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Updates found with 'observations'

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Updates found with 'observations'

IEEE 2016 MAT LAB PROJECTS ABSTRACT2D SEGMENTATION USING A ROBUST ACTIVE SHAPE MODEL WITH THE EM ALGORITHM ABSTRACT: Statistical shape models have been extensively used in a wide range of applications due to their effectiveness in providing prior shape information for object segmentation problems. The most popular method is the Active Shape Model (ASM). However, accurately fitting the shape model to an object boundary under a cluttered environment is a challenging task. Under such assumptions, the model is often attracted towards invalid observations (outliers), leading to meaningless estimates of the object boundary. In this paper, we propose a novel algorithm that improves the robustness of ASM in the presence of outliers. The proposed framework assumes that both type of observations (valid observations and outliers) are detected in the image. A new strategy is devised for treating the data in different ways, depending on the observations being considered as valid or invalid. The proposed algorithm assigns a different weight to each observation. The shape parameters are recursively updated using the Expectation-Maximization method, allowing a correct and robust fit of the shape model to the object boundary in the image. Two estimation criteria are considered: 1) the maximum likelihood criterion; and 2) the maximum a posteriori criterion that uses priors for the, unknown parameters. The methods are tested with synthetic and real images, comprising medical images of the heart and image sequences of the lips. The results are promising and show that this approach is robust in the presence of outliers, leading to a significant improvement over the standard ASM and other state of the art methods.
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NS2 PROJECTS ABSTRACT 2016-2017 OPTIMIZED IN-BAND FULL-DUPLEX MIMO RELAY UNDER SINGLE-STREAM TRANSMISSION ABSTRACT: This article presents a coherent scheme to optimize an in-band full-duplex multiple-input multiple-output (MIMO)relay via beam forming and transmit power allocation in a two hop single-input single-output (SISO) link under full channel knowledge and perfect hardware assumptions. First, we derive in closed form the optimal pair of transmit power and receive filter for a fixed transmit filter by unifying the minimum mean square error filtering with the SISO-equivalent power allocation, as an iterative approach is not guaranteed to converge to global optimum. Second, we propose a heuristic algorithm to approximate the optimal transmit filter for a fixed receive filter. Furthermore, we study the well-known null-space projection constraint and derive a singular value decomposition based solution for the arbitrary-rank self-interference channel by generalizing the optimal solution under the assumption of rank-1 self-interference channel. Finally, we combine these solutions into a partially iterative algorithm in order to address the global optimization as our observations justify that some of the aforementioned schemes converge to the optimal solution under certain criteria. The numerical analysis of the proposed iterative algorithm demonstrates close-to-optimal performance relative to the theoretical upper bound of the end-to-end link in terms of maximum achievable throughput.
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IEEE 2016 JAVA PROJECTS ABSTRACT SUPER-RESOLUTION OF MULTI-OBSERVED RGB-D IMAGES BASED ON NONLOCAL REGRESSION AND TOTAL VARIATION ABSTRACT: There is growing demand for accuracy in image processing and visualization, and the super-resolution (SR) technique for multi-observed RGB-D images has become popular because it provides space-redundant information and produces a detailed reconstruction even with a large magnification factor. This technique has been thoroughly investigated in recent years. Nevertheless, technical challenges remain, such as finding sub pixel correspondences with low-resolution (LR) observations, exploiting space-redundant information, formulating space homogeneity constraints and leveraging cross-image similarities in structures. To address these challenges, this paper proposes a unified optimization framework to estimate both the super resolved RGB image and the super-resolved depth image from the multi-observed LR RGB-D images using their correlations. Using depth-assisted cross-image correspondences, the RGB image SR problem is formulated as an effective regularization function by incorporating the normalized bilateral total variation (NBTV) regularize, and it is efficiently solved by a first-order primal dual algorithm. The depth image SR estimate can be obtained by minimizing a non-local regression based energy, which integrates the structural cues of the super-resolved RGB image in a detail-preserving fashion. Essentially, our unified optimization framework uses the RGB image and depth image as a priori knowledge that the SR process uses for better accuracy. Our extensive experiments on public RGB-D benchmarks and real data and our quantitative comparison with several state-of-the art methods demonstrates the superiority of our method in terms of accuracy, versatility and reliability of details and sharp feature preservation.
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IEEE 2016 DOTNET PROJECT ABSTRACTLEVERAGING DATA DEDUPLICATION TO IMPROVE THE PERFORMANCE OF PRIMARY STORAGE SYSTEMS IN THE CLOUD ABSTRACTWith the explosive growth in data volume, the I/O bottleneck has become an increasingly daunting challenge for big data analytics in the Cloud. Recent studies have shown that moderate to high data redundancy clearly exists in primary storage systems in the Cloud. Our experimental studies reveal that data redundancy exhibits a much higher level of intensity on the I/O path than that on disks due to relatively high temporal access locality associated with small I/O requests to redundant data. Moreover, directly applying data deduplication to primary storage systems in the Cloud will likely cause space contention in memory and data fragmentation on disks. Based on these observations, we propose a performance-oriented I/O deduplication, called POD, rather than a capacityoriented I/O deduplication, exemplified by iDedup, to improve the I/O performance of primary storage systems in the Cloud without sacrificing capacity savings of the latter. POD takes a two-pronged approach to improving the performance of primary storage systems and minimizing performance overhead of deduplication, namely, a request-based selective deduplication technique, called SelectDedupe, to alleviate the data fragmentation and an adaptive memory management scheme, called iCache, to ease the memory contention between the bursty read traffic and the bursty write traffic. We have implemented a prototype of POD as a module in the Linux operating system. The experiments conducted on our lightweight prototype implementation of POD show that POD significantly outperforms iDedup in the I/O performance measure by up to 87.9% with an average of 58.8%. Moreover, our evaluation results also show that POD achieves comparable or better capacity savings than iDedup.
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IEEE 2016 JAVA PROJECTS ABSTRACTSUPER-RESOLUTION OF MULTI-OBSERVED RGB-D IMAGES BASED ON NONLOCAL REGRESSION AND TOTAL VARIATIONABSTRACT: There is growing demand for accuracy in image processing and visualization, and the super-resolution (SR) technique for multi-observed RGB-D images has become popularbecause it provides space-redundant information and produces a detailed reconstruction even with a large magnification factor. This technique has been thoroughly investigated in recent years. Nevertheless, technical challenges remain, such as finding sub pixel correspondences with low-resolution (LR) observations, exploiting space-redundant information, formulating space homogeneity constraints and leveraging cross-image similarities in structures. To address these challenges, this paper proposes a unified optimization framework to estimate both the super resolved RGB image and the super-resolved depth image from themulti-observed LR RGB-D images using their correlations. Using depth-assisted cross-image correspondences, the RGB image SR problem is formulated as an effective regularization function by incorporating the normalized bilateral total variation (NBTV)regularizer, and it is efficiently solved by a first-order primal dual algorithm. The depth image SR estimate can be obtained by minimizing a non-local regression based energy, which integrates the structural cues of the super-resolved RGB image in a detail-preserving fashion. Essentially, our unified optimization framework uses the RGB image and depth image as a priori knowledge that the SR process uses for better accuracy. Our extensive experiments on public RGB-D benchmarks and real data and our quantitative comparison with several state-of-the art methods demonstrates the superiority of our method in terms of accuracy, versatility and reliability of details and sharp feature preservation.
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IEEE 2016-2017 BIG DATA PROJECTS ABSTRACT FEEDBACK AUTONOMIC PROVISIONING FOR GUARANTEEING PERFORMANCE IN MAPREDUCE SYSTEMS ABSTRACT:-  Companies have fast growing amounts of data to process and store, a data explosion is happening next to us. Currently one of the most common approaches to treat these vast data quantities are based on the MapReduce parallel programming paradigm. While its use is widespread in the industry, ensuring performance constraints, while at the same time minimizing costs, still provides considerable challenges. We introduce the first algorithm to create dynamic models for Big Data MapReduce systems, running a concurrent workload. Furthermore, EXISTING SYSTEM:- Furthermore, one can notice that, due to the unpredictability of new deployment environments, such as the cloud, traditional adaptation approaches become increasingly difficult to use. Therefore, more and more attention is given to approaches used in different fields for controlling complex systems. Our work is in line with these latter approaches as we develop on-line feedback, feed forward control techniques that don’t require complex tuning and that, contrary to existing heuristic approaches. This, together with the generality of the developed techniques presented in this paper, allows for the applicability of our approach to a wide variety of cloud systems. PROPOSED SYSTEM:- The high complexity of a MapReduce system and the continuous changes in its behaviour because of software upgrades and improvements prompted us to avoid the use of white-box modelling and to opt for a technique which is agnostic to these issues. This leads us to a grey-box or black-box modelling technique. The line between these two techniques is not well defined, but we consider our model a grey-box model since the structure of the model was defined based on our observations of linearity regions in system behavior. We propose a dynamic model that predicts MapReduce cluster performance, in our case the average service time, based on the number of nodes and the number of clients. To the best of our knowledge this is the first dynamic performance model for MapReduce systems. ADVANTAGES:- The advantages of control theory are that it can provide a solid mathematical basis for synthesizing feedback control loops, for handling safely complexity and for having theoretically guaranteed results. H/W SYSTEM CONFIGURATION:- System - Pentium –IV 2.4 GHzSpeed - 1.1 GhzRAM - 256MB(min)Hard Disk - 40 GBKey Board - Standard Windows KeyboardMouse - LogitechMonitor - 15 VGA Color.S/W SYSTEM CONFIGURATION:-Operating System : Windows/XP/7.ApplicationServer : Tomcat 5.0/6.0 Front End : HTML, Java, Jsp Scripts : JavaScript.Server side Script : Java Server Pages.Database : MongoDBDatabase Connectivity : Robomongo-0.8.5-i386. CONCLUSION:- This paper presents the design, implementation and evaluation of the first algorithm for creating dynamic performance models for Big Data Map Reduce systems. Moreover we identify two major performance constraint use cases: relaxed - performance, minimal resource and strict performance constraints. For the first case we develop and implement a PI feedback control mechanism. For the second case we develop and implement a feed forward controller that efficiently suppresses the effects of large workload size variations. All the control algorithms are validated online on a real node Map Reduce cluster, running a data intensive Business Intelligence workload.
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STRUCTURAL PROJECTS ABSTRACT 2016-2017 SIMPLE TUNED MASS DAMPER TO CONTROL SEISMIC RESPONSE OF ELEVATED TANKS & STRUCTURES ABSTRACT:- A simple tuned mass damper is proposed to control seismic response of RCC elevated tanks. The simplicity of proposed TMD lies in the fact that it has been derived from the existing components of tank. This TMD consists of roof slab of tank and container columns, which support the roof slab. Usually tanks are analysed as 2-DOF system, in which sloshing and impulsive modes of vibration are included. With the deployment of such a TMD, tank becomes a 3-DOF system, in which sloshing mass and TMD are not attached to each other. To retain the simplicity of proposed TMD, its damping is kept as structural damping of its material. In this sense, proposed TMD is a non-optimum TMD. Effectiveness of proposed TMD is demonstrated by considering an example tank. Response spectrum analysis using design acceleration spectra of IS 1893 has shown that such a non-optimum TMD reduces the tank response by 20%. Further it is noted that for a TMD with mass equal to 5% mass of tank, the required sizes of container column and roof slab thickness are practically feasible and stresses in TMD columns are within permissible limits. Using time history analysis, performance of such a TMD is also shown to be effective under past earthquakes. Some observations are noted on further enhancement in TMD’s performance by increasing its damping and by including frictional damping
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