Search

Techniques of data compression involve de-duplication of data that plays an important role in eliminating duplicate copies of information and has been widely employed in cloud storage to scale back the storage capacity and save information measure. A secure AES encryption de-duplication system for finding duplication with the meaning and store up it in the cloud. To protect the privacy of sensitive information whereas supporting de-duplication, The AES encryption technique and SHA-256 hashing algorithm have been utilized to encrypt the information before outsourcing. Pre-processing is completed and documents are compared and verified with the use of wordnet. Cosine similarity is employed to see the similarity between both the documents and to perform this, a far economical VSM data structure is used. Wordnet hierarchical corpus is used to see syntax and semantics so that the identification of duplicates is done. NLTK provides a large vary of libraries and programs for symbolic and statistical natural language process (NLP) for the Python programming language that is used here for the unidentified words by cosine similarity. Within the previous strategies, cloud storage was used abundantly since similar files were allowed to store. By implementing our system, space for storing is reduced up to 85%. Since AES and SHA-256 are employed, it provides high security and efficiency.

The modern society runs over the social media for their most time of every day. The web users spend their most time in social media and they share many details with their friends. Such information obtained from their chat has been used in several applications. The sentiment analysis is the one which has been applied with Twitter data set toward identifying the emotion of any user and based on those different problems can be solved. Primarily, the data as of the Twitter database is preprocessed. In this step, tokenization, stemming, stop word removal, and number removal are done. The proposed automated learning with CA-SVM based sentiment analysis model reads the Twitter data set. After that they have been processed to extract the features which yield set of terms. Using the terms, the tweets are clustered using TGS-K means clustering which measures Euclidean distance according to different features like semantic sentiment score (SSS), gazetteer and symbolic sentiment support (GSSS), and topical sentiment score (TSS). Further, the method classifies the tweets according to support vector machine (CA-SVM) which classifies the tweet according to the support value which is measured based on the above two measures. The attained results are validated utilizing k-fold cross-validation methodology. Then, the classification is performed by utilizing the Balanced CA-SVM (Deep Learning Modified Neural Network). The results are evaluated and compared with the existing works. The Proposed model achieved 92.48 % accuracy and 92.05% sentiment score contrasted with the existing works.

Information are generated over the internet for every second. These information are not fully secured. To increase the security of these information send over the internet there are two methods Cryptography and Steganography are combined to encrypt the data using RSA algorithm as well as to hide the data in multimedia image in Hadoop Cluster. Features of the resultant image such as color are extracted and stored separately in Hadoop cluster to enhance security. Then combining features of the Stenographic image for secret image retrieval, which has been then split into image and secret information. At last, decrypting the secret information, we retrieve the actual information. Application of this system in Hadoop will increase the speed of execution of the process.

In this study, an Internet of Things (IoT)-based analysis system is proposed, which can be used to design a consumer electronic device. This IoT-based analysis system will alert the user when any of the parameters related to his/her health is above or below the normal range. The data collected using this system are uploaded to the cloud using a mobile application and then transferred to the field-programmable gate array (FPGA) analysis system. The raw data are computed and processed by the FPGA system, and pathological conditions are displayed on the patient’s wearable IoT device. The novelty of this work lies in the development of a fuzzy classifier, which indicates the pathological conditions of diseases with a higher accuracy. The FPGA implementation of the fuzzy classifier results in a lower execution time in comparison with that of previous models, such as K-nearest neighbour, decision tree, support vector machine, and naive Bayes.

The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been introduced to the market with unique features that assist service developers collaborate and migrate services among multiple cloud service providers to address the varying requirements of cloud consumers. Many interfaces and proprietary application programming interfaces (API) are available for migration and collaboration services among cloud providers, but lack standardization efforts. The target of the research work was to summarize the issues involved in semantic cloud portability and interoperability in the multi-cloud environment and define the standardization effort imminently needed for migrating and collaborating services in the multi-cloud environment.

Objective: The main aim is to de-duplicate the redundant files in the cloud and also to improve the security of files in public cloud service by assigning privileges to the documents when it is uploaded by confidential user. Methods: To achieve the objective the authors have used the AES algorithm to encrypt the file stored after de-duplication in the cloud. De-duplication is done based on comparison of contents, file type and size. For an authorized user to access the file from the cloud, generation of OTP using SSL protocol is adopted. Findings: Files uploaded in the cloud are encrypted using traditional encryption algorithms which don't provide high levels of security. Files can be accessed by anyone who is authorized. Privileges are not considered. During de-duplication, only the name and size of the files are considered. Application: Files within the public cloud can't be viewed by everyone who has registered with the cloud. Those who have the respective privileges can only view the file. Proof of Ownership is assured. Since de-duplication is done based on the content redundancy within the cloud storage is avoided. Usage of OTP ensures that the content is viewed by the individuals who have the respective privileges related to the file. These concepts provide additional security to the files stored in the public environment.

A grid computing is a collection of distributed com puting resources owned by many organizations across boundaries and not dedicated for a single user avai lable in the Virtual Organization. Grid computing environment provides more computational capabilities to increase the scalability and efficiency of the infrastructure. Resource selection in computational grid is the open issue for executing complex jobs. Trus t is the one of the important factor for selecting the resou rces in computational grid. We introduced a new technique for finding the trusted resource. In our proposed method, we computed the trusted resource from user’s feedback and performance. While selecting th e resource, overall trustworthiness is compared. Th e result of the resource selection algorithm is that most trust resource weight is above the threshold l evel. In our proposed algorithm job is submitted to the trus t resource available in grid.

A smart antenna is actually a fusion of an array that changes individual antenna elements patterns in response of a dedicated signal to improve performance. Moving one step beyond of beam forming is adaptive algorithm which allows for the computation of continuously amend weights. The process of adaptation must satisfy a specified optimization principle. In this project two Adaptive algorithm Least Mean Square and Recursive Least Mean Square are implemented to revamp beam. Added optimization of beam is accomplished with the help of genetic algorithm which makes beam more optimize. This optimization method is used for the synthesis of antenna array radiation pattern in adaptive beam forming. The optimum value of weights of each antenna element is determined which produces a radiation pattern with minimum side lobe level. Analysis is being carried out with varying different element and space in between element to obtain the minimum weight with the help of genetic algorithm. This improves the performance to achieve the maximum reduction in side lobe level (of the order −20dB) with minimum function calls.

Smart antenna attempt to enhance the received signal, suppress all interfering signals, and increase capacity. Beamforming is one of the mostly used antenna technique. It adjust the radiation beam in one specific direction also reduces multiple access interference. It also reduces common channel interference and multiple path fading, in result it increases capacity of mobile network. In this paper two antenna beam forming algorithms are analyzed. These two algorithms are Least Mean Square and Minimum Variance Distortionless Response. Outputs of these two algorithms are in weights. By using these outputs weights, two parameters i.e. Mean Square Error and Power Beam Width are calculated and two algorithm are compared by this parameters and respective results are discussed.

In this paper we describe an algorithm for distributed, BDD-based bounded property checking and its implementation in the verification tool SymC. The distributed algorithm verifies larger models and returns results faster than the sequential version.The core algorithm distributes partitions of the state set to computation nodes after reaching a threshold size. The nodes proceed with image computation on the nodes asynchronously. The main scalability problem of this scheme is the overlap of state set partitions. We present static and dynamic overlap reduction techniques.

A divide-and-conquer approach in BDD-based verification to handle larger designs is to partition BDDs exceeding a threshold size and to deal with the partitions separately. Crossover transitions to the same state cause the main problem of this methodology, because they result in overlap of the partitions and thus introduce redundant computations when dealing with the partitions. In this paper we describe an algorithm for splitting Boolean functions representing state sets of synchronous systems such that in subsequent symbolic traversal of the resulting subsets the state set overlap is reduced. We demonstrate the effectiveness of our splitting algorithm by applying it in sequential and distributed versions of a bounded property checking algorithm. Also, a dynamic extension to static overlap reduction is sketched.

Current statistics attribute up to 75% of the overall design costs of digital hardware and embedded system development to the verification task. In recent years, the trend to augment functional with formal verification tries to alleviate this problem. Efficient property checking algorithms allow automatic verification of middle-sized designs nowadays. However, the steadily increasing design sizes still leave verification the major bottleneck, because formal methodologies do not yet scale to very large designs. In this paper we present the formal verification tool SymC based on forward state space traversal and so-called AR-automata for property checking, both internally represented with BDDs. Furthermore, we introduce a new methodology called dynamic guiding. This methodology best suits multimodule concurrent finite state machine (FSM) designs. The aim of guiding is to reduce the intermediate and final BDD size, which in turn makes this verification technique applicable to larger designs. Our approach exploits abstract information of the design in the form of regular expressions and effectively guides the symbolic traversal depending on the verified property.