Information and Communication Technologies

The Data could be provided from the MAC Layer to the Security Sub Layer that is going to be designed & simulated. In this paper the security sub-layer will provide subscribers with total privacy across the fixed broadband wireless network. This is done by encrypting connections between Subscriber Station (SS) and Base Station (BS). Also, strong protection from theft of service is provided through the BS protection to data transport services, by enforcing encryption of the associated service flows across the network. The data when entered into the security sub layer first will be certified. After the certification of the data, it could be encrypted through techniques like Data Encryption Standard (DES), Advanced Encryption Standard (AES) that we will call as the Data Encryption. After the encryption, the data is again encrypted through Secure Hash Algorithm (SHA-1) which is used for the message digest of that encrypted data. This operation will take place in the Integrity Unit (part).

A fully automated and effective method of detection of Assamese numerals captured under varied recording conditions and moods is presented here. The work also deals with gender variations taken as part of the speech recognition using a combination of Linear Predictive Code (LPC) and Vector Quantization applied to a combination of Self Organizing Map (SOM) and Multi Layer Perceptron (MLP) based system. The SOM and MLPs are used to constitute a Learning Vector Quantization (LVQ) block which is necessitated by the fact that the LPC-VQ methods fail to produce the expected outcome while dealing with numerals of Assamese- a language spoken by a sizable population in the North-Eastern part of India. The performance of the LPC feature set is further compared with that obtained PCA features applied to the same LVQ block.

Arithmetic and Logic Unit (ALU) is a combination circuit that performs a number of arithmetic and logical operations within a microprocessor.We present guidelines for low- and ultra low-power & high performance 32- bit ALU [1] units and analyze structures for logical operations to determine the most suitable for these regions of operations for low power applications.An ALU design is then modeled in VHDL for further testing with pre-manufactured parts of the 32 bit ALU using XILINX ISE tools. A novel method that chooses better designs of flipflops and latches in different places of the data path, based on the data and clock activities.Our simulation results indicate that, for the 180nm-65nm CMOS technologies [6] it is possible to reduce the ALU total energy by 18%-24%, with minimal delay degradation. In addition, there is up to 22%-32% reduction in leakage power in the standby mode.

As various applications of wireless ad hoc networks have been proposed, security has become one of the salient research challenges and is receiving increasing attention. Recently, several security schemes for wireless ad hoc networks have been proposed using encryption schemes. Our proposed scheme is more secure against adaptive chosen cipher text attacks because of randomness. In this paper we propose an algorithm that focuses on encryption of plain text over a range of languages supported by Unicode .Our assumption is to make sure that sender and receiver must have share Secret key and Mapping array before establishing communication.

In this paper, fuzzy controller for the trajectory control of PUMA560 robot manipulator arm is approximated into a Bi-variate Polynomial using Genetic Algorithm. In the proposed method, initially Fuzzy PD+I controller is used for control of PUMA560. The input and output data points are collected from the Fuzzy PD controllers of individual joints. With these data points a bi-variate polynomial approximation function is obtained through interpolation process using genetic algorithm. This Fuzzy PD controller approximate is then employed and evaluated.

This paper introduces a multi resolution cascaded transform domain method for copyright protection of digital images. An algorithm is developed that embeds the watermark information, with out much distortion to the cover image, while allows us to extract the watermark by use of correlation. The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is achieved by Dual Tree Complex Wavelet Transform (DTCWT), which is modeled to show directional selectivity and as a result consistent with the Human Visual System (HVS). The Discrete Cosine Transform (DCT) is resistant to several attacks. In this paper combined DTCWT-DCT technique is proposed, which uses a unique method for spreading, embedding and extracting the watermark. This paper compares various watermarking algorithms like DCT, Discrete Wavelet Transform (DWT), DTCWT, DCT-DWT with DTCWT-DCT method. This hybrid DTCWT-DCT shows superior performance in the presence of geometric and signal processing operations.

A new differential CMOS dynamic comparator using positive feedback with less power dissipation, less offset has been proposed. For the performance verification, the design was simulated in CADENCE GPDK 90nm CMOS Technology at 1.8Vsupply voltage. Nearly 18mV Offset Voltage, power dissipation 2.564mW, speed 2.812GHZ, propagation delay 0.3556ns is easily achieved with the proposed structure. Inputs are reconfigured from typical differential pair comparator such that near equal current distribution in the input transistors can be achieved for a meta-stable point of the comparator. Restricted signal swing clock for the tail current is also used to ensure constant currents in the differential pairs.

A new CMOS voltage-mode Four-quadrant analog Multiplier is proposed and analyzed. By applying inputs signals to set of complementary diode pair connection & to that of voltage difference circuit. The circuit is formed by cascading the complementary diode pair connection with the voltage difference circuit. Based on the proposed multiplier circuit, a low voltage high performance CMOS four quadrant analog multiplier is designed and fabricated by using 0.35micron technology. The measured 3dB bandwidth is 15 MHz Simple structure, low-voltage, low power, and high performance makes the proposed multiplier quite feasible in many applications.

DoS / DDoS(Distributed Denial of Service) attacks deny regular, internet services accessed by legitimate users, either by blocking the services completely, or by disturbing it completely, so as to cause customer baulking. Several traceback schemes are available to mitigate these attacks. The simulation approach also can be used to test the performing effects of different marking schemes in large-scale DDoS attacks. Based on the simulation and evaluation results, more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed. DGT8, directional geographical trackback scheme, with 8 directions is one of them. Having a limited set of 8 directions, DGT8 may not work for routers with more than 8 interfaces. In this paper, we propose M-DGT i.e DGT 16, a 16 directional geographical traceback scheme having all the advantages of DGT. The 16 directions, though not having exactly equal interface, have nearly equal measures, and are identified using a novel scheme of Segment Direction Ratios (SDR). The SDR concept and the associated marking scheme allow the victim to defend against DDoS attacks independent of its ISP and also the generalization to DGT2n, having 2n directions (n>4).

Caspases are enzymes that can cleave other proteins and control normal and abnormal cell death. Cancer cells generally lack apoptosis. In this research work, a computational approach has been adopted to design a promotor that targets the inactivated caspases particularly Pro-caspase-3 or caspase-7, which are the effector caspases that cleave the downstream substrates like lamin-A, ICAD and PARP. Out of the 38 anti-carcinomic compounds selected for the analysis, some of them are found to have positive charged substituents similar to the known drug; PAC1, which cleaves the safety catch mode that blocks the IETD active site. Site specific interactions of the proteins with these ligands were performed. From the interaction analysis, it was found that 3 compounds; Choline, Glaziovine, Dasatinib can effectively target caspases and activate them. It has been suggested that these compounds favor the activation of the effector caspase proteins, thereby giving a better option in cancer therapy.

This article presents Learning Classifier Systems (LCS) approach for automated discovery of Hierarchical Censored Production Rules (HCPR). A LCS is an adaptive system that learns to perform the best action given its input. By

best

is generally meant the action that will receive the most reward or reinforcement from the system’s environment. A classifier system consists of three main components: rule and message system, apportionment of credit system, genetic algorithm (GA). In the proposed LCS, concatenate of the Hierarchical Censored Production Rule-trees form the genotype, and therefore the GA operates on a population of HCPR-trees. More recently, LCSs have proved efficient at solving automatic classification tasks. Hierarchical Censored Production Rules is a system of knowledge representation that exhibited variable certainty as well as variable specificity and offered mechanisms for handling the trade off between the two. An appropriate chromosome representation scheme, suitable genetic operators, appropriate fitness function and also appropriate credit assignment scheme is proposed to evolve the best HCPR-trees. Experimental results are presented to demonstrate the performance of the proposed system.

The central idea of this paper is to develop an algorithm that embeds the watermark information in host image to authenticate it. The host image is divided into non-overlapping blocks of size 2x2. Minimum value is obtained from each block and the resultant matrix is scrambled for three times with Arnold transform to enhance security. From the transformed matrix a binary watermark is constructed and is embedded within the host image. The operation of embedding and extraction of watermark is done in high frequency domain of Discrete Wavelet Transform since small modifications in this domain are not perceived by human eyes. Furthermore, the proposed algorithm is checked against various common image processing attacks. This watermarking scheme deals with the extraction of the watermark information in the absence of original image, so the blind scheme was obtained.

The reconstruction of gene networks is crucial to the understanding of cellular processes which are studied in Systems Biology. The success of computational methods of drug discovery and disease diagnosis is dependent upon our understanding of the biological basis of the interaction networks between the genes. Better modelling of biological processes and powerful evolutionary methods are proving to be a key factor in the solution of such problems. However, most of these methods are based on processing of genotypic information. We present an evolutionary algorithm for inferring gene networks from expression data using phenotypic interactions. The benefit of this is that we avoid the need for an explicit objective function in the optimization process. In order to realize this, we have implemented a method called as the Phenomic algorithm and validated it for stability and accuracy in the reconstruction of gene networks.

Web mining is one of the mining technologies, which applies data mining techniques in large amount of web log data. Web navigational mining discovers users’ access patterns from web logs. This information can be used to identify the behavior of the web user. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behavior may be changed when the new web data is inserted into and the old web data is deleted from web logs. Therefore, the user behavior must be re-discovered from the updated web logs. However, it is very time-consuming to re-find the users’ access patterns. Hence, many researchers pay attention to the incremental mining, which utilizes the previous mining results and finds new patterns just from the inserted or deleted part of the web logs such that the mining time can be reduced.

The present paper proposes an efficient incremental web navigational mining algorithm for discovering web navigational patterns when the user sequences are inserted into and deleted from original database. It avoids re-finding the original web navigational patterns and re-counting the original candidate sequences. It uses lattice structure to keep the previous mining results such that just new candidate sequences need to be computed. Hence, the web navigational patterns can be obtained rapidly when the navigational sequence database is updated. Besides, maximal web navigational patterns can also be obtained easily by traversing the lattice structure. The experimental results show that the present algorithm is more efficient than the other approaches.

A Mobile ad-hoc network (MANET) is an autonomous system of routers (and associated hosts) connected by wireless links. The Ad hoc On-Demand Distance Vector (AODV) routing protocol meant for MANETs is an improvement over Destination-Sequenced Distance Vector (DSDV) because it typically minimizes the number of required broadcasts by creating routes on demand basis, as opposed to maintaining a complete list of routes as in the DSDV algorithm. AODV is vulnerable to both external and internal security attacks. In Specification Based Intrusion Detection, the correct behavior of critical objects are manually abstracted and crafted as security specifications, which are compared with the actual behavior of the objects. We propose a technique to analyze the vulnerabilities of AODV protocol, specifically to monitor the network layer attacks such as Black Hole attack and to develop a Specification Based Intrusion Detection System (IDS) using soft computing technique. The proposed system is based on fuzzy logic which analyzes the performance of the wireless nodes in a MANET and provides relevant information about the various attacks. The Fuzzy logic control (FLC) system specifies a set of Fuzzy rules based on the essential features of the AODV routing protocol such as RREQ forwarding rate, Packet forwarding rate and so on. The performance of the MANET is analyzed based on the FLC system results.

In this paper, we proposed a new image content-based authenticity verification procedure for wireless image authentication scheme. The existing image content-based authentication procedure for wireless image authentication scheme has the following shortcomings: 1) High computational cost and 2) Low performance. To overcome above drawbacks, a new image content authenticity verification procedure has been proposed. The proposed method enhances the authentication results with low computational cost and high performance by comparing with existing content-based authentication procedure. The proposed scheme implemented the existing methods like secret wavelet filter parameterization, wireless image authentication and structural digital signature.

This paper proposes an enhanced substitution-diffusion based image cipher using improved chaotic map. The first step consists of permutation which uses the odd key values. Byte substitution is applied in the second step to improve the security against the known/chosen-plaintext attack. Finally, confusion and diffusion are obtained using the sub diagonal diffusion of adjacent pixels and XORing with the chaotic key. The numbers of rounds in the steps are controlled by combination of pseudo random sequence and original image. The security and performance of the proposed image encryption technique have been analyzed thoroughly using statistical analysis, key sensitivity analysis, differential analysis, key space analysis and entropy analysis. Results of the various types of analyzes are showing that the proposed image encryption technique is more secure and fast.

Network forensics involves the capture, recording, and analysis of network events in order to discover the source of security attacks and other problem incidents. We extend our previously proposed model for collecting network data, identifying suspicious packets, examining protocol features misused and correlating attack attributes. This model is capable of handling attacks on the TCP/IP suite. The results obtained by this model are validated.

Providing real-time data delivery in wireless sensor networks is a challenging research problem. In this paper we propose a centralized control plane incorporating the timed token protocol in the MAC layer for providing real-time data delivery in wireless sensor networks. In this approach hard real time guarantee is provided by a dynamic ring structure, where high priority stations have more chance of admittance and stations with low priority can be removed from the ring. Soft real time guarantee is provided by using proactive wireless routing protocol (DSDV) for path finding and maintenance, timely delivery of data is done through a prior bandwidth reservation. Simulation results show that the proposed control plane and bandwidth reservation ensures higher priority traffic more bandwidth than lower priority traffic and guarantees real-time data delivery.

Scheduling a task with multiple QoS needs such as deadline, reliability, cost, trust, etc., is called QoS based scheduling. Several heuristics have been proposed for QoS based scheduling and it has been proved that it is a NP-hard problem. In this paper, we have proposed a heuristic for multiple QoS based scheduling for independent tasks. The heuristic considers multiple QoS needs of a task and finds the total utility of the task. It groups the tasks on the basis of their utility values. It schedules tasks in the descendent order from higher to lower utility values. The results show that the proposed heuristic is better in makespan and load balancing than other heuristics such as QoS Guided Min-Min and Weighted Mean Time Min-Min Max-Min Selective.

Network security approach addresses attacks from perspective of prevention, detection and mitigation. The alternative approach of network forensics involves investigation and prosecution which act as deterrence. Our paper presents a generic process model and reviews various implementations for network forensics. We propose a novel framework to address the research gaps and discuss the work-in-progress.

In this paper, we propose a new statistical predictive model of Trust based on the well-known methodologies of the Markov model and Local Learning technique. Repeatedly appearing similar subsequences in the trust time series constructed from history of direct interactions or recommended trust values collected from intermediaries over a sequence of time slots are clustered into

regime

. Each regime is learnt by a local model called as

local expert

. The time series is then modeled as a coarse-grain transition network of regimes by using a Markov process and value of the trust at any future time is predicted by selecting the local expert with the help of the Markov matrix.

Data mining services play an important role in the telecommunications industry. Considering the importance of data mining services to provide intelligence locally on devices on mobile environments, we propose a data mining service that adopts the embedded data mining algorithm according to situation. In this paper, we propose a novel data mining algorithm named J2ME-based Mobile Progressive Pattern Mine (J2MPP-Mine) for effective mobile computing. In J2MPP-Mine, we first propose a subset finder strategy named Subset-Finder (S-Finder) to find the possible subsets for prune. Then, we propose a Subset pruner algorithm (SB-Pruner) for determining the frequent pattern. Furthermore, we proposed the novel prediction strategy to determine the superset and remove the subset which generates a less number of sets due to different filtering pruning strategy. Finally, through the simulation our proposed methods were shown to deliver excellent performance in terms of efficiency, accuracy and applicability under various system conditions.

The programmable Dynamic Petri Nets(DPN) can efficiently model interactive and iterative distributed multimedia presentations. However, the dynamic adaption of the presentation is not possible using isolated DPNs. This paper proposes the concept of communicating Dynamic Petri Nets (CDPN). The declaration and utilization of global variables and functions has been used in the paper to augment the existing DPNs with the communicating feature. There are several distributed systems that can be modeled using the proposed CDPN. In this paper a domain specific application illustrating the potential of CDPN to model adaptive e-learning system is presented.

The proposed system is mainly based on mining closed sequential web access patterns. Initially, the PrefixSpan algorithm is employed on the preprocessed web server log data for mining sequential web access patterns. Subsequently, with the aid of post-pruning strategy, the closed sequential web access patterns are discovered from the complete set of sequential web access patterns. Then, a pattern tree, a compact representation of closed sequential patterns, is constructed from the discovered closed sequential web access patterns. The Patricia trie based data structure is used in the construction of the pattern tree. For a given user’s web access sequence, the proposed system provides recommendations on the basis of the constructed pattern tree. The experimentation of the proposed system is performed using synthetic dataset and the performance of the proposed recommendation system is evaluated with precision, applicability and hit ratio

Trajectory data mining is an emerging area of research, having a large variety of applications. This paper proposes a nearest neighbour based trajectory data as two-step process. Extensive experiments were conducted using real datasets of moving vehicles in Milan (Italy). In our method first, we build a classifier from the pre-processed 03 days training trajectory data and then we classify 04 days test trajectory data using class label. The resultant figure shows the our experimental investigation yields output as classified test trajectories, significant in terms of correctly classified success rate being 98.2. To measure the agreement between predicted and observed categorization of the dataset is carried out using Kappa statistics.

Most recent Mobile ad hoc network (MANET) research has focused on providing routing services without considering security. The malicious node can easily disturb the functioning of routing protocols. Route discovery phase of Dynamic Source Routing (DSR) requires the ability to verify that no node has been deleted from the path, and no node can be inserted in the path without a valid authentication. In this paper, we present a security mechanism that provides message integrity, mutual authentication and two-hop authentication mechanism without the assistance of online certification authority. Our mechanism not only prevents identity impersonation, replay attacks, but also detect and drop inconsistent RREQ to save network bandwidth.

In this paper Regression Modeling Technique is proposed for the retention of customer and maintains customer loyalty. Prediction attempts to predict the pattern of events on the basis of the input data Here the aim of the paper is to launch desktops and laptops of various configurations on the basis of age, Gender, Price and monthly income. This paper aims at how the concepts of data mining and regression analysis can be applied to achieve the response of the customer by analyzing the relationship among the various customer related attributes.

A wireless transmission channel with multipacket reception capability is expected to be a common feature of next generation telecommunication systems. Probability of correctly receiving simultaneously transmitted packets at the base station in a given time slot depends on the number of transmitted packets as well as the geographical proximity of the transmitter to the base station. This paper formulates a congestion game with player-specific costs to model the situation, characterizes its Nash equilibrium and analyses the slot allocation at the operating point.

Biometric systems are subjected to a variety of attacks. Stored biometric template attack is very severe compared to all other attacks. Providing security to biometric templates is an important issue in building a reliable personal identification system. Multi biometric systems are more resistive towards spoof attacks compared to unibiometric counterpart. This work provides security and revocability to iris and fingerprint templates using password hardened multimodal biometric fuzzy vault. Password hardening provides security and revocability to biometric templates. Security of the vault is measured in terms of min-entropy.

This paper proposes a high-speed multi-diameter CNFET-based 7T (seven transistor) SRAM (static random access memory) cell. It investigates the impact of process, voltage and temperature (PVT) variations on its design metrics and compares the results with its counterpart – CMOS-based 7T SRAM cell. The proposed design offers 77.4× improvement in write access time along with 88.1× reduction in write access time variation and 117.8× saving in write power along with substantial reduction in write EDP/write EDP variation. The proposed memory cell shows 40% improvement in SNM (static noise margin) and better robustness against PVT variations.

One of the challenging issues in computational grid is load balancing. In many approaches load balancing is done only at the local scheduler level, which is applicable to small application and leads to more communication overhead between the resources. For the large scale application load balancing at the local scheduler level will not provide the feasible solution. So the novel Load Balancer algorithm is proposed, which provides the load balancing at the meta-scheduler level. To initiate the load balancing triggering policy is used, which determines the appropriate time period to start the load balancing operation by using Boundary value approach. This approach increases the performance by reducing the waiting time of jobs and by maximizing the utilization resource which is least loaded.

Classification of Streaming Data has been recently recognized as an important research area. It is different from conventional techniques of classification because we prefer to have a single pass over each data item. Moreover, unlike conventional classification, the true labels of the data are not obtained immediately during the training process. This paper proposes ILEP, a novel instance-based technique for classification of streaming data with a modifiable reference set based on the concept of Emerging Patterns. Emerging Patterns (EPs) have been successfully used to catch important data items for addition to the reference set, hence resulting in an increase in classification accuracy as well as restricting the size of the reference set.

This paper proposes a modified version of Go-left Balls & Bins algorithm for server load balancing using K-Partite property of a graph. The previous algorithms all had to keep knowledge of the past load distribution information while distributing new load -an issue that is virtually impossible in real life as it itself congests the server with load. But ours algorithm is random in nature and hence sheds this overhead; this is also quite realistic in nature and close to the implementation domain.

Current context aware e-learning system lacks in providing highly customized information to the learner, which considers context parameters discretely and there is no complete standardized set of learner contexts. The proposed three layered adaptation model gives all most all learner contexts in a standardized way, which improves the efficiency of learning process. The design solution provides a three layered architecture based on learner’s characteristics, which is divided into three layers such as conceptual layer, logical layer and physical layer which helps to improve the efficiency of learning process.

In this paper, we propose an agreement-based fault detection and recovery protocol for cluster head (CH) in wireless sensor networks (WSNs). The aim of protocol is to accurately detect CH failure to avoid unnecessary energy consumption caused by a mistaken detection process. For this, it allows each cluster member to detect its CH failure independently. Cluster members employ distributed agreement protocol to reach an agreement on failure of the CH among multiple cluster members. The detection process runs concurrently with normal network operation by periodically performing a distributed detection process at each cluster member. To reduce energy consumption, it makes use of heartbeat messages sent periodically by a CH for fault detection. Our algorithm would provide high detection accuracy because of agreement protocol.

The primary use of wireless sensor networks (WSNs) is to collect and process data. Most of the energy consumption is due to data transmission. Because of the unique properties of WSNs all raw data samples are not directly sent to the sink node instead data aggregation is preferred. Since sensor nodes are deployed in an open environment such as a battlefield or similar applications, data confidentiality and integrity are vital issues in such conditions, hence secure aggregation is required. End to end secure aggregation is less demanding compared to hop by hop secure aggregation so former is superior. When aggregation is performed on data, crucial information is lost which may be indicating alarming situation. This paper presents an idea to reduce the amount of information transmitted with retention of critical data so that the problem area could be identified. Privacy Homomorphism(PH) preserves the data characteristics even in the encrypted form. This paper is based on the PH technique which provides secure data aggregation without significant loss of individuality of data.

A novel approach to the design of high-performance operational-amplifier-based differential charge amplifiers is proposed. It is based on a two-stage topology: The first stage performs a differential measurement to single ended signal conversion, providing a common mode rejection that only depends on the matching between two resistors; the second stage filters the signal. These novel topologies that are based on this technique are presented, analyzed, and measured, and design criteria are finally given. Their performance is compared with there topology that is used as the benchmark, and it results in a better common- mode rejection ratio (CMRR).

With the advancement in technology in the field of transistors it has become easy to have millions of transistors on one dice. It is still a challenge to translate the available resources into convenient application. Many conventional processors has failed to achieve that level of performance. A new alternative to the conventional processors is the scalable WaveScalar. WaveScalar is a dataflow instruction set based execution model with low complexity and high performance features. It can run real world programs, non-real world programs without changing the language and still having the same parallelism. It is designed as a intelligent memory system where each instruction executes in its place and then communicates with its dependent. If a high-performance processor is to realize its full potential, complexity should be least. Here is this paper, we have proposed solution to reduce the complexity of the wavescalar processor without affecting its performance

This paper presents a new method for tracking of an object in video sequence which is based on dual tree complex wavelet transforms. Real valued wavelet transform, mostly used in tracking applications, suffers from lack of shift invariance and have poor directional selectivity. We have used dual tree complex wavelet transform in tracking because it avoids shortcomings of real wavelet transform. In the proposed method, object is tracked in next frames by computing the energy of dual-tree complex wavelet coefficients corresponding to the object area and matching this energy to that of in the neighborhood area. The proposed method is simple and does not require any other parameter except complex wavelet coefficients. Experimental results demonstrate performance of the proposed method.

Information Retrieval [IR] is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web. This paper describes a document representation method instead of keywords ontological descriptors. The purpose of this paper is to propose a system for content-based querying of texts based on the availability of ontology for the concepts in the text domain and to develop new Indexing methods to improve RSV (Retrieval status value). There is a need for querying ontologies at various granularities to retrieve information from various sources to suit the requirements of Semantic web, to eradicate the mismatch between user request and response from the Information Retrieval system. Most of the search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. The indexes do not contain synonyms, cannot differentiate between homonyms and users receive different search results when they use different conjugation forms of the same word.

The common attacks on the internet are denial of service and spoofing. Spoofing hides the identity of the attacker by modifying source IP address field and can cause the denial of service which makes the services unavailable to the legitimate users. Tracing the source of the attacking packet is very difficult because of stateless and destination based routing infrastructure of Internet. In this paper we propose a system which uses packet marking mechanisms along with Intrusion Prevention Systems for efficient IP traceback. The data mining techniques can be applied to the data collected from the packet marking scheme for detecting attack. The resultant database of knowledge can be further used by network Intrusion prevention systems for decision making. The data mining techniques are providing very efficient way for discovering useful knowledge from the available information. The combination of packet marking scheme, Intrusion prevention system and data mining can give us very effective results.

Wireless sensor networks (WSN) have been identified as being useful in a variety of domains such as environment monitoring, target tracking, etc. Time synchronization is an important component of sensor networks to provide a common clock time in sensor nodes. Time synchronization protocols provide a mechanism for synchronizing the local clocks of the nodes in a sensor network. Some of the sensor nodes may be malicious, which can disrupt the normal operation of a sensor network. In this paper, we find out malicious nodes out of existing nodes and propose multi-hop time synchronization based secure protocol for a group of non-malicious nodes.

The player experience is a significant parameter in evaluating the overall success of a game. It is necessary to create a game that provides: (1) satisfaction and (2) challenge. Dynamic difficulty adjustment (DDA) helps in producing interesting games. This paper focuses on improving DDA systems by introducing: (a) dynamic weight clipping, (b) differential learning and (c) adrenalin rush. Experimental results indicate that these features can implement an ideal DDA system that can engage the human player by creating equally competent opponents.

Information retrieval (IR) is a crucial area of natural language processing (NLP). One of the fundamental issues in bilingual retrieving of information in search engines seems to be the way and the extent users call for phrases and chunks. The main problem arises when the existing bilingual dictionaries are not able to meet the users’ actual needs for translating such phrases and chunks into an alternative language and the results often are not reliable. In this project a heuristic method for extracting the correct equivalents of source language chunks using monolingual and bilingual linguistic corpora as well as text classification algorithms is to be introduced. Experimental results revealed that our method gained the accuracy rate of 86.13% which seems very encouraging.

As neural network modeling of learning continues, further applications to education could become more apparent. Some implication of such model is to predict how the students will perform in the course, during the admission procedure. Many researches implemented mathematical models and concluded that they are not very effective to predict. However, advancement of artificial intelligence has proved many innovation and renovation in various fields. This paper discusses the Self Organizing Map (SOM), neural network architecture, to predict the student’s performance.

The Content based medical image retrieval, which aims at searching the image database using invariant features, is an important research area for manipulating large amount of medical images. So designing and modeling methods for medical image search is a challenging task. This paper proposes an approach by combining DICOM header information (high level features) and content features (low level features) to perform the retrieval task. A novel approach of rotation invariant contourlet transform (CT) is proposed for texture feature extraction and fixed resolution format is used to derive the shape features. Initially the DICOM header information is extracted which is used to perform a pre-filtering on the original image database. Content based search is performed only on these pre-filtered images which speed up the retrieval process. The retrieval performance of this method is tested using a large medical image database and measured using commonly used performance measurement.

Supplier selection negotiation is a challenged, complex, and nondeterministic problem. To solve the problem well, it is necessary to develop an intelligent system for negotiation support in supplier selection process. Reinforcement Learning (RL) is a powerful algorithm which can be used for the price offer in supplier selection negotiation with the aim of maximizing the demander’s profits. In this paper, we formulate the supplier selection as a RL problem. States, actions, and reinforcement function are defined in this problem. In the next step, we compare the proposed RL method with traditional method.

An algorithm is presented for generating trees in A-order using z-sequences series. This algorithm generates both k-ary and (k,m)-ary trees and it is the first algorithm which generates these trees independently from their structures. This algorithm has implemented in java with two classes completely and works successfully. I have used an array to store all information which are necessary about nodes. Both space and time complexities of this algorithm are optimal.

The discrete image transforms are used for energy compaction primarily and so used in image data compression. The level of energy in the image depends on level of colors used. In this paper we use two discrete image transforms namely Discrete Hadamard Transform (DHT) and Discrete Wavelet Transform (DWT). These transforms are applied on two different color models namely HSV and YCbCr separately in a given large standard database with 1000 images formed from 10 different classes taken from the Corel collection. The proposed features are effective and useful for image indexing and retrieval.

A rough set incorporated fuzzy C-means (FCM) algorithm for color image segmentation is introduced. It aims construction of more appropriate clusters in the domain. Dominant peaks in hue (H), saturation (S) and intensity (I) histograms are captured from the input image and all possible combinations of them are taken as initial set of points for processing. Reduction theory of rough set is applied for refinement to the set. The centers thus obtained represent overall pixel colors and hence generate improved clusters when given as input to FCM algorithm. Experiments on several images exhibit effectiveness of the proposed approach.

The researchers have proposed several local monitoring based reputation mechanisms to identify and isolate the misbehaving nodes in Mobile Ad hoc Networks. The simulation results of these mechanisms shows a considerable amount of improvement in overall network throughput but at the expense of false detection of a good node as a misbehaving one by the monitoring node due to lack of alerting the source of the packet about misbehaving link. So there exists a need for designing a new mechanism to minimize such false detections. This paper proposes a Local Monitoring based Reputation System with Alert

[LMRSA]

mechanism to minimize the effect of false detections without compromising the overall network performance. The simulation results were compared with the existing model that lacks the forwarding traffic rejection and alert mechanism.

Due to various reasons, the cluster based architecture has been rarely used for efficient broadcasting. In this paper, we propose to develop an Energy Efficient Broadcasting protocol using cluster based approach for the MANETs. Our protocol uses a target radius and a locally defined connected graph, to preserve the connectivity. In our algorithm, the broadcasting nodes select a subset of their neighbors using an efficient clustering technique, to forward the message using an efficient forward node selection mechanism. By simulation results, we show that our proposed protocol attains good delivery ratio with reduced delay, energy and overhead.

Data mining analysis methods are increasingly being applied to data sets derived from science and engineering domains which represent various physical phenomena and objects. In many of data sets, a key requirement of effective analysis is the ability to capture the relational and geometric characteristics of the underlying entities and their relationships with vertices and edges, which provide a natural method to represent such data sets.In Apriori-based graph mining, to determine candidate sub graphs from a huge number of generated adjacency matrices, where the dominating factor is, the overall graph mining performance because it requires to perform many graph isomorphism test . The pattern-growth approach is more flexible for the expansion of an existing graph.

The Service Level Agreements (SLA) are e-Contracts that need to be established among business partners and monitored to ensure that web services comply with the agreed Quality of Service (QoS) values. Existing approaches deal with automated contract generation for simple Web Services. Many business enterprises implement their core business service, while outsourcing other application services. When a single service cannot satisfy the user requirements, multiple Web Services must be composed which can together fulfill the request. Therefore, establishment of SLA among the component services of a composite service and the users becomes important. Hence, we have designed and implemented a framework for generating and monitoring e-Contracts for business applications involving composite Web Services. We have demonstrated our work using the scenarios of an Insurance application. A template based approach is used for composing the Web Services dynamically.

The issue of synchronization of authorization flow with work object flow in a document production workflow environment is presented and discussed in this paper. We have shown how a work object flow is synchronized with the authorization flow using a central arbiter in Web service paradigms. The co-ordination of Web services is done using WS-BPEL which supports orchestration and XACML provides authorization for Web services. The synchronization is achieved by exploiting the obligation provisions in XACML.

Networking in Wireless Sensor networks is a challenging task due to the lack of resources in the network as well as the frequent changes in network topology. Although lots of research has been done on supporting QoS in the Internet and other networks, but they are not suitable for wireless sensor networks and still QoS support for such networks remains an open problem. In this paper, a new scheme has been proposed for achieving QoS in terms of packet delivery, multiple connections, better power management and stable routes in case of failure. It offers quick adaptation to distributed processing, dynamic linking, low processing overhead and loop freedom at all times. The proposed scheme has been incorporated using QDPRA protocol and by extensive simulation the performance has been studied, and it is clearly shown that the proposed scheme performs very well for different network scenarios.

A small shorted square microstrip antenna with air dielectric (

ε

r

= 1.001) substrate is presented. The probe fed square microstrip antenna incorporates a single shorting post of radius 0.6mm which significantly reduces the overall size or area by 82% from a conventional square microstrip antenna of same substrate and height. After connecting a shorting post 55% reduction in resonant frequency is achieved for the same square patch. The maximum gain and directivity of the shorted square microstrip antenna are 3.72 dBi and 4.68 dBi respectively at resonant frequency 3.29 GHz. Simulated return loss, gain, directivity and radiation patterns are shown.

Biclustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. In this paper, MAXimal BICluster algorithm (MAXBIC) identifies coherent biclusters of maximum size with high Average Spearman Rho (ASR). This proposed query based algorithm includes three steps viz. three tier pre-processing, identifying a bicluster seed and growing the seed till an optimal bicluster is obtained. Experimental results show the effectiveness of the proposed algorithm.

Organizations, these days are deploying Data Warehouse for integrating data from various heterogeneous sources for management of information more efficiently and cost- effectively. Data Warehouse is acting as the main asset of the organization because strategic decision making ability of a business manager largely depends on the effectiveness of data warehouse. One of the main issues that influence the Quality of the information is the Data Warehouse Model Quality. A set of metrics have been defined and validated [9] to measure the Quality of the conceptual Data Model for Data Warehouse. However, this set of metrics contains some redundant metrics. Using Linear Regression (LR), we reduce this set of metrics so as to obtain the effective predictor metrics only. Further, these metrics will be empirically validated so as to use them as Quality indicators.

Knowledge Discovery and Data Mining (KDD) is an interdisciplinary area focusing on methodologies for extracting useful knowledge from data. Patterns of relations of data and information have the capacity to signify knowledge. Image pattern collection and management is the hottest subject of the digital world. The demand for image recognition knowledge of various kinds of real world images becomes greater.

Kohenen’s Self Organizing Maps (SOM)

algorithm is one of the particular neural network algorithms, which is used for pattern learning and retrieval. The conventional SOM learning method represents poor knowledge and hence their applicable targets are restricted. In this paper SOM is scrutinizing with various standard distances and remarkable similar measures. Reliable image learning is achieved with City block, Lee, Maximum value distance, Jaccard and Dice coefficient. Image gallery can be mined well by using SOM with the above said measures. The composed knowledge is useful for various significant services.

Predicting occurrence of events in the network has always been a notable problem in computer networks especially in wireless sensor networks. Estimating the location in which the next event occurs, can be used as an important routing factor in these networks. Events that occur randomly in the network can’t be modeled with methods like curve fitting, therefore, to reach this goal, other methods like clustering are needed. This paper proposes a novel algorithm using clustering methods with the aim of estimating the future events locations. Simulation results illustrate that using methods like “Complete-Link” can be an efficient help in leading routing agents - in algorithms based on routing agents- toward new events in network.

NC94 dataset is an agricultural dataset which consists of climate, crop, and soil data and is considered as a completely dataset available. The data is organized based on county and is treated as an object. It is adapted in interdisciplinary research and widely used in various research domains and once again has been exploited in our parametric database research. In this research work, we exploit the climate data of NC94 dataset to calculate the cumulative degree of coldness in Iowa in last 30 years. To demonstrate the degree of coldness, an xml based scheme is used considering blue as the base color with the notion that darker shade reflects higher degree of coldness. This work is a baby step towards the generation of climate atlas of North Central Region of USA in one single click.

A three layer laminate consisting of absorbing material, conductive polymer and conductor was developed to enhance the performance of electromagnetic compatibility of a shield. In this paper, equations were developed for the estimation of reflectivity and shielding effectiveness of the three layer laminate and analysis of the laminate was carried out to investigate the electromagnetic compatibility performance. Different types of microwave absorbers and conductive polymer materials were considered for achieving optimum reflectivity and shielding effectiveness. The investigations were carried out in X-band frequency range for the three layer laminate at different thicknesses of the layers.

In this paper we present a Pre-Confirmation Neural Network (PCNN) to reduce the processing time of the general face detection Neural Networks (NNs) by reducing the region of interest in an image up for face detection. The other algorithms commonly used for most face detection works by applying one or more NNs, directly to portions of the input image, and arbitrating their results. This requires that the whole image be passed several times through different NNs thereby increasing the processing time required for face detection. We present a smaller and less complex PCNN which operates on the image to produce a relatively small set of image portions which have the possibility of being a Face. When only this small set is passed through the NNs, generally used for face detection, the time required to detect faces in an image reduces.

Designing analog circuit to operate at low voltage levels for applications like battery powered analog and mixed mode electronic device is the need of the day. MOS devices are required to be used in the weak inversion region or the sub-threshold inversion region to minimize DC source power. In this paper the design of low voltage low power operational amplifier operating at ±1V with power consumption of 110μW has been implemented in 0.18μm CMOS technology. Simulated results of the amplifier using CADENCE, Spice simulation tool showed a DC gain of around 70dB with GBW of 20MHz, phase margin of 33

0

.The designed op amp has various advantages over its existing counter-parts like high gain, GBW, phase margin with the only relevant drawback being marginal power dissipation enhancement making it suitable to be used in battery-powered applications.

The topology of the ad hoc network has a significant impact on its performance. The dense topology has produce high interference and low capacity while the thinly scattered topology is Vulnerable to link failure. Some research work has been done on topology control in wireless networks. The existing topology control algorithms utilize either a purely centralized or purely distributed approach. In this work we have presented the traffic load analysis of the mobile node in the wireless network, by used of cluster concept. The wireless domain has been partitioned into different zones or clusters. Here we present analytically, the hops distance between random cluster heads. We have considered the minimum hops distance between the clusters and shown the traffic load at the gateway node, destination node. We considered that all the nodes randomly move. In this paper we have shown, how clustering affects on the performance with respect to throughput, delay, packet sent and packet received by simulation.

In this paper, we presented a morphological analyzer for the classical Dravidian language Telugu using machine learning approach. Morphological analyzer is a computer program that analyses the words belonging to Natural Languages and produces its grammatical structure as output. Telugu language is highly inflection and suffixation oriented, therefore developing the morphological analyzer for Telugu is a significant task. The developed morphological analyzer is based on sequence labeling and training by kernel methods, it captures the non-linear relationships and various morphological features of Telugu language in a better and simpler way. This approach is more efficient than other morphological analyzers which were based on rules. In rule based approach every rule is depends on the previous rule. So if one rule fails, it will affect the entire rule that follows. Regarding the accuracy our system significantly achieves a very competitive accuracy of 94% and 97% in case of Telugu Verbs and nouns. Morphological analyzer for Tamil and Malayalam was also developed by using this approach.

Visualizations predominantly require high computational and communication complexity. In extremely large scale, real world applications, the efficiency in question, is debatable. Parallelization, being one of the current methods of increasing efficiency, is pertinent to the divide and conquers strategies used in most visualization algorithms today. Our choice for parallelization, are state transition systems, owing to the simplistic applicability of the aforementioned strategy, and the use of simple data structures. Interactive systems that lend themselves to parallelization, when implemented, drastically reduce computation time and hence greatly improve performance, in concurrence with Amdahl’s law.

In the recent years, Data mining has attracted a great deal of attention in the information industry to turn huge volumes of data into useful information and knowledge. In this research work, it has been proposed to build Semantic Web Architecture for effective Information Retrieval and to display the result in visual mode. Hence, the first motivation of this paper is towards clustering of documents. The second motivation is to invent a data structure called BOOKSHELF for community mining in the search engine, using which the storage and time efficiency can be enhanced. The third motivation is to construct a novel semantic search engine to give results in visual mode. This paper proposes a web search results in visualize web graphs, representations of web structure overlaid with information and pattern tiers by providing the viewer with a qualitative understanding of the information contents.

Recently much attention have been paid to region of interests in an image as they are useful in bridging the gap between high level image semantics and low level image features. In this paper we have proposed a method for classification of image objects produced by a standard image segmentation algorithm using multiclass support vector machine classifier integrated with histogram intersection kernel. SIFT is a relatively new feature descriptor which describes a given object in terms of a number of interest points. They are invariant to scaling, translation and partially invariant to illumination changes. This paper primarily focuses on the design of a fast and efficient image object classifier by combining the robust SIFT feature descriptor with intersection kernel SVM which is comparatively better than the existing kernel functions in terms of resource utilization. The experimental results show that the proposed method has good generalization accuracy.

A major approach for palmprint recognition today is to extract feature vectors corresponding to individual palmprint images and to perform palmprint matching based on some distance metrics. One of the difficult problems in feature- based recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. This paper presents a palmprint recognition using Zernike moments feature extraction. Unsharp filtered palmprint images makes possible to achieve highly robust palmprint recognition. Experimental evaluation using a palmprint image database clearly demonstrates an efficient matching performance of the proposed system.

The success of MANET will depend on its ability to support existing, applications and protocols. Such a dynamic setting poses fabulous design challenges at each layer of the network. Hence this paper proposes a new concept of Blending Routing Protocols (BRP) in MANET which provides the advantages of the three types of routing protocols and addresses the QOS requirements and repeatability issues calculated for better scalability in streaming multimedia MANET with Comparison results were appraised.

Relevance Feedback is an important tool for grasping user’s need in Interactive Content Based Image Retrieval (CBIR). Keeping this in mind, we have build up a framework using Support Vector Machine Classifier in interactive framework where user labels images as relevant and irrelevant. The refinement of the images shown to the user is done using a few rounds of relevance feedback. This relevant and irrelevant set then provides the training set for the SVM for each of these rounds. The framework uses Histogram Intersection kernel with this interactive SVM (IKSVM). It has a retrieval component on top of this which searches for those images for retrieving which falls in the nearest neighbor set of the query image on the basis of histogram intersection based similarity ranking (HISM). The experimental results shows that the proposed framework shows better precision when compared with Active learning based SVMActive implemented with Radial Basis or Polynomial Kernels.

Banyan network plays an important role in today’s broadband technology. One of the problem faced in the banyan network is the loss of packets and blocking probability. To address this problem, the concept of increasing the queue size to one is applied. The simulation is carried out for both cases i.e for normal queue size and increased queue size. The output obtained is used for plotting graph in MS-Excel. With the obtained graphs, the performance of the network for both cases and a report is prepared. This analysis provides a valuable analysis for network designers and operators in terms of network performance.

A MANET is a dynamic multi – hop network established by a group of mobile nodes on a shared Wireless channels by virtue of their proximity to each other. To support mobility to users generally low configure nodes are in use, so limited resources, dynamic network topology and link variations are some of the issues in MANET. Routing in such dynamic environment is a challenge issue, lots of work have been done for routing but still QoS(Quality of Service) requirements of the network is not satisfied because to find an optimal path in dynamic networks is a NP complete problem. The main objective of this paper is to find a feasible path that has sufficient resources to satisfy the network constraints. In this paper we have applied multi object genetic algorithm optimize four QoS parameters such as delay ,bandwidth, traffic from adjacent nodes and number of hops that assists a QoS model in meeting timing requirements and improves in global network performance. Analyze is done for OLSR protocol based on how the protocol finds out its MPR sets and MPR to route a packet from source to destination.

This paper focuses on identifying defects in a handloom silk fabric using image analysis techniques such as first order statistical features. Any disparity in the knitting process that leads to an unpleasant appearance or dissatisfaction of the customer is termed as a defect in the fabric. Even today, the defect detection in a silk fabric is done using skilled manual labour. An automated defect detection and identification system would naturally enhance the quality and result in improved productivity to meet both customer demands and also reduce the costs associated with off-quality. This paper also classifies about the various defects that can occur in a silk fabric. As a supplementary need for the proposed machine vision based defect detection in textile fabric images, we would require a hardware implementation of the proposed method. This has been done using a soft core processor such as a NIOS processor of Altera Semiconductors.

The lack of any centralized infrastructure in mobile ad hoc networks (MANET) is one of the greatest security concerns in the deployment of wireless networks. MANET functions properly only if the participating nodes cooperate in routing without any malicious intention. However, some of the nodes may be malicious in their behavior by initially attracting a large amount of traffic and later on launching active security attacks like worm hole. Wormhole attack is severe attack in ad hoc networks and particularly challenging to defend against. The wormhole attack is possible even if the attacker has not compromised any hosts and even if all communication provides authenticity and confidentiality. This approach evaluates the impact of various wormhole attacks by evaluating different performance parameters like jitter, frame dropped, end to end delay, throughput and packet delivered on various routing protocols and recommends the safest and weakest routing protocol against wormhole attack.

Our case study used a Bayesian approach to find out the classification report requirements from a Oracle based Time and attendance application schema. Oracle data Miner was used to get the reports and though our chosen approach was simple, its strong assumption that attributes are independent within each class gave our model remarkably high accuracy. The study was conducted as a part of the process to make an airline profitable by re-structuring.

Low-voltage and low-power circuit structures are substantive for almost all mobile electronic gadgets which generally have mixed mode circuit structures embedded with analog sub-sections. Using the reconfigurable logic of multi-input floating gate MOSFETs, 4-bit full adder has been designed for 1.8V operation. Multi-input floating gate (MIFG) transistors have been anticipating in realizing the increased functionality on a chip. A multi-input floating gate MOS transistor accepts multiple inputs signals, calculates the weighted sum of all input signals and then controls the ON and OFF states of the transistor. This enhances the transistor function to more than just switching. Implementing a design using multi-input floating gate MOSFETs brings down transistor count and number of interconnections. Here in this we have presented how to eliminate the propagate and generate signals This tends the design to become more efficient in area and power consumption. The following information is about Carry look ahead adder circuit, tested with 45nm technology and is extended to ALU. The proposed circuit has been implemented in 45n-well CMOS technology.

Greenhouses in India are being deployed in the high-altitude regions where the sub-zero temperature up to -40° C makes any kind of plantation almost impossible and in arid regions where conditions for plant growth are hostile. So what we intend to do is to design a device to control and monitor this greenhouse environment. This device has been designed using simple equipments to meet the needs of Indian farmers requiring no skilled knowledge.

Handwritten to Digital Text Conversion Tool is designed using digital image processing technique to make data conversion (hand written scanned paper document to digital document) an easy and cost effective method using MATLAB. The input handwritten text is scanned and its Digital image form is obtained. The image is handled with the help of Enhancement techniques, segmentation, image recognition and neural network with an ultimatum of achieving higher efficiency. The Recognition system is designed along with the multilayer feed forward neural network, so that higher level of efficiency is obtained for the cursive handwriting recognition. The flexibility of this design allows it to extend to other languages easily.

In traditional data networks, a multicast tree which is starting at the source is built with branches across all the destinations to admit a multicast session. In a highly dynamic and traffic changing environment, a routing and bandwidth allocation scheme has to be developed in the optical WDM networks. Without requiring detection of traffic changes in real time, the dynamic traffic will be routed with the quality-of-service (QoS) guarantees. In this paper, we propose to develop a Resource Allocation and Multicast Routing (RAMR) protocol. In this protocol, the incoming traffic is sent from the multicast source to a set of intermediate junction nodes and then, from the junction nodes to the final destinations. The traffic is distributed to the junction nodes in predetermined proportions that depend on the capacities of intermediate nodes. Bandwidth required for these paths depends on the ingress–egress capacities, and the traffic split ratios. The traffic split ratio is determined by the arrival rate of ingress traffic and the capacity of intermediate junction nodes. By simulation, we show that our proposed protocol attains increased throughput and bandwidth utilization with reduced delay.

The problem of rearranging manufacturing facilities over time is known as Dynamic Facility Layout Problem (DFLP). The objective is to minimize the sum of the material handling and the rearrangement costs. The problem is NP-hard and has begun to receive attention very recently. In this paper, an approximate algorithm/ heuristic for solving DFLP is presented. The proposed heuristics has been applied to eight different data sets of a problem set (containing 48 data sets) given by Balakrishnan and Cheng [1], and it has been found that the proposed heuristic provide good solutions having about 12% of deviation from the best known solution available in the published literature. Further, as a future scope of research work, an improvement heuristic can be developed or some meta-heuristic approach such as simulated annealing, and tabu search can be further applied to improve the solution quality obtained from the proposed approximate algorithm.

Chikungunya fever is an overwhelming, but non-fatal viral illness that has been reported in many parts of the country. The E1 domain of Q1El92_CHIKV virus that helps in binding with the host has been determined by using comparative homology modeling program MODELLER based on crystal structure of the homotrimer of fusion glycoprotein E1 from Semliki Forest virus as a template protein and it had 63% sequence identity. The modeled structure‘s energy was minimized and validated using structure validation server in which 82.8% of the residues were present in the most favored regions of the Ramachandran plot. Disulphide bonds which help in protein folding of the proteins were analyzed and it was found to be conserved for both the homologous and the modeled structures. The ion pairs which contribute to fusion of viral membranes and that help in solvent protein interactions were analyzed. Docking studies was carried out with various phytochemicals and it was found that osltamivir had the most stable interaction with the E1 domain of the Q1El92_CHIKV virus. Thus from the complex scoring and binding ability it was interpreted that Osltamivir could be a promising inhibitor for E1 domain of Q1El92_CHIKV virus as the drug target yet pharmacological studies have to confirm it.

The goal of automatic speech recognition (ASR) is to accurately and efficiently convert a speech signal into a text message independent of the device, speaker or environment. In ASR, the speech signal is captured and parameterized at front-end and evaluated at back-end using the Gaussian mixture hidden Markov model (HMM). In statistical modeling, to handle the large number of HMM state parameters and to minimize the computation overhead, similar states are tied. In this paper we present a scheme to find the degree of mixture tying that is best suited for the small amount of training data, usually available for Indian languages. In our proposed approach, perceptual linear prediction (PLP) combined with Heteroscedastic linear discriminant analysis (HLDA) was used for feature extraction. All the experiments were conducted in general field conditions and in context of Indian languages, specifically Hindi, and for Indian speaking style.

Process variation has emerged as a major concern in the design of circuits including interconnect pipelines in current nanometer regime. Process variation results in uncertainties of circuit performances such as propagation delay, noise and power consumption. Threshold voltage of a MOSFET varies due to changes in oxide thickness; substrate, polysilicon and implant impurity level; and surface charge. This paper provides a comprehensive analysis of the effect of threshold variation on the propagation delay through driver-interconnect-load (DIL) system. The impact of process induced threshold variations on circuit delay is discussed for three different technologies i.e 130nm, 70nm and 45nm. The comparison of results between these three technologies shows that as device size shrinks, the process variation issues becomes dominant during design cycle and subsequently increases the uncertainty of the delays.

The aim of the paper is to explore the application of 2-D dual tree discrete wavelet transform (DDWT) which is directional and redundant over the critically sampled transform like discrete wavelet transform (DWT) for image coding. In this paper image coding application is investigated with DDWTs along with iterative projection based noise shaping (IP-NS) algorithm. IP-NS is one of sparsifying method for DDWT coefficients used to modify large coefficients to compensate for the loss of small coefficients, without substantially changing the original image. Promising results are compared with DWT and DWT with noise shaping also. After thorough investigations, it is proposed that by employing DDWT along with noise shaping algorithm significantly improve the performance over DWT.

Privacy-preservation in distributed databases is an important area of research in recent years. In a typical scenario, multiple parties may wish to collaborate to extract interesting global information such as class labels without revealing their respective data to each other. This may be particularly useful in applications such as car selling units, medical research etc. In the proposed work, we aim to develop a global classification model based on the Naïve Bayes classification scheme. The Naïve Bayes classification has been used because of its simplicity and high efficiency. For privacy-preservation of the data, the concept of trusted third party with two offsets has been used. The data is first anonymized at local party end and then the aggregation and global classification is done at the trusted third party. The proposed algorithms address various types of fragmentation schemes such as horizontal, vertical and arbitrary distribution.

In this paper, we describe a method for extraction of facial features of 2D still faces with variations in view in a certain viewing angle range. The images we have considered vary beyond left 30 degrees to right 30 degree out of plane rotation. Our technique applies skin separation and corner detection for extraction of features of faces in different poses. Just detecting the location of two facial points namely the corner of eyes and location of nose tip, the other features will be derived from them automatically; thus saving the time during the feature extraction.

Heuristics are based on the experiences and solves problems approximately that cannot be solved exactly. In handwritten documents recognition, the most difficult phase is touched character segmentation as incorrectly segmented characters cannot be recognized correctly. Accordingly, this paper presents a heuristic approach for multiple touched cursive characters. Initially, a possible segmentation zone is detected using peak to valley function. Next, greedy best search algorithm is implemented in the possible segmentation zone for touched characters segmentation. Experimental results on a test set extracted from the IAM benchmark database exhibit high segmentation accuracy up to 91.63%. Moreover, proposed approach is very fast and can handle multiple cursive touching characters.

Integrating computing and computing applications into surroundings instead of having computers as discrete objects is the objective of pervasive computing. Applications must adjust their behavior to every changing surroundings. Adjustment involves proper capture, management and reasoning of context. This paper proposes representation of context in a hierarchical form and storing of context data in an object relational database than an ordinary database .Semantic of the context is managed by ontology and context data is handled by object relational database. These two modeling elements are associated to each other by semantics relations build in the ontology. The separation of modeling elements loads only relevant context data into the reasoner therefore improving the performance of the reasoning process.

Support Vector machine (SVM) has become an optimistic method for data mining and machine learning. The exploit of SVM gave rise to the development of a new class of theoretically refined learning machines, which uses a central concept of kernels and the associated reproducing kernel Hilbert space. The performance of SVM largely depends on the kernel. However, there is no premise about how to choose a good kernel function for a particular domain. This paper focuses in this issue i.e. the choice of the Kernel Function is studied empirically and optimal results are achieved for binary class SVMs. The performance of the Binary class SVM is illustrated by extensive experimental results. The experimental results of the datasets show that RBF Kernel or any other kernels is not always the best choice to achieve high generalization of classifier although it is often the default choice.

A new approach to the restoration of color images corrupted by Gaussian noise is presented. The proposed technique adopts a multipass processing approach that gradually reduces the noise in the color information components of the image. Two different models for data smoothing are proposed based on the different classes of noisy pixels. The subsequent algorithm for edge detection is designed to better appraise the noise cancellation behavior of our filter from the point of view of human perception. This method does not require any “a priori” knowledge about the amount of noise corruption. Experimental results show that the filtering performance of the proposed approach is very satisfactory and accurate edge maps are achieved even in the presence of highly corrupted data.

In many object oriented softwares, there are recurring patterns of classes. Design pattern instances are important for program understanding and software maintenance.Hence a reliable design pattern mining is required. Here we are applying decision tree approach followed by subgraph isomorphism technique for design pattern detection.

Patch antenna arrays are used extensively due to their low profile structure, light weight and low cost. Patch antenna arrays have been widely used for a variety of wireless applications. However, a major drawback of this type of antenna arrays is mutual coupling and bandwidth. Mutual Coupling losses can be reduced effectively by placing Electromagnetic band gap (EBG) structures, also called photonic band gap (PBG) structures. In this paper, different types of Electromagnetic Band Gap (EBG) structures are proposed to be placed in between the patch antenna arrays to reduce the mutual coupling loss. These EBG structures are designed as small as possible because of system compactness. Hence the design of novel compact hybrid EBG structures are more challenging for wireless applications. In this paper various hybrid EBG structures showed with and without vias are compared with the defined antenna parameters.

Melanoma is a form of cancer that begins in melanocytes. The occurrence of melanoma continues to rise across the world and current therapeutic options are of limited benefit. Researchers are studying the genetic changes in skin tissue linked to a life-threatening melanoma through SNP genotyping, Expression microarrays, RNA interference etc. In the spectrum of disease, identification and characterization of melanoma proteins is also very important task. In the present study, effort has been made to identify the melanoma protein through Support Vector Machine. A positive dataset has been prepared through databases and literature whereas negative dataset consist of core metabolic proteins. Total 420 compositional properties of amino acid dipeptide and multiplet frequencies have been used to develop SVM model classifier. Average performance of models varies from 0.65-0.80 Mathew’s correlation coefficient values and 91.56% accuracy has been achieved through random data set.

Compression of data in any form is a large and active field as well as a big business. Image compression is a subset of this huge field of data compression, where the compression of image data is taken specifically. Wavelet transform is one of the popular transforms used in this field and its lifting based variant has become very popular for its easy hardware implementability. For images, the inter-pixel relationship is highly non-linear and unpredictive in the absence of a prior knowledge of the image itself. The back propagation based neural network (BPNN) takes into account the psycho visual features, dependent mostly on the information contained in images. Thereby preserving most of the characteristics of the data while working in a lossy manner and maximize the compression performance. So here image compression based on the lifting wavelet transform is taken in to account along with the BPNN based adaptive technique. Firstly by varying quantization levels for the lifting wavelet transform and number of hidden neurons for the BPNN an optimized compression percentage is reached for suitable adaptive hardware implementation of image compression with both the techniques.

In this paper, combined off-line signature verification using Neural Network (CSVNN) is presented. The global and grid features are combined to generate new set of features for the verification of signature. The Neural Network (NN) is used as a classifier for the authentication of a signature. The performance analysis is verified on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm.

Selecting a query plan for executing continuous aggregate queries over dynamic data using data dissemination networks by optimally distributing the query among different data aggregators is the key issue in improving performance of dynamic data dissemination networks. Optimal execution of continuous queries over dynamic data using data dissemination networks is useful in many online applications like stock market applications. In this paper, we propose a better algorithm, Enhanced greedy algorithm with withdrawals (EGAWW), for dividing the client query into sub-queries and assigning these sub-queries to different data aggregators of dynamic data dissemination networks.

IEEE 802.16m is the standard representing the security architecture for multi hop relay of broadband wireless access. The security sublayer is provided within IEEE 802.16m MAC layer for privacy and access control, which includes the Privacy and Key Management (PKM) protocol. This paper models the PKMv3 key agreement protocol using CasperFDR and analyzes the output. A few Attacks are found in this version. The specifications through which these attacks are found are presented.

In this paper, a copyright protection scheme that combines the Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is proposed. The proposed scheme first extracts the DCT coefficients of secret image by applying DCT. After that, image features are extracted from cover image and from DCT coefficients by applying DWT on both separately. Hiding of extracted features of DCT coefficients in the features of cover image is done using two different secret keys. Experimentation has been done using eight different attacks. Experimental results demonstrate that combining the two transforms improves the performance of the steganography technique in terms of PSNR value and the performance is better as compared to that achieved using DWT transform only. The extracted image has good visual quality also.

Perception and pose estimation are still some of the key challenges in the area of robotics, and hence the basic requirement for an autonomous mobile robot is its capability to elaborate the sensor measurements to localize itself with respect to a global reference frame. For this purpose the odometric values or the sensor measurements have to be fused together by means of particle filters. Earlier particle filters were limited to low-dimensional estimation problems, such as robot localization in known environments. More recently, particle filters are used in spaces with as many as 100,000 dimensions. This paper presents some of the recent innovations on the use of particle filters in robotics.

Purpose is an inherent relation in artifact-related text. This information is available as a pair of components, called ‘purpose-action’ and ‘purpose-upon’ in text. This paper presents the Neural Networks as a possible solution to detecting the existence of ‘purpose_action’ in corpus. Two types of Neural Networks, i.e., RBF Networks and Multilayer Perceptron Neural Network have been tried and compared with a Naïve Bayes approach to detection. The corresponding results have been tabulated. The MLP method is found to be more efficient among the two.

Mobile ad hoc networks (MANETs) appear nowadays as one of the most promising architectures to flexibly provide multimedia services in multiple wireless scenarios. However, the dynamic nature of this environment complicates the supporting of the heavily demanded QoS. In this paper, an attempt has been made to performing the individual and comparative performance analysis of QoS-aware routing on proactive protocol (DSDV) and two prominent on-demand source initiated routing protocols: AODV and DSR protocols using network simulator NS-2.34.The performance matrix includes the following QoS parameters such as PDR (Packet Delivery Ratio), Throughput, End to End Delay and Routing overhead. We are also analyzing the effect in performance of QoS parameters on these routing protocols when packet size changes, when time interval between packet sending changes, when mobility of nodes changes.

The need for graphical user interfaces in industrial and consumer applications is steadily increasing. To address this need, free scale is introducing a family of cost-effective and highly integrated Atmega microcontrollers that feature an integrated touch screen controller module. The idea behind the selection was channelization of human thoughts to automated realization. It is decided to implement the theme of automatic maneuvering of vehicles and the unanimous choice of sensor was touch screen. It was started with the thought of being able to replace the steering of a car completely by a touch screen. It is drawn on the experience of driving to reach at the choice of touch screen as a drive interface. Another innovation was the touch screen controller being wireless. The idea is application of the user input tracking capability of the touch screen to an RC car control scheme. The Radio controlled car should follow the path drawn by user on a touch screen in real time.

This paper presents a novel system for vehicle collision avoidance based on microcontroller which will be very effective for reducing accidents. In order to develop automobile collision avoidance system, the vehicles involved should be able to exchange the information in real time. The system adopts ultrasonic sensor and light intensity meter to monitor the distance of approaching vehicle. Based on simulation and tests conducted a light intensity threshold is set. Upon exceeding this threshold, the device triggers the relay which in turn dips the headlights of vehicles involved. Apart from this, it will have a life saving capability. The system is also equipped with GSM and GPS modules. In case the collision occurs, then the exact location of collision will be sent by GPS to the stored numbers through GSM circuit, so that medical care is attended in short time.

Face recognition is very important in many research areas from machine vision to complex security systems and the important cue to detect the human face is skin color. The proposed paper focuses on isolating the regions of an image corresponding to human skin region through the use of fusion technique. Fusion is performed on the skin region detected from RGB, YCbCr and CIEL*a*b color spaces of the given input image. Then the fused image is filtered by the median filter, in order to avoid the noise. This technique will be able to clearly identify skin region of the image.

A Wireless Data Modem using Tele Typewriter Signals and Frequency Shift Keying (FSK) Modulation Technique is proposed here in order to neglect Electromagnetic Interference with minimal Bit Error Rate. The wireless modem designed here consists of 5 stages namely a) Modulation stage, b) Transmission stage, c) Reception stage, d) Filtering stage and e) Demodulation stage. The Modulation stage consists of IC555 Timer generating FSK signal. Transmission is wireless and is achieved using IRLED. The transmitted signal is received using a spectrally matched Phototransistor. The Filtering stage consists of a second order low pass filter. Demodulation is achieved using IC565. The modem designed here makes use of Tele Typewriter signals which neglects conduction and radiation interference from external environment. The Probability of error for this wireless data modem is better with minimal Bit Error Rate. Hence the whole setup is stable and accurate device for transmitting digital data.

A collection of technologies that operates, analyzes, and controls materials at the nanoscopic level, is now merging into the main stream of electronics with examples ranging from quantum-electronic lasers to memory devices, even Nano Electro Mechanical Systems (NEMS) known as the Nanotechnology. State variables are physical representations of information used to perform information processing via memory and logic functionality. Advances in material science, emerging nanodevices, nanostructures, and architectures have provided hope that alternative state variables based on new mechanisms, nanomaterials, and nanodevices may indeed be plausible. The review and analysis of the computational advantages that alternate state variables may possibly attain with respect to maximizing computational performance via minimum energy dissipation, maximum operating switching speed, and maximum device density is performed. An outlook of some important state variables for emerging nanoelectronic devices is suggested in the work.

A feature based steganalytic method used for detecting both transform and spatial domain embedding techniques was developed. We developed an unconditional steganalysis which will automatically classify an image as having hidden information or not using a powerful classifier Support Vector Machine which is independent of any embedding techniques. To select the most relevant features from the total 269 features extracted, they apply Principal Component Analysis. Experimental results showed that our steganalysis scheme blindly detect the images obtained from six steganographic algorithms- F5, Outguess, S-Tool, JP Hide & Seek, LSB flipping and PVD. This method is able to detect any new algorithms which are not used during the training step, even if the embedding rate is very low. We also analyzed embedding rate versus detectability performances.

Mobile Adhoc Network Environment poses its unique challenges to the existing Transaction models, which are fail to solve. The main challenges of mobile computing environment are its heterogeneous environment, low and width and power resources. The transaction must be able to handle frequent disconnection because mobile user can move anywhere. In this paper, we presented a greedy booster approach with fair task distribution for MANET.Network topology is highly dynamic in mobile environment and no restriction is applicable to the nodes, if any node is performing some important transaction, it may leave the network then performance degraded abruptly. There is no mechanism and work is available, to control the performance in this critical situation. Our approach is able to overcome this situation and to utilize the maximum efficiency and resources of efficient node. Also applying this approach a Fair load can be distributed among nodes according to their efficiency by using Work Division Factor. The Fair load Distribution approach improves the performance of overall MANET .

This paper proposes image encryption and decryption process using new transformation matrix. The image is divided into zones .The zones are constructed from blocks of size 3 x 3 using secret key. All the zones are combined together to form a new transformation matrix and is used for encryption purpose. The sender sends the secret in the form of polynomial’s function value for their ID value. The receiver reconstructs the secret from the polynomial’s function value. Then form the transformation matrix and decrypt it to get the original image. The comparison of the proposed method with the Cross Chaotic map image encryption method [7] reveals that the proposed method is higher in security and superior in encryption quality and also the share to be sent is smaller.

Software Testing approaches are playing essential role to satisfy the clients’ needs without defects in delivered systems. Different testing methods will be used to filter the systems defects and improve the system quality. In testing method wise here proposed ‘Reusability testing scripts’ can be considered for finding the reusable packages in the current system development to fulfill the requirement of future requirement. To find the reusable level, the different package metrics such as ‘Coupling’, ‘Cohesion’, ‘Stability’, and ‘Complexity’ are analyzed to define the reusability level of software packages.

Wavelets are considered as powerful signal processing tools that provide an effective alternative to conventional signal transforms. Spread spectrum plays an important role in CDMA communication system. Because of its unique characteristics, it has been first used for military applications. So spread spectrum communication via, discrete wavelet transform and thresholding becomes effective now a days. This paper describes a near optimal threshold estimation technique for signal denoising in spread spectrum communication using Double Density Discrete Wavelet filter. A new threshold calculating formula is utilized. Performance of the proposed system is found to be better than the conventional spread spectrum receiver.

Today’s architectures are moving towards to exploit more and more parallelism. Instruction level parallelism (ILP) is where multiple instructions are executed simultaneously. Superscalar architecture was one of such evolutions. To exploit ILP superscalar processors fetch and execute multiple instructions in parallel thereby reducing the clock cycles per instruction (CPI). ILP can be exploited either statically by the compiler or dynamically by the hardware. In this paper the basic superscalar approach and the improvements made to the superscalar architectures to exploit more parallelism in execution have been discussed.

In this paper, the problem of color image restoration using a neural network learning approach is addressed. Instead of explicitly specifying the local regularization parameter values, we modify the neural network weights, which are considered as the regularization parameters. These are modified through the supply of appropriate training examples. The desired response of the network is in the form of estimated value for the current pixel. This estimate is used to modify the network weights such that the restored value produced by the network for a pixel is closer to this desired response. In this way, once the neural network is trained, images can be restored without having prior information about the model of noise/blurring with which the image is corrupted.

This paper gives a simple and straightforward method for designing spline based biorthogonal wavelets. Biorthogonal wavelets differ from orthogonal wavelets in that the former has more flexibility in its design. This is because, they enable the design of wavelets which are symmetric and smooth, which is not possible in the case of orthogonal wavelets (except Haar wavelet). However, the compromise made to achieve the symmetry property is that the non-zero coefficients in the analysis and synthesis filters are not the same for biorthogonal wavelets. The existing algorithm for spline biorthogonal wavelet design involves complex formulas, whose proof is also not easily understandable. In this paper, we present a very simple way of constructing spline based biorthogonal wavelets, which results in the same nonzero coefficients for the analysis and synthesis filters.

Password security has emerged as a promising field in the Computer science and technology. The innovative strategies are found to be costly and also require expertise to use them. The widely used methods of password security are pass-faces and biometrics password authentication schemes. Though they serve their purpose but are found to be cost ineffective.This paper looks at the new concepts of password security based on text-based authentication, ensuring the security from dictionary attacks. It is based on the principle of conversion of the characters of password in some control points, an unrecognizable form for intruders.

The basic device that is used in a Flash memory block is a floating gate metal oxide semiconductor field effect transistor (FGMOS). Storage of the charge on the floating gate allows the threshold voltage (V

T

) to be electrically altered between a low and a high value to represent logic 0 and 1, respectively. Typically, 8 or 16 cells are connected together in series to manufacture SLC NAND ah memory. In this paper experimentation has been done on string of 4 such cells using UMC 0.18 m CMOS process technology. Transient analysis is performed for measurement of Read access time operation. This also includes Design of Sense amplifier, Decoder and Buffer.

Securing the sensitive data and computer systems by allowing ease access to authenticated users and withstanding the attacks of imposters is one of the major challenges in the field of computer security. Traditionally, ID and password schemes are most widely used for controlling the access to computer systems. But, this scheme has many flaws such as Password sharing, Shoulder surfing, Brute force attack, Dictionary attack, Guessing, Phishing and many more. Biometrics technologies provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working with a keyboard. In this paper, neural network approaches with three different passwords namely weak, medium and strong passwords are taken into consideration and accuracy obtained is compared.

Research in motion analysis has evolved over the years as a challenging field, such as traffic monitoring, military, automated surveillance system and biological sciences etc. Tracking of moving objects in video sequences can offer significant benefits to motion analysis. In this paper an approach is proposed for the tracking of moving objects in an image sequence using object segmentation framework and feature matching functionality. The approach is amenable for SIMD processing or mapping onto VLIW DSP. Our C implementation runs at about 30 frames/second with 320x240 video input on standard Window XP machine. The experimental results have established the effectiveness of our approach for real world situations.

The wireless body area network (WBAN) allows the data of a patient’s vital body parameters and movements to be collected by wearable or implantable sensors and communicated using short-range wireless communication techniques. WBANs provide unprecedented opportunities to monitor the patient’s health status with real- time updates to the physician. Furthermore, these devices are used to collect life-critical information and may operate in hostile environments, so they require strict security mechanisms to prevent the malicious interaction with the system. In this paper the technique that can be used to monitor patients by the use of body area networks is reviewed. Also, the current secure strategies that can impede the attacks faced by wireless communications in healthcare systems and improve the security of mobile health care are discussed.

The paper “A State Variables Analysis for Emerging Nanoelectronic Devices” appearing on pages 632-637 of this publication has been retracted due to a severe case of plagiarism.