due to the increased application in the area of human action detection, automatic capture and action analysis becomes a great research area. This paper provides a novel method to detect human action by considering features from the positive space and negative space region. Usually, in the literatures features have been considered from the positive space of the subject. Positive space features alone cannot provide solutions to the challenges such as occlusion and boundary variations. Therefore, in this method we have also considered the surrounding regions of the subject along with the positive space features. Initially, the input video has been segmented using background subtraction. Then the features are extracted from both positive and negative space of the subjects. Later, action descriptor has been defined for each pose of an action and proposed a new way for detecting number of cycles required to describe an action. Later, nearest neighbor classifier has been applied to classify the actions. The proposed system is evaluated using Weizmann dataset, KTH dataset and the results seem to be promising.

T. Pradeepa, S. Abirami, M. Sivarathinabala, S. Murugappan

Protecting the Augmented Browser Extension from Mutation Cross-Site Scripting

Browser extensions have a great role in bringing changes to the web browser behavior and improving browser performance. However, nowadays, many browser extensions fail to meet the security requirements. Due to this, they become a medium for various attackers to steal the credential information of the users. The proposal in this paper makes a pitch for the protection of an augmented browser extension against mutation-based cross-site scripting attack. A method is introduced for hardening the browser extension script along with dead code injection. A mutation-based cross-site scripting identifier to identify the attacks that affect the extensions is also discussed in this paper. These methods will protect browser extensions from various malicious script injection attacks.

S. Remya, K. Praveen

Large-Scale Data Management System Using Data De-duplication System

Data de-duplication is the process of finding duplicates and eliminating it from the storage environment. There are various levels where the data de-duplication can be performed, such as file level, where the entire file as a whole is considered for the purpose of duplicate detection. Second is chunk level, where the file is split into small units called chunks and those chunks are used for the duplicate detection. Third is byte level, where the comparisons take byte-level comparison. The fingerprint of the chunks is the main parameter for the duplicate detection. These fingerprints are placed inside the chunk index. As the chunk index size increases, the chunk index needs to be placed in the disk. Searching for the fingerprint in the chunk index placed in the disk will consume a lot of time which will lead to a problem known as chunk lookup disk bottleneck problem. This paper eliminates that problem to some extent by placing a bloom filter in the cache as a probabilistic summary of all the fingerprints in the chunk index placed in the disk. This paper uses the backup data sets obtained from the university labs. The performance is measured with respect to the data de-duplication ratio.

S. Abirami, Rashmi Vikraman, S. Murugappan

Movement Detection Using LabVIEW by Analysis of Real-Time Video

Movement detection is the key to solving many simple and complex problems in the real world. In its simplistic form, movement detection involves capturing the subject in question and comparing it with previous knowledge of shape, size, and displacement of subject from the captured snapshot/reference. This paper explores different noise filtering techniques

Averaging/Mean filter, Median filter, Gaussian smoothing

and object detection methods such as

Background subtraction, Optical flow method, Temporal differencing, Sum of Absolute difference

along with its advantages and disadvantages. The paper describes the

Gaussian smoothing


absolute difference

method that was used to detect movement in real-time video using LabVIEW. The approach involved processing set of consecutive video frames, extracting absolute difference of each other to detect foreground and background objects and its relative displacement from previous position. Subsequent to movement detection, the method also aims to highlight the region of object movement along with a Boolean indicator to visually inform the end user about movement detection.

D. V. Sumalatha, A. V. Ravi Kumar

Connectivity Model for Molecular Communication-Based Nanomachines Network in Normal and Sub-diffusive Regimes

Nanomachines network is an interconnection of nanomachines (NMs) capable of communicating with each other. NMs networks are expected to provide an intelligent alternative to contemporary wireless sensor networks due to their biocompatibility, pervasiveness, and energy efficiency. However, connectivity issues of NMs networks are yet to be explored fully. This paper presents a probabilistic connectivity model for molecular communication-based NMs network which involves transmission of a message via diffusion of messenger molecules. This model has been developed through signal to interference and noise ratio (SINR) analysis considering effects of co-channel interference (CCI) and intersymbol interference (ISI). It is found that ISI is the dominating factor in degrading the network connectivity than CCI. Also, results have shown that selection of symbol time is crucial and should depend on internode distance (or transmission range), for higher network connectivity. Physical obstructions in transmission media lead to anomalous diffusive behavior. This paper has investigated effects of sub-diffusion on the connectivity of NMs network to reveal that presence of physical obstructions can be a favorable condition in MC-based NMs networks if symbol time is adjusted accordingly.

Prachi Raut, Nisha Sarwade

Application of Locally Weighted Regression for Predicting Faults Using Software Entropy Metrics

There are numerous approaches for predicting faults in the software engineering research field. Software entropy metrics introduced by Hassan (Predicting faults using the complexity of code changes, 78–88, 2009) [


] are also popularly used for fault prediction. In previous studies, statistical linear regression (SLR) and support vector regression (SVR) for predicting faults using software entropy metrics have been validated. However, other machine learning approaches have not yet been explored. This study explores the applicability of locally weighted regression (LWR) approach for predicting faults using the software entropy metrics and compares it with SVR. It is noticed that the LWR performs better than SVR in most of the cases.

Arvinder Kaur, Kamaldeep Kaur, Deepti Chopra

Effect of Defects on Current-Voltage Characteristics of a Silicene ZNR-Based Field Effect Transistor

In this paper, we investigated the behavior of negative differential resistance (NDR) and analysis on various deformations like twist, wrap, and ripple/buckler and defects like vacancy and rough edge on short channel bilayer silicene zigzag nanoribbon (ZNR). Effects are caused by deformations like wrap with 5


and by rippling the channel by 0.5 Å amplitude on 6 nm silicene. FET is evaluated by density functional theory (DFT) and by nonequilibrium green’s function (NEGF) approach. We studied the I–V characteristics of deformations and defects. These characteristics of device with different conditions and mainly negative differential resistance (NDR) behavior are studied.

E. Meher Abhinav, M. Chandra Mohan, A. Suresh Reddy, Vemana Chary, Maragani Thirupathi

Performance Improvement of Read Operations in Distributed File System Through Anticipated Parallel Processing

A distributed system (DS) consists of a set of computers networked together and gives an impression to the users that it is a single system. The main storage component to be used in a DS is distributed file system (DFS). The DFS is used as the main file storage system in the cloud computing systems. Most of the cloud-based applications require a good performance from the DFS as for as read operations are concerned. Many caching and pre-fetching techniques have been proposed in the literature for improving the performance of the DFS. But all of these techniques use synchronous approach which increases read access time. In the literature, a speculation-based technique has been proposed for improving the performance of read access in the DFS which implements asynchronous reading using client-side caching technique. In this paper, we have proposed a new read algorithm for the DFS based on asynchronous reading, client-side caching technique, and anticipated parallel processing. We have carried out performance analysis of the speculation-based algorithm and our proposed algorithm based on anticipated parallel processing. The results of analysis indicate that our proposed algorithm performs better than the earlier speculation-based algorithm.

B. Rangaswamy, N. Geethanjali, T. Ragunathan, B. Sudheer Kumar

Steerable Texture Descriptor for an Effective Content-Based Medical Image Retrieval System Using PCA

Digital images have increased in quantity especially in the medical field used for diagnostics. Content-Based Medical Image Retrieval System will retrieve similar medical images from large database based on their visual features like texture, color, and shape. This paper focuses a novel method to increase the performance using Boundary detection, Steerable filter, and Principal Component Analysis. The content of the image was extracted with the help of region-based texture descriptor using steerable decomposition followed by extracting Principle Component Analysis which has better feature representation capabilities. The similar medical images are retrieved by comparing the extracted feature vector of the given query image with the corresponding database feature vectors using Euclidian distance as a similarity measure. The effectiveness of the proposed method is evaluated and exhibited via various types of medical images. With the experimental results, it is obvious that the region-based feature extraction method outperforms the direct feature extraction-based image retrieval system.

B. Jyothi, Y. MadhaveeLatha, P. G. Krishna Mohan, V. S. K. Reddy

A Novel Multi-view Similarity for Clustering Spatio-Temporal Data

With the enhanced usage of sensors and GPS devices, obtaining spatial and spatio-temporal data has become easy and analyses of these data in real-time applications are increasing day to day. Clustering is a data mining technique used for analyzing and obtaining unknown/hidden knowledge from the data/objects. Distance-based methods are helpful for analyzing and grouping the objects. In general, based on the type of data, Euclidean or Cosine distance-based techniques are used for grouping the data. Traditional techniques are point-based techniques and are based on single-view point, which may not produce efficient information and cannot be utilized for analyzing spatio-temporal objects. Hence, this paper presents a novel multi-view similarity technique for clustering spatio-temporal objects. Authors demonstrated the effectiveness of the proposed technique by adopting DBSCAN and implementing JDK1.2 on benchmarked datasets with respect to FMI indicator.

Vijaya Bhaskar Velpula, M. H. M. Krishna Prasad

WRSP-Miner Algorithm for Mining Weighted Sequential Patterns from Spatio-temporal Databases

Not allowing priorities in the mining process does not support user-directed or focus-driven mining. The work proposed in this paper provides support to include user prioritizations in the form of weights into the mining process. An algorithm




is proposed for the purpose of mining

Weighted Regional Sequential Patterns

(WRSPs) from spatio-temporal event databases.


uses two interestingness measures

sequence weight


significance index

for efficient mining of WRSPs. Experimentation has been performed on synthetic datasets and results proved that the proposed WRSP-Miner algorithm has achieved the purpose of its design.

Gurram Sunitha, A. Rama Mohan Reddy

Performance Analysis in Free Space Optical Communication System Using Aperture Averaging

The advancement of the terrestrial free space optical communication system (FSO) has shown a drastic increase. But despite the various advantages provided by FSO communication, there are various issues related to the design of optical links. To overcome the problems like scattering, diffraction, turbulence, etc., different techniques were developed, and one of the most widely employed techniques is aperture averaging. The aperture averaging is the main focus of this paper and using on–off (OOK) modulation technique, the bit error rate (BER) performance curves have been analysed for varying data rates and aperture sizes at the receiver using optisystem software.

Saloni Bhatia, Monika Singh, Hemani Kaushal

Performance Evaluation of Free Space Optical Link Under Various Weather Conditions

Free space optics (FSO) is emerging as a viable complementary technology to address the need for larger bandwidth and high data rate at affordable cost. FSO communication systems face severe link availability and reliability challenges under different weather conditions, and this is a limitation for the wide-scale acceptability of the FSO technology. The main objective of this paper is to analyze the impact of fog, snow, and rain on FSO link, and hence evaluate the performance of the FSO system for various weather conditions. It is analyzed that bit error rate (BER) and link margin of FSO system are very poor for heavy fog, rain, and snow. However, decreasing the data rate for a particular weather condition can improve both these performance parameters.

Monika Singh, Saloni Bhatia, Hemani Kaushal

Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis

Improvement in the quality of cluster centers and minimization of intra-cluster distance are two most challenging areas of K-means clustering algorithm. Due to predetermined number of clusters, it is difficult to predict the exact value of


. Furthermore, in case of non-globular clusters, K-means fails to get optimal cluster center in a data set. In this paper, a hybrid improved particle swarm optimization-based evolutionary K-means clustering method has been proposed to obtain the optimal cluster center. The hybridization of improved PSO and genetic algorithm (GA) along with K-means algorithm improves the convergence speed as well as helps to find the global optimal solution. In the first stage, IPSO has been used to get a global solution in order to get optimal cluster centers. Then, the crossover steps of GA are used to improve the quality of particles and mutation is used for diversification of solution space in order to avoid premature convergence. The performance analysis of the proposed method is compared with other existing clustering techniques like K-means, GA-K-means, and PSO-K-means.

Janmenjoy Nayak, D. P. Kanungo, Bighnaraj Naik, H. S. Behera

Analysis of Energy Efficient, LEACH-Based Cooperative Wireless Sensor Network

Energy efficient transmission is the foundation for energy efficient communication. Similarly, for (Wireless Sensor Network) WSN energy efficiency is of prime importance because of its constrained size that results in constrained battery. The transmission medium being wireless, fading plays a major role on the performance of WSN. It detoriates the overall performance which leads to indirect consumption of battery of a sensor node. Cooperative communication combats fading and saves energy of a network. In this paper application of cooperative communication has been implemented and analyzed. Experimental results show that when cooperation is there the total energy consumed by the WSN is much less than the energy consumed by the WSN without cooperation. Cooperation makes WSN more energy efficient. It is a known fact that wireless transmission is energy consuming operation. LEACH is considered as most popular routing protocol which has better performance in saving the energy consumption. It is implemented in cooperative WSN and experimental results show that LEACH makes cooperative WSN more energy efficient.

V. Murali, Sindhu Hak Gupta

A Monotonic Digitally Controlled Delay Element-Based Programmable Trigger Pulse Generator

This paper presents a resourceful utilization of a monotonic digitally controlled delay element (DCDE) to propose a programmable high frequency trigger pulse generator circuit (TPG). Performance evaluation of various analog and digital programmable delay elements (DEs) have been carried out to reach the conclusions presented. Further, this work exploits a monotonic DCDE along with an efficient XOR circuitry, to realize the proposed TPG. The proposed design generates a very high frequency ultra-thin pulses of pulse duration ranging from 56 to 170 ps for digital input vector ranging from ‘00000’ to ‘11111’, respectively. The proposed design has been extensively verified using SPICE @ 16-nm predictive technology model.

Amit Krishna Dwivedi, Manisha Guduri, Rishab Mehra, Aminul Islam

Layered-Encoding Video Multicasting in Mobile WiMAX Networks

The accessibility of good smartphones in 4G is encouraging mobile users to use video services. This huge increase in the usage of video communication over wireless networks poses many challenges according to fluctuations of wireless channel conditions along with user mobility. The challenge in providing video multicasting over wireless networks would most likely be the channel condition of each user in the same multicast group which is probably not identical due to location of the user and/or congestion of the network. In this paper, an adapted multicasting scheme has been proposed, which gracefully adapts the video quality to heterogeneous receivers and varying network conditions. Results obtained from simulation indicate that an enhancement of video quality under numerous channel conditions was through graceful degradation of these channels in terms of average throughput and packet loss ratio.

Jamil M. Hamodi, Ravindra C. Thool

Intuitionistic Fuzzy Similarity and Information Measures with Physical Education Teaching Quality Assessment

Information and similarity measures have a vital place in the fuzzy set theory. It has been investigated by many researchers with different aspects. In this paper, new intuitionistic fuzzy similarity and information measures based on sine function are proposed. Comparison of proposed information measures with the existing ones is listed. Numerical results clearly indicate the efficiencies of these measures over others. New technique for multi-criteria decision-making (MCDM) quandaries to rank the alternatives is introduced. This technique is developed on the application intuitionistic fuzzy information measure and weighted averaging operator (IFWAO). A case of five colleges ranking of a district region is studied and discussed.

Arunodaya Raj Mishra, Divya Jain, D. S. Hooda

Molecular Level Insight into the Interactions of SoxC and SoxD from Epsilonproteobacteria Sulfurimonas denitrificans: A Biomolecular Computational Approach

In deep seabeds, sulfide oxidation is essential for metabolism by microbes all through the seafloor. For the purpose, the organism

Sulfurimonas denitrificans

utilizes the gene cluster-






idizing). It comprises two units:




. SoxCD complex formation is paramount for entire oxidation of sulfide and thiosulfate to sulfate. Herein for a computational molecular-level analysis, 3D models of SoxC and SoxD proteins were constructed by discrete molecular modeling techniques. Protein–protein docking generated SoxCD complex. Few stability calculating parameters revealed the simulated final protein complex as a highly stable one. Solvent accessibility value of the MD-simulated complex also disclosed it as the most interactive one. Identification of responsible amino acids for protein–protein interactions investigated that Asn145 from SoxD and His8 from SoxC played pivotal roles for the interactions to turn out stronger. Current study thereby provides a proposal for molecular mechanism and biophysical analysis of sulfur oxidation process to render a safe biota.

Sujay Ray, Arundhati Banerjee, Angshuman Bagchi

“Jugaad”—The Creativeness for Selection of Software Development Methodology Advisory System—Fuzzy Expert System

Globalization and technical revolution are raising several challenges to the software development sector. Over the past 50 years, software has evolved as a specialized problem-solving and information analysis tool to an industry, but now it is facing multiple challenges. The objective of this study is to landscape current knowledge, in terms of productivity and find out its impact on the software development. To resolve such problems, “Software Development Practitioner” needs to find out a flexible way (Jugaad) for development. “


” is the Indian common man’s philosophy to achieve the dream goal within the available resources. The researcher has made earnest attempts to study the steps to be undertaken to make the flexible module. The goal of this paper is to rectify the present hurdles and hassles in development approaches by representing “Selection of Software Development Methodologies Advisory System” on the basis of reference of fuzzy expert system.

Kardile Vilas Vasantrao, Chitra G. Desai

A Framework for Data Clustering of Large Datasets in a Distributed Environment

The chief motivation is to develop a framework for handling clustering of large datasets in a distributed manner. The proposal presented in this work addresses both numerical and categorical data with effective noisy information handling approach. Two basic models are developed known as primary and connected model to design the distributed approach. After forming clusters separately based on numerical and categorical features, an evolutionary approach is suggested to merge the clusters for optimization. A modification of multiple kernel-based FCM algorithm (MKFCM) Chen et al. (A multiple kernel fuzzy c-means algorithm for image segmentation 41:1263–1274, 2011) is used to implement the proposal. A comprehensive view of the designed method and algorithm is presented in this paper. Comparison of the results on few sample datasets shows the effectiveness of the proposed approach over existing one.

Ch. Swetha Swapna, V. Vijaya Kumar, J. V. R. Murthy

Segmentation of Ancient and Historical Gilgit Manuscripts

The Gilgit manuscripts belong to fifth century A.D. and are oeuvre of texts which deal with Buddhist work. It is one of the oldest manuscripts in the world and is considered to be a milestone in the history of Buddhist works in India. It is a collection of both official and unofficial Buddhist works which are believed to have helped in the evolution of many literatures including Chinese, Japanese, and Sanskrit. Since this manuscript is almost seventeen centuries old it has not been able to fully decipher the text yet. It has been laminated by the National Archives of India which proves it is one of the most important literatures concerning India. In this paper, we perform character-based image segmentation on Gilgit manuscript in order to simplify and to better identify character in the image of the manuscript. The employed method gives an accuracy of nearly 87 %.

Pinjari Hameed, Rosemary Koikara, Chethan Sharma

Semantic-Based Approach for Automatic Annotation and Classification of Medical Services in Healthcare Ecosystem

A vast amount of related healthcare information exists over the web without any explicit semantic association. Healthcare ecosystem makes use of medical services for the services entities of publishing and classification. However, before the emergence of healthcare ecosystems, where ecosystems are generally present in the environment, medical service and healthcare information are diverse. Therefore, the first medical service is a key issue to deal with information systems in the healthcare environment. In this paper, we propose health-related clinical data annotation, classification, and interpretation of medical data in relation to the level of classification based on the existence of the frame, and for improving the customer’s request to present a semantic-based Web mining. In addition, we classify medical data in relation to the level of clustering based on the use of healthcare information. Information relevant to the development of semantic information extraction can be achieved using a better phrase. Highly relevant improved information requested can be retrieved by deployment of additional medical terms. Our experimental evaluation results and the feasibility of assessing the impact of the proposed mining method show improvisation.

Vijayalakshmi Kakulapati, Rishi Sayal, Ravi Aavula, Sunitha Devi Bigul

Real-Time Graphs for Communication Networks: A Fuzzy Mathematical Model

For a given alive network, in many situations, its complete topology may not always be available because of the reason that few of its links may be temporarily disabled. Thus, at any real-time instant, only a subgraph, rather than the complete graph may be available to the system for executing its activities. Besides that, in most of the cases, the cost parameters corresponding to its links are not crisp numbers, rather fuzzy numbers. Under such circumstances, none of the existing algorithms on the shortest path problems or fuzzy shortest path problem can work. In this paper, the authors propose a mathematical model for such types of graphs to be called by real time graphs (RT-graphs) in which all real-time information (updated every q quantum of time) are incorporated so that the network can serve very efficiently with optimal results. Although the style of Dijkstra’s Algorithm is followed, the approach is a completely new in the sense that the SPP is solved with the real-time information of the network.

Siddhartha Sankar Biswas, Bashir Alam, M. N. Doja

PWLCM-Based Random Search for Strong Substitution-Box Design

Substitution-boxes are the only source of nonlinearity in various symmetric encryption systems and responsible for inducing confusion of plaintext data. The robustness of these systems exclusively depends on the potentiality of S-boxes. The design methods having fast and simple computations which can yield effective S-boxes are preferred. In this paper, a new chaos-based random search is applied to construct cryptographically potent 8 × 8 S-box. The method explores the features of piecewise linear chaotic map for candidate generation and random search. The optimized S-box obtained is tested against standard statistical tests like bijectivity, equiprobable I/O XOR distribution, nonlinearity, and strict avalanche criteria revealing its superior performance. The proffered substitution-box is further compared with some contemporary chaotic substitution-boxes. The results confirm a consistent design, suitable for building strong block encryption systems.

Musheer Ahmad, Danish Raza Rizvi, Zishan Ahmad

Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering

The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers. To alleviate this issue, a new approach of using an evolutionary technique known as Teaching–Learning-Based Optimization (TLBO) is used hybridized with fuzzy approach. The proposed approach is able to deal with the sensitiveness of cluster centers. Results presented are very encouraging.

P. Gopala Krishna, D. Lalitha Bhaskari

A Mean-Based Thresholding Approach for Broken Character Segmentation from Printed Gujarati Documents

The major problem faced by a Gujarati optical character recognition (OCR) can be attributed to the presence of broken character in machine printed Gujarati document image. This character could cause the error in character segmentation process. Broken characters are generated due to noise scanning, older documents with low-quality printing, and thresholding error. It is necessary to identify and segment it properly. So this paper presents mean-based thresholding technique for broken character segmentation from printed Gujarati documents. Line segmentation is used to extract lines from Gujarati document image. Individual characters are extracted using vertical projection profile method. Then, broken characters are identified using mean-based thresholding (MBT) algorithm. Heuristic information is used to merge the identified broken characters. The main purpose of this paper is to merge vertical and naturally broken Gujarati characters as a single glyph from the document image. Experimental results are carried out using various types of Gujarati documents (A, B, C, and D). 79.93 % accuracy is achieved from experimental results.

Riddhi J. Shah, Tushar V. Ratanpara

Entity Resolution-Based Jaccard Similarity Coefficient for Heterogeneous Distributed Databases

Entity Resolution (ER) is a task for identifying same real world entity. It refers to data object matching or deduplication. It has been a leading research in the field of structure database. Due to its significance, entity resolution continues to be a most important challenge for heterogeneous distributed databases. Several methods have been proposed for the Entity resolution, but they have yielded unsatisfactory results. In this paper, we propose an efficient integrated solution to the entity resolution problem based on Jaccard similarity coefficient. Here we use Markov logic and Jaccard similarity coefficient for providing an efficient solution towards ER problem in heterogeneous distributed databases. The approach that we have implemented gives an overall success rate of about 98 %, thus proving better than the previously implemented algorithms.

Ramesh Dharavath, Abhishek Kumar Singh

Energy Efficient Algorithms for Hot Spot Problem in Wireless Sensor Networks

The major bottleneck in the operation of a wireless sensor network (WSNs) is the limited power sources of the sensor nodes. Therefore, effective use of sensors’ energy is the most challenging issue for improving network lifetime. In general, the sensor nodes near to the sink have huge traffic load as they relay data from other nodes to reach the sink. Thus, their energy is exhausted quickly and it results in network segmentation. This problem is generally known as hot spot problem. In this paper, we deal with the hot spot problem and present energy efficient clustering and routing algorithms. The simulated results demonstrate that the proposed algorithms perform better compared to the existing algorithms in terms of various performance metrics.

Srikanth Jannu, Prasanta K. Jana

Mining Community-Based Top-k Experts and Learners in Online Question Answering Systems

Online Question Answering Systems are very popular and helpful for programming community. In these systems, users can post questions, answer the questions, collaboratively tag the questions, and vote for quality answers. This paper implements a link structure-based Top-


Experts and Learners finding algorithm using Stanford Network Analysis Project (SNAP) Library. Experiments are done on real data taken from Stack Overflow that mainly focuses on computer programming and the results show that link analysis techniques are more suitable for analyzing online question answering systems.

S Rao Chintalapudi, M. H. M. Krishna Prasad

An Approach for Creating Framework for Automated Question Generation from Instructional Objective

Generating questions for evaluation and assessment of learner’s knowledge, in an e-learning system, is usually a tedious job for the instructor. There are approaches being made to automatically generate questions from content of an e-learning course. However, there is a general procedure of using only the content provided by the instructor, to generate questions, which are applicable on these automated question generation systems. Here a proposed approach to develop questions from the instructional objectives of an e-learning system, irrespective of the e-learning content, is presented. These questions which are being generated are aimed to satisfy the instructional objectives of the instructor and learning objectives to the learner. This reduces the necessity of questions being generated from a particular content and provides a proposed methodical framework to generate questions having similar instructional and learning objectives.

Syaamantak Das, Rajeev Chatterjee, Jyotsna Kumar Mandal

Adaptive Output Feedback Control System Design for Low-Cost Electronic Knee Prosthesis

Electronic knees provide a wide range of mobility for the amputees. These can often cover K2 and K3 grades of mobility. To achieve a wider mobility, these electronic knees depend upon feedback sensors which provide real-time data to a microcontroller. Various methods are used to calculate the joint motion and knee angle which include magnetic encoder, electro goniometer, and inertial measurement units. The goal of this paper is to develop a sensor to measure angular change of the “stump” socket using accelerometer and ultimately that of the thigh movement, and to use it as feedback signal to the microcontroller in the electronic knee. Sensing the thigh position directly can help in achieving a more natural gait. This feedback system can be used instead of the passive feedback systems currently used in the electronic knees. Results show the potential of low-cost sensing method as a reliable feedback system which can reduce complexity of the hardware as well as the algorithms currently used in the development of modern electronic knee, and hence the overall cost of the electronic knee.

Salman Shaikh, Akshay Malhotra

Low-Leakage, Low-Power, High-Stable SRAM Cell Design

This paper proposes a technique for designing low-leakage stable SRAM cell which can mitigate impact of



(threshold voltage) variation. The architecture of the proposed transmission gate-based 9-transistor SRAM cell (TG9T) is almost similar to that of 7-transistor SRAM cell (7T) except the access transistors, which are replaced with transmission gates. In this study, various key design metrics like noise margin, leakage current, and hold power are simulated for both cells and compared. The proposed design provides 1.25× lower leakage current and 1.46× higher SINM (static current noise margin) while bearing 3.8× penalty in WTI (write trip current) compared with 7T. Proposed design exhibits its robustness by achieving 1.1× tighter spread in hold power compared to 7T.

Soumitra Pal, Y. Krishna Madan, Aminul Islam

Optimizing Routes in Mobile Ad Hoc Networks Using Genetic Algorithm and Ant Colony Optimization

A network formed with the collaboration of mobile nodes which can communicate among themselves is called as Mobile Ad hoc NETwork (MANET). These networks are infrastructure less in nature causing the mobile nodes to act like routers and which in turn forward the packets from the source node to the destination node. Different protocols have been used to maintain connectivity between mobile nodes. Continuous movement of nodes, radio transmission and low battery power of mobile nodes can lead to break the connectivity between nodes. Thus, the performance of network may depend upon the protocol used for routing purpose. To measure quality of service (QoS) of the network, various factors can be used like ratio of packet delivery, end to end delay, control and routing overhead, and distance in terms of nodes present between the source and destination nodes. To search an optimized path between source and destination node pair, different optimization methods are applied. In this paper, the proposed algorithm uses ant colony optimization (ACO) Technique to explore most outstanding feasible paths in collaboration with genetic algorithm (GA) which assists to give globally optimal solution among feasible paths generated by the ACO. The experiments carried out use the AODV protocol with GAAPI protocol in terms of packet delivery, delay required in end to end communication and energy consumption.

Pankaj Uttam Vidhate, R. S. Bichkar, Yogita Wankhade

Concatenation Technique for Extracted Arabic Characters for Efficient Content-based Indexing and Searching

This research paper demonstrates the work accomplished in the last phase of the ongoing research project with an objective of developing a system for moving Arabic video text extraction for efficient content-based indexing and searching. The novelty of this paper is the technique used for concatenation of the individual stand alone Arabic characters which are extracted and recognized from image frames. Unicode format of Arabic characters is used for concatenation of extracted characters which is never done before. The concatenated characters are written into the text file in incessant way. This text files are indexed using Lucene and search for the desired string is done in a faster and precise manner.

Abdul Khader Jilani Saudagar, Habeeb Vulla Mohammed

Harmonic Elimination Using DSTATCOM

There has been an increasing awareness admits customers and utilities regarding superior quality and reliable electric power. The awareness has been triggered by a stupendous increase in the number of nonlinear loads such as power electronic devices. Due to nonlinear loads and low power factor the gravest power quality problems today are voltage sags and swells and harmonic distortion. Therefore, devices like DSTATCOMs are extensively used in power system applications and are able to provide efficient reactive power support to maintain voltage stability. Simulation of DSTATCOM is performed using simpowersystems. Analysis of instantaneous reactive power theory and instantaneous active and reactive current techniques is done which are used for extracting the reference current in the control of the DSTATCOM. These compensation techniques recuperate the ability of DSTATCOM to sustain voltage stability in steady-state and transient conditions. Investigation of the simulation results for both the techniques is done.

Shabana Urooj, Pikasha Sharma

Fault Tolerance in Wireless Sensor Networks: Finding Primary Path

Wireless sensor networks (WSN) are prone to be affected by faults which may be caused due to a variety of reasons, namely hardware malfunction, software problems, inadequate energy resources, and range or environmental hazards. A WSN is required to be equipped to handle such situations else would suffer an overall curtailment of the lifetime and ultimately not meet the required goal. Tolerance to faults thus forms one of the guiding parameters in WSN design. In this paper, we have proposed a method to find a reliable routing protocol using fuzzy logic based on Link Quality Indicator (LQI), Received Signal Strength Indicator (RSSI), and number of hops to the base station. Additional to the primary reliable path, each node has a secondary path which will be alternate path for each sensor in case of failure of the primary path. Implementation of this approach has been done as TinyOS module and evaluated through TOSSIM simulations. The experimental results show promising results in terms of packet delivery and reliability of the network.

Pritee Parwekar, Sireesha Rodda

PSO-Based Multiple-sink Placement Algorithm for Protracting the Lifetime of Wireless Sensor Networks

Optimal deployment of multiple sinks has been proven to be one of the energy efficient techniques for prolonging the lifetime of wireless sensor networks (WSNs). In this paper, we propose a particle swarm optimization (PSO) based algorithm called PSO-MSPA for placement of multiple-sink in WSNs. The algorithm is developed with an efficient scheme of particle encoding and novel fitness function. For the energy efficiency of the PSO-MSPA, we consider various parameters such as Euclidian distance and hop count from the gateways to the sinks. The algorithm is tested extensively on various scenarios of WSNs by varying number of gateways and sensor nodes and the results are analyzed to show the efficacy of the proposed algorithm.

C. Srinivasa Rao P., Haider Banka, Prasanta K. Jana

Denoising of GPS Positioning Data Using Wavelet-Based Hidden Markov Tree

Precise position and navigation with GPS is always required for both civil and military applications. The errors and biases associated with navigation will change the positional information from centimeters to several meters. To estimate and mitigate the errors in GPS positioning data, the wavelet transform is most significant technique and proven. The traditional wavelet threshold methods will work to a certain extent but are not useful to estimate the signal levels to the expected level due to their incapability for capturing the joint statistics of the wavelet coefficients. The wavelet-based hidden Markov tree (WHMT) is designed to capture such dependencies by modeling the statistical properties of the wavelet coefficients as well. In this paper, a WHMT is proposed to reduce positioning error of the GPS data. To establish proposed method, the position data are decomposed using wavelets. The obtained wavelet coefficients are subjected to Discrete Wavelet Transform (DWT) as well-proposed WHMT for noise removal. In this proposed methodology, an Expectation Maximization (EM) algorithm used for computing the model parameters. The root-mean square error (RMSE) of proposed method shows better performance comparatively classical DWT.

Ch. Mahesh, K. Ravindra, V. Kamakshi Prasad

Parent Selection Based on Link Quality Estimation in WSN

Wireless sensor network (WSN) is a dynamic and infra-structure less communication system. These are used for any specific target monitoring. The meaningful data returned by the sensor nodes are used for significant decision making. So the quality of data plays a significant role in correctness of decision. Resource scarcity of WSN makes it vulnerable and prone to faults. Faults in WSN are unintended errors, which may lead to wrong decision making. Moreover, due to faults, the reliability and performance of the WSN may get affected; hence, fault tolerance is necessary over here. This paper aims to design a built-in learning-based link quality estimation approach for WSN. This will enable each sensor node to select an appropriate parent through a good-quality link for forwarding data to the base station (BS). Parameters for link quality estimation over here mainly include received signal strength (RSS), signal to interference plus noise ratio (SINR), and packet reception rate (PRR). Simulation results along with discussion are also presented here.

Siba Mitra, Sarbani Roy, Ajanta Das

Enhanced Higher Order Orthogonal Iteration Algorithm for Student Performance Prediction

Predicting Student Performance is the process that predicts the successful completion of a task by a student. Such systems may be modeled using a three-mode tensor where the three entities are user, skill, and task. Recommendation systems have been implemented using Dimensionality reduction techniques like Higher Order Singular Value Decomposition (HOSVD) combined with Kernel smoothing techniques to bring out good results. Higher Order Orthogonal Iteration (HOOI) algorithms have also been used in recommendation systems to bring out the relationship between the three entities, but the prediction results would be largely affected by the sparseness in the tensor model. In this paper, we propose a generic enhancement to HOOI algorithm by combining it with Kernel smoothing techniques. We perform an experimental comparison of the three techniques using an ITS dataset and show that our proposed method improves the prediction for larger datasets.

Prema Nedungadi, T. K. Smruthy

A One-to-One Communication Model to Facilitate Conversation Between Differently-Abled People by a Portable and Handy Machine

The research study presented in this paper focuses on the process of communication between differently-abled people via technological machine model stated in the methodology section of this research paper. This challenging issue of human interaction between dissimilar impairments has sought a medium of communication between a normal person and a differently-abled person troubled by a single disability, be it blindness, be it deafness, or be it dumbness. But, apart from all such scenarios, no such solution exists in this world, full of advancements that include the word “technology” where a remedy to dual or multiple (combination of blind, deaf, dumb) defects in a person right from their births is available. So, keeping this fact alive in the woes and wits, authors have proposed a machine model by help of which the sufferers suffering with similar or dissimilar disabilities could have dialogue with one another in the easiest possible manner. The prime focus of the authors is to shed some light in the field of communication between disabled people suffering with mono, dual, or multiple defects who could meet the basic day-to-day necessities of their life by using this proposed machine model, who were using finger sensations and lip movement learning methods that were miles away from technological grounds till yet. The algorithms have been presented to convey the proper working and functioning mechanism of the machine model to ease the task of live conversation between the sufferers. The various case studies involving every possible use case diagram have been included, showing the parameters and considerations on which the recommended machine model will be best suited for working, have seeked its wide discussion. The final phase of this research study will show the descriptive images highlighting the working methodology and usage of tables has also been done for providing a better knowledge of the proposed concept.

Rajat Sharma, Swarnima Gupta

Malicious File Hash Detection and Drive-by Download Attacks

Malicious web content has become the essential tool used by cybercriminals to accomplish their attacks on the Internet. In addition, attacks that target web clients, in comparison to infrastructure components, have become prevalent. Malware drive-by downloads are a recent challenge, as their spread appears to be increasing substantially in malware distribution attacks. In this paper we present our methodology for detecting any malicious file downloaded by one of the network hosts. Our detection method is based on a blacklist of malicious file hashes. We process the network traffic, analyze all connections, and calculate MD5, SHA1, and SHA256 hash for each new file seen being transferred over a connection. Then we match the calculated hashes with the blacklist. The blacklist of malicious file hashes is automatically updated each day and the detection is in the real time.

Ibrahim Ghafir, Vaclav Prenosil

Parameter Optimization of Support Vector Machine by Improved Ant Colony Optimization

Support vector machine (SVM) is one of the significant classification technique and it can be applied in various areas like meteorology, financial data analysis, etc. The performance of SVM is influenced by parameters like C, which is cost constant and kernel parameter. In this paper, an improved ant colony optimization (IACO) technique is proposed to optimize the parameters of the SVM. To evaluate the proposed approach, the experiment adopts two benchmark datasets. The developed approach was compared with the ACO–SVM algorithm proposed by Zhang et al. The experimental results of the simulation show that performance of the proposed method is encouraging.

Srujana Rongali, Radhika Yalavarthi

KT3F: A Key-Based Two-Tier Trust Management Filtering Scheme for Intrusion Detection in Wireless Sensor Network

The air and ambience of Wireless Sensor Network (


) are always suitable for intruders and they always gear up to hit this network. So the safety of


is very much essential and extremely challenging. Lots of intrusion detection schemes have been proposed and trust management is a significant part of them. The traditional trust management schemes were developed on the basis of calculation of trust value of nodes and provide one or few levels of intrusion detection within the network. This paper has a new Key-based Two-Tier Trust Management Filtering Scheme (KT3F), which not only allows us to spot intruder several times but also strictly filters the network by using filtering and key management scheme. By serving strict filtering, this scheme boosts the security factor much higher than the previous techniques. This approach is simple and strict filtering of network helps to mark the intruder more accurately and provides high level of security.

Moutushi Singh, Rupayan Das, Mrinal Kanti Sarkar, Koushik Majumder, Subir Kumar Sarkar

Cascading De-noising Algorithm for Improving GPS Positional Accuracy

The temporal and spectral variations of GPS errors severely degrade the precise positioning. These errors may change the positional information from centimeters to several meters and may result catastrophic errors in navigational information of civil aviation. Applying of de-noising methods by targeting both temporal and spectral features of GPS errors is a new strategy. With conventional code carrier smoothing algorithms, the maximum amount of temporal variations is eliminated, and by novel wavelet multiresolution analysis, the other systematic and random errors of GPS are eliminated. In present research, a combination of code carrier smoothing algorithm and wavelet multiresolution analysis is used to improve the GPS receiver position smoothing. The proposed method is well suited for critical application like GAGAN of Indian SBAS augmentation for improving positional accuracy. In this analysis, the raw GPS data is firstly processed with traditional code carrier smoothing to remove the temporal variations before position is estimated. Subsequently, the Wavelet Hidden Markov Tree (WHMT) is used to reduce the systematic and random biases of GPS.

Ch. Mahesh, R. Pavan Kumar Reddy, K. Ravindra, V. Kamakshi Prasad

A Corpus of Word-Level Offline Handwritten Numeral Images from Official Indic Scripts

Dataset development is one of the most imperative tasks in document image processing research. The problem becomes more challenging when it comes about Numeral Image Database (


) for official Indic scripts. Few efforts are made so far but they were restricted on single script which is basically a local script of the fellow researcher who prepared the database. In this paper, a technique for development of a handwritten


of four popular Indic scripts namely Bangla, Devanagari, Roman and Urdu is proposed. Initially data were collected in unconstrained manner at Word-level from different writers with varying age, sex and educational qualification. All the images are stored in grey-level at .jpg format so that the data can be used in various ways as per need. A benchmark result on the present dataset is proposed using a novel hybrid approach with respect to Handwritten Numeral Script Identification (


) problem.

Sk Md Obaidullah, Chayan Halder, Nibaran Das, Kaushik Roy

A Planar Ultra-Wide Band Antenna Design Using Circularly Truncated Corners and Notches

This paper describes the design and parametric study of a planar rectangular ultra-wide band antenna (UWB). The proposed design exploits


-shaped notch and


-shaped slot with circularly truncated corners on the radiating patch along with stepped microstrip feed. Circular truncation is also used at the corners of the partial ground plane with a rectangular notch along with an electromagnetically coupled truncated rectangular strip. This antenna operates in a frequency band ranging from 2.975 to 10.684 GHz with an impedance bandwidth nearly 7.709 GHz. This antenna finds its applications in 5.2/5.8 GHz WLAN, 3.5/5.5 GHz WiMAX, 4 GHz C band, and lower frequencies of


band. The effect of the truncated ground plane and the dimensions of




and rectangular shaped notches on the optimization of the return loss is parameterized and discussed in detail.

H. S. Mewara, M. M. Sharma, Mayank Sharma, Mukesh Gupta, Ajay Dadhich

Genetic Algorithm for k-Connected Relay Node Placement in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used for many applications including health care, environment monitoring, underground mines, and so on. In WSN, deployment of relay nodes to cover specific region or target is an important issue. In a target-based WSN, it is important that all the targets must be covered by sensor nodes, and the sensor nodes are connected with the backbone network. In this paper, we propose two algorithms for relay node placement which provide


-connectivity of the sensor nodes. The first algorithm is based on Genetic Algorithm (GA), and the second one is based on greedy approach. We have also to extensively simulate both the algorithms to study their performance.

Suneet K. Gupta, Pratyay Kuila, Prasanta K. Jana

Segmentation of Cotton Bolls by Efficient Feature Selection Using Conventional Fuzzy C-Means Algorithm with Perception of Color

Ad hoc method for segmentation of mature or nearly mature cotton bolls is proposed based on proper feature vector selection and efficient application of Fuzzy c-means (FCM) on images. Perception of color is used as fundamental criteria for segmentation. The results obtained are compared with conventional FCM and supremacy of the proposed work is presented. Since the technique is ad hoc, it will work only for the said purpose in the natural setting of cotton fields. Any improper acquisition of images of cotton bolls, like intense illumination or deep shadows (which is of course absent in natural settings) will produce improper results.

Sandeep Kumar, Manish Kashyap, Akashdeep Saluja, Mahua Bhattacharya

System Dynamics Modeling for Analyzing Recovery Rate of Diabetic Patients by Mapping Sugar Content in Ice Cream and Sugar Intake for the Day

Ice cream is a complex colloidal system, usually formed by structural compounds viz., air bubbles, fat, and ice crystals which are dispersed in a matrix consisting of a solution of sugars, proteins, stabilizers, emulsifiers, dyes, and scents. This paper presents system dynamics model based on the Vensim tool and this model contains equations derived for diabetic and non-diabetic patients and common health conscious people as well. Very effectual results have been obtained from the derived model suggesting best suitable ice cream for mapping diabetes patients, which is different from health conscious people and normal crowd.

Suhas Machhindra Gaikwad, Rahul Raghvendra Joshi, Preeti Mulay

Enhanced Stable Period for Two Level and Multilevel Heterogeneous Model for Distant Base Station in Wireless Sensor Network

In last decade, Wireless Sensor Network (WSN) has gained popularity because of its little or no infrastructure-based network communication. The key concerned in WSN is energy optimization. Several researches are being carried out focusing on the parameters that affect the energy of the network. Introduction of heterogeneity enhances the network lifetime. In this paper, we propose a heterogeneity-based energy efficient clustering scheme for distant base station. In this protocol, cluster head selection is based on localized parameters of the node. The proposed work is simulated for two level and multilevel heterogeneous energy model. Simulation results validate the extended stability of proposed work. The proposed protocol remarkably outperforms SEP and DEEC.

Pawan Singh Mehra, M. N. Doja, Bashir Alam

Dynamic Timetable Generation Using Constraint Satisfaction Algorithm

Manual method of generating timetable has always been a time-consuming, laborious, and tedious task. It is neither efficient nor effective in terms of utilization of resources. The complicated relationships between time periods, classes (lectures), classrooms, and instructors (staff) make it difficult to attain a feasible solution. In this paper, timetabling problem is modeled as a constraint satisfaction problem. The algorithm dynamically builds the timetable adjusting resources in order of complexity. The main focus is to satisfy all the hard constraints and maximum soft constraints without any conflicts among resources. In order to reach a subsolution state, we use various heuristics that guide the search. Along with this, chronological backtracking and look-ahead techniques are also discussed. This software is ergonomic in nature as it also provides a way to alter the given inputs.

Urmila Kalshetti, Deepika Nahar, Ketan Deshpande, Sanket Gawas, Sujay Sudeep

Application of Genetic Algorithm for Evolution of Quantum Fourier Transform Circuits

Quantum Fourier Transform finds a variety of applications in quantum computing. It is the most important building block in a number of quantum algorithms like Shor’s algorithm, phase estimation algorithm, etc. This paper illustrates the ability of Genetic algorithm for evolving these quantum fourier transform circuits on a classical computer. Circuits for two, three, four, and five qubits have been discussed in the paper, however. the algorithm has been generalized for evolving circuits for any number of qubits.

Swanti Satsangi, C. Patvardhan

An Improved Polygon Clipping Algorithm Based on Affine Transformation

Today, Computer Graphics is used almost in all the domains including gaming, entertainment, education, CAD/CAM, etc. One of the most important operations in computer graphics is clipping, such as line clipping and polygon clipping. Its importance emerges from the fact that polygon clipping can be applied in VLSI CAD, GIS, garment industry, etc. Many algorithms exist at the present moment but the intersection calculations incur huge costs. Our paper proposes an algorithm based on affine transformation which eliminates degeneracies while clipping self-intersecting and multi-polygons. Experimental results show that the new algorithm outperforms Greiner-Hormann and Vatti Algorithms for real-time datasets which are used in the packing industry.

Mugdha Sharma, Jasmeen Kaur

Alternative Design Space Analysis for Electronic Commerce System

Electronic commerce system is a popular way of web application services for business people, customers, and Employees. We observed so many challenges of electronic commerce system in an information system. Electronic commerce system is used for doing business transactions, funds transfer that involves transfer of information through the internet. Electronic commerce system is used for buying the products or selling the products through the internet. This paper presents the model of electronic commerce system with different design alternatives. Implementation of different design alternatives is used for modeling of electronic commerce system. In this paper, alternative design space analysis is used for deriving different design alternatives of the system. This paper presents an architecture view of the system based on UML modeling language. Alternative design space analysis can also applicable for designing industrial applications in an electronic commerce system.

P. Rajarajeswari, D. Vasumathi, A. Ramamohanreddy


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Globales Erdungssystem in urbanen Kabelnetzen

Bedingt durch die Altersstruktur vieler Kabelverteilnetze mit der damit verbundenen verminderten Isolationsfestigkeit oder durch fortschreitenden Kabelausbau ist es immer häufiger erforderlich, anstelle der Resonanz-Sternpunktserdung alternative Konzepte für die Sternpunktsbehandlung umzusetzen. Die damit verbundenen Fehlerortungskonzepte bzw. die Erhöhung der Restströme im Erdschlussfall führen jedoch aufgrund der hohen Fehlerströme zu neuen Anforderungen an die Erdungs- und Fehlerstromrückleitungs-Systeme. Lesen Sie hier über die Auswirkung von leitfähigen Strukturen auf die Stromaufteilung sowie die Potentialverhältnisse in urbanen Kabelnetzen bei stromstarken Erdschlüssen. Jetzt gratis downloaden!