The GPS signal is a combination of more than one signal. This paper deals with a model based design technique to implement GPS signal generator for L1 frequency. It helps in proper understanding of GPS signal structure. SFF-SDR Lyrtech board has been used for this purpose. Once the proper results are obtained, the GPS signal can be down converted for further processing in the GPS receiver.
Wireless sensor networks are highly susceptible to security attacks due to its open and infrastructure less configuration, autonomous operation, limited battery power and computational capability. As a result, secure routing of information by the sensor nodes has become an increasingly important topic of research interest in the recent years. In this paper, we have proposed a simple fuzzy logic based trust evaluation model for the individual nodes in a WSN mostly appropriate for routing data using the Link State Routing Protocol. The paper has been divided into two sections with the first section dealing with the evaluation of trusts of the sensor nodes. The second section involves selection of the most appropriate route for routing within the WSN from Source to Sink using the calculated trust values in the first part. Finally the protocol has been simulated on TOSSIM, and the results have shown the effectiveness of our protocol with smaller delay, increase in residual battery life and higher percentage of successful packet transmission compared to the already existing algorithms.
The theory of Rough set is a new mathematical tool to deal with intelligent data mining proposed by Z Pawlak. This paper implements the concept of tolerance relation on incomplete information system using variable precision rough set(VPRS) with flexible classification, which is an extension to the classical rough set theory. Here we have analyzed a cancer data set provided by national cancer institute from (1975-2008) and estimated the value of tolerance factor for each country people based on their age, using VPRS with flexible classification model.
Knowledge Representation is largely based on ontologies in the Semantic Web. One of the major difficulties to construct ontology such that it meets the requirements of users is the high cost incurred and time consumed in building them. Gathering complete knowledge about the domain of disclosure is time consuming and may not guarantee expected results too. Therefore reuse of existing ontologies can offer a cost and time benefit alternative than building new ones from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web and online libraries to build new ontologies. This paper presents an approach to retrieve the required ontologies on basis of important terms of domain from online repositories using semantic web search engines, preprocess them as per our requirement and merge them to form the entire domain ontology.
Ours is an age of computing power where we have millions of computing devices present and developing every day. Multinational organizations, local bodies and even individual users are all dependent on computers for their day to day computing needs and this need is ever increasing. This involves storing and manipulating critical to personal data. Thus giving rise to various security and misuse issues related to user's data. Employees of these organizations are found chatting, playing games, wasting time on social networking and other useless web-sites during the office hours. Many web-sites are not allowed by the organizations proxy, however there are very simple ways to bypass the proxy and browse these web-sites. This gave rise to the need of a monitoring system. This paper aims to present a solution, which is a developed software which provides better security mechanism by incorporating USB attachment detection, desktop screen shot capture and network connection detection with visited website information, all at one point only. The developed software can be used as a desktop application by any enterprise or an individual user. The software can be installed by the administrator of the system and can monitor user's activity on the system. It keeps a log which the administrator can view any time he wants and monitor his machine. There's also an additional feature to email the log to the admin or send him an alert SMS on his cell-phone so that he can have correct information about the system even when he is located at a different location. It can be used by an enterprise to monitor the activity of their employees or even by individuals to safeguard their system from misuse and hence making employees to make optimal use of the office time.
Epilepsy is one of the frequent brain disorder that may consequence in the brain dysfunction and cognitive disorders. Epileptic seizures occurred because of the transient and unexpected electrical interruptions of brain. EEG (ElectroEncephaloGram) is one of the most important methods for inquiring the human brain dynamics that affords a direct evaluation of cortical behavior. Data recording create very lengthy data, and therefore the inspection and identification of epilepsy will take more time for completion. In recent years, computerized diagnoses of systems are usually established to make the diagnosis simpler. This paper discusses an implementation of automated epileptic EEG detection system using Fast Walsh-Hadamard Transform and Hybrid Extreme Learning Machine. In this paper, Spatially-Constrained Independent Component Analysis (SCICA) with otsu's thresholding is used to separate the exactly the artificate Independent Components (ICs) from the initial EEG signal. Fast Walsh-Hadamard Transform and Sample Entropy (SampEn) is used for feature extraction to the task of classifying EEG signals, that which are normal, ictal and interictal. This paper uses Hybrid ELM as a classification model. This classification model uses the Analytical Hierarchy Process (AHP) method to select the input weights and hidden biases, the ELM algorithm to analytically determine the output weights and the Levenberg--Marquardt (LM) algorithm to learn the network. Experimental results show that automatic epilepsy detection using Fast Walsh-Hadamard Transform and Hybrid Extreme Learning Machine achieves batter accuracy in lesser time than standard ELM.
Various kinds of audio and video data are generated everyday like audio and video chatting, blog posts, e-communities, social networks, customer reviews on wide range of products and online audio and video helpline for different technical problems. Providing keywords for these audio files, thus allow the users to quickly grab the gist of the lengthy recordings and helps information access effectively. Nowadays online reviews are having greater impact on consumers and companies compared to the traditional data. New methodologies are available for automated sentiment analysis and discovering the hidden knowledge from unstructured audio and video data. Among various sentiment analysis tasks, one of them is sentiment classification, ie., identifying whether the input of the given text is positive or negative. In this paper it is proposed to combine both keyword extraction and sentiment classification into a single model which will perform both the works at a single time.
Comparative or Evaluative questions are categorized as non-factoid questions where user asked to compare between entities (for comparative) based on some criteria and constraints or asked to evaluate certain criteria of the entities (for Evaluative). The answers of this type questions can't be directly lifted from the underline document collection rather answers are hidden. To answer the Comparative or Evaluative questions system must have potentiality to understand the comparative evaluative expression and user needs. This paper contains trivial approach to give answer of Comparative or Evaluation questions.
The demand of a mobile phone and its various applications are increasing rapidly in recent era and as a result, it becomes vital to design and/or improve the existing PKI (Public Key Infrastructure) useful for mobile phones. Since a mobile phone has small screen, low computing power, small storage capacity etc, the present paper proposes an ECC-based PKI that overcomes all the limitations for the mobile phones as mentioned above. For this, we introduce a Mobile Home Agent (MHA) and Registration Authority (RA) that minimize the major work/processing loads of mobile phone and Certificate Authority (CA), respectively. In addition, the use of ECC reduces the computation cost, message size and transmission overhead over RSA significantly. The security analysis of the proposed PKI against relevant attacks and the comparisons with existing schemes shows overall improved performance.
Scarcity of bandwidth for fixed allocation of frequency and also current growth trends in wireless communications shows that Cognitive Radio Networks (CRNs) are followed strictly. Fluctuating nature of the radio spectrum with a variety of quality services which needs causes some challenges in CRNs, such as spectrum sharing. In many previous works, the problem of performance loss is seen due to the sudden presence of primary user. In such a situation, secondary user has to vacate bandwidth at the moment. This often leads to long wait for secondary user on access the channel. However, Spectrum Pooling concept is used with the ability of secondary utility for licensed frequency band, without reducing quality of service and without requiring new hardware. In this paper, a fair queuing algorithm with weighting and prioritization between different users is recommended. This type of queuing model with weighted fair queuing policy is efficient to share the bandwidth among systems with different priority levels. The proposed algorithm offers shared use of spectrum to invest a fair sharing of bandwidth between cognitive users. The proposed model involves a different service rates for unlicensed users and can be used as an acceptable model in heterogeneous networks. By the use of analytical and simulation results, we can achieve a desirable level of performance and an improvement in fairness.
Ontologies are very popular in building knowledge base. In this paper we have created ontology based student profile. There are many advantages of using ontology based student profile which are explored in this paper. This paper gives a detailed description of each step involved in student profile creation. Student profile is created using protégé 4.0 alpha tool.
Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. One of the most common and effective ways to detect worm attacks is to implement a signature-based IDS. An IDS samples suspicious flow in the network with the goal of detecting previously encountered worms. The two significant drawbacks in these approaches are manual signature generation and lack of accurate signatures to detect polymorphic worms. This approach proposes a new Network Signature Generator (NSG), Extended PolyTree that automatically and quickly generates accurate signatures for worms, especially polymorphic worms. It is observed that signatures from worms and their variants are relevant and a tree structure can properly reflect their familial resemblance. Therefore, the signatures extracted from worm samples are organized into a tree structure called Signature Tree. This approach comprises of five phases namely, traffic data collection, SRE signature generation, signature tree generation, signature selection for IDS and worm detection & removal. Based on the suspicious traffic collected, SRE signatures are generated. These signatures are aligned in such a way that they represent their familial resemblance in the form of signature tree. From the generated most specific signatures, few signatures are selected and given to IDS for worm detection. The simulation analysis of this work shows the increase in time consumption to construct the tree and worm detection time. The accuracy in signature generation in this work is better than any existing system.
The Extensible Authentication Protocol (EAP) is a framework for transporting authentication credentials. EAP offers simpler interoperability and compatibility across authentication methods. In this paper, we have modeled the Extensible Authentication Protocol is modeled as a finite state machine. Then the model is checked for conformance with its specifications to detect possible flaws. The various entities in our model are Authenticator, EAP Server, User and User Database. The messages exchanged between various entities are modeled as transitions. The model is represented in PROMELA. Then the model is verified using SPIN model checker. This enables us to check working of protocol before implementation.
Automatic visual inspection is the backbone of any manufacturing industry. Manual inspections of textile fabrics are ineffective due to the fatigue and speed requirement. The Gabor wavelets network provides an effective way to analyze the input images and to extract the fabric features. This paper addresses the functionality of Gabor Wavelet with statistical features and Morphological filtering. The first method extracts statistical features of the input image using Gabor wavelet. Another method combines Gabor wavelet with morphological filtering to select appropriate structuring element. Finally, thresholding of the features are done to produce a binary image. In addition, the performance of the algorithms is evaluated to verify their efficiency in identifying the defective fabric image based on the segmented results.
Research in Automatic Speech Recognition (ASR) has attracted a great deal of attention over the past five decades. It aims to provide an efficient way for human to communicate with computers. With the extensive development of these systems permits the user to talk almost naturally with computers. Hence, today's researchers are mainly focusing on developing a system for recognizing continuous speech to accomplish tasks such as answering emails and creating text documents etc. But developing such system is still found to be a difficult task due its own complexity. This paper presents a Continuous Speech Recognition (CSR) system for Tamil language using Hidden Markov Model (HMM) approach. The most powerful and widely used MFCC feature extraction is used as a front-end for the proposed system. The monophone based acoustic model is chosen to recognize the given set of sentences from medium vocabulary. The results are found to be satisfactory with 92% of word recognition accuracy and 81% of sentence accuracy for the proposed developed system.
Medical image segmentation plays a crucial role in identifying the shape and structure of human anatomy. The most widely used image segmentation algorithms are edge-based and typically rely on the intensity homogeneity of the image at the edges, which often fail to provide accurate segmentation results due to the intensity inhomogeneity. This paper proposes a boundary detection technique for segmenting the hippocampus (the subcortical structure in medial temporal lobe) from MRI with intensity inhomogeneity without ruining its boundary and structure. The image is pre-processed using a noise filter and morphology based operations. An optimal intensity threshold is then computed. We have used mean, top-hat and bottom hat filters for noise removal and Ridler Calvard method to compute the threshold value. Our method has been validated on human brain sagittal MRI, with desirable performance in the presence of intensity inhomogeneity. The proposed method works well even for weak edge. Experimental results show that our method can be used to detect boundary for accurate segmentation of hippocampus. The proposed method takes no more than 2 seconds for boundary detection.
In this paper we propose a fully automatic method for segmenting the brain portion from the MRI of head scans. We make use of Bond Number (No) to detect the edges of brain and head. Block truncation is used as a filter in edge detected image and finally morphological operations are done to segment the brain portion. The segmented brain portions are compared with the gold standard images provided by the IBSR. Experimental results on a number of MRI volumes show that the proposed method gives satisfactory and comparable results to that of few existing brain extraction method.
very few research works have been done on XML security over relational databases despite that XML became the de facto standard for the data representation and exchange on the internet and a lot of XML documents are stored in RDBMS. In [14], the author proposed an access control model for schema-based storage of XML documents in relational storage and translating XML access control rules to relational access control rules. However, the proposed algorithms had performance drawbacks. In this paper, we will use the same access control model of [14] and try to overcome the drawbacks of [14] by proposing an efficient technique to store the XML access control rules in a relational storage of XML DTD. The mapping of the XML DTD to relational schema is proposed in [7]. We also propose an algorithm to translate XPath queries to SQL queries based on the mapping algorithm in [7].
Field Programmable Gate Arrays (FPGAs) is a general-purpose, multi-level programmable logic device which allows perfect customization of the hardware at an attractive price even in low quantities. Modern FPGAs became viable ASIC replacement because of very expensive fabrication process and time consuming test process. Unfortunately, the amount of reconfigurable resources is fixed and limited. While using the resources as well the logic implemented needs optimizations in order to meet the desired constraints. As the capacity of FPGAs increases, synthesis tools and efficient synthesis methods for target device become more significant to efficiently exploit the logic capacity. The synthesis tool provides a variety of design constraints which essentially helps the designer to meet the design goal such as area and speed to obtain the best implementation. This paper presents the implementation of modified ripple carry adder with various block optimizations for speed and area constraints. This modified structure produces better optimized output when compare to conventional ripple carry adder and parallel prefix adders like brent kung, Sklansky, Kogge-Stone etc. The module functionality are described using Verilog HDL and performance issues like slice utilized, simulation time, percentage of logic utilization, level of logic are analyzed at 90 nm process technology using SPARTAN6 XC6SLX150 XILINX ISE12.1 tool.
Voice transformation is a process of changing voice personality; i.e. speech uttered by a source speaker is modified to sound as if a target speaker had uttered it. In this paper we study a new approach based on the GMM model and using the first and the second derivate of the pitch and the spectrum as additional information to calculate the joint probability of the source and target. We use the standard Arabic to measure the performance of this new approach.
