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About this book

This book comprises selected articles from the International Communications Conference (ICC) 2018 held in Hyderabad, India in 2018. It offers in-depth information on the latest developments in voice-, data-, image- and multimedia processing research and applications, and includes contributions from both academia and industry.

Table of Contents

Frontmatter

Moving Object Recognition and Detection Using Background Subtraction

Motion detection and object recognition algorithms are a significant research area in computer vision and involve building blocks of numerous high-level methods in video scrutiny. In this paper, a methodology to identify a moving object with the use of a motion-based segmentation algorithm, i.e. background subtraction, is explained. First, take a video as an input and to extract the foreground from the background apply a Gaussian mixture model. Then apply morphological operations to enhance the quality of the video because during capture the quality of a video is degraded due to environmental conditions and other factors. Along with this, a Kalman filter is used to detect and recognize the object. Finally, vehicle counting is complete. This method produces a better result for object recognition and detection.

Loveleen Kaur, Usha Mittal

Fog Computing: Overview, Architecture, Security Issues and Applications

There is a famous saying that goes “Necessity is the mother of invention”. In today’s globalized world people are getting stuck with many problems like data management, time management, and security and privacy concerns etc. There are traditional methods like cloud computing, cloudlet, and mobile management techniques to sort out the processing, storing, and executing of the data. But with the passage of time, the world is exploring new areas and these traditional methods are on the wane in terms of data handling. In this paper we discuss the technology that helps in data management, time management and security issues. We also addresses some real time scenarios.

Kishore Dasari, Mounika Rayaprolu

Proposal of Linear Specific Functions for R-L-C as Fundamental Elements in Terms of Considered Specific Electric Constants

In this paper electric physical quantities which can be characterized as being of either of the scalar or phasor type, are discussed. The scalar physicalities of resistance, inductance and capacitance are regarded as being of a fundamental type as they provide the initial basis of circuit designing, while the phasor physicalities are voltage and current etc. These fundamental elements may also be regarded as conventional in comparison to those of semiconductors devices, which are introduced later. In taking these scalar elements into consideration, the linearized specific functions are consider in terms of the considered specific electric constants possible from the ground of already existing electromagnetic constants of the wave propagating through the free space, namely intrinsic impedance, permeability, and permittivity.

Vineet Kumar

Efficient Video Delivery Over a Software-Defined Network

This paper proposes a framework called SDN Streamer for an OpenFlow controller in order to provide QoS support for scalable video streaming over an OpenFlow network. OpenFlow is a protocol that decouples control and forwarding layers of routing. Abstracting control from the forwarding plane lets administrators dynamically adjust network-wide traffic flow and keeps the network agile. Software-Defined Networking (SDN) aims to improve the reliability of multimedia streaming while reducing utilization of server resources. It optimizes video delivery using a Scalable Video Coding (SVC) algorithm that sends layers of different quality via discrete paths. Dynamic rerouting capability is ensured using a Lagrange Relaxation-based Aggregate Cost (LARAC) algorithm. Unlike Dijikstra’s algorithm, the LARAC algorithm does not calculate least “hop-counts” to find the optimal path but calculates the optimal path based on “link statistics.” The SDN Streamer can guarantee seamless video delivery with little or no video artifacts experienced by the end-users. This project makes use of HTML5 for browser display. The SDN server allows everyone to access the media files irrespective of their platform, device or browser. Performance analysis shows there is significant improvement on the video’s overall PSNR under network congestion.

R. Thenmozhi, B. Amudha

A Viewpoint: Discrimination Between Two Equivalent Statements of Kirchhoff’s Current Law from the Ground of Precedenceness

In this paper, on the ground of precedenceness, the two equivalent statements in regard to the basic law of electrical from Kirchhoff’s current are discriminated. Here, this viewpoint of statements discrimination for the law of same does not means to regard that there is a differences in between, but to regard that the statement of one out of the two exist due to the existence of other. In addition, the current regulation function is discussed, which is always limited to the range of 0 to 1 as it determines the ratio of totality at each node for either sides of the branches collected by the converger and diverger respectively. In the case of a condition without unity, it determines that the law of conservation of charge does not hold.

Vineet Kumar

A DES-Based Mechanism to Secure Personal Data on the Internet of Things

The Internet of Things (IoT) helps users in their day to day activities such that they can communicate with each other through sensors very easily and in less time. Communication through intermediate sensor nodes may violate security because it may harm confidential user data or information by either modifying it or not forwarding to it. To deal with such problems, a number of cryptographic secure mechanisms are available that provide symmetric and asymmetric keys to secure data and each mechanism has its own pros and cons. In this paper, a secure DES-based mechanism is proposed in which a DES algorithm is used to transfer the data between users through sensor nodes without no loss of security.

Pragya Chandi, Atul Sharma, Amandeep Chhabra, Piyush Gupta

A Reputation-Based Mechanism to Detect Selfish Nodes in DTNs

A delay tolerant network (DTN) is a complete wireless network. In a DTN there is no base station as it is in the case of existing wireless networks. Nodes may behave selfishly to transmit a message to save their own resources, such as energy. The cooperation requires detecting routes and transmitting the packets for other nodes, even though it consumes network bandwidth, buffer, and energy. A selfish node is a node that may be unwilling to cooperate to transfer packets. Such a node wants to preserve its own energy while using the services of others and consuming their resources. Many approaches have been used in the literature to implement the concept of non-cooperation in a simulated environment. However, none of them is capable of reflecting real cases and thus, the implementation of non-cooperative behavior needs improvement. In this paper, we focus on malicious and selfish node behavior, and we present a new classification and comparison between existing methods and algorithms to implement selfish nodes. Finally, we propose a new algorithm to implement selfish nodes in a DTN environment.

Rakhi Sharma, D. V. Gupta

Improved Target Detection in Doppler Tolerant Radar Using a Modified Hex Coding Technique

In every corner of the globe, nations want to improve the monitoring mechanism of the country, so that no one can enter their territory in an unwanted manner easily. Well-known equipment, called Radar, is commonly used for monitoring. However, only a small amount of work is done to monitor multiple moving targets in the presence of Doppler. This important issue diverts the attention of the research community away from working on this platform. In the present literature, the merit factor (MF) is improved by increasing the amplitude of the main lobe. However, these particular approaches did not attach more importance to the effects of noise side peaks of fast moving targets. The drawback of noise peaks masks slow-moving targets and cannot be clearly seen by the radar receiver. As a result it reduces the performance of the Doppler radar system. In this paper, an approach is presented which not only improves multiple moving target detection, but also reduces the energy of code generation. This approach is simple and effective in detecting multiple moving targets at the desired Doppler. The presented technique is called Improved Target Detection in Doppler Tolerant Radar Using a Modified Hex Coding Technique. MATLAB is used to formalize the results by simulation.

Majid Alotaibi

Enhanced Packet Loss Calculation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are autonomous and structure-less dynamic networks which consist of spatially distributed sensor nodes to support real-time applications. However, due to limited resource availability these networks face certain challenges. Many researchers address bandwidth and delay using different approaches to increase the quality of service (QoS). Almost all researchers address loss calculation using sliding window flow control protocol, which may not always give an optimum solution. So, accurate loss calculation is necessary to increase the packet delivery ratio (PDR) which in turn increases QoS. In this paper, a mathematical model is proposed to enhance the loss calculation in WSNs using Poisson theory.

Saud S. Alotaibi

Enhanced Security of MANETs Against Black Hole Attacks Using AS Technique

A mobile ad hoc network (MANET) is an autonomous structureless arrangement of mobile nodes to figure a momentary network. Communication between any two nodes is possible directly if the two nodes belong to the same sensing range; otherwise communication can be achieved by means of the nodes which are present between source and destination. As the network nodes are mobile, any node can enter or leave the network at any particular time interval. Thus, whichever node is present in between the source and destination can perform as a router or the host node in the arranged network. Therefore this poses security challenges to MANETs. This paper presents a solution to black hole attacks. The presented method is easy to use and efficient in detecting black hole attacks. The presented approach is validated by the use of network simulator 2 (NS2).

Ishrath Unissa, Syed Jalal Ahmad

Design of a Smart Water-Saving Irrigation System for Agriculture Based on a Wireless Sensor Network for Better Crop Yield

Precision agriculture is a decision-support system that helps farmers to make better decisions in the management of their farms, thus increasing returns while preserving resources. An automated irrigation system facilitates continuous and efficient irrigation under conditions of water and labor scarcity. Overwatering of crops causes nutrients to flow off the land surface and this can lead to lower crop yields. This wireless technology helps farmers to address the problem of overwatering and underwatering their crops. Currently, automation is one of the more important aspects affecting human life. It not only provides comfort but also reduces energy, increases efficiency and saves time. The proposed system uses wireless technology to irrigate crops in need of water. Water requirement varies depending on the type of crop, for example, paddyfields needs more water while crops like ragi needs less water. The proposed system irrigates based on the water requirements of particular crops in particular areas and the system as designed also provides smart irrigation technology at a low cost, usable by Indian farmers. Temperature, humidity, moisture of the land, and the water level in the tank will be measured and sent to the user via GSM communication. The water pump is automatically operated through the messages and Android application. Data is stored in the cloud for analysis. The proposed moisture-sensing method has the ability to be incorporated into an automated drip irrigation scheme and perform automated, precision agriculture in conjunction with decentralized water control.

Meeradevi, M. A. Supreetha, Monica R. Mundada, J. N. Pooja

SVM—A Way to Measure the Trust Ability of a Cloud Service Based on Rank

Trust management is one of the most serious and demanding issues facing by cloud computing. In spite of having some surprising qualities, such as virtualization potential, highly optimized storage capacity, multi-tenancy features, and 24-7 service availability, cloud technology still faces security and authenticity issues, which have created an obstacle for adapting cloud computing as a widespread technology. Threat is a qualitative factor rather than its quantitative approach. Trust is also a quality factor, which can be more useful when trust can be established and proven quantitatively. Previously, cloud users had to show blind faith towards cloud service providers and vendors. Today, the importance of cloud auditing has increased. This paper focuses on the establishment of trust and measuring the quality of service for SaaS cloud service model, by using some measurement indices and with the help of some known parameters.

Sharmistha Dey, Vijender Kumar Solanki, Santanu Kumar Sen

An Optimized Five-Layer Model with Rainfall Effects for Wireless Propagation in Forests

This paper presents a new propagation model for evaluating the fading of wireless communication signals in forests. The model considers rainfall and snowfall effects, and allows for the estimation of attenuation at varying frequencies in the VHF/UHF bands that are used by cognitive radios. The structure of the vegetation environment is represented here by five material layers, namely soil, scrubs and small plants under the trees, trunks of trees, foliage of trees, and free space. The model parameters are optimized using the least squares technique. The resulting model is verified by comparison with measured data where acceptable agreement is observed. The average rain rate R0.01% that will probably be exceeded for at most 0.01% of the year is computed using real measured data in Jordan. R0.01% is found to be 22.9 mm/h which agrees with the ITU recommended value of 22 mm/h.

Mohammed Saleh H. Al Salameh

Leak Detection Methods—A Technical Review

For safe transmission of various fluids or gases leakage detection in pipelines is very important. The leak of hazardous/dangerous fluids and gases can cause loss of property and lives (e.g., the Bhopal gas tragedy). Hence review of various available technologies should be necessary in order to identify a technology which provides an easy, adaptable, flexible, inexpensive, and efficient approach for real-time distributed data acquisition and monitoring. Based on review one can able to know that which technology has a very low false alarm rate and cost effective one etc. In this paper the performance and ability of the different systems is compared in terms of their leak detection capability.

R. Ramadevi, J. Jaiganesh, N. R. Krishnamoorthy

Text Message Classification Using Supervised Machine Learning Algorithms

In recent years, as the popularity of mobile phone devices has increased, the short message service (SMS) has grown into a multi-billion dollar industry. At the same time, a reduction in the cost of messaging services has resulted in the growth of unsolicited messages, known as spam, one of the major problems that not only causes financial damage to organizations but is also very annoying for those who receive them. Findings: Thus, the increasing volume of such unsolicited messages has generated the need to classify and block them. Although humans have the cognitive ability to readily identify a message as spam, doing so remains an uphill task for computers. Objectives: This is where machine learning comes in handy by offering a data-driven and statistical method for designing algorithms that can help computer systems identify an SMS as a desirable message (HAM) or as junk (SPAM). But the lack of real databases for SMS spam, limited features and the informal language of the body of the text are probable factors that may have caused existing SMS filtering algorithms to underperform when classifying text messages. Methods/Statistical Analysis: In this paper, a corpus of real SMS texts made available by the University of California, Irvine (UCI) Machine Learning Repository has been leveraged and a weighting method based on the ability of individual words (present in the corpus) to point towards different target classes (HAM or SPAM) has been applied to classify new SMSs as SPAM and HAM. Additionally, different supervised machine learning algorithms such as support vector machine, k-nearest neighbours, and random forest have been compared on the basis of their performance in the classification of SMSs. Applications/Improvements: The results of this comparison are shown at the end of the paper along with the desktop application for the same which helps in classification of SPAM and HAM. This is also developed and executed in python.

Suresh Merugu, M. Chandra Shekhar Reddy, Ekansh Goyal, Lakshay Piplani

Error Assessment of Fundamental Matrix Parameters

Stereo image matching comprises of establishing epipolar geometry based on fundamental matrix estimation. Accuracy of the epipoles is governed by the fundamental matrix. A stereo image pair may contain errors on a systematic and/or random basis which determine the accuracy of the fundamental matrix required for matching. The algorithm used to extract the image pair correspondence, and the method used to estimate the correct parameters of the matrix, controls its accuracy further. A performance analysis of widely adopted matrix estimators over the point pairs found by correspondence determiners is undertaken in this chapter. The methods are modified for the best combination of results, based on the properties of the resulting fundamental matrix. Permutations are analyzed over the possible paths for obtaining the matrix parameters with the expected characteristics, followed by error analysis. Amongst the estimators analyzed, RAndom SAmple Consensus (RANSAC) and M-estimator SAmple Consensus (MSAC) estimators were found to produce the best results over the features detected by the Harris–Stephens corner detector.

Bankim Chandra Yadav, Suresh Merugu, Kamal Jain

A Two-Band Convolutional Neural Network for Satellite Image Classification

The advent of neural networks has led to the development of image classification algorithms that are applied to different fields. In order to recover the vital spatial factor parameters, for example, land cover and land utilization, image grouping is most important in remote sensing. Recently, benchmark classification accuracy was achieved using convolutional neural networks (CNNs) for land cover classification. The most well-known tool which indicates the presence of green vegetation from multispectral pictures is the Normalized Difference Vegetation Index (NDVI). This chaper utilizes the success of the NDVI for effective classification of a new satellite dataset, SAT-4, where the classes involved are types of vegetation. As NDVI calculations require only two bands of information, it takes advantage of both RED- and NIR-band information to classify different land cover. The number and size of filters affect the number of parameters in convolutional networks. Restricting the aggregate number of trainable parameters reduces the complexity of the function and accordingly decreases overfitting. The ConvNet Architecture with two band information, along with a reduced number of filters, was trained, and high-level features obtained from a tested model managed to classify different land cover classes in the dataset. The proposed architecture, results in the total reduction of trainable parameters, while retaining high accuracy, when compared with existing architecture, which uses four bands.

Anju Unnikrishnan, V. Sowmya, K. P. Soman

Dimensionally Reduced Features for Hyperspectral Image Classification Using Deep Learning

Hyperspectral images (HSIs) cover a wide range of spectral bands in the electromagnetic spectrum with a very finite interval, and with high spectral resolution of data. The main challenges encountered with HSIs are those associated with their large dimensions. To overcome these challenges we need a healthy classification technique, and we need to be able to extract required features. This chapter analyzes the effect of dimensionality reduction on vectorized convolution neural networks (VCNNs) for HSI classification. A VCNN is a recently introduced deep-learning architecture for HSI classification. To analyze the effect of dimensionality reduction (DR) on VCNN, the network is trained with dimensionally reduced hyperspectral data. The network is tuned in accordance with the learning rate and number of iterations. The effect of a VCNN is analyzed by computing overall accuracy, classification accuracy, and the total number of trainable parameters required before and after DR. The reduction technique used is dynamic mode decomposition (DMD), which is capable of selecting most informative bands using the concept of eigenvalues. Through this DR technique for HSI classification using a VCNN, comparable classification accuracy is obtained using the reduced feature dimension and a lesser number of VCNN trainable parameters.

K. S. Charmisha, V. Sowmya, K. P. Soman

Asymptotic Symbol Error Rate Analysis of Weibull/Shadowed Composite Fading Channel

In this work, we derive the asymptotic expressions of the average symbol error probability (SEP) of a wireless system over the Weibull-lognormal fading channel. First, we evaluate an approximation of the multipath distribution at the origin then the composite distribution is obtained by averaging the approximate multipath probability density function (PDF) with respect to shadowing. The result is further extended to include maximal ratio combining (MRC), equal gain combining (EGC), and selection combining (SC) PDF at the origin. The derived expressions of the composite PDF are further utilized to evaluate the average SEP for both coherent and non-coherent modulation schemes. The derived expressions have been corroborated with Monte-Carlo simulations.

Puspraj Singh Chauhan, Sanjay Kumar Soni

Transmission Spectrum of a Typical Waveguide in Photonic Crystal with Tunable Width: Simulation and Analysis

In this paper a typical waveguide in 2D photonic crystal of air holes in dielectric slab structure has been simulated to explore the possible transmission spectrum as shown later in various figures. The waveguide width is variable and correspondingly its transmission spectrum changes. This may be improved upon and used to design optical communication devices and photonic sensors. The algorithms used for simulation are finite difference time domain (FDTD), and plane wave expansion method (PWEM).

Neeraj Sunil, V. Jayakrishnan, Harish Somanathan, Alok Kumar Jha

An Anamnesis on the Internet of Nano Things (IoNT) for Biomedical Applications

This paper holds the data of broadly anamnesis and summery on internet of nano things (IoNT) for human services. This makes great possible to give the systematic and prognostic techniques and which in this way help in the medi cations of patients through correct bound pharmaceutical transport, tranquilize convey, tumor and various distinctive contamination’s. The proposed study discusses the different network models of the IoNT and the architectural requirements for its implementation, which involves the different networking models, electromagnetic and molecular communication, channel modeling, information encoding, telemedicine aspects, and IoNT protocols.

Amruta Pattar, Arunkumar Lagashetty, Anuradha Savadi

Minimization of the Size of an Antipodal Vivaldi Antenna for Wi-MAX and WLAN Applications

In this paper, miniaturization of the antipodal Vivaldi antenna is discussed. The antipodal Vivaldi antenna is a broadband antenna and thus it is suitable for use in many wireless applications. The proposed antenna has a center frequency at 3.6, 5.2, and 5.8 GHz and so it can be used in WiMAX as well as WLAN systems and to avoid potential interference from narrowband communication systems, it is advised to design a miniaturized broadband antenna with intrinsic band-notched characteristics which can be used in narrowband communication. The circular slots are applied on the edge at the width of the Vivaldi antenna and are etched properly and as a result this helps in the area minimization of the antenna. The circular slot with the greatest diameter is used to achieve the different center frequencies so that the proposed antenna can be used for wireless communication, i.e. for WiMAX and WLAN. The rectangles are used on the alternative circular slots so as it can be used as notched structure and the interference in between the two different wireless applications can be minimized. The bandwidth of the proposed antenna is 2.8–6.2 GHz. A comparison of a conventional Vivaldi antenna with an antipodal antenna is also undertaken. The simulation of the proposed antenna was performed using HFSS13.0 software. The return loss, gain, radiation pattern, z-parameters, and VSWR are shown.

Sneha Tiwari, Trisha Ghosh, Janardhan Sahay

Physical Layer Impairment (PLI) Aware Lightpath Selection in WDM/DWDM Networks

The demand for high data rate with low bit error rate (BER) and large bandwidth is highly satisfied in wavelength division multiplexing/dense wavelength division multiplexing (WDM/DWDM) networks. However, the signal traveling inside the optical fiber can be affected by various physical layer impairments (PLIs). These impairments are caused due to fiber non-linearities and the non-ideal nature of optical components. Dispersion is one of the PLI constraint which affects signal quality. That needs to be compensated. This research work presents the approach of dispersion penalty (DP). It also suggests a PLI-aware lightpath selection algorithm based on DP.

Vikram Kumar, Santos Kumar Das

Miniaturized MIMO Wideband Antenna with L-Shaped DGS for Wireless Communication

In this paper, a MIMO antenna is designed consisting of two planar symmetrical monopole antennas and the ground plane is slotted into L-shaped. The simulation results indicate that the antenna works well in the ultra-wide band and hence can be used in a wide range of applications. The return loss is below −62 dB at 7.1 GHz and below −32 dB at 3 GHz approximately, which is highly desirable. This antenna has a wide frequency range of 2.4–10 GHz. The overall antenna size is as low as 35 mm × 22 mm but the effect of the mutual coupling among the antenna elements is reduced and is below −10 dB over a wide range of frequencies, i.e. 2.4–8 GHz. Reducing the effect of mutual coupling is a challenge in MIMO antennas, and has been achieved in this case. The maximum gain achieved is approximately 2.2 dB. The design has been simulated using Ansoft HFSS software. The attributes of S-parameters, VSWR, gain, radiation pattern, and Smith chart are shown and its applications are discussed.

Trisha Ghosh, Sneha Tiwari, Janardhan Sahay

An Enhanced Reputation-Based Data Forwarding Mechanism for VANETs

A vehicular ad hoc network (VANET) is a self-configuring and infrastructureless network connecting high mobility random vehicles by wireless links. Due to high mobility, data transmission between two vehicles may possible through other intermediate vehicles but it is difficult to transmit messages through these intermediate vehicles because intermediate vehicles may violate security by sending the wrong messages or by not forwarding messages. So transmission of messages using trust-based VANETs is a difficult task. Various techniques are proposed by researchers to forward packets in trust-based VANETs. Each technique has its own mechanism as well as its pros and cons. In this paper we propose an enhanced trust-based mechanism to select trusted nodes through which messages are transmitted. The proposed mechanism has been implemented using ONE (opportunistic network environment) simulator. Results shows that the proposed mechanism has a high delivery ratio and less message delay than existing reputation-based mechanisms.

Aman Kumar, Sonam Bhardwaj, Preeti Malik, Poonam Dabas

Statistical Metric Measurement Approach for Hazy Images

A novel statistical metric measurement approach for the evaluation of enhancement of hazy images. Metric measurement plays a critical role in picture enhancement in hazy weather conditions and leads to a lessening in pixel resolution, a distortion in color, and gray images. In this paper hazy and foggy images are considered for evaluation using contrast-to-noise ratio (CNR) which dehazes the original hazy images. We propose a unique novel effective parameter based on an image filtering approach. The results demonstrated show a better CNR for dehazed images.

T. Saikumar, K. Srujan Raju, K. Srinivas, M. Varaprasad Rao

Image Enhancement for Fingerprint Recognition Using Otsu’s Method

The internal surfaces of human hands and feet of have minute ridges with furrows between each ridge. Fingerprints have very distinctive features and have been used over a long period of time for the identification of individuals and are now considered to be a very good authentication system for biometric identification. For successful authentication of fingerprint, features must be extracted properly. The different types of fingerprint enhancement algorithms used in image processing all provide different performance results depending on external and internal conditions. External conditions include types of sensors and pressure applied by the subject etc. Internal conditions include the body temperature of a subject and skin quality etc. In this paper, we enhance an image using Otsu’s method, which is one of the segmentation steps of image processing. This algorithm can improve the clarity of ridges and furrows of a fingerprint and enhances performance by reducing the total time for extraction of minutiae compare to other algorithms.

Puja S. Prasad, B. Sunitha Devi, Rony Preetam

Estimation of Success Probability in Cognitive Radio Networks

In this paper, we considered a cooperative spectrum sensing over fading and non-fading channels. We proposed a model of a Rayleigh fading channel and a non-fading additive white Gaussian noise channel. Total error rates and the optimal number of cooperative secondary users over the non-fading channel and the success probability over the fading channel are calculated and the simulation results plotted. The simulation results convey that the optimal number of secondary users is five in both cases. We hope that our results will be useful in improving energy efficiency in identifying the unutilized spectrum.

Chilakala Sudhamani, M. Satya Sai Ram, Ashutosh Saxena

Analysis of Road Accidents Through Data Mining

There is currently a great deal of interest relating to road accidents that result in the loss of life or harm to an individual. GIS is capable of storing information regarding road accidents like vehicle accidents, hour wise accidents, day wise accidents. Apart from this, road accidents are also addressed by road traffic database. In this research on the city of Hyderabad, road traffic databases is taken into considerations where road accidents impact on the socioeconomic growth of society. A data mining technique is used to discover hidden information from the warehouse to handle road accident analysis. We implement algorithms, such as prediction and classification in Weka version 3.7. We use k-Madrid to form a cluster of related information. Different attributes are subjected to analysis with the conclusion that prediction is the most suitable and accurate algorithm.

N. Divya, Rony Preetam, A. M. Deepthishree, V. B. Lingamaiah

An Assessment of Niching Methods and Their Applications

Populace-based metaheuristics have been demonstrated to be especially powerful in taking care of MMO issues if furnished with particularly planned decent variety saving systems, commonly known as niching strategies. This paper provides a fresh review of niching techniques. In this paper, an assessment of niching methods is presented along with their real-time applications. A rundown of fruitful applications of niching techniques to genuine issues is used to show the capacities of niching strategies in giving arrangements that are hard to other enhancement techniques to offer. The critical viable benefit of niching techniques is clearly exemplified through these applications.

Vivek Sharma, Rakesh Kumar, Sanjay Tyagi

A Novel Method for the Design of High-Order Discontinuous Systems

A new procedure is suggested for the design of high order discontinuous systems using an order reduction technique. The method is computationally very simple and straightforward. The proposed method is based on an improved bilinear Routh approximation method and illustrated using typical numerical examples.

G. V. K. R. Sastry, G. Surya Kalyan, K. Tejeswar Rao

Efficient Integration of High-Order Models Using an FDTD–TDMA Method for Error Minimization

In this research paper, we have developed a hybrid FDTD–TDMA technique which is used to focus on solving the time domain equations of non-uniform structure applications by exploiting the finite difference time domain method. A mathematical model has been designed for the proposed FDTD–TDMA technique. The proposed technique shows better performance than existing FDTD–ADI method in terms of error minimization. The simulated comparison showed good results and an agreement between the two methods, which confirms the theory and validates the proposed FDTD–TDMA method.

Gurjit Kaur, Mayank Dhamania, Pradeep Tomar, Prabhjot Singh

Bearing Fault Detection and Classification Using ANC-Based Filtered Vibration Signal

The defective bearing in a rotating machine may affect its performance and hence reduce its efficiency. So the monitoring of bearing health and its fault diagnosis is essential. A vibration signature is one of the measuring parameters for fault detection. However, this vibration signature may get corrupted with noise. As a result this noise must be removed from the actual vibration signature before its analysis to detect and diagnose the fault. ANC (adaptive noise control)-based filtering techniques are used for this noise removal and hence to improve the SNR (signal-to-noise ratio). In our study an experimental setup is developed and then the proposed work is executed in three stages. In the first stage the vibration signatures are acquired and then ANC is implemented to remove the background noise. In the second stage the time (statistical) and the frequency analysis of the filtered vibration signals are done to detect the fault. In the third stage the statistical parameters of the vibration signatures are used for the classification of the fault present in the bearing using random forest and J48 classifiers.

Sudarsan Sahoo, Jitendra Kumar Das

A New Approach to Securing Online Transactions—The Smart Wallet

For many years, two-factor authentication has been the only means of preventing cyber attacks and providing cyber security to online transactions. However, it seems to be vulnerable due to weak spots through which hackers are able to easily find ways of either intercepting message codes or exploiting account recovery mechanisms. Most of the available systems provide a onetime text password as an SMS to the registered mobile number of the user, while a few of them deliver it via telephone call leaving users to worry about its misuse through their phone being stolen or the SMS being seen by a hacker who can easily hack the SIM network provider and read the message, or by specific calls being diverted to his/another mobile number without the knowledge of the original recipient. The result was a huge detriment to the user, leaving him to worry about his hard-earned money. As a result, we are presenting this Smart Wallet approach.

K. L. Anusha, G. Krishna Lava Kumar, Aruna Varanasi

A Global Dispatcher Load Balancing (GLDB) Approach for a Web Server Cluster

With the volatile expansion of the internet, numerous innovative online applications and services are in development. In conventional internet architecture, the innovative disputes are imposed by the fashionable applications. By using multiple servers, web server performance is improved and the effectiveness of a simulated web server system depends upon the process of distributing client requests. The distribution of client requests must occur in a way that is transparent to users among multiple server nodes, which affects availability and scalability in the distributed web server system. Thus, in this study, an efficient load balancing architecture called global dispatcher-based load balancing (GDLB) is proposed, which uses both domain name system and dispatcher. With this approach, performance is estimated to be better than with existing approaches. To analyze performance, a JMeter testing tool is used for dynamic load generation and performance measurement in a real-life internet scenario.

Kadiyala Ramana, M. Ponnavaikko, A. Subramanyam

Automation of Railway Crossing Gates Using LabVIEW

Train accidents at level crossings have always been a concern for the railways. The aim of this paper is to propose an automatic gate system at unmanned level crossings to replace gates operated by gatekeepers. By using this automatic railway gate management system, the arrival of the train is detected by the proximity detector placed with reference to the gate. Hence, the time that the gates are closed is less than with hand-operated gates and the amount of human labor needed is reduced too. This system can be employed at unmanned crossings wherever the probabilities of accidents is high and reliable operation is needed. It provides safety to road users by reducing accidents that occur as a result of carelessness on the part of either road users or gatekeepers. Mistreatment IR sensors and servo motors work automatically to open and shut the gates depending on the presence of train before the crossing. With relevance to the paper that used a single IR sensor to trace the train’s position by employing a controller, we used two IR sensors on either side of the track. And later we programmed them by connecting them to myRIO through National Instruments LabVIEW platform.

N. Nagaraju, L. Shruthi, M. S. D. Hari

RF Energy Harvesting Using a Single Band Cuff Button Rectenna

In this paper a small-sized cuff button rectenna is proposed. The proposed antenna structure is a circular patch antenna of cuff button shape. It is fabricated and tested for suitability to power wearable electronics at 2.4 GHz. The wearable antenna, which resembles a cuff button, is made of a PTFE taconic ceramic substrate. The substrate has a permittivity εr equal to 10. RF-DC conversion is achieved by a diode rectifier and a DC-DC step-up converter. The measured efficiency is 51% at 2.4 GHz or 0 dBm. This rectenna can be used in wireless power transmission systems that transmit power by radio waves.

R. Sreelakshmy, G. Vairavel

Hexagonal Intersection-Based Inner Search to Accelerate Motion Estimation

The computational complexity of motion estimation (ME), increases proportionally with the number of search points. As a result, rapid ME techniques reduce complexity by using different search patterns. Of these techniques, hexagonal search (HS) with a small diamond search pattern (SDSP) significantly reduces complexity compared to other fast ME algorithms. The proposed hexagonal intersection search (HIS) algorithm improves the inner search of HS. The new algorithm cares the inner points near to the intersection of a hexagon rather than a SDSP or a full search. The proposed algorithm reduce 7–15% search points compared with HS. Compared with other popular algorithms, the HIS algorithm made the lowest number of average search points with negotiable PSNR loss.

P. Palaniraj, G. Sakthivel

A Comprehensive Study of 1D and 2D Image Interpolation Techniques

Image interpolation plays an important role in converting a low resolution image into a high resolution image. This paper provides a comprehensive study of perdurable image interpolation techniques, such as nearest neighbor, bilinear, bicubic, cubic spline, and iterative linear interpolation. The usage of a Lagrange polynomial and a piecewise polynomial gives a better fitting curve for interpolated pixel values. The parameters of interest are the signal-to-noise ratio, peak signal-to-noise ratio, mean square error and processing time. Experiment results are used to analyze the performance of interpolation algorithms. These results help us to choose an appropriate algorithm for better usage.

V. Diana Earshia, M. Sumathi

Reinforcement Learning-Based DoS Mitigation in Software Defined Networks

A software defined network (SDN) is an OpenFlow-based network that initiates innovative traffic engineering and also simplifies network maintenance. Network security is still as stringent as that of traditional networks. A denial of service (DoS) attack is a major security issue that makes an entire network’s resources unavailable to its intended users. Blocking the flows based on the number of flows per port threshold was the most common method employed in the past. At some occasions legitimate traffic also takes the huge flow will punish by default rules. In order to address this issue, I proposed a reinforcement learning-based DoS detection model that detects and mitigates huge flows without a decline in normal traffic. An agent periodically monitors and measures network performance. It also rewrites the flow rules dynamically in the case of rule violation.

A. VishnuPriya

Design of a Low Power Full Adder with a Two Transistor EX-OR Gate Using Gate Diffusion Input of 90 nm

A full adder is the one of the main parts of an arithmetic logic unit (ALU). In this paper a full adder is developed using gate diffusion input (GDI) to perform fast arithmetic operations. The main aim of this paper is the design of a two transistor XOR gate-based full adder using a gate diffusion input (GDI) technique. A two transistor (2T) EX-OR gate is a suitable gate in the design of a full adder. The intention behind the novel method of a 2T EX-OR gate-based full adder design is to reduce power and improve speed in an optimized area with a lower transistor count compared with CMOS technology. A GDI approach is the one of better methods available for the design of digital logic circuits and tends to run the improved conditions. The proposed technique is then applied to a full adder design. The complete work is carried out using the 90 nm technology of a cadence tool to calculate power, delay, and area for the 2T EX-OR gate. The resulting analysis shows that the proposed method is better than conventional CMOS technology.

J. Nageswara Reddy, G. Karthik Reddy, V. Padmanabha Reddy

Analysis of e-Recruitment Systems and Detecting e-Recruitment Fraud

Recruitment is the one of the major tasks for the human resource department of any organization. At present, human resource management recruits employees using a manual procedure. This manual procedure means that employees must attend interviews and this is time consuming for the organization as well as for the candidates who attend interviews. To overcome this limitation, several e-recruitment tools are available. In this paper we analyze these e-recruitment tools along with potential fraud detection tools. We also look at the advantages and disadvantages of e-recruitment.

M. Niharika Reddy, T. Mamatha, A. Balaram

Issues in Wireless Sensor Networks with an Emphasis on Security

Wireless sensor networks (WSNs) are spatially dispersed systems with self-configuring sensor nodes connected via a wireless medium. The applications of WSNs are growing exponentially regardless of their known shortcomings. The very first applications of WSNs were military and surveillance based and even now military applications form a major portion of WSN applications. Due to the aforementioned reason and the sensitive nature of the data collected by WSNs, security has become a prime concern. This paper is focused on various issues present in WSNs with a minor emphasis on security.

Kartik Sharma, Sheeba Sharma

Evaluation of Selected Tree- and Mesh-Based Routing Protocols

This paper researches various routing protocols, problems and necessities comparatively in MANET routing and layout concerns which include classifications primarily based on layers and other aspects. The layout and implementation of PUMA is a declarative constraint-fixing platform for coverage-based total routing and channel selection in multi-radio wi-fi mesh networks. PUMA integrates a high-performance constraint solver with a declarative networking engine. PUMA achieves a high data delivery ratio with very restricted manage overhead, which is almost constant in a large range of community situations. PUMA uses an unattached manipulate packet format for querying the receivers while ODMR has separate manage packets for querying exclusive manipulate information. The outcomes from a huge range of eventualities of varying mobility, organization members, a wide variety of senders, traffic load, and a wide variety of multicast organizations show that PUMA attains higher packet delivery ratios than ODMRP and MAODV, whilst incurring some distance less manipulate overhead.

T. Harikrishna, A. Subramanyam

Reduction of Kickback Noise in a High-Speed, Low-Power Domino Logic-Based Clocked Regenerative Comparator

The comparator is the most significant element in the design of ADCs. Also there is a lot of demand for low-power, high-speed VLSI circuits. Therefore to maximize power efficiency and speed in ADCs, there is a desire to design high-performance clocked regenerative comparators. The regenerative latch of the comparator is responsible for taking decisions quickly and accurately. Normally, the accuracy of an ADC is degraded due to disturbance in the input voltage called kickback noise, which usually occurs with large variations of voltage at coupled regenerative nodes. This paper describes an analysis of the minimization of kickback noise in a clocked regenerative double tail comparator. To improve further on the double tail comparator, a new domino logic-based regenerative comparator is realized with high speed, low power and reduced kickback noise at low supply voltages. The simulated results using 130 nm CMOS technology confirm the theoretical results. The analysis of the proposed design demonstrates that kickback noise, power, and delay are considerably reduced. The simulation work was carried out using Mentor Graphics tools.

N. Bala Dastagiri, K. Hari Kishore, G. Vinit Kumar, M. Janga Reddy

Two-Level Intrusion Detection System in SDN Using Machine Learning

Software Defined Networking (SDN), the new paradigm in network architecture is changing how we design, manage, and operate an entire network, making networks more agile, flexible, and scalable. Such admirable features arise from the design factor that, in SDN, the control plane is decoupled from the data plane and instead resides on a centralized controller that has complete knowledge of the network. As SDN continues to flourish, security in this realm remains a critical issue. An effective intrusion detection system (IDS), which can monitor real-time traffic, detect and also identify the class of attack would greatly help in combating this problem. This work aims to heighten the security of SDN environments by building an IDS using the principles of machine learning and genetic algorithms. The proposed IDS is divided into two stages, the former to detect the attacks and the latter to categorize them. These stages reside in the switches and the controller of the network respectively. This approach reduces the dependency and the load on the controller, as well as providing a high attack detection rate.

V. Vetriselvi, P. S. Shruti, Susan Abraham

Geometric Programming-Based Automation of Floorplanning in ASIC Physical Design

The ASIC physical design process is a complex optimization problem with various objectives such as minimum chip minimum wire length, area, minimum of vias. The main aims of optimization are to improve the performance and reliability etc., of the ASIC design process. The objectives mentioned can be achieved through the effective implementation of floorplanning before other steps are implemented. In this study, a pseudo code is developed for floorplanning using geometric programming to achieve global optima. This study uses simulations performed using MATLAB GGP toolbox.

N. Bala Dastagiri, K. Hari Kishore, Vinit Kumar Gunjan, M. Janga Reddy, S. Fahimuddin

Design of a Power Efficient ALU Using Reversible Logic Gates

Today’s VLSI design technology is moving very quickly into low power, high speed and micro areas of development. Reversible logic has played an important role in this, notably in quantum computing and DNA computing, and presently moving into optical computing also. It is also found that under some ideal conditions it can produce zero power dissipation. A known fact that an arithmetic logic unit (ALU) is one of the core components of a the CPU in a computer. The design of an ALU using different reversible logic gates is proposed. The proposed reversible logic-based ALU is implemented using a Mentor Graphics tool in 130 nm technology for power efficiency. The power dissipation of two proposed ALU designs and a conventional area-based ALU have been compared. The conventional ALU dissipates the power 10% reversible logic-based ALU.

B. Abdul Rahim, B. Dhananjaya, S. Fahimuddin, N. Bala Dastagiri

Modelling and Mitigation of Open Challenges in Cognitive Radio Networks Using Game Theory

Cognitive radio networks (CRNs) are being envisioned as drivers of the next generation of ad hoc wireless networks due to their ability to provide communications resilience in continuously changing environments through the use of dynamic spectrum access. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by CRNs still remains a daunting challenge. In this survey, we put forward concerns relating to security, spectrum sensing and management, and resource allocation and performance of CRNs and model mitigation techniques using game theory. Game theory can be a useful tool with its ability to optimize in an environment of conflicting interests. Finally, we discuss the research challenges that must be addressed if CRNs are to become a commercially viable technology.

Poonam Garg, Chander Kumar Nagpal

On Control Aspects of Quality of Service in Mobile Ad Hoc Networks

Provisioning Quality of Service (QoS) in a MANET is a prominent research area due to the ongoing increasing range of MANET applications. The need to improve QoS in these networks has been vital due to the traits which include dynamically changeable network topology, be short of facts about state, unavailability of a primary controller, and insufficient availability of resources. To quantitatively evaluate QoS in a MANET several associated metrics are preferred. This paper explores QOS aspects and metrics, after which mentioned the scope and relevance of manipulated aspects in view of the divisible and non-divisible traffics in the network for QoS.

C. Siva Krishnaiah, A. Subramanyam

Securing CoAP Through Payload Encryption: Using Elliptic Curve Cryptography

The vision of the IoT is to not only make our everyday lives easier but also at the same time to ensure a secure environment. As the networking world moves closer towards an environment comprising of minimalistic ubiquitous nodes, IoT-based protocols cannot afford to accommodate security vulnerabilities. In this paper, we identify and mitigate the existing security flaws in the CoAP protocol of the IoT. A real-time system is developed to put the mitigated system into use and analyze the enhanced security. Additionally, we quantitatively look to evaluate the vulnerability of current implementation of CoAP and the magnitude of mitigation the method suggested in this paper provides. A secondary quantitative measure is used to prove that the overhead of the applied encryption is acceptable in terms of efficiency for the mitigation achieved.

M. Harish, R. Karthick, R. Mohan Rajan, V. Vetriselvi

A Survey of Fingerprint Recognition Systems and Their Applications

Recognition for authentication using biometrics is an intricate pattern recognizing technique. The process is really hard to architect and design, and choosing precise algorithms competent of fetching and extracting significant features and then matching them correctly, particularly in the cases where the quality of the fingerprint images are poor quality image capturing devices are used. Problems also occur where minutia are clearly visible on very small fingerprint area that are not exactly capture by camera. It is a false assumption that fingerprint recognition is a completely settled area regarding the authentication of a person just because it always give the correct identity of an individual. Fingerprint identification remains a very complex and intricate pattern-recognition system for authentication of a person.

Puja S. Prasad, B. Sunitha Devi, M. Janga Reddy, Vinit Kumar Gunjan

Iris Recognition Systems: A Review

Recognition for authentication using biometric features is an intricate pattern-recognizing technique. The process is extremely hard to design and build, and choosing the exact algorithms competent to fetch and extract significant features and then match them correctly, particularly in cases where the quality of the captured images is poor or low-quality image capturing devices with very small capturing areas are used. It is a false assumption that biometric recognition is a completely settled area regarding the authentication of a person just because it always gives the correct identity of an individual. Iris identification remains a very complex and intricate pattern recognition system for authenticating a person. This paper focuses on the different techniques used for authentication.

Puja S. Prasad, D. Baswaraj

Efficient Image Segmentation Using an Automatic Parameter Setting Model

Most image analysis methods perform segmentation as a first step towards producing the object description. In these methods both input and output are images only, but the output is an abstract representation of the input. Image segmentation is responsible for partitioning an image into multiple sub-regions based on a desired feature, such as edge, point, line, boundary, texture, and region. The most common segmentation methods use intensity-based images. Snakes or active contours (AC) are used extensively in computer vision and image processing applications, particularly to locate object boundaries. During a literature survey, it has been identified that many segmentation issues like gray values of pixels remain equal for a area, rapid change of image gradient, large gradient due to noise and identification of boundary between areas. The traditional adaptive distance preserving level set evolution method works fine for natural and synthetic images. However, it is necessary to set the performance parameters manually every time with respect to the type of input image. Hence, the proposed method of an automatic parameter setting model (APSM) improves the adaptive distance preserving level set evolution based on region by setting the performance parameters automatically with the help of an analysis of the image quality. The results show the effective performance of segmentation by reducing the number of iterations with an improved output quality.

D. Baswaraj, Puja S. Prasad

Quantitative Evaluation of Panorama Softwares

Image stitching has been practiced in various computer vision and scientific study areas. Many different image stitching algorithms have been proposed by different research groups in the past, and there are many different image stitching software products available on the market. However, a comparison between different stitching software products and an evaluation of them has not been performed so far. Furthermore, most previous quality assessment approaches have not had an adequate number of performance matrices, while others have suffered from the adverse effects of computational complications. Our objective is to identify the best software for panoramic image stitching. In this paper we measure the robustness of different software products by assessing image quality of a set of stitched images. For the evaluation itself, a varied set of assessment criteria is used, and evaluation is performed over a large range of images captured in different scenarios using differing cameras. Results show that Autostitch performs relatively well for all types of scenes and for all types of dataset.

Surendra Kumar Sharma, Kamal Jain, Merugu Suresh

Emerging Trends in Big Data Analytics—A Study

Big data refers to exceptionally large datasets that are growing exponentially with time. The three key enablers for the growth of big data are (1) data storage, (2) computation capacity, and (3) data availability (Grobelnik M, Big-Data tutorial, 2012 [1]). This massive, heterogeneous, and unstructured digital content cannot be processed by traditional data management techniques and tools effectively, but this problem is overcome by using big data analytics. In this paper, we have discussed various big data services, languages, and data visualization tools. Big data helps organizations to increase sales and improves marketing results. It also improves customer service, reduces risk, and improves security. Both high storage and computation are important requirements for big data analytics. Information technology researchers and practitioners have faced the major challenge of designing systems for the efficient handling of data and its analysis for the decision-making process as the amount of data continues to grow. Big data is available in three forms, namely structured, unstructured, and semi-structured. The top ten big data technologies are (1) predictive analytics, (2) NoSQL databases, knowledge discovery and searching, (4) stream analytics, (5) data fabric for in memory computing, (6) distributed file stores, (7) virtualization of data, (8) integration of data, (9) preparation of data, and (10) quality of data. Amazon Elastic MapReduce, Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB are data integration tools used to manipulate big data accurately.

G. Naga Rama Devi

A Novel Telugu Script Recognition and Retrieval Approach Based on Hash Coded Hamming

Due to their many applications, optical character recognition (OCR) systems have been developed even for scripts like Telugu. Due to the huge number of symbols utilized, identifying the Telugu words is a very complicated task. Pre-computed symbol features are stored by these types of systems to be recognized or retrieved from a database. Hence, searching of Telugu script from the database is a challenging task due to the complication involved in finding the features of the Telugu word images or scripts. Here, we implement a novel Telugu script recognition and retrieval approach based on a method called hash coded hamming (HCH). Hash coding will be used as a feature extractor and the hamming distance will be utilized as a replacement for conventional Euclidean distance in order to measure the similarity between query and database images. Simulation analysis shows that the proposed scheme has a superior performance to the conventional approaches presented in the literature.

K. Mohana Lakshmi, T. Ranga Babu

Comparison-Based Analysis of Different Authenticators

In the modern era, advances in technology are endless and information security has a vital role to play, in order to overcome or provide security related things. Authentication is an important factor when considering security. This paper focuses on evaluating different modes of security, such as passwords, biometrics, and security tokens etc. which we can state as authenticators or unique output for the combination. Here we focus on biometric techniques for the purpose of authentication. Every individual is recognized by parameters or characteristic features which are physiological in nature. In order to provide services to an individual, a verification system should be used or upgraded in order to avoid anonymous user and grant authenticated user based on authentication for any service. This paper details a review of authentication as it relates to different users and an evaluation based on source criteria that are unique in nature.

K. Kishore Kumar, A. M. Deepthishree

Clustering Method Based on Centrality Metrics for Social Network Analysis

The significance of a node in a social network is quantified through its centrality metrics, such as degree, closeness, and betweenness. However, many methods demonstrating the relevance of a node in the network have been proposed in the literature. In this digital smart world, the evolution of social networks occurs in various different directions at an unprecedented speed. A network evolution mechanism that provides the state of each node and its changes from its inception to its extinction over time will help in understanding its behavior. Often the strategy behind evolution is unknown and would not be reproduced in its totality. However, it is essential to understand behavior of the network as this can greatly facilitate its management before it becomes uncontrollable. A heuristics-based cluster method is proposed in this paper which combines centrality metrics and categorizes the entire network.

Siddapuram Arvind, G. Swetha, P. Rupa

Future Aspects and Challenges of the Internet of Things for the Smart Generation

The internet is now a basic necessity for human beings, especially in modern cities and metropolitan areas. Without the internet, an educated person feels helpless and unable to understand and follow events. At present, most people depend on machines. The field of computer engineering has helped the process of automation and the control of software as well as hardware devices. The internet of things (IoT) is a field of computer engineering that presents a synchronous behavior of components in a real-time system. Every piece of hardware and software that assists in accessing the internet or is used by the internet constitutes a main part of the IoT. The IoT includes the applications used in every field, e.g., healthcare, engineering, designing, inventory control, machine control, selling-purchasing, and the export-import of goods etc. In modern cities almost everyone uses the internet with individuals being linked to it through variable bandwidths and network ranges. People can access internet easily but they are not aware of the various issues, problems, and challenges of providing data to everyone at the same time on an unlimited number of topics. In this paper, the architecture of the IoT, the functioning of the IoT, the applications of the IoT in different fields, along with the research challenges and problems relating to the IoT are discussed.

Chander Diwaker, Pradeep Tomar, Atul Sharma

Impact of Node Mobility and Buffer Space on Replication-Based Routing Protocols in DTNs

A delay-tolerant network (DTN) is a kind of network in which nodes are not directly connected with each other so they communicate through intermediate nodes. As the mobility of nodes is so high in DTNs it is difficult to deliver a message without the creation of duplicate copies for distribution in the network. In this paper the impact of node mobility and the impact of buffer spaces on replication-based routing techniques called epidemic routing and sprays and waits routing has been assessed. To evaluate performance metrics, measures such as delivery ratio, drop rate, overhead ratio, and the number of replications have been used. To simulate the above routing protocols ONE (opportunistic network simulator) simulation was used.

Atul Sharma, Chander Diwaker

A New Surgical Robotic System Model for Neuroendoscopic Surgery

During endoscopic surgery, the surgeon holds and manipulates the endoscope inside the operating area. Using a robotic handle for these tasks has beneficial points which have been covered by a rich literature. Most of the previous works have involved laparoscopy rather than neuroendoscopy which is fairly new in comparison. In this paper the difference between the two is discussed and the design of a suitable robotic handle for neuroendoscopy is proposed.

Velappa Ganapathy, Priyanka Sudhakara, Amir Huesin, M. Moghavvemi

Survey on Security in Autonomous Cars

The improvements made in automotive control systems and sensory technologies have led to the rise in autonomous cars. These cars use a wide range of networking and sensory technologies to control the car and interact with the environment. One recent application which is making headway is in the alliance of the IoT and autonomous cars. Since the IoT focuses on connectivity between different isolated devices found over the web, it is also used to provide services to autonomous cars. It is therefore, imperative to ensure that data privacy and security is maintained by the system. Hence, this paper surveys and discusses the security issues faced in autonomous cars.

K. V. Harish, B. Amutha

Identification of Vegetable Plant Species Using Support Vector Machine

This study proposes a method for the identification of vegetable plant species. Each plant leaf has its own features that can be used to identify the species it belongs to. Some of the features of a leaf that enable specific identification of a plant species are its shape, vein pattern, apical and basal features, and color patterns. Those salient features extracted from the leaf image are used along with a data mining algorithm, such as support vector machine, to identify of the species that the leaf belongs to. In this study two vegetable species, namely eggplant and ladies’ fingers were considered. Support vector machine is suited to situations where the data need to be classified into two groups.

K. Deeba, B. Amutha

Review of Wireless Body Area Networks (WBANs)

This comprehensive study guides the researchers to continue research in Wireless Sensor Networks and understanding of patient monitoring systems, protocold, and communication standards etc. This paper covers general wireless body area network (WBAN) architecture, methodologies, communication standards, and challenges to understanding. We summarize the frequency range, bandwidth, channel capacity, and bit rates of different communication standards and look at how to design sensor nodes and coordinator nodes for WBANs.

B. Manickavasagam, B. Amutha, Priyanka Sudhakara

Association Rule Mining Using an Unsupervised Neural Network with an Optimized Genetic Algorithm

The best known and most widely utilized pattern finding algorithm in data mining applications is association rule mining (ARM). Extraction of frequent patterns is an indispensable step in ARM. Most studies in the literature have been implemented on the concept of support and confidence framework utilization. Here, we investigated an efficient and robust ARM scheme based on a self-organizing map (SOM) and an optimized genetic algorithm (OGA). A SOM is an unsupervised neural network that efficaciously produces spatially coordinated internal feature representations and detected abstractions in the input space and is the most efficient clustering technique that reveals conventional similarities in the input space by performing a topology maintaining mapping. Hence, a SOM is utilized to generate accurate clustered frequency patterns and an OGA is used to generate positive and negative association rules with multiple consequences by studying all possible patterns. Experimental analysis on various datasets has shown the robustness of our proposed ARM in comparison to traditional rule mining approaches by proving that a greater number of positive and negative association rules is generated by the proposed methodology resulting in a better performance when compared to conventional rule mining schemes.

Peddi Kishor, Porika Sammulal

An Optimal Heuristic for Student Failure Detection and Diagnosis in the Sathvahana Educational Community Using WEKA

The study offered in this paper aims to explore students characteristics and to determine unsuccessful student groups in respective subjects based on their earlier education and the impact of other factors in multiple dimensions. Predictive data mining techniques such as as classification analysis is applied in the analysis process. Datasets used in the investigation were collected from all academic years in the Sathavahana educational community contains different professional disciplines through online. The method adopted is to know the number of students failing in each subject and analyze the reasons for failure using data mining tools like WEKA. This model works effectively with large datasets. It has been tested on WEKA with different algorithms.

P. Vasanth Sena, Porika Sammulal

Computer Vision Model for Traffic Sign Recognition and Detection—A Survey

Computer vision is an interdisciplinary field which deals with a high level understanding of digital videos or images. The result of computer vision is in the form of a decision or data. This also includes methods such as gaining, processing, analyzing, understanding, and extracting high dimensionality data. Object recognition is used for identifying the objects in any image or video. The appearance of objects may vary due to lighting or colors, viewing direction, and size or shape. The problem we identify here is accuracy at nighttime and in certain weather conditions is less that when compared to daytime and also we enable to detect some signs at the night time. In this paper, we present a detailed study of computer vision, object recognition, and also a study of traffic sign detection and recognition along with its applications, advantages, and disadvantages. The study focuses on several subject, e.g., proposal theme, model, performance evaluation, and advantages and disadvantages of the work. The performance evaluation part is further discussed w.r.t. the experimental setup, different existing techniques, and the various performance assessment factors used to justify the proposed model. This study will be useful for researchers looking to obtain substantial knowledge on the current status of traffic sign detection and recognition, and the various existing problems that need to be resolved.

O. S. S. V. Sindhu, P. Victer Paul

Color-Texture Image Segmentation in View of Graph Utilizing Student Dispersion

The Image segmentation is that for investigation is a noteworthy part of discernment and up to date it is still testing issue for machine recognition. Numerous times of concentrate in PC view demonstrate that dividing a picture into important districts for ensuing preparing (e.g., design acknowledgment) is similarly as troublesome issue as never changing case identification. In this paper work, the proposed one uses the particular sort of frameworks had been taken after to complete shading surface picture division. Division strategies are intended to incorporate more component data, with high exactness and agreeable visual total. The division procedure depends on MSST and understudy’s t-conveyance technique.

Viswas Kanumuri, T. Srinisha, P. V. Bhaskar Reddy

A Novel Approach for Digital Online Payment System

Nowadays digital transaction security is seen as essential in an online payment system. Earlier, cryptographic authentication techniques were used to make transactions very secure with third-party verification. However, in recent times, digital transactions have developed to allow online payments to be made directly from one party to another, without the intervention of the third party. This kind of P2P network transaction is achieved by utilizing the blockchain innovation. A blockchain uses the idea of Bitcoin. It isn’t observed by the central authority, but its clients direct and approve exchanges when one individual pays another for merchandise or administrations, dispensing with the requirement for outside confirmation. All finished exchanges are freely recorded as a block and each block is added one after other to form as a blockchain. In this paper, we present the workflow of a digital online payment system using the blockchain technique. We explain the secure sign-in procedure of the blockchain strategy. We elaborate on the effects of blockchain technology on the online transaction management system in terms of security and usability.

M. Laxmaiah, T. Neha

Ensemble-Based Hybrid Approach for Breast Cancer Data

Classification of datasets with characteristics such as high dimensionality and class imbalance is a major challenge in the field of data mining. Hence to restructure data, a synthetic minority over sampling technique (SMOTE) was chosen to balance the dataset. To solve the problem of high dimensionality feature extraction, principal component analysis (PCA) was adopted. Usually a single classifier is biased. To reduce the variance and bias of a single classifier an ensemble approach, i.e. the learning of multiple classifiers was tested. In this study, the experimental results of a hybrid approach, i.e. PCA with SMOTE and an ensemble approach of the best classifiers obtained from PCA with SMOTE was analyzed by choosing five diverse classifiers of breast cancer datasets.

G. Naga RamaDevi, K. Usha Rani, D. Lavanya

Probabilistic-Based Rate Allocation Flow Control Technique for Traffic Governance in Wireless Sensor Networks

The proposed control mechanism delivers higher data traffic flow over a wireless sensor network. The proposed work is evaluated based on the probability of rate flow control method where the queue length is controlled by the traffic model based on the given traffic flow. The approach defines traffic governances based on the node mobility approach, where nodes are dynamically moved from short range to a more distant range. The link overhead and end-to-end delay are minimized in this technique, in comparison to the conventional controlling technique. Due to an early evaluation of congestion probability in the buffer unit, the blockage probability has been controlled.

Sudha Arvind, V. D. Mytri, Siddapuram Arvind

Amended Probabilistic Roadmaps (A-PRM) for Planning the Trajectory of Robotic Surgery

Trajectory planning is an essential aspect of research into the use of pliable needles for surgical processes. Sampling-based algorithms can generate trajectories and reach the target avoiding obstacles. However, the trajectories cannot match the physical constraints of injecting the pliable needle into human flesh, as the trajectories are not continuous. Aimed at solving this problem, an enhanced probabilistic roadmap (PRM) is used in this work. A PRM generates trajectories for surgeries that are minimally invasive and simultaneously guarantees the effectiveness and continuity of the trajectory. In this research work, the classical PRM method is enhanced by using a shape preserving piecewise cubic hermite interpolation (PCHIP) technique, used to generate smooth trajectories, which are important for navigating the curved path of the pliable needle in surgery. Trajectories that have been generated using the PRM satisfy direction constraints approach in terms of both source and target positions. As a result, the trajectories produced by the pliable needle are dynamically and geometrically feasible. Results of simulations performed show the validity of the algorithm implying that it can be efficiently used in trajectory planning of pliable needles in real-time surgical operations.

Priyanka Sudhakara, Velappa Ganapathy, B. Manickavasagam, Karthika Sundaran

Region-Based Semantic Image Clustering Using Positive and Negative Examples

Discovering various interest of users from massive image databases is a strenuous and rapid impel expedition region. Understanding the needs of users and representing them meaningfully is a challenging task. Region-based image retrieval (RBIR) is a method that incorporates the meaningful description of objects and an intuitive specification of spatial relationships. Our proposed model introduces a novel technique of semantic clustering in two stages. Initial semantic clusters are constructed in the first stage from the database log file by focusing on user interested query regions. These clusters are further refined by relevance feedback in the second stage based on probabilistic feature weight using positive and negative examples. Our results show that the proposed system enhances the performance of semantic clusters.

Morarjee Kolla, T. Venu Gopal

A Cost Effective Hybrid Circuit Breaker Topology for Moderate Voltage Applications

Compared to mechanical circuit breakers, with respect to speed and life, solid state circuit breakers based on modern high power semiconductors offers considerable advantages. During a short circuit the voltage profile of the power grid can be improved since the fault current is reduced. The distortion in voltage caused by a three-phase short circuit can be limited to fewer than 100 μs. In this paper, a theoretically approached active thyristor circuit based on a new hybrid topology of connecting the semiconductor devices in series and parallel is proposed. This permits an increase in supply voltage and fault clearance without arcing. In turn, and to get benefit from a current limitation, the circuit breaker is realized with the extinction of an electrical arc when the breaker is opened. Hence the proposed topology has led to a wider integration of solid state circuit breakers (SSCB) in existing power grids because of their cost effective nature when compared to turn-off semiconductor devices.

D. S. Sanjeev, R. Anand, A. V. Ramana Reddy, T. Sudhakar Reddy

Multi-criteria Decision Analysis for Identifying Potential Sites for Future Urban Development in Haridwar, India

Decadal population growth and increasing demand for land have led to the present study to identify potential sites for future development in Haridwar City, Uttarakhand. This study is conducted using remote sensing (RS) and geographical information system (GIS) using various thematic layers, such as slope, elevation, land use land cover (LULC), a digital elevation model (DEM), normalized difference vegetation index (NDVI), urban landscape dynamics (ULD), and other physical parameters which can affect the growth of urban expansion. GIS provides an opportunity to integrate various parameters with population and other relevant data associated with features which will help to determine potential sites for expansion. The appropriate weights are assigned to each layer using an analytical hierarchical process (AHP), with a multi-criteria decision analysis (MCDA) technique. The weights are assigned using expert opinion on the factors which are most suitable to least suitable for urban expansion according to their importance and are used in the study to extract the best result from the given data.

Anuj Tiwari, Deepak Tyagi, Surendra Kumar Sharma, Merugu Suresh, Kamal Jain

Configurable Mapper and Demapper for the Physical Layer of a SDR-Based Wireless Transceiver

In this paper, field programmable gate array (FPGA) implementation of an adaptive constellation mapper and demapper is discussed for use in a SDR-based wireless transceiver. The adaptive functions at the physical layer of wireless technologies play a key role in achieving optimum performance. These functions include forward error correction (FEC) coding, puncturing, orthogonal frequency division multiplexing (OFDM), constellation mapping, sub-channelization, and frame assembly etc. We present here constellation mapping and demapping that supports gray-coded BPSK, QPSK, 16QAM, and 64QAM modulation schemes. This block can be configured at run time so that you can use them in multi-user systems where each user may be operating with a different modulation scheme. The proposed mapper/demapper block is implemented on a Xilinx Virtex-II pro FPGA and it is characterized in terms of functional correctness, area, power, and speed. Experimental results show that our design takes 1360 gates and can run at a maximum frequency of 320 MHz.

Zuber M. Patel

Experimental Investigation to Analyze Cognitive Impairment in Diabetes Mellitus

Metabolic disorders and cognitive impairment are very common age-associated disorders. Alzheimer’s disease and diabetes are two common diseases and their possibility of occurrence increases as the age of a person increases. This paper proposes the percentage of type 2 diabetes patients that have a chance of developing Alzheimer’s disease. Pathophysiology and clinical patterns share common features in both the diseases as shown by epidemiological studies. There are a number of studies that show evidence of a connection between type 2 diabetes and Alzheimer’s disease but there is no proof of any biochemical mechanisms present yet. We focused our study and experiments on whether the alteration of factors like glycogen synthase kinase-3β activity, olfactory function, and ApoE genotypes can identify early cognitive impairment, which leads to Alzheimer’s disease.

Vinit Kumar Gunjan, Puja S. Prasad, S. Fahimuddin, Sunitha Devi Bigul

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