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This project deals with the performance evaluation on the comparison of model-based control for drying process of paper industry. The dryer section is the last part of the paper machine and consists of a large number of rotating steam-heated cast iron cylinders by adjusting the set point of the stream pressure controller to the cylinders. In the design of model reference adaptive control, schema is used, in which the adaptive law has been developed by MIT rule. Similarly, design of PID and MRAC controller is used. This paper presents a nonlinear dynamic control, based on heat and mass balance for steam, cylinder, and paper. The control was performed to the combined drying process system using both the adaptive control algorithm and MPC controller method and its results were analyzed. A simulation is carried out using MATLAB. Simulation results reveal clear benefits of the model reference adaptive control over traditional controller and MPC controller methods. Thus, by controlling, this process proves real incentives for industrial implementation.

In the research field we require more time to read a single research paper; it also consumes more time to find the algorithms and limitations of the paper. So we require a fast reading system to identify this problem. This paper identifies the algorithms or techniques, or methods, and limitations of a research paper, and also classifies the area of the algorithm. Key phrases are sets of words that elucidate the relationship between context and content in the document. Key phrases are identified from the document and algorithms or techniques, or methods of that paper are extracted. Keywords are a subset of words that contain important information and the area is classified. Cue words are those that contain meaningful information used to identify the limitations of the paper.

Wireless sensor networks (WSN) is the collection of thousands sensor nodes. WSN faces many problems related to communication failures, data storage, and limited power supply. This paper includes the existing paradigms, such as fuzzy logic, neural network, evolutionary computing with some hybrid paradigms of computational intelligence, including some major challenges which help to reduce the gap between researchers and developers of WSN.

In this paper, we have tried to describe the details about the strategies and methods we have adapted to design and develop a digital Odia corpus of newspaper texts. We have also attempted to identify the scopes of its utilization in different domains of Odia language technology and applied linguistics. The corpus is developed with sample news reports produced and published by some major Odia newspapers published from Bhubaneswar and neighboring places. We have followed several issues relating to text corpus design, development, and management, such as size of the corpus with regard to number of sentences and words, coverage of domains and sub-domains of news texts, text representation, question of nativity, determination of target users, selection of time span, selection of texts, amount of sample for each text types, method of data sampling, manner of data input, corpus sanitation, corpus file management, and problem of copyright. The digital corpus is basically in machine readable format, so that the text becomes easy to process very quickly. We presume that the corpus we have developed will come to a great help to look into the present texture of the language as well as to retrieve various linguistic data and information required for writing a modern grammar for Odia with close reference to its empirical identity, usage, and status. The electronic Odia corpus that we have generated can also be used in various fields of research and development activities for Odia.

IEEE 802.11 wireless networks are becoming omnipresent nowadays, providing mobility as well as flexibility to the users accessing the information. Presently, it acts as an alternative to the wired network, but soon it may replace the wired network completely. The protocol is based on multiple access where a node competes with other nodes to get access to the communication medium and to transmit the data. A major aspect of wireless technology is roaming, which is defined as the ability to seamlessly change from one wireless AP to another. TCP used in wired networks is not appropriate for wireless networks because TCP assumes all loses as a result of congestion, which is not the case always in wireless links. Also, wireless networks are more vulnerable to security threats as compared to wired networks.

This paper introduces a Z-test for two correlation coefficients under neutrosophic statistics. The necessary steps to implement the proposed test are given. The statistic of the proposed test under indeterminacy introduced the first time. The application of the proposed test is given using temperature and wind speed data. From the analysis of the energy example, it is concluded that the proposed test is more efficient than the Z-test for two correlation coefficients under classical statistics in terms of the measure of indeterminacy, information, and flexibility. Based on the comparative study, it is recommended to apply the proposed test in the areas of energy and weather.

The paper presents Self-Study (SS) component as a pedagogical tool for enhanced learning in a set of identified course. The objective of the SS component is to enhance the understanding of the course beyond the classroom teaching. The traditional modes of course design, course delivery and course assessment provide less scope for in-depth learning of courses. Outcome-Based Education (OBE) is an effort to overcome limitations of traditional education by using many progressive pedagogical models, ideas, to meet a-k student outcomes of ABET and NBA. To meet the expectations of present engineering education, few courses of Electronics and Communication engineering programme are modified using SS component. Typical methods of SS component are course project, term paper/seminar, field work, modelling and simulation, case studies, proof of concept/prototype development, etc. The attainment of depth of learning is demonstrated using two activities, namely, course project and term of SS component. The student outcomes are assessed by using designed set of rubrics for each course-SS combination. The tradition mode of delivery facilitates the attainment of technical outcomes of ABET, whereas along with technical outcomes professional outcomes are also achieved through SS component. The assessment of attainment of student outcomes and student feedback of SS component act as inputs for the continuous improvement of teaching-learning process.

Digital watermarking at the least significant bit (LSB) for copyright protection is proposed in this paper based on the saliency map. The projected algorithm can embed more information into less perceptive areas within the original image determined by Itti-Koch saliency map. It gives a concept of the areas which has excellent data hiding capacity in an image. The area/region with less perceptibility denotes the most insignificant region from the aspect of visibility in an image, so any modification within these areas will be less evident to any observer. Here the algorithm is being evaluated by means of imperceptibility and robustness. Thus images embed with a watermark will project higher bit capacity.

In a rapidly digitizing country, Digital Financial Services (DFS) hold the promise of bringing financial inclusion to the last mile. Through the use of DFS, vulnerable and marginalized groups can reduce the risk and cost of using cash, and increase their access to a suite of financial services that would have been otherwise unavailable, or difficult to obtain, such as credit, savings, insurance, and pension. This paper provides an overview of the policy environment regulating digital finance in India as well as presents high-level statistics on the level of digitization across population segments in the country. It then moves on to describe the digital readiness, current usage of digital financial services, and specific issues and solutions for three vulnerable and marginalized groups with high digitization potential: women, micro-entrepreneurs, and domestic migrants. This is complemented by insights drawn from LEAD at Krea University’s primary research on these topics. Finally, the paper provides recommendations to accelerate the penetration of digital financial services among vulnerable groups. These include monitoring the implementation of recently introduced policies on data privacy, grievance redressal, and KYC simplification. It is also recommended to develop customized digital financial services that address the unique needs and profiles of vulnerable groups, such as those specifically analysed in this paper.

The COVID -19 pandemic has seen a rise in the deployment of digital health technologies. This includes those aimed at identifying the infected and making sure they did not spread the infection any further as well as other technologies providing data driven insights aimed at improving the effectiveness of decisions such as locking down certain areas and allowing others to re-open. This paper attempts to evaluate the socio-legal implications of the use of these technologies with a particular focus on privacy. It does that by examining a range of data technologies that were deployed during the pandemic with a view to assess the socio-legal implications of their use. It analyses the technologies themselves, the data collected, the manner in which it was intended to be used and the safeguards if any built into these technologies. Based on this analysis we will attempt to evaluate the privacy implications of these technologies. Never before have data technologies been used in this manner at the frontlines of our battle against a virulent disease. As a result there are no precedents that directly address what does or does not constitute a violation of personal privacy. Notwithstanding that, the paper attempts to arrive at a conclusion as to the legitimacy of the use of these technologies and the safeguards that would be appropriate under the circumstances.

In this paper, it is proposed to understand the opinion of the public regarding the policy of demonetization that is implemented recently in India through Aspect-based Sentiment Analysis (ABSA) that predicts the sentiment of specific aspects present in the text. The major aim is to identify the relevant contexts for various aspects. Most of the conventional techniques have adopted attention mechanisms and deep learning concepts that decrease the prediction accuracy and generate huge noise. Another major disadvantage with the attention mechanisms is that the sentiment related to few context words alters with various aspects, and hence it cannot be concluded from itself alone. This paper adopts the optimized deep learning concept for performing the ABSA for demonetization tweets. The proposed model involves various phases such as pre-processing, aspect extraction, polarity feature extraction, and sentiment classification. Initially, the different demonetization tweets collected from the Kaggle dataset are taken. Pre-processing is done with the help of four phases like stop words removal, punctuation removal, lower case conversion, and stemming from minimizing the data to its reduced format. This pre-processed data is further performed with aspect extraction to extract the opinion words. These extracted aspect words are converted to the features with the help of polarity score computation and Word2vec. The weight of the polarity scores is optimized using hybridization of two meta-heuristic algorithms like FireFly Algorithm (FF), and Multi-Verse Optimization (MVO), and the new algorithm is termed as Fire Fly-oriented Multi-Verse Optimizer (FF-MVO). Further, combined features are subjected to a deep learning algorithm called Recurrent Neural Network (RNN). As a modification to the existing RNN, the hidden neurons are optimized by the hybrid FF-MVO, FF-MVO-RNN classifies the positive and negative sentiments. Finally, the comparative analysis of different machine learning algorithms proves the competent performance of the proposed model.

The green technology wavelength division multiplexing based on polymer optical fiber (GT-WDM-POF) network solutions is presented in this paper. Green technology polymer optical fiber (GT-POF) splitter has been fabricated by environment-friendly handmade fusion technique, as an effective transmission media to split and recombine a number of different wavelengths. Two different wavelengths from ecologically friendly light emitting diode (LED) were fully utilized to transmit two different sources of systems; Ethernet connection and video transmission system. Red LED which in 650 nm wavelength capable to download and upload data through Ethernet cable while green LED in 520 nm wavelength transmits a video signal. Special filter has been placed between the splitter and receiver-end to ensure GT-WDM-POF network system can select and generate a single signal as desired. The material, fabrication method, system, and application approach in this study are based on the environment-friendly solution to reduce the power consumption and wastage without affecting the system performance. Our GT-POF splitter and GT-WDM-POF network solution proposed in this paper are the first reported up to this time.

Today World Wide Web has emerged as a second world. Everything we can think of is now available on this digital world. In the real world there exists many kinds of people. Some are good, some are bad, and some are trustworthy while the rest are liars. Similarly, in the digital world, there exits many websites out of which some are good while the rest are useless. So the big question is how can one know which content on www is trustworthy as every information can be changed with a few keystrokes. A lot of algorithms have been developed to identify the trust rank of a website, but none of them are up to the mark. So through this paper, we propose a simple mechanism by which we can test a website and automatically calculate the trust score of the website on the basis of not a single parameter but a cumulative combination of five parameters. Various test results have been included in this paper which prove that the method proposed in this paper is much better than the conventional methods which test websites manually.

This paper presents to ameliorate the performance of text-independent speaker recognition system in a noisy environment and cross-channel recordings of the utterances. In this paper presents the combination of Gammatone Frequency Cepstral Coefficients (GFCC) to handle noisy environment with i-vectors to handle the session variability. Experiments are evaluated on NIST-2003 database.

Mobile Ad hoc Network (MANET) is a wireless infrastructure less network in which nodes communicates with each other by establishing temporary network. One of the most important issues in the MANET is the congestion. It leads to the network performance degradation. In order to transmit real time data reliably in MANET, TCP can be used. But the congestion control techniques used by the TCP is inadequate for such networks because of node mobility and dynamic topology. Also the routing protocols designed for MANET do not handle the congestion efficiently. In this paper we proposed a new TCP congestion control scheme called TCP-R for detecting the congestion and proposed ADV-CC (Adhoc Distance Vector with Congestion Control) as a new dynamic routing algorithm to control congestion in MANET. ADV-CC improves the network performance than AODV due to its congestion status attribute as an additional feature. The main strength of this paper is performance results and analysis of TCP-R and ADV-CC.

This paper presents a study about effect of series compensation on double-circuit transmission lines. It improves power transfer capability of a long distance transmission lines. Series capacitors connected reduce inductive reactance of transmission lines and hence reduce the losses. It controls load flow between parallel circuits and improves dynamic and transient stability. In this paper, simulations are carried out for a double-circuit transmission line for different faults and for different compensation levels using Matlab/Simulink. This study is done on double-circuit transmission line feeding both industrial and residential customers. Simulation results are shown for different levels of compensation (15, 40, and 80 %) with different faults (single-phase to ground and three phase). Voltage regulation and power generation are studied in the results to check effectiveness of series compensation. Improvement of voltage profile during transient and steady state shows the effectiveness of series compensation.

Brain-computer Interfaces (BCIs) use the inherent brain activity of subjects in order to communicate with external devices. BCIs have been utilised by disabled users to navigate and interact with their environments using a host of application platforms such as mouse cursor controllers, virtual keyboards and mobile wheelchairs. These platform highlight the wide assistive potential of BCIs. These applications however are restricted to the subjects local environment and do not readily present an opportunity to communicate over long distances or allow messaging to multiple recipients. Social media platforms allow long distance communication over the internet and messaging to multiple recipients which can be leveraged for BCI-based communication. However, the integration of BCIs with social media platforms for communication is limited. This paper investigates the integration of a BCI with Twitter for the communication of public posts and private messages. An online P300-based BCI was developed which allowed for communication to Twitter. Three subjects participated in a programme of experimentation using the developed BCI. The BCI yielded an average classification accuracy of 86 % for the identification of user messages. All user messages however were successfully communication to Twitter. This paper therefore highlighted social media as a viable communication platform which can further improve the assistive value of BCIs. Possible areas of future study include the investigation of other classification methodologies and the integration of the BCI with further social media platforms such as Facebook and LinkedIN.

High-frequency switching is used to minimize the size and to improve the system performance. Most of the power supplies derived from ac mains source. The rectifier based PWM inverter has front end bridge rectifier with smoothing capacitor. As a result, the input has distorted line current and poor factor. The square wave inverter has non-sinusoidal output current. It affects the system performance and increases the switching loss and reduces the efficiency. All these problems affect the Ozone production and system efficiency. These problems are solved through proposed method. This paper presents the Power Factor Corrected (PFC) converter with parallel resonant inverter based power supply for ozone generator system. The proposed system has active power factor correction converter and Parallel resonant inverter which are used to achieve sinusoidal current and improve the supply power factor and zero-voltage switching across all switches. The active PFC converter with parallel resonant inverter fed ozone generator generates more ozone output compared to the conventional inverter. Thus the proposed system has less switching loss, less current harmonics, and better input power factor compared to the conventional system. The performance of the both inverters are compared and analyzed with the help of simulation results presented in this paper.

Graph partitioning is a key step in developing scalable data mining algorithms on massive graph data such as web graphs and social networks. Graph partitioning is often formalized as an optimization problem where we assign graph vertices to computing nodes with the objection to both minimize the communication cost between computing nodes and to balance the load of computing nodes. Such optimization was specified using a cost function to measure the quality of graph partition. Current graph systems such as Pregel, Graphlab take graph cut, i.e. counting the number of edges that cross different partitions, as the cost function of graph partition. We argue that graph cut ignores many characteristics of modern computing cluster and to develop better graph partitioning algorithm we should revise the cost function. In particular we believe that message combing, a new technique that was recently developed in order to minimize communication of computing nodes, should be considered in designing new cost functions for graph partitioning. In this paper, we propose a new cost function for graph partitioning which considers message combining. In this new cost function, we consider communication cost from three different sources: (1) two computing nodes establish a message channel between them; (2) a process creates a message utilize the channel and (3) the length of the message. Based on this cost function, we develop several heuristics for large graph partitioning. We have performed comprehensive experiments using real-world graphs. Our results demonstrate that our algorithms yield significant performance improvements over state-of-the-art approaches. The new cost function developed in this paper should help design new graph partition algorithms for better graph system performance.

In the present era, use of Automated Guided vehicles (AGV) has emerged for automation in material handling applications. AGVs are equipped with DC brushed/brushless motors with encoders. The encoders help AGV to move accurately on the track as encoders keep on monitoring the actual shaft position of DC motor. This is called a positional analysis of DC motor. The analysis will be helpful in track following as well as to prevent collision (Between two AGVs or between object and AGV). In the first phase of paper, the details of AGV, Interfacing of Arduino with DC encoder motor and Interfacing of Arduino with MATLAB are shown. In the second phase, the transfer function of the system, type of the system, order of the system, steady state and transient analysis are shown. In the final phase of the paper, system’s response with P, PI and PID controllers are shown with the help of Ziegler-Nicholas (Z-N algorithm) algorithm (Real time and simulation both) with respect to step input.

In this paper, an unsupervised (e.g. autonomous) shape recognition is performed in a structured environment with the help of rotation, translation and scale invariant parameters and fuzzy logic. In a practical scenario, it is undoubtedly difficult to apply a sharp cut-off for defining a particular object shape using an object parameter in crisp parameter based object recognition. To overcome this difficulty, we have proposed to use a well-known concept of fuzzy logic. Applying object’s shape parameters as fuzzy logic inputs, it was observed that oval-shaped, triangular, rectangular, pentagonal and hexagonal objects are autonomously perceived with an overall recognition rate of 91.45 %. The proposed scheme has also been compared with two existing algorithms, presents better results. The detailed description of the results is provided towards the end of this paper.

This paper presents THD responses of ZSI-fed induction motor drive system for the buck and boost operation. The proposed mechanism is simulated for various fault conditions, viz. MOSFET blown off fault, fault at MOSFET gate terminals for both the buck and boost operations. The simulation results are presented with corresponding THD values in this paper.

For last many years video authentication and detection of tampering in a video are major challenges in the domain of digital video forensics. This paper presents detection of one of the temporal tampering (frame drop) under no reference mode of tampering detection. Inspirit of the scheme presented by Upadhyay and Singh, this paper extends the features to train the SVM classifier and accordingly classify frames of given video as tampered or non-tampered frames, i.e. detects the tampering of frame drop. Subsequently given video is classified as tampered or non-tampered video. The obtained results with enhanced features show significant improvement in classification accuracy.

Cascaded multilevel inverters with photovoltaic arrays are becoming famous of its application like hybrid vehicles, solar plants, etc. This paper presents cascaded asymmetrical multilevel inverter with low harmonic distortion. Photovoltaic cells are used for given inputs to the asymmetrical multilevel inverter. Thirteen-level and fifteen-level multilevel inverters with THD values are discussed. LC filters are used with less number of switches. Number of output levels is also increased in this paper. The asymmetrical multilevel inverters are simulated using Mat lab/Simulink.

This research paper mainly concentrates on introducing DSL(Domain Specific language) approach in developing gaming applications. DSL approach hides the lower level implementation in C, C Sharp, C++, and JAVA and provides abstraction of higher level. The higher level of abstraction provided by the Domain Specific Language approach is error-free and easy to develop. The aim of this paper is to propose an approach to use GaML (Gamification Modelling Language, a form of DSL for gaming) for Unity based complex games efficiently in this paper. The paper doesn’t focus on the How and Whys of the Gaming Modelling Language usage, but rather focuses on the run-time enforcement. At the end of the paper, survey has been made on total lines of code and time invested for coding using a case study. The case study proves that DSL approach of automated code generation is better than manual.

The dynamic programming paradigm involves various important optimization problems. The set of optimization problems includes optimal binary search tree, longest common subsequence, binary knapsack, Matrix chain multiplication (MCM), and many more. In dynamic programming problems, the MCM of n matrices comprises the computation of the parenthesization for an optimal matrix product, which requires the computation time of O(n 3) using O(n 2) table size. We propose the MCM parallelization techniques for thread-level of multi-core CPU and group of threads on NVIDIA GPU. The prime objective of this paper is to present and analyze massively parallel implementations of MCM algorithm using OpenMP and CUDA on parallel systems such as Intel Xeon CPU and NVIDIA GPU. The implemented parallel MCM algorithm achieved a speedup of 10× on an Intel Xeon using OpenMP and a speedup of 7× on NVIDIA Quadro FX 3800 GPU with reference to its serial implementation. So the speedup achieved on multi-core CPU dominates the speedup achieved by the GPU. This paper also presents performance comparisons for OpenMP, when chunk size of iterations of a loop and scheduling techniques of those chunks among core changes.

This paper proposes an process/environmental parameters monitoring framework based on Wireless Sensor Networks (WSNs) using embedded systems. The developed system will have many applications particularly in the area of aquaculture, tea plantations, vineyards, precision agriculture, green houses monitoring etc. The complexity of the system increases for applications involving aquaculture. The developed systems are tailored for sensing in wide farmland’s without (or) very little human supervision. The fully designed and developed system consists of sensors for monitoring the parameters, sensor node with wireless network topology, gateway for collecting the information from the sensor node and transmitting the information to the central control unit. Data base, real-time monitoring and visualization facilities, controlling of sensor nodes and sensors are provided at the central control unit. Sensor nodes, sensor assembly and gateways should with stand demanding environmental conditions like rain, dust, heat, moisture, relative humidity etc. With these systems deployed in large farm’s bodies the scientist’s will have more and more field data with very little human intervention. The acquired data can be used to provide quantitative indications about the farm’s status, thus easy for analysis and decision making. In this paper we are focusing our discussion about WSNs in the field of aquaculture.

This paper designs a Multi-scale Spectral transformation technique for Voice Conversion. The proposed algorithm uses Spectral transformation technique designed using multi-resolution wavelet feature set and a Neural Network to generate a mapping function between source and target speech. Dynamic Frequency Warping technique is used for aligning source and target speech and Overlap-Add method is used for minimizing the distortions that occur in the reconstruction process. With the use of Neural Network, mapping of spectral parameters between source and target speech has been achieved more efficiently. In this paper, the mapping function is generated in three different ways, using three types of Neural Networks namely, Feed Forward Neural Network, Generalized Regression Neural Network and Radial Basis Neural Network. Results of all three Neural Networks are compared using execution time requirements and Subjective analysis. The main advantage of this approach is that it is speech as well as speaker independent algorithm.

Corneal arcus is a white ring or arc deposited in the corneal region of the human eye. This corneal abnormality is significantly associated with the lipid disorders and atherosclerosis. In this paper, we proposed a computer-aided diagnosis system to detect the corneal arcus. The proposed method detects the corneal arcus using the statistical features extracted from the iris region of the eye image. The iris region is segmented from the other regions of the eye image using circular Hough transform (CHT). In order to achieve the better classification results, a morphological operation-based specular reflection removal and colour transformation-based enhancement methods are also developed in this paper. The proposed method was implemented and evaluated using the abnormal eye images from our own database and normal eye images collected from UBIRIS.v1 database. Our database contains the eye images with different grades of corneal arcus abnormality. The performance of our method was evaluated using the confusion matrix-based metrics. In the training phase, our method achieved a classification accuracy of 1. In the testing phase, our method achieved a classification accuracy of 0.96 with a positive predictive value 0.9791 and negative predictive value 0.9423.

This paper presents an effective supervised learning approach for static security assessment of power system. The approach proposed in this paper employs Least Square Support Vector Machine (LS-SVM) to rank the contingencies and predict the severity level for a standard IEEE-39 Bus power system. SVM works in two stage, in stage 1st estimation of a standard index line Voltage Reactive Performance Index (PIVQ) is carried out under different operating scenarios and in stage II (based on the values of PI) contingency ranking is carried out. The test results are compared with some recent approaches reported in literature. The overall comparison of test results is based on the, regression performance and accuracy levels. Results obtained from the simulation studies advocate the suitability of the approach for online applications. The approach can be a beneficial tool to fast and accurate security assessment and contingency analysis at energy management centre.

A service rented or leased from cloud resource providers follows a systematic procedure on working with the resource and returning them in the same accordance. In relevant terms it can be stated like policies that a user need to adhere in order to utilize the resources. These polices are clearly declared on an agreement to define the service level policies to the cloud users. This agreement stances or acts as a legal document between the user and the resource provider. The most important role of an SLA treaty is to provide quality assured service to its users as stated on the agreement. Quality Agreement of negotiation among the contributors helps in defining the Quality of Service necessities of critical resource based progressions. Though, the negotiation process for users is a momentous job predominantly when there are frequent SaaS providers in the Cloud souk. Consequently, this paper proposes a novel briefing on negotiation agenda where a SaaS broker is employed as a resource provider for the customers to achieve the required service efficiently when negotiating with multiple providers. Negotiation framework simplifies intelligent mutual negotiating of SLAs between a SaaS agent and multiple providers to achieve different objectives for different participants. To capitalize on revenue and mend customer’s contentment levels for the broker, the paper also proposes the design of strategies based counter generation techniques.

Clustering algorithms are popular algorithms used in various fields of science and engineering and technologies. The k-means is example unsupervised clustering algorithm used in various applications such as medical images clustering, gene data clustering etc. There is huge research work done on basic k-means clustering algorithm for its enhancement. But researchers focused only on some of the limitations of k-means. This paper studied some of literatures on improved k-means algorithms, summarized their shortcomings and identified scope for further enhancement to make it more scalable and efficient for large data. From the literatures this paper studied distance, validity and stability measures, algorithms for initial centroids selection and algorithms to decide value of k. Then proposing objectives and guidelines for enhanced scalable clustering algorithm. Also suggesting method to avoid outliers using concept of semantic analysis and AI.

This paper presents a technical review on various watermarking techniques applied to different videos. In this paper watermarking techniques are categorized according to two domains: spatial domain and transform domain. These techniques are studied and research progress in both the techniques in the field of watermarking has been shown in this paper. A summary of various watermarking technique in both domain are also presented in tabular form which provides an overview about the recent research work going on about video watermarking.

A cloud based data storage technique is going to be proposed in this research. Cloud based multiple data storage technique exhibits multiple data storage within a particular memory location using indexing. Proposed cloud based technique involves searching and storing data with less time consumption. Data analyzer, data transmission, and data acquisition in cloud have been introduced in this paper. Dynamic memory space allocation in cloud has been demonstrated. The paper introduces data searching and indexing techniques. Time and space analysis are represented graphically in this paper. Hit ratio in real-time scenario has been demonstrated in our work. Proposed cloud based multiple data Storage technique reduces memory access time. Comparisons have been shown for time difference realization.

In this paper we institute a baseline technique for human identification based on their body structure and gait. This paper presents a unique human identification system based on self-extracted gait biometric features. Recurring gait analysis is done to deduce key frames from the gait sequence. The gait features extracted are height, hip, neck and knee trajectories of the human silhouette from the body structure Here, we propose two new parametric curves Beizer curve and hermite curve, based on gait pattern. The projected approach has been applied on the SOTON covariate database, which comprises eleven subjects. The testing samples are compared to training samples using normalized correlation, and subject classification is performed by nearest neighbor matching among correlation scores. From the conducted experimental results, it can be accomplished that the stated approach is successful in human identification.

The manufacturing organizations around the globe have been observing a paradigm shifting from mass production to mass customization (MC). Mass customization capability (MCC) is the ability of a firm to produce varieties of customized products quickly, on a large scale and at a cost comparable to mass-production through flexible production processes and responsive supply chain. The goal of this paper to report a existing mass customization capability level (MCCL) appraisement of Indian bicycle manufacturing firm using multi-grade fuzzy approach. Through this study, a MCCL measurement model integrated with multi-grade fuzzy approach was designed. After the computation of MCCL index, the areas for improvement have been identified to adopt and implement mass customization strategy in the case firm. The approach contributed in this paper could be employed as appraisement equipment for estimating an organization’s capability to turn out to be a mass customizer.

This paper presents area and latency aware design of Discrete Fourier Transform (DFT) architectures using Winograd Fast Fourier Transform algorithm (WFFT). WFFT is one of the Fast Fourier algorithms which calculate prime sized DFTs. The main component of DFT architectures are Adders and Multipliers. This paper presents DFT architectures using Winograd Fast Fourier Algorithm with Carry Look Ahead Adder and add/shift multiplier and also with Semi-complex Multipliers. In this paper, different prime size DFTs are calculated using polynomial base WFFT as well as conventional algorithm. Area and latency are calculated in Xilinx synthesizer. Polynomial WFFT include Chinese Remainder theorem which increases complexity for higher orders. This paper mainly focuses on prime size 5-point and 7–point WFFT architectures, implemented in Verilog and simulated using Xilinx ISE 13.1. Each sub module is designed using data flow style and finally top level integration is done using structural modeling. DFT architecture has wide range of applications in various domain includes use in Digital Terrestrial/Television Multimedia Broadcasting standard.

In recent years the need for using cloud environment has increased abundantly. Threats to cloud computing are also rising with its usage. Among various threats to cloud, distributed denial-of-service (DDoS) attack is a main threat today. DDoS attack compromises the availability of the services and risks everything including financial loss, losing reputation, and loss of confidence of cloud users over the organization. To avoid this we need more profound and elegant way to secure cloud structure from DDoS attack. In this paper, we discuss recent development and protection mechanisms (i.e., prevention, detection, and response) against DDoS attack in cloud environment. These methods include preventive measures that are applied before the attack, detection method that can be applied while the attack is live, and responsive methods that are applied after the attack. This paper discusses about DDoS attack problem, currently proposed solutions, and research scope possible in future to deal with such attacks efficiently.

Information retrieval systems developed during last 2–3 decades have marked the existence of web search engines. These search engines have become an important role player in the field of information seeking. This increasing importance of search engines in the field of information retrieval has compelled the search engine companies to put their best for the improvement of the search results. Therefore the measurement of the search efficiency has become an important issue. Information retrieval is basically used for identifying the activities which makes us capable to extract the required documents from a document repository. Information retrieval today is done on the basis of numerous textual and geographical queries having both the textual and spatial components. The textual queries of any IRS are resolved using indexes and an inversion list. This paper mainly concentrates on the indexing part and the analysis of the algorithm. Several structures are in existence for implementing these indexes. Hash tables, B-trees, sorted arrays, wavelet trees are few to name. For an efficient data structure there are different deciding parameters that are to be taken into account. Some important parameters considered in this paper are index creation time, storage required by inverted file and retrieval time. This paper provides a detailed comparative study of different data structures for the implementation of inverted files.

This paper mainly focuses on the various technological improvements that need to be made for the current-integrated gasification combined cycle (IGCC)-based power plants in the near future through which efficiency of above 50 % would be achievable. This paper also elaborates about the working and the parts of an IGCC-based power plant. A discussion on the premier technologies that make IGCC the foremost prominent and promising technology for power generation today and tomorrow has been done, such as the advanced turbine technology, hot-gas cleanup technology, and the efficiency enhancement of the process, while taking into account the various aspects of each of these. If all of these novel technologies are adopted, then not only this will make the process very efficient, but also will be a promising solution for the years to come.

This paper implements the regenerative braking concept with a bidirectional DC–DC converter-fed DC motor to charge the ultracapacitor and also to reduce power consumption. This paper uses the ultracapacitor power source for fast current changes and while battery responds for slow current changes which in turn increases the lifetime of the battery. Energy developed at the time of braking is not useless; it is reutilized to store the energy in the ultracapacitor. An experimental prototype is developed. The advantage is that state of charge (SOC) of the battery and ultracapacitor voltage and current and voltage are within the limits.

Increased use of nonlinear loads injects current harmonics in electrical distribution networks and creates new problems of power quality in the power system. In today’s competitive electrical market, electric utilities strive to supply consumers with reliable and pure sinusoidal electric power that does not represent a damaging threat to their equipment. In the restructured power system, prices of the power are associated to power quality. Hence, it is very important to monitor and ensure power quality. This paper presents an innovative way of real-time harmonic computing using intelligent microcontroller and its assessment with automatic meter reading (AMR) on smart phone. During preparation of this paper, fifty site visits are done covering the nonlinear loads like IT industries, steel industries, paper industries, workshops, etc. It is observed that the current harmonics generated are beyond the tolerable limits. Readings are taken at consumer end with the help of standard power analyzer for a period of 24 h. It is also observed that a good number of researchers have worked on harmonic studies in the power system network; however, their work does not fit into the framework of the standards applicable. The developed solution has the capacity to identify the normal and abnormal presence of harmonics with color code. Through this color coding technique, an unskilled worker can easily identify the objectionable presence of harmonics. The computing results are observed to be satisfactory and promising. It creates awareness in the utility for measurement and control of harmonics which in turn helps to improve the power quality.

A MANET is an infrastructure-less mobile network whose principle is multi-hop relaying between nodes. They are prone to malicious attacks as no authentication or security architectures can be implemented because the nodes can continuously join or leave the network. Numerous schemes have been proposed for intrusion detection in MANETs. Recently, game theoretic approach is emerging as a major research area. The intrusion detection is a tedious task because the nodes are dynamic. A few papers have analysed the problem and provided appropriate solutions. In this paper, a game theoretic model has been developed for modelling network intrusions for colluding attacks from multiple packets. Detection is done by sampling a portion of the packets moving through the network. Depending on a sampling budget, a sampling strategy is developed and an optimal solution is found out to increase the chances of successful detection of the intrusion.

In this paper new Phase based Mel frequency Cepstral Coefficient (PMFCC) are used for speaker identification. GMM with VQ are used as a classifier for classification of speakers. The identification performance of proposed features is compared with identification performance of MFCC features and phase features. The performance of PMFCC features has been found superior compared to MFCC features and phase features. Ten Hindi digits database of fifty speakers is used for simulation of results. This paper also explore the usefulness of phase information for speaker recognition.

In the era of mobile computing, the number of mobile users has increased day by day. The computing capability, storage capability, processing speed of mobile, etc., required by a mobile user is higher, which may not be affordable by a mobile device. The advanced customers of e-Science require high performance, flexibility, and scalability (according to their demand, they can increase and decrease their use of resources), reduced cost, mobility, easy upgrades, etc. The cloud computing paradigm can fulfill the requirements of customers. The cloud computing is becoming the most profitable technology for service industries and can provide a new paradigm for business computation. Cloud provides the paradigm by which the users can access the resources from any geographical location at any time like using Google Doc, Microsoft SharePoint, GoGrid, etc. The different geographical locations and large number of users lead to different security issues for cloud. The main focus of this paper is to pinpoint the security threads for cloud computing. In this paper, we discuss design objectives, different models, challenges, and security of cloud with intrusion detection techniques.

Named entity extraction is the most primitive task in the field of text mining. This paper is a preliminary attempt to identify domain specific named entities, specifically crop names from text documents in Agriculture domain. The task is challenging as the names of these entities are very generic and hence word level features are not very helpful in differentiating them from routine words. Thus in this paper we have suggested a semantic vector based approach. Two different methods have been suggested, that are based on exploiting the context of the words in order to extract these entities. The methods accept few seed entities, identify their context and then find other words that are sharing the similar context. These words sharing the similar context are expected to be newly identified entities. Considering this as an initial attempt, the results are motivating and inspire us to move further in this direction.

The state-of-the-art developments in robotics reveal a scenario wherein human motion imitation has been developed through advancements in features and performance of sensors. Most of such robots uses flex sensors to acquire the information about the orientation of the body being imitated. However, these are slow and are highly vulnerable to damages and physical changes. There are image processing algorithms that are focused on a robots learning through ANN and are not into imitation without memory. This paper presents an overview of research attempts to use a robotic assembly to imitate a real object using image processing. The paper describes results in force, tactile and visual sensing, sensor-based control, and the configuration of a test-based robot integrated system for imitation of a human hand, using vision through image processing using RGB scheme; and one-on-one mapping for the acquisition of the current position of a body and orientation of the various parts of the robot in same fashion. This research has resulted in the development of a prototype capable of imitation and its applications are in the areas of medicine, defense, safety, and explorations for both research and commercial applications.

The purpose of this paper is to scrutinize the nature of the humanitarian aid supply chain and discuss the degree to which certain business supply chain concepts. The main objective of this paper is to develop a comprehensive model to find the optimal location of supplies and to assist planners during the design of the logistics efforts for humanitarian relief that mitigates and reduces the risk of disruption on the humanitarian aid. In this paper, we develop a mathematical model for location-routing models and pre-position of supplies for disaster areas. Our main purpose is the use of integer programming model to find the optimal location of supplies. It is shown that the problem can be solved more effectively and efficiently even for medium- and large-scale transportation networks.

In this paper, automatic real-time object detection and tracking is implemented via means of Kalman filter in which the system output is actually tracking the input canceling out any variation due to input and output noises. This paper can be used to develop a surveillance system of static camera and robotic automation visual systems. Whenever a new object comes in camera frame, system uses the concepts of frame subtraction then threshold image by Otsu’s method, and later Kalman filtering is being processed to estimate the next following coordinates of its movement. The work presented here is extended to work at video processing stage. And finally, least square mean optimization technique is used to evaluate the set of system parameters for perfect tracking of forthcoming new objects, and once that parameter is evaluated it be can used to execute tracking process perfectly.