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This article reports the Block based cipher concept followed by the affine cipher technique. The Image considered was grouped into squared (16, 32 and 64) pixel blocks then each column was shifted by specific values. These values were randomly generated prime numbers and worked as the key for scrambling. These images were investigated for their quality of scrambling using histogram and adjacent pixel correlation. The adjacent pixel correlations for 16, 32 and 64 pixel-based ciphered images were found as 0.7907, 0.7292, and 0.4783 respectively. The analysis gave the information that the level of scrambling was not satisfactory, therefore; the affine cipher technique was applied to each of the images. These images were converted into the matrix format and each element was transformed using the affine cipher. This transformed matrix is again converted inform from the image to visualize. The Histogram and adjacent pixel correction for these images were much improved.

This paper investigates the problem of nonsmooth feedback stabilization for the higher order uncertain chain of integrators. For achieving the specified goal, the integral term of classical Proportional-Integral (PI) controller is replaced by an integral of the discontinuous function. Replacing this integrator, the overall control becomes absolutely continuous rather than discontinuous as in the first order sliding mode control. With this proposed scheme, the property of invariance concerning the matched Lipschitz uncertainty is still preserved. The main technical contribution of the paper is a sound and non-trivial Lyapunov analysis of the closed loop system controlled by nonsmooth PI controller. The effectiveness of the proposed controller is illustrated with the help of numerical simulation on the magnetic suspension system.

Oceans are consequential provenance of resources, transportation and bio-diversity on a grand scale. Humankind uses oceans for various motives such as haulage, victuals, trade, mineral extraction, power generation, warfare, sustention and leisure. Many of these activities fabricate marine pollution and straining in sustaining aquatic life, despite several protection barriers.

Some monitoring applications in wireless sensor networks (WSNs) may require that the target is tracked by at least $k$ directional nodes in $m$ -connected WSNs, where $k \geq 1$ and $m \geq 1$ . In this paper, we address the target tracking problem in term of the number and orientation of tracking region of directional nodes required to achieve $k$ -target tracking in $m$ -connected WSNs. We consider the different values of $k$ , $m$ , target range, tracking angle, communication range of nodes, and shape of the field of interest (FoI) for solving the problem. The FoI is divided into an equilateral triangle, square, and hexagon patterns to estimate the optimal side length of a polygon and tracking direction of the nodes for $k$ -target tracking and $m$ -connectivity of the network. Next, the optimal side length in a polygon is used to estimate the node locations required for the desired level of target tracking and connectivity. Finally, we select a regular deployment pattern, which requires the least number of nodes for the designed level of tracking and connectivity. We also propose a target tracking system using the proposed analysis for tracking the moving targets in the FoI.

The Robotic Process Automation (RPA) is a new wave of the future technologies. Robotic Process Automation is one of the most advanced technologies in the area of computers science, electronic and communications, mechanical engineering and information technology. It is a combination of both hardware and software, networking and automation for doing things very simple. In this light, the research manuscript investigated the secondary data - which is available in google, academic and research databases. The investigation went for totally 6 months, i.e., 1-1-2018 to 30-6-2018. A very few empirical articles, white papers, blogs and were found RPA and came across to compose this research manuscript. This study is exploratory in nature because of the contemporary phenomenon. The keywords used in searching of the database were Robotic Process Automation, RPA, Robots, Artificial Intelligence, Blue Prism. The study finally discovered that Robots and Robotic Process Automation technologies are becoming compulsory as a part to do business operations in the organizations across the globe. Robotic Process Automation can bring immediate value to the core business processes including employee payroll, employee status changes, new hire recruitment and on boarding, accounts receivable and payable, invoice processing, inventory management, report creation, software installations, data migration, and vendor on boarding etc. to name a few applications. Besides, the Robotic Process Automation has abundant applications including healthcare and pharmaceuticals, financial services, outsourcing, retail, telecom, energy and utilities, real estate and FMCG and many more sectors. To put in the right place of RPA in business operations, their many allied technologies are working at the background level, artificial intelligence, machine learning, deep learning, data analytics, HR analytics, virtual reality (second life), home automation, blockchain technologies, 4D printing etc. Moreover, it covers the content of different start-ups companies and existing companies - their RPA applications used across the world. This manuscript will be a good guideline for the academicians, researchers, students, and practitioners to get an overall idea.

The Internet of Things (IoT) is one of the most promising scientific fields of the last decade. The number of devices developed that support Internet things makes the application of internet things more interesting. The field of use includes the insurance sector, intelligent monitoring Environment, Smart City, sector-health care, Intelligent City, and also many other applications enabled for smart devices.The vision of the introduction of the internet, forming a network called IoT, where various information technology devices are connected, security and trust are most important for the user, because this network presumably used a large amount of data.However, trust is a significant obstacle that can hinder the growth of the IoT and even slow down the significant compression of many applications.This paper presents an approach using trust management (TM) with next-generation IoT networks for healthcare, agriculture, and sustainable development goals. Implementation of IoT conveys various problems of security and confidentiality. The use of IoT to ensure confidentiality, integrity, authentication, authorization, trust, secrecy, and management challenges are addressed. The paper uses decentralized blockchain technology to solve security and privacy issues in applications with the support of the IoT.Ethereum virtual machine is used to implement theBlockchain distributed networkand healthcare insurance claims are taken as anexample to test the proposed solution. Also, it is provided that the lessons and opinions discussed are primarily about the purpose of the trust, which can be reproduced in the future on the internet.The results show that distributed blockchain technology provides trust management and can improve the existing security and privacy of the IoT in the health and agriculture sectors, which is helpful for next-generation IoT networks.

Cloud computing offers various features such as creates, configure and customize application online. User can access applications over the Internet and they can also access database via Internet. Open nature and public access make their application vulnerable for several security attacks. This paper attempt to integrated different previously defined security approaches to propose a new and novel model of security. Here, proposed solution approach to improve performance of confidentiality, authentication, integrity and access control with better and strong way. Proposed solution is implemented using java technology and evaluated based on computation time and memory consumption. A detailed comparison with graph is also demonstrated in experimental analysis section.

Data mining techniques of classification and prediction model are used for analyzing the customer of bank through their real data, customer is a performing as asset or non-performing asset for the bank. Every year each bank faces a similar problem that most of them customers do not refunding loan installment on time and many precisely become defaulter for the bank. So every bank needs a system that can predict in future any customer in future will be profitable or not profitable asset for the bank. If any customer found as non-performing asset means, it has bad credit score. In such case if a customer further requests for new loan, bank can easily identify him as defaulter and reject his/her request on the basis of our new proposed models. In this way, bank extracts their non-performing assets. Besides, bank can also identify their new customers for having good credit score and may offer them other services for the beneficial of bank. There are many as such models that are used for prediction. This paper compares different classification and prediction algorithms to developed best suitable model to analyze loan requesting customer’s data set using their credit score. Mainly in this paper, there is a comparison between random forest algorithms and logistic regression which is best suitable for prediction of credit scores of customers with apply k best feature selection on data set. The aim of proposed model is to reduce bankruptcy, non-performing assets and the losses of bank.

The earned value method (EVM) is used as a standard tool for tracking of traditional software projects in order to report their status during execution. However, the status reported by EVM is not reliable as it has a fundamental limitation of not considering the partially completed tasks for the evaluation of the project status. This paper suggests considering the partially completed tasks for the earned value or budgeted cost of work performed (EV) calculation for project status evaluation. The state-space approach is used for modeling EVM, which provides foundation for integrating issues related to partially completed tasks effectively and explicit representation of the project status during planning and execution. The EVM is applied for tracking a project, and the tracking results obtained using Monte-Carlo simulation, with or without considering the partially completed tasks during execution, are presented to show the project’s execution behavior in comparison to planned behavior.

All projects are required to be tracked and reported their status information to the management for controlling their execution, and have limited resources in terms of budget and time, so agile projects are. Their tracking is an overhead for the software projects as the tracking is a management activity, not a technical one, for the development of the software product. Various tracking techniques have been developed for tracking of agile projects. We have classified them as heavy-weight and light-weight, in terms of the information maintained, or not, about which phase of the project lifecycle a partially completed task belongs to. Light-weight techniques consider the tasks which have been completed but do not consider the partially completed tasks; while heavy-weight ones consider the tasks which have been completed as well as the state information about which phase of the project lifecycle the partially completed tasks belong to. Light-weight techniques are not effective due to not considering the partially completed tasks and heavy-weight ones are not utilizing the resources efficiently. In this paper, a light-weight tracking technique is designed using the state-space approach, which considers both the completed tasks and the partially completed tasks but without maintaining the state information for the partially completed tasks in order to tracking the projects effectively and utilizing the resources efficiently. The application of the technique, using Monte-Carlo simulation, is demonstrated by tracking a project which was carried out with the agile software development philosophy.

Every year around 1.35 million people are cut off due to numerous crashes in case of road traffic accident. As per the statistics 20 to 50 million people suffer as a result of its injuries. As a consequence of such traffic accidents people pays off their lives. These conditions are caused due to the lack of co-ordination among the organizations involving in it. Also not properly practising the rules and ways as it to be followed magnifies the graph upwards. The risk factors include speeding, drink and drive, distraction in driving, bad infrastructure, in-proper vehicles, breaking rules and many more. As such a system is needed which is perfectly able to co-ordinate between the different actions that is to be taken for the quick response at the accident location. As per the research such detection system involves different technologies such as Global Positioning System [GPS] & Global System for Mobile Communication [GSM], applications of mobile phones, etc. All the vehicles are included under these detection systems and other technologies are also considered for the same. As this paper represents an overview related to the technologies that interconnected with that of road accidents by automated road [traffic] accident detection system.

Today, we are living in the world where the data is present in enormous amount. There are many web applications and social networking sites where users share their thoughts in form of sentiments or opinions. Sentiment analysis provides great practical value on user’s viewpoint. Sentiment analysis is a research area which extracts the proper meaning of the user’s viewpoint that includes text analytics and classifies the polarity of the user’s opinion. Here, we are discussing some papers of sentiment analysis of data with the help of machine learning approaches and trying to propose a new algorithm by combining two feature extraction methods. Training and testing are done by neural network. Accuracy is checked by Precision, Recall, F-Score. ROC curve is plotted for checking the performance.

The aim of this paper is to detect and find out risk in sanctions of loan of banks. The approach here applying data mining methods on particular data set on current customers of bank to which bank has already sanction loan. Now data mining methods play a major role, analyse the data records of current customers and give a result on the value of different attributes of customers’ record. Now the result of mining give a basic idea to banking system that which attribute value is important for any customer to return their loan on time or not returning the loan on time. So on that basis of result a framework is designed to which bank used to analyse the new customer for sanctioning the loan or not. In this way bank can reduce their losses and non-performing assets and can increase their profit. There are different data mining algorithms, using some one of them and also comparing which algorithm is the best. To decide this, firstly trained the different algorithm with some number of percentages of data set and then test with some number of percentage of data set and on that basis accuracy result will decide that which algorithm is best for the bank.

[EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies., The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.

Two Active Inductor design has been designed and simulated using UMC 0.18μm technology and Cadence Virtuoso. Active Inductor Design I show inductive behavior up to 2.75 GHz and more suitable for voltage controlled oscillator. It also achieves a high-value Q of 700 and draws the current of 116 uA at 750m bias voltage. Besides, a novel approach to tune the inductor is investigated using Active Inductor Design-II. Use of bulk or body bias voltage can tune the inductance. The results of the proposed design-I are discussed and compared. The approach is validated by using the Simulation result.

Generating High Dynamic Range (HDR) image in the presence of camera and object motion is a tedious task. If uncorrected, these motions will manifest as ghosting artifacts in the fused HDR image. On one end of the spectrum, there exist methods that generate high-quality results that are computationally demanding and too slow. On the other end, there are few faster methods that produce unsatisfactory results. With ever increasing sensor/display resolution, currently we are very much in need of faster methods that produce high-quality images. In this paper, we present a deep neural network based approach to generate high-quality ghost-free HDR for high-resolution images. Our proposed method is fast and fuses a sequence of three high-resolution images (16-megapixel resolution) in about 10 s. Through experiments and ablations, on different publicly available datasets, we show that the proposed method achieves state-of-the-art performance in terms of accuracy and speed.

International audience; The impact of the extraction technologies on product yield and analytical profile is relevant. This review is aiming to discuss recent experiences of successes and failures in non-conventional extraction scalingup, to better understand the challenges and designing of existing and new techniques. Understanding the crucial extraction factors at laboratory and pilot scale is of paramount value for the engineering work of scaling-up. Besides, a careful analysis and modelling of heat and mass transfer and energy consumption, the design of industrial extraction plants driven by economic and environmental factors, are well covered in the present review. Current trend strongly favors scaling-up of green approaches that consumes less organic solvent(s), involves minimal operational steps, provides high throughput capability and assures highest yield at lower costs. Current and future challenges in scaling-up extraction of bioactive compounds require a parallel development of suitable analytical methods to monitor the process and ensure high yield and quality.

Data mining and sentiment analysis are two most versatile research areas in field of real time knowledge extraction. Real time twitter data analysis can plays very crucial role to observe the thinking and view point of people and users. Nowadays, social networking sites have become centric points to share your thoughts and viewpoints. Analysis of social networking data can help a lot to observe trend of society. It can also help to derive user interest and hidden activities. Sentiment analysis is the approach to determine whether piece of writing is positive, negative or neutral. It also help to derive user opinion and attitude of writer. Sentiment analysis of twitter user can help to track eventual view point of user. Country wise accumulative view point can help to derive overall opinion of country citizens and their thinking criteria. This work has proposed sentiment analysis model to observe positive and negative view point of different countries based on sentiment analysis approach. The complete work will be implemented using Hadoop Ecosystem to perform parallel processing on large data.

This paper deals with non-smooth feedback stabilisation in presence of stochastic unbounded(normally distributed) noise. The integral term of classical proportional-integral controller is replaced by a discontinuous integrator. The overall control effort is still continuous. The behaviour of the proposed scheme under stochastic perturbations is presented. We have given a sound and non-trivial Lyapunov analysis of the closed loop system controlled by the proposed controller on stochastic dynamics.

In this modern era, a technological development is taking shape from universities to schools. This paper throws light on an automatic identification technology which has been used to diminish man force and time to input file or information manually. RFID (radio-frequency identification) is a wireless technology which is getting popularity among identification technologies due to its cost-effective maintenance, low cost, less weight and minimized size. RFID (radio-frequency identification) is one of wireless technology that incorporates the use of EM fields to automatically identify and trace tags attached to items/objects. Radio-Frequency Identification tag enables the schools/universities to oversee the student movement inside and outside of the premises.

We experience a daily reality such that we can be overpowered with data; in this manner it has turned out to be progressively critical to separate pertinent data from the unstable measure of information for. Information Mining is the iterative and intuitive procedure of finding substantial, novel, valuable, and justifiable examples or models in gigantic databases. Information Mining implies looking for important data in extensive volumes of information, utilizing investigation and examination, via programmed or self-loader implies, of expansive amounts of information so as to find significant examples and standards. Soft Computing (SC) alludes to methods of figuring in which accuracy is exchanged for tractability, heartiness and simplicity of usage. For the most part, SC encompasses the technologies of fuzzy logic, genetic algorithms, and neural networks, and it has emerged as an effective tool for dealing with data mining, control, modeling, and decision problems in complex systems. This is a review of the role of various soft-computing tools for different data mining tasks.

This paper is aimed at discussing the efficiency gained in the process of sorting by embedding parallelism in the task of comparing the values. The interconnection networks to implement the logic and the criteria embedded to generate parallelism in the process are discussed. Depending on the logic embedded to generate parallelism in sorting, improvement in the performance of the process of sorting varies. Speedup gained and efficiency generated by three sorting algorithms: enumeration sort, odd-even transposition sort, parallel quicksort are discussed. As improvement in performance demands extra cost, hence the criteria chosen to embed parallelism is a trade off between the increase in overall cost and decrease in time taken in generating the result. The scenario in which each of the algorithms generates good performance and is preferred to be implemented is discussed and the efficiency gained by each criteria of parallelism is illustrated.

Increasing demand for large capacity data storage can only be fulfilled by hard disk drives (HDDs) and to some extent by solid-state drives (SSDs). However, HDDs are favorable in many applications, as they are approximately 5-10 times cheaper than SSDs. Attempts are being made to increase the capacity of HDDs by technologies such as heat-assisted magnetic recording and microwave assisted magnetic recording. However, increasing the capacity has been a slow process and there are limitations in achieving areal density above 10 Tbpsi. Thus, the introduction of new technologies is important for attaining high capacity. In this scenario, domain wall (DW) memory is a potential candidate, but there are still many unsolved issues. One of these is ensuring controlled and reliable motion of DWs along the nanowire. In this paper, we provide an overview of existing technologies and our attempts to control DW motion. Many methods of fabricating pinning centers have been proposed and demonstrated. These methods can mainly be categorized as 1) geometrical and 2) non-geometrical methods. In the first part, we review the geometrical approach to pin DWs. Later, we provide an overview of our approaches to create pinning centers using non-geometrical means. Non-geometrical approach provides more advantages as it provides a variety of choices to tailor the properties. In particular, this approach suits scalability. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Accepted version

Topic modeling methods like LSI, pLSI, and LDA have been widely studied in text mining domain for various applications like document representation, document clustering/classification, information retrieval, etc. However, such unsupervised methods are effective over corpus with well separable topics. In real-world applications, topics might be of highly overlapping in nature. For example, a news corpus of different terror attacks has highly overlapping keywords across reporting of different terror events. In this paper, we propose a variant of LDA, named as Prioritized Named Entity driven LDA (PNE-LDA), which can address the issue of overlapping topics by prioritizing named entities related to the topics. From various experimental setups, it is observed that the proposed method outperforms its counterparts in entity driven overlapping topics.

Device-to-Device (D2D) communication, an influential addition to long term evolution- advanced (LTE-A) standards, enables direct communication between closely located cellular users to improve the throughput, latency and spectral efficiency of a cellular network. In this paper, we augment the development of D2D by proposing an orthogonal frequency division multiple access (OFDMA) based cooperative D2D (C-D2D) framework wherein multiple D2D pairs exist with a cellular user. Among M available D2D pairs, a D2D transmitter (DT) that can achieve the largest quality-of-service (QoS) improvement of uplink cellular transmission is selected to serve as a user relay for the cellular transmission. In addition, a novel round robin process with priority cube method is proposed to facilitate the fair distribution of available resources among M DTs. A generic mathematical probability model is developed to analyze the performance of the proposed scheme. In particular, the closed-form expression of the outage probability for the best D2D user selection for C-D2D framework is derived. Results show that the proposed scheme improves the QoS of the cellular user as compared to the conventional C-D2D framework.

We report here a simplified and improved technique for modeling and simulation for the Photovoltaic module using MATLAB/Simulink environment. Parameters of the equivalent-circuit model are obtained using the iterative algorithm by adjusting the output characteristics curve. The present work evaluates the current-voltage (I-V) and power-voltage (P-V) characteristics under different ambient conditions employing a single-diode model. The module performance is significantly affected by solar irradiance, ambient temperature and partial shading. This report aims to perform the detailed analysis of partial shading effect on module performance, which is considered as important real-time problem. The proposed simulation technique provides an accurate and reliable method to extract different sources of resistance, such as series and shunt resistance in the PV module. A MSX60 photovoltaic module is used as a reference for the present study. The characteristic curves of the simulated model are in good agreement with the available experimental data. The developed simulation model could also be extended for performance assessment of other photovoltaic modules and in the design of efficient power converters.

The landing gear system of an aircraft absorbs energy from the impact of landing. Numerical simulation has become great tool for the assessment of the landing gear dynamics as well as for aircraft structure and landing gear interaction. This paper basically describes the normal structural behavior of a simple landing gear structure model system and accurately simulates the energy absorbed by the gear system without adding substantial complexity with the model. It carries the structure aircraft weight at all required ground operations including landing, take off, taxiing and towing. The stress results obtained from the finite element analysis is used to calculate the factor of safety. This work also focuses on landing gear of small unmanned aerial vehicles, where the landing gears used are of leaf spring type. It has been seen that the stresses are very high during greater impact speeds, which resulted in less factor of safety.

Face recognition is not a new phenomenon, but still there is a lot which can be done. We are aiming in this work to develop an improved face recognition system for person-dependent and person-independent variants. To extract relevant facial features, we are using the convolutional neural network. These features allow comparing faces of different subjects in an optimized manner. The system training module, firstly recognizes different subjects of dataset, in another approach, the module processes a different set of new images. Thus, the predictions improve on previously trained data. The accuracy on Yale dataset of 15 people, 11 images per person with a total of 165 images, yielded 96.19% accurate results; the system can be easily scaled for more images and different datasets.

In the current digital world, big data concept is increasing very rapidly. Data is generated in very large volume and in a variety of forms. This large and complex dataset is used by the business organizations for finding their customer needs or insights. Therefore, the security and privacy over the large datasets become too much necessary for the organizations and users. This paper mainly deals with the issues related to big data while storing and processing it. In the proposed architecture clients data is distributed among different Hadoop machines and computation is done through a single machine using a random method and joint computation is performed here that announces the final result to the clients. Therefore, our architecture provides the anonymity of users to maintain the high level of privacy, that means machine who performs computation only knows the data as a whole of all clients and does not know to whom the data belongs and thus the privacy of different user during data processing remains anonymous.

Secure sum allows cooperating parties to compute sum of their private data without revealing their individual data to one another. Many secure sum protocols exists in the literature. Most of them assume network to be secure. In this paper we drop that assumption and provide a protocol that is applicable to insecure networks as well. We used additive homomorphic encryption technique for secure sum computation.

Encryption is a method that converts readable form of data to encoded data that can be read after decryption. Encryption ensures that the original message cannot be retrieved by someone who is not supposed to get the message. It is always an important aspect to secure the data, because data is the key to rule. There are various cryptographic algorithms available with their own advantages and drawbacks. This paper is introducing a new symmetric key encryption algorithm. This algorithm involves three steps decoding to obtain final encrypted data.

It is 2017 and mobile communication technology has completely engraved itself into our lives. Mobile communication technology usage has been quickly develop internationally in the last 10 years, which has ensued in concern of public about the harmful health effect of electromagnetic (EM) radiation that are discharged by cell phones. The exposure of electromagnetic radiation (EMR) has received an equitable share of participation in literature; however, this paper is one to represent the effects of the electromagnetic fields induced by cell phones on human body, plans related to electromagnetic vulnerability in mobile communication and present feasible ways of reducing the effects.

Minimizing Energy consumption in a wireless sensor network has become a challenging issue. Energy consumption in transmission is higher in the presence of interfering nodes due to more re-transmissions required for a successful transmission. In this paper, energy consumption model has been presented. Mathematical models for Rayleigh interference have been derived. Energy efficient algorithm for interference minimization has been investigated. The simulation result has been carried out in MATLAB. The proposed model consumes low energy and reduces interference in the presence of one interferer, two interferer and multiple interferers.

With the continuous growth in human population, rising incidence and predominance of various diseases, there is growing need for development of natural as well as engineered diagnostic systems and drugs in order to meet the therapeutic demands. Since microorganisms have evolved in nature with an astounding set of mechanisms utilized in detecting and responding to varied, transient and enduring external stimuli, such microbial systems can be utilized in diagnosis as well as giving competition to animal cells in producing value added products due to its easy and low cost processing. The re-making of various biosensing systems by incorporating whole cells intend to provide efficient biological detection and measurable response. Microbes governing the synthesis of biopolymers are also found to be exploited in developing new generation of novel drug delivery systems and as repair material of tissues. The area of synthetic biology together with novel microbial systems and whole cells is gaining rapid attention in diagnostics and global health challenges. With this background, the present chapter highlights various applications of microbes in pharmaceutical industries with special emphasis on diagnosis and drug delivery.

This innovative system is made for operations which have high risk for the men to enter at any zone, especially for some case like bomb blast and this may prove very beneficial for military area for spying purposes. This system makes use of robotic arm as well as robotic vehicle which helps to diffuse the bombs, not only to diffuse but to dispose also by entering an area involving high risk to pick the bomb it wants to and carry it to safer zone. The robot includes night vision camera which will view whatever will seen by the robot in day time but also during night time. The system also includes IR sensor which will detect the obstacle comes in its path, this will tend the robot to move opposite direction for a while to protect its arms by getting accidental damage. The whole system can be controlled via android application or laptops with the help of server. The system sends commands to the receiving circuit mounted on the vehicle through Internet. The receiving circuit involves Raspberry pi microcontroller with Wi-Fi enabled which receives commands through internet sent by the server called as VNC server. The android application or laptops involves buttons K, L, M, N, O, and F so as to control the robotic arm. The user can press the desired buttons in order to control the movement of vehicle. Thus this application involves both Robotic arm and Robotic vehicle so that the system can not only be used to enter a high risk area but also to pick, move and place whichever objects it wants to. Each and every movement of the robot will be recorded and can be viewed in a PC wirelessly by the operator.

Multimedia Communication is the new age of communication. Image Communication is one of the most popular types of multimedia communication. This type of communication always faces security challenges. Security breach is rising at a rapid rate. Image privacy is facing an even bigger threat. There are several image cryptography techniques having both positive and negative consequences. Via this paper, we are presenting a new approach for image encryption and decryption by using pixel shifting and using Quasigroup (Latin Square) without performing translation. It requires low computation as a key. Image pixel reshuffling is done randomly and in a non-repeated manner.

The gain flatness of EDFA plays a awfully crucial role in DWDM system applications in all told sorts of optical networks style. The main purpose of this paper is to attain gain flatness for thirty two channels so as to equalize amplitude gain of DWDM system. this method is simulated using Opti system package to attain gain flatness of EDFA through variation of EDFA pump power and input power of system. The gains are planner inside 41±0.9 decibel from Wavelength varying between 1546 to 1560 nm having noise figure but 8dB, Optimized EDFA fiber length of 6.245 m and frequency spacing between channels is 0.4 nm simultaneously amplified in a EDFA of single stage. The working model consists of WDM transmitter, MUX, Pump laser, dual port WDM analyzer and EDFA as key components.

calculation of large scale MANETs is an NP computational problem, and this complexity can be reduced by identifying critical nodes in a network. The identification of critical nodes itself is a computationally hard problem. The present work provides an empirical Algorithm for detecting critical nodes in a MANET which is computationally efficient than the already existing detection methods. The algorithm proposed for critical node detection is based on the pattern a critical node may exhibit uniquely in a connection matrix.

The Mobile Adhoc Network Extensibility is an issue of concern and research. The wireless network extensibility seems to be an inherent capability of MANETs taking into consideration the Multi-hop data transfer scheme. The limits of extensibility and the tradeoff between MANET stretching and the reliability concerns need to be exercised with a deterministic model. The reliability calculation methods proposed in past have an exponential growth factor for time complexity as number of nodes increase, thus making it computationally unfeasible to calculate reliability of large scale mobile networks .This work proposes a reliability calculation scheme which is realistic for calculating reliability of large scale MANETs. The proposed approach calculates the reliability of Mobile Adhoc networks by identifying critical links within a network . General Terms Reliability, Wireless Networks. Keywords Manet, Reliability, Critical Node, Two-terminal-reliability, Scalable Networks

With the advancements in World Wide Web and high speed internet the scenario of traditional shopping system has changed completely and e-shopping has gained popularity as it furnishes human with a feeling of easiness. But this e-shopping system is also being badly off for the involvement of human efforts and time to a great extent. To minimize this human intervention automation in e-commerce is like a blessing and use of agent technology is the best way to achieve this. In this context use of negotiation strategies and protocols mentioned in literature in developing systems which automates the whole e- shopping system is required. And so, here in this paper we present a multi-agent application using JADE, based on real world e- commerce; it automates an English auction by applying Kasbah negotiation strategies in order to minimize human intervention and ensures that the product is being sold at its best price.

In this paper, the performance investigation of artificial neural network (ANN) controller-assisted small hydro power generation system (SHPGS) has been presented. The mathematical modeling of SHPGS is implemented in MATLAB/Simulink software. As the turbine speed and output power are directly proportional to the water flow rate and net head, an ANN controller has been implemented to control these parameters under varying load demand. A comprehensive investigation of the system under steady state and dynamic state has been done in terms of demand power, water flow rate, net head, turbine power, load voltage and current. The results validate that the ANN controller has satisfactory performance under dynamic conditions.

In this work, a mathematical model has been developed to analyze the spread of a distributed attack on critically targeted resources in a network using firewall security coefficient. The model provides an epidemic framework with two sub-frameworks to consider the difference between the overall behavior of the attacking population and the targeted population. The targeted population and attacking population is divided into five compartments of nodes, viz. Susceptible-Latent-Breaking out-Recovered-Antidotal. With cyber mass action incidence, the boundedness of the system, the feasibility of equilibrium states and their stabilities are analyzed. Basic reproduction number R0 is calculated and it is observed that when R0 1, then endemic steady state exists and will be locally asymptotically stable. The impact of firewall security rule base in controlling transmission of malicious objects is analyzed. We are taking firewall security as a media coverage factor in our computer network model of malicious code propagation. Asymptotic local stability method is used as an alternate to find the stability of the system. Finally, a sensitivity analysis of the system parameters for basic reproduction number and endemic equilibrium points has been carried out using normalized forward sensitivity index. Numerical experimentation has been carried out to simulate the system of equations in support of analytical findings.

Secure sum protocol allows computing sum of individual data of a group of cooperating parties without divulging the individual data to other parties. Protocols exist in the literature for the secure sum computation but these protocols presume the network to be secure. We propose secure sum computation protocol when the network lines are insecure. When a cooperating party sends its private data to another party, the data can be encrypted using either symmetric or asymmetric cryptographic techniques. We also proposed a protocol which is suitable against colluding neighbors who may try to learn the secret data of some party. It ensures that an intruder cannot learn the data over the network. We show that how we achieve better security during secure sum computation.