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Reliable control of cement kiln under sensor faults is vital to ensure cement clinker quality. To accomplish this objective, an active fault-tolerant control (FTC) scheme is proposed for burning zone temperature control of cement kiln even under inaccurate measurements. FTC is achieved by integrating adaptive fading memory Kalman filter (AFMKF) in an already existing model predictive controller (MPC) framework. Residue from the Kalman filter is used to detect faults, and the model prediction replaces sensor measurements. Hence, there is a need for an accurate model and its associated noise covariance. Model accuracy is ensured by the online estimation of sensor noise covariance using the proposed AFMKF. Hyperparameters governing the AFMKF are tuned using the multi-objective genetic algorithm (GA), aiming to minimise the modelling and tracking error. Sensor measurement failure, bias, and stuck are considered for performance validation of the proposed FTC. Percentage recovery from fault is evaluated in terms of Integral square error (ISE) and energy utilisation compared with a conventional MPC. Experimental results illustrate the significant improvement in model accuracy and fault-tolerant behaviour using the proposed GA tuned AFMKF.

In recent years, the use of data mining techniques has gained significance in healthcare applications. The appropriate data mining techniques extract interesting affinities/associations between pat...

In medical field, the detection of abnormalities in breast is essential to find earlier stage of breast tumor. Conventional semi-supervised ensemble framework based on the normalized cut algorithm developed and it strongly improves the detection accuracy of the resulting images. However, further development of classification algorithm with minimum computational time and cost, several conventional methods limits the classification of tumor. In this paper, Statistical Gradient and Dynamic Weight based LogitBoost (SG-DWL) Ensemble approach is presented to improve the detection rate of malicious tumor. The key objective of SG-DWL Ensemble approach is to increase the malicious tumor performance with higher accuracy and lesser time consumption. The Statistical Gradient Boosting model is introduced as a numerical technique to improve feature set identification in test images. The proposed feature ensemble is formed by concatenating the probability density function, gradient vector and powerful algorithmic framework for feature selection with the best fit feature set is selected. Dynamic Weight based LogitBoost classifier (DW-LC) is applied for malicious tumor detection. This Dynamic Weight based LogitBoost classifier uses Hoeffding tree to achieve high malicious tumor detection rate by reducing the computational complexity involved in the classification of benign and malignant tumor. The performance of the proposed approach is evaluated by comparing it with the existing approaches, and the results improve the classification accuracy with minimum time period for malicious tumor detection.

The Fast Fourier Transform (FFT) is a digital signal processing (DSP) function most commonly used one in many applications such as imaging, wireless communication, and multimedia. The FFT processors are consists of butterfly structure operations, which includes multiplication, addition and subtraction of complex value data. In this paper, an FFT butterfly structure is designed using the Vedic multiplier for high speed applications. In this Vedic multiplier, Urdhava Triyakbhyam algorithm is utilized to improve its efficiency. Then, detector block is introduced to identify the unwanted portion of the input data to be processed in the data processing unit. Therefore, data computation time is reduced in the detector based Vedic multiplier that supports full range and half range input data. The detector is developed based on Boolean function, to detect the valid ranges of two input operands during input data computation. The detector result is used to select the operand with half range input data for Vedic multiplication and it is disabled the surplus computation. So, it reduces the switching activities in the logic gates and proportionally reduces the power consumption. The proposed design-I is consists of Vedic algorithm and the detection unit. Then, the 3-1-1-2 compressor is designed and it is utilized in the multiplier. The proposed design-II is developed with modified Vedic algorithm, detection unit and proposed 3-1-1-2 compressor. Finally, the radix-2, radix-4, and radix-8 FFT butterflies are implemented using the detection unit based Vedic multiplier, the 3-1-1-2 compressor based multiplier and various existing multiplier. The proposed design-I and proposed design-II is designed and implemented in Spartan-6, Virtex-4 and Virtex-5 FPGA family devices. The proposed reconfigurable Vedic multiplier is simulated and synthesized using Synopsys tools using the 90 nm standard cell library.

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

High-contrast imaging instruments have advanced techniques to improve contrast, but they remain limited by uncorrected stellar speckles, often lacking a “second stage” correction to complement the Adaptive Optics (AO) correction. We are implementing a new second stage speckle-correction solution for the Gemini Planet Imager (GPI), replacing the instrument calibration unit (CAL) with the Fast Atmospheric Self coherent camera Technique (FAST), a new version of the self-coherent camera (SCC) concept. Our proposed upgrade (CAL2.0) will use a common-path interferometer design to enable speckle correction, through post-processing and/or by a feedback loop to the AO deformable mirror. FAST utilizes a new type of coronagraphic mask that will enable, for the first time, speckle correction down to millisecond timescales. The system's main goal is to improve the contrast by up to 100x in a halfdark hole to enable a new regime of science discoveries. Our team has been developing this new technology at the NRC's Extreme Wavefront control for Exoplanet and Adaptive optics Research Topics (NEW EARTH) laboratory over the past several years. The GPI CAL2.0 update is funded (November 2020), and the system’s first light is expected late 2023., Adaptive Optics Systems VII, December 14-18, 2020, Online Only, United States

We present our development of the Multi-Object Adaptive Optics (MOAO) system for the Gemini Infra-Red Multi-Object Spectrograph (GIRMOS). The GIRMOS MOAO system consists of four identical arms patrolling over a large field-ofregard (2 arcmin) and able to deliver an image quality close to the diffraction-limit in the near-infrared. The AO system of GIRMOS will performed MOAO correction on top of the wavefront delivered by the Gemini North AO (GNAO) system. We are currently prototyping one arm in the laboratory in order to validate the simulated performances and characterize the hardware as well as different MOAO control strategies. GIRMOS MOAO is a complex AO system and a complete lab characterization would require a full GNAO simulator with a pick-off system and multiple wavefront sensors (WFS), light sources, etc. To circumvent the cost and complexity of such a system we are using a spatial light modulator (SLM) allowing the generation of residual turbulences in specific directions in the field without the need of pick-off system. Coupled with a numerical end-to-end model of the system, our bench is focused on open-loop control rather than tomography. In this paper, we review the GIRMOS MOAO preliminary design of the system, the baseline performances and the status of the testbed.

GIRMOS is a multi-object adaptive optics spectrograph in the near-infrared currently being designed for the Gemini North telescope. It will allow to observe with high-quality adaptive optics correction multiple targets selected from a large field. To increase the efficiency of the nightly observations, it is important to provide a feedback in real time to operators and observers on the quality of the point-spread function, using intuitive metrics such as the full width at half maximum, the Strehl ratio and the encircled energy. We present a set of simple analytical expressions derived from other works that use the measurement of the wavefront residuals. We then compare the results to an end-to-end AO simulation of GIRMOS. We show that this approach works well with the estimation of the full width at half maximum and the Strehl ratio, while it is less successful with the encircled energy.

Residual phasing errors from segmented mirror telescopes can significantly inhibit the performance of an adaptive optics (AO) system. One approach to quantify these errors utilizes the imaging capabilities of a telescope’s instrument suite; quantification requires images that have observed the primary, therefore these images should be on-sky and AO-corrected in the absence of a source that views the primary. An image-based phase estimation technique that can potentially make this quantification is Phase Diversity, whereby focussed/defocussed images of a bright star are compared with models to estimate phase. Traditional use of this technique with on-sky images can be difficult due to atmospheric evolution between image pairs. This is further compounded due to both NCPA and the partial AO correction of phasing errors persisting in on-sky images. Here we propose a technique that can mitigate the atmospheric effects, estimate the NCPA and assess phasing errors under partial AO correction.

In this paper we discuss the mechanical design of the GIRMOS Cryostat. GIRMOS is an adaptive optics fed multi-object Integral-Field Spectrograph with a parallel imaging capability and will be installed at the Gemini North Observatory. This instrument includes four separate identical spectrograph channels arranged symmetrically around the central axis of the instrument which provide it its multiplexing capability. Each spectrograph channel starts off at the object selection mechanism. The object selection mechanism contains four motorized fold mirror assemblies which scan the incoming light from the telescope to look at four separate objects simultaneously or combine their efforts to look at a single object in a tiled mode. Each of the four individual beams from the object selection system are then directed into the instrument dewar via separate entrance windows. Within the dewar each IFS beam moves through an anamorphic relay, an optical image slicer assembly and eventually makes it to a Spectrograph unit. All of these assemblies are located on a single cold bench within the dewar. The instrument imager is located along the central axis of the dewar and is housed in the cold bench as well. In this paper we will provide some details regarding the Cryostat design, the mechanical packaging of the IFS and imager along with some of the thermal load mitigation techniques employed. We will also discuss some key performance requirements that were expected from the Cryostat and the design choices we made in order to achieve them.

Systems engineering as a discipline is relatively new in the ground-based astronomy community and is becoming more common as projects become larger, more complex and more geographically diverse. Space and defense industry projects have been using systems engineering for much longer, however those projects don’t necessarily map well onto groundbased astronomy projects, for various reasons. Fortunately, many of the processes and tools have been documented by INCOSE, NASA, SEBoK and other organizations, however there can be incomplete or conflicting definitions within the process and implementation is not always clear. For ground-based systems engineers, adopting these existing processes can be confusing. One area of particular uncertainty involves how, when and where to document operations concepts in a way that captures astronomers’ needs, translates them into concise and complete requirements without over-constraining the design teams or over-burdening the project with complex requirements and document management procedures. We present the criteria and outcome of the solution(s) chosen from the perspective of two different projects: a new observatory that is planning its operations (Maunakea Spectrocopic Explorer, MSE) and a new instrument at a longestablished observatory (Gemini - GIRMOS). Quite possibly, this will not answer the question in the title and may raise more questions. We welcome that discussion., Modeling, Systems Engineering, and Project Management for Astronomy IX, December 14-18, 2020-, Online Only, United States, Series: Proceedings of SPIE

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

The objective of this research is the assessment of circuit design compression strategy carrying a greater number of similar output chains. The proposed method decreases the testing time while keeping lesser area overhead. The decompression technique depends upon constant LFSR diffusion which is utilized as a part of such a route, to the point that it empowers LFSR lockout getting away inside the least amount of clock series. It involves a different scheming of the LFSR de-compressor and the output set timer inputs. The proposed work presents the tetrad state skip decompression adequacy for various LFSR dimensions, examine chain measurements and quantities of similar LFSR data. Our observations depict that it is equipment sparing to utilize an LFSR by skipping individual states as opposed to employing an LFSR deceased along with a stage shifter. In other words, it uses a lesser amount of interior XOR doors, ensures most last partition between sweep chains without presenting any additional postponement on the LFSR outcomes. Exploratory outcomes have demonstrated that the displayed test design decompression on conventional circuits gives an unlimited blame scope and lesser test duration besides the minor equipment overhead is contrasting the forms composed and the assistance of the recent days’ mechanical instruments.

Fruit disease detection is vital at early stage since it will affect the agricultural field. In this paper, mainly consider the detection and analysis of fruit infections which is available in the plant areas and storage of data about the agricultural filed and details of farmers in database and recovering the data using Cloud computing. There are more fruit diseases which occur due to the surrounding conditions, mineral levels, insects in the farm area and other factors. The detected data from the plant area is determined by image processing and stored in the database.

MAPS, MMT Adaptive optics exoPlanet characterization System, is the upgrade of legacy 6.5m MMT adaptive optics system. It is an NSF MSIP-funded project that includes (i) refurbishing of the MMT Adaptive Secondary Mirror (ASM), (ii) new high sensitive and high spatial order visible and near-infrared pyramid wavefront sensors, and (iii) the upgrade of Arizona Infrared Imager and Echelle Spectrograph (ARIES) and MMT high Precision Imaging Polarimeter (MMTPol) science cameras. This paper will present the design and development of the visible pyramid wavefront sensor. This system consists of an acquisition camera, a fast-steering tip-tilt modulation mirror, a double pyramid, a pupil imaging triplet lens, and a low noise and high-speed frame rate based CCID75 camera. We will report on hardware and software and present the laboratory characterization results of the individual subsystems, and outline the on-sky commissioning plan., Comment: Fixed an author typo. SPIE, Adaptive Optics Systems VII, Proceedings Volume 11448, 114485J, Dec 14, 2020, See, https://doi.org/10.1117/12.2576353

The indistinguishability of ciphertext under the chosen ciphertext attack IND-CCA2 is often considered to offer the strongest security notion for a public key encryption system. Nowadays, because of the availability of powerful malwares, an adversary is able to obtain "more" information than what he could obtain in the CCA2 security model. In order to realistically model the threats posed by such malwares, we need to empower the adversary to obtain additional information. This paper initiates a research to counter malwares such as RAM scrapers and extend the CCA2 model with oracles providing additional information to capture the effect of RAM scrapers precisely. We call this more stronger security notion as glass box decryption. After discussing the new kind of attack/threat and the related oracle, we show that almost all CCA2 secure systems are vulnerable to this kind of attack. We then propose a new system that offers security against glass box decryption and provide the formal security proof for the new system in the standard model. Copyright © 2016 John Wiley & Sons, Ltd.

Due to the excessive utilization of memory, data compression is an evergreen research topic. Realizing the constant demand of compression algorithms, this article presents a compression algorithm to analyse the digital VLSI circuits for constraint optimization, such as test data volume, switching power, chip area overhead and processing speed of testing. This article proposes a new power transition X filling based selective Huffman encoding technique, which achieves better data compression, switching power reduction, chip area overhead reduction and speed of testing. The performance of the proposed work is examined with the help of ISCAS benchmark circuits. Initially, the test set is occupied by using the power transition X filling technique to replace the don't care bits and the filled test set is further encoded by selective Huffman encoding technique. The experimental results show that the proposed power transition X filling based selective Huffman encoding gives effective results compared to the related data compression techniques with minimal time and memory consumption.

We report the optical design of an infrared (0.85-1.8 μm) pyramid wavefront sensor (IRPWFS) that is designed for the 6.5m MMT on telescope adaptive optics system using the latest developments in low-noise infrared avalanche photodiode arrays. The comparison between the pyramid and the double-roof prism based wavefront sensors and the evaluation of their micro pupils’ quality are presented. According to our analysis, the use of two double-roof prisms with achromatic materials produces the competitive performance when compared to the traditional pyramid prism, which is difficult to manufacture. The final micro pupils on the image plane have the residual errors of pupil position, chromatism, and distortion within 1/10 pixel over the 2×2 arcsecond field of view, which meet the original design goals., Series: Proceedings of SPIE; no. 10401

While deep learning has led to breakthroughs in many areas of computer science, its power has yet to be fully exploited in the area of adaptive optics (AO) and astronomy as a whole. In this paper we describe the first steps taken to apply deep, convolutional neural networks to the problem of wavefront reconstruction and prediction and demonstrate their feasibility of use in simulation. Our preliminary results show we are able to reconstruct wavefronts comparably well to current state of the art methods. We further demonstrate the ability to predict future wavefronts up to five simulation steps with under 1nm RMS wavefront error.

We explore the application of phase diversity to calibrate the non common path aberrations (NCPA) in the Gemini Planet Imager (GPI). This is first investigated in simulation in order to characterize the ideal technique parameters with simulated GPI calibration source data. The best working simulation parameters are derived and we establish the algorithm's capability to recover an injected astigmatism. Furthermore, the real data appear to exhibit signs of de-centering between the in and out of focus images that are required by phase diversity; this effect can arise when the diverse images are acquired in closed loop and are close to the non-linear regime of the wavefront sensor. We show in simulation that this effect can inhibit our algorithm, which does not take into account the impact of de-centering between images. To mitigate this effect, we validate the technique of using a single diverse image with our algorithm; this is first demonstrated in simulation and then applied to the real GPI data. Following this approach, we find that we can successfully recover a known astigmatism injection using the real GPI data and subsequently apply an NCPA correction to GPI (in the format of offset reference slopes) to improve the relative Strehl ratio by 5%; we note this NCPA correction application is rudimentary and a more thorough application will be investigated in the near future. Finally, the estimated NCPA in the form of astigmatism and coma agree well with the magnitude of the same modes reported by Poyneer et al. 2016.

The Gemini Infrared Multi-Object Spectrograph (GIRMOS) is a powerful new instrument being built to facility- class standards for the Gemini telescope. It takes advantage of the latest developments in adaptive optics and integral field spectrographs. GIRMOS will carry out simultaneous high-angular-resolution, spatially-resolved infrared (1 - 2.4 µm) spectroscopy of four objects within a two-arcminute field-of-regard by taking advantage of multi-object adaptive optics. This capability does not currently exist anywhere in the world and therefore offers significant scientific gains over a very broad range of topics in astronomical research. For example, current programs for high redshift galaxies are pushing the limits of what is possible with infrared spectroscopy at 8 -10- meter class facilities by requiring up to several nights of observing time per target. Therefore, the observation of multiple objects simultaneously with adaptive optics is absolutely necessary to make effective use of telescope time and obtain statistically significant samples for high redshift science. With an expected commissioning date of 2023, GIRMOS’s capabilities will also make it a key followup instrument for the James Webb Space Telescope when it is launched in 2021, as well as a true scientific and technical pathfinder for future Thirty Meter Telescope (TMT) multi-object spectroscopic instrumentation. In this paper, we will present an overview of this instrument’s capabilities and overall architecture. We also highlight how this instrument lays the ground work for a future TMT early-light instrument.

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Cloud computing is an emerging technology which provides various services like virtualization, service-level agreement, autonomic scaling, and utility computing. Cloud computing is composed of several layers like IaaS, PaaS, and SaaS, where they provides service at resource layer, service layer user QoS (Quality of Service), and cloud user in application layer. So resource allocation for optimization problem can be divided into sub-problems: resource allocation problem, service provisioning problem, and user QoS maximization problem. The resource allocations in cloud computing is one of the major challenge. The proposed resource allocation algorithm improves the efficient resource allocations performances. The proposed method has been compared with existing linear algorithm method. The proposed method performance is better than the existing linear algorithm method and it reduces the execution time and cost. The experimental results shows that our resource allocation algorithm efficiently allocates...

Computational grids have been used to solve large scale problems in science, engineering and commerce. The task scheduling in computational grid is a complex optimization problem. Task scheduling is the fundamental issue in grid scheduling. The heuristic algorithms play a vital role in solving complex optimization problems. The distributive nature of Ant Colony Optimization (ACO) helps to find optimal or near optimal solution in efficient manner. Artificial Bee Colony Algorithm is one of the latest heuristics, which out performs classical heuristics, such as Tabu Search (TS), Simulated Annealing (SA), or even ACO. Its exploration capacity can be improved by modifying in the fitness value computation. The hybridization and the modifications in ACO and ABC improve the exploration and exploitation capability of the algorithms and enhance the convergence ability of the algorithm. The proposed hybridization of ABC algorithm with ACO algorithm reduces the waiting time, communication delay and makespan time of the schedule with good load balancing. It improves the convergence to the optimal solution. The proposed Pareto based hybrid ABC-ACO algorithm finds a population of solutions, then uses Pareto ranking to sort these solutions, and then derives the Pareto front for optimal task scheduling. The group of non-dominated solutions assists to schedule the tasks to the best available resources with a tradeoff between makespan and cost in the computational grid.

Microarray experiments are a centerpiece of postgenomics life sciences and the current efforts to develop systems diagnostics for personalized medicine. The majority of antibody microarray experiments are fluorescence-based, which utilizes a scanner to convert target signals into image files for subsequent quantification. Certain scan parameters such as the laser power and photomultiplier tube gain (PMT) can influence the readout of fluorescent intensities and thus may affect data quantitation. To date, however, there is no consensus of how to determine the optimal settings of microarray scanners. Here we show that different settings of the laser power and PMT not only affect the signal intensities but also the accuracy of antibody microarray experiments. More importantly, we demonstrate an experimental approach using two fluorescent dyes to determine optimal settings of scan parameters for microarray experiments. These measures provide added quality control of microarray experiments, and thus help to improve the accuracy of quantitative outcome in microarray experiments in the above contexts.

Using transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

Line of sight (LoS) and security are the fundamental requirement for any secure visual MIMO communication based vehicular networks. Uneven speed, irregular terrains, and uncertain appearance of obstacles disgrace LoS between the visual multiple-input multiple-output (MIMO) transmitter and receiver. This paper proposes optimal tracking techniques to maintain LoS using a novel quick response (QR) inspired light emitting array (LEA) pattern. Automatic geometric transformation of the QR code in conjunction with the particle filter is used to track the dynamically varying LEA position. In addition to that, an adaptive threshold technique (ATT) has been proposed to minimize error caused by ambient light variations and interferences. A novel key generation algorithm is also proposed to improve the security of the visual MIMO communication system. It utilizes a customized linear feedback shift register (LFSR) and synthetic color image to generate a stronger key. The randomness test demonstrates the robustness of the proposed key generation technique with a minimal probability value of 0.1223 (&gt, 0.01). Simulation and real-time studies have been carried out in the presence of geometric distortions, and the bit error rate (BER) performance is evaluated. Results illustrate that the proposed techniques make the visual MIMO system more secure and reliable for vehicular networks.

As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path) to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.

In image processing, image segmentation is one of the important tasks to extract information from the images. A variety of segmentation algorithm is developed to satisfy increasing requirement of image segmentation. Fuzzy CMeans is unsupervised method that has been applied for the variety of purposes such as clustering, classification, image segmentation and target recognition. This method can classify the image, which can be represented in various feature spaces by grouping the similar data points in the feature space into clusters. Especially the FCM can be used to obtain the segmentation with the pixel classification where this method allows pixels to be the property of multiple classes with varying degree of membership. This method can produce the flexibility in processing of Magnetic Resonance Image (MRI). In our proposed first, proposing the K-Means with FCM method and color model to improve the existing system. This algorithm is based on maximum measure of the distance function which is found for cluster center detection process using the Mahalanobis concept. The objective of this research is to develop an enhanced k-means and fuzzy c-means for a segmentation of brain magnetic resonance images. Also we are implementing the Fuzzy Membership based function to select the initial centers for the segmentation process. The firefly algorithm is implemented to optimize the Fuzzy Cmeans membership function for better accuracy segmentation process. At the same time the convergence criteria is fixed for the efficient clustering method. On the whole the proposed technique produces more accurate results compare with other techniques.

Objective: Artificial Neural Networks became a powerful tool for dynamic modelling of non linear physical systems and for prediction of specific parameters of complex systems. This work aims to model a heat exchanger using different neural networks and to investigate their performance in predicting its outlet temperature. Methods: In this work four different neural networks namely Elman Recurrent Neural Networks (ERNN), Time Delay Neural Networks (TDNN), Cascade Feed Forward Neural Networks (CFFNN) and Feed Forward Neural Networks (FFNN) are modelled for the prediction of the outlet liquid temperature of a saturated steam heat exchanger from its liquid flow rate. A benchmark dataset consisting of 4000 tuples is used to train, validate and test the performance of each neural network model. Findings: All the four ANN models are trained, validated and tested which predicted the outlet temperature of the Heat exchanger with acceptable accuracy and the Elman Recurrent Neural Network is found to have the best accuracy by having the lowest Mean Square Error (MSE) and best Regression due to its feedback connections. Applications: The Artificial Neural Network Models simulated, especially the Elman Neural Networks are good at prediction of operational parameters of a physical system and hence can be used in prediction and forecasting of operational parameters of engineering, medical, financial and environmental systems.

Background/Objectives: Foreign currency Exchange (FOREX) plays a vital role for currency trading in the international market. Accurate prediction of foreign currency exchange rate is a challenging task. The paper investigates the FOREX prediction using feed forward neural network. Methods/Statistical analysis: This paper employs artificial neural network to forecast foreign currency exchange rate in India during 2010-2015.The exchange rates considered between Indian Rupee and four major currencies Euro, Japanese Yen, Pound Sterling and US Dollar. The network developed consists of an input layer, hidden layer and output layer. The neural network was trained with Levenberg-Marquardt (LM) learning algorithm. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Forecasting Error (FE) are used as indicators for the performance of the networks. Findings: Simulation results are presented to show the performance of the proposed system. The paper also aims to suggest about network topology that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed technique gives the evidence that there is possibility of extracting information hidden in the foreign exchange rate and predicting into the future. Applications/Improvements: Finally, this paper presents the best network topology for FOREX prediction by comparing the effectiveness of various hidden layer performance algorithm using MATLAB neural network software as a tool.

Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System.

In the upcoming 4G networks can provide better quality parameters values than 3G networks. Video conferencing is IP based connectivity to N number of users in the geographical coverage area in order to obtain good quality parameters like throughput, delay, jitter, traffic pattern in sending/receiving end of users. Streaming protocol for audio/video conferencing over VoIP based protocols over VLAN scenario.

The study represents a novel approach taken towards car detection, feature extraction and classification in a video. Though many methods have been proposed to deal with individual features of a vehicle, like edge, license plate, corners, no sy stem has been implemented to combine features. Combination of four unique features, namely, color, shape, number plate and logo gives the application a stronghold on various applications li ke surveillance recording to detect accident percentage(for every make of a company), authentication of a car in the Parliament(for high security), learning system(readily available knowledge for aut omobile tyro enthusiasts) with increased accuracy o f matching. Video surveillance is a security solution for government buildings, facilities and operation s. Installing this system can enhance existing securit y systems or help start a comprehensive security so lution that can keep the building, employees and records s afe. The system uses a Haar cascaded classifier to detect a car in a video and implements an efficient algori thm to extract the color of it along with the confi dence rating. An gadabouts trained classifier is used to detect the logo (Suzuki/Toyota/Hyunadai) of the car whose accuracy is enhanced by implementing SURF matching. A combination of blobs and contour tracing is applied for shape detection and model classificatio n while number plate detection is performed in a smart and efficient algorithm which uses morphological op erations and contour tracing. Finally, a trained, s ingle perceptron neural network model is integrated with the system for identifying the make of the car. A thorough work on the system has proved it to be eff icient and accurate, under different illumination conditions, when tested with a huge dataset which h as been collected over a period of six months.

Problem statement: Identifying the new lossless image compression alg orithm for high performance applications like medical and satellite imaging; a high quality lossless image is most important when reproduction which leads to classify the data for decision making. Approach: A new lossless hybrid algorithm based on simple selective scan order with Bit Plane Slicing method is presented for lossless Image compression of limited bits/pixel images, such as medical images, satelli te images and other still images common in the world. Efficient coding is achieved by run length and modified Huffman coding. This approach is combined with efficient selective scan order for entire image in one pass through. Results: The new hybrid algorithm achieves good compression rate, compared to the existing schemes of coding with dif ferent test images. Conclusion: Compared to the existing standards JPEG-LS and CALIC, the compression rate is reduced with our proposed algorithm for different standard test images.

paper employing Routh"s table, a geometrical stability criterion for the analysis of linear time-invariant system is formulated. The proposed stability criterion is applied for the system, whose characteristic equation having complex coefficients. For this Routh like table is presented with complex terms and the signs pair-wise elements with the first column of the table are observed. The proof for the criterion is also given which is based on the Hurwitz"s matrix and its determinants. It is found that the proposed method is termed as ""SIGN PAIR CRITERION"" and is illustrated with suitable examples.

Problem statement: Digital transmission over band-limited communication channel largely suffers from ISIS and various noise sources. The presence of ISI and noise causes bit errors in the received signal. Equalization is necessary at the receiver to overcome these channel impairment to recover the original transmitted sequence. Traditionally equalization is considered as equivalent to inverse filtering and implemented using linear-perform under severe distortion conditions when Signal to Noise Ratio (SNR) is poor. Equalization can be considered as a non-linear classification problem and optimum solution is given by Bayesian solution. Non-linear techniques like Artificial Neutral Networks (ANN) are very good choice for non-linear classification problems. Several non-lines are equalizers have been implemented using ANN which outperformed LTE and solved the problem of equalization to the varying degree of sources. Approach: Forward neural network architecture with optimum number of nodes was used to achieve adaptive channel equalization. Summation at each node was replaced by multiplications which result in powerful mapping. The equalizer was tested on Rayleigh fading channel with a BPSK signal. Results: Results showed that proposed equalizer provides simplified architecture and improvement in the bit error rate at various levels of signal to noise ratio for Rayleigh faded channel. Conclusion: A high order feed forward network equalizer with multiplicative neuron is proposed in this study. Use of Multiplication allows direct computing of polynomial inputs and approximation with fewer nodes. Performance comparison in terms of network architecture and BER performance suggest the better classification capability of the proposed MNN equalizer over RRBF.

Problem statement: An Ad hoc network has been defined as a self-organizing, dynamic topology network formed by a group of wireless mobile nodes. Minimal configuration, absence of infrastructure and quick deployment, would make ad hoc networks convenient for emergency situations other than military applications. In recent years, security in Ad hoc Networks as a research topic had started to receive attention from a growing number of researchers. Approach: Several ad hoc network routing protocols have been proposed; only some of them consider the security problems. To secure an ad hoc network, the attributes like availability, confidentiality, integrity, authentication and non-repudiation may be considered. In this study, authentication is taken into consideration. Here, Double Hash Authentication Technique (DHT) has been incorporated for ad hoc networks. The security associations between nodes were established, when they were in the vicinity of each other, by exchanging appropriate cryptographic information. This security mechanism has been simulated on Dynamic Source Routing (DSR) protocol in addition to Self-Healing and Optimized Routing Technique (SHORT) made the routing secure for ad hoc networks. Result: The results have shown that in a moderately changing network, the Double Hash Technique has provided secure routing even in the presence of malicious nodes. Conclusion: The Double Hash Technique in association with SHORT has improved the performance of the DSR protocol.

Problem statement: In peer-to-peer networks, Byzantine fault tolerance refers to the capability of a system to tolerate Byzantine faults. It can be achieved by replicating the server and by ensuring all server replicas reach an agreement on the input despite Byzantine faulty replicas and clients. Since malicious attacks and software errors can cause faulty nodes to exhibit Byzantine behavior, Byzantine-fault-tolerant algorithms are increasingly important. Approach: In the study, we wish to develop a robust Byzantine Fault-Tolerance Replication (BFTR) technique for peer-to-peer content distribution systems which contains fault detection and fault recovery. It is based on collaborative monitoring of each node to detect the occurrence of a fault. Already we proposed a QoS based overlay network architecture (QIRM) involving an intelligent replica placement algorithm to improve the network utilization of the P2P system. Results: By simulation results, we show that the proposed technique involves less overhead and recovery time with increased accuracy. Conclusion/Recommendations: Here the result obtained is that BFTR Technique is much efficient than the QIRM with respect to packet drop ratio, average end-to-end delay, throughput and overhead.

Purpose – The aim of this paper is to optimize the capacitor value of a single phase open well submersible motor operating under extreme voltage conditions using fuzzy logic optimization technique and compared with no-load volt-ampere method. This is done by keeping the displacement angle (α) between main winding and auxiliary winding near 90 o , phase angle (φ) between the supply voltage and line current near 0 o . The optimization work is carried out by using Fuzzy Logic Toolbox software built on the MATLAB technical computing environment with Simulink software. Findings – The optimum capacitor value obtained is used with a motor and tested for different supply voltage conditions. The vector diagrams obtained from the experimental test results indicates that the performance is improved from the existing value. Originality/value – This method will be highly useful for the practicing design engineers in selecting the optimum capacitance value for single phase induction motors to achieve the best performance for operating at extreme supply voltage conditions.

the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. The ontology for multiple search engines is written such that in this search engine for single query the final result is got from multiple search engines. After getting the user query result we can use the clustering. In this clustering the user query results is formed in the a to z form, the several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of Semantic Web resources, that is, relations. We can use web cache optimization in search engine to get fast retrieval of user query results. In this work I have used web cache optimization based on eviction method for semantic web search engine. In this paper, analization of both advantages and disadvantages of some current Web cache replacement algorithms including lowest relative value algorithm, least weighted usage algorithm and least unified-value algorithm is done. Based on our analysis, we proposed a new algorithm, called least grade replacement (LGR), which takes recency, frequency, perfect- history, and document size into account for Web cache optimization.

Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. A computational GRID is typically heterogeneous in the sense that it combines clusters of varying sizes, and different clusters typically contains processing elements with different level of performance. In this, heuristic approaches based on particle swarm optimization and ant colony optimization algorithms are adopted for solving task scheduling problems in grid environment. Particle Swarm Optimization (PSO) is one of the latest evolutionary optimization techniques by nature. It has the better ability of global searching and has been successfully applied to many areas such as, neural network training etc. Due to the linear decreasing of inertia weight in PSO the convergence rate becomes faster, which leads to the minimal makespan time when used for scheduling. To make the convergence rate faster, the PSO algorithm is improved by modifying the inertia parameter, such that it produces better performance and gives an optimized result. The ACO algorithm is improved by modifying the pheromone updating rule. ACO algorithm is hybridized with PSO algorithm for efficient result and better convergence in PSO algorithm.

Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.

In peer-to-peer content distribution the lack of a central authority makes authentication difficult. Without authentication, adversary nodes can spoof identity and falsify messages in the overlay. This enables malicious nodes to launch man-in-the-middle or denial-of-service attacks. In this paper, we present a trust based content distribution for peer-to-peer overlay networks, which is built on the trust management scheme. The main concept is, before sending or accepting the traffic, the trust of the peer must be validated. Based on the success of data delivery and searching time, we calculate the trust index of a node. Then the aggregated trust index of the peers whose value is below the threshold value is considered as distrusted and the corresponding traffic is blocked. By simulation results we show that our proposed scheme achieves increased success ratio with reduced delay and drop.

Problem statement: Design of Induction motors is an engineering art and needs an extensive experience for obtaining an optimal design solution for a given design problem. An optimized GD2 value for the development of S4 Duty, cage induction motor to meet the specifications of a particular designated application (the location of application is kept as trade secret due to IP barrier) is proposed and validated by a physical model. Approach: The Genetic Algorithm (GA) is used to optimize inertia of rotating member as a single objective function for the designated application. A formulation based on the violations of normalized constraints is used here to transform the problem as unconstrained one. Results: The design variables for the developed model for the rating of 30 W for S4 duty operation were examined with the GA operators such as Initial population-15; two point crossover probability-0.8; mutation-0.05; number of generations-50; fitness scaling-rank; selection-Roulette wheel; Conclusion: The GD2 value obtained using the GA-constraints normalization technique and from the proto model developed are 26.24 and 33.04 respectively as against the specified value 40.75 kg-cm2.