Relevant bibliographies by topics / Constrained optimization. Electronic data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Constrained optimization. Electronic data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Constrained optimization. Electronic data processing"

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Gosain, Anjana, and Kavita Sachdeva. "Materialized View Selection for Query Performance Enhancement Using Stochastic Ranking Based Cuckoo Search Algorithm." International Journal of Reliability, Quality and Safety Engineering 27, no. 03 (September 18, 2019): 2050008. http://dx.doi.org/10.1142/s0218539320500084.

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Materialized view selection (MVS) improves the query processing efficiency and performance for making decisions effectively in a data warehouse. This problem is NP-hard and constrained optimization problem which involves space and cost constraint. Various optimization algorithms have been proposed in literature for optimal selection of materialized views. Few works exist for handling the constraints in MVS. In this study, authors have proposed the Cuckoo Search Algorithm (CSA) for optimization and Stochastic Ranking (SR) for handling the constraints in solving the MVS problem. The motivation behind integrating CS with SR is that fewer parameters have to be fine tuned in CS algorithm than in genetic and Particle Swarm Optimization (PSO) algorithm and the ranking method of SR handles the constraints effectively. For proving the efficiency and performance of our proposed algorithm Stochastic Ranking based Cuckoo Search Algorithm for Materialized View Selection (SRCSAMVS), it has been compared with PSO, genetic algorithm and the constrained evolutionary optimization algorithm proposed by Yu et al. SRCSAMVS outperforms in terms of query processing cost and scalability of the problem.
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Li, Guangshun, Jiping Wang, Junhua Wu, and Jianrong Song. "Data Processing Delay Optimization in Mobile Edge Computing." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/6897523.

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With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three-layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing.
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Wang, Gaojian, Gerd Ascheid, Yanlu Wang, Oner Hanay, Renato Negra, Matthias Herrmann, and Norbert Wehn. "Optimization of Wireless Transceivers under Processing Energy Constraints." Frequenz 71, no. 9-10 (September 26, 2017): 379–88. http://dx.doi.org/10.1515/freq-2017-0150.

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Abstract Focus of the article is on achieving maximum data rates under a processing energy constraint. For a given amount of processing energy per information bit, the overall power consumption increases with the data rate. When targeting data rates beyond 100 Gb/s, the system’s overall power consumption soon exceeds the power which can be dissipated without forced cooling. To achieve a maximum data rate under this power constraint, the processing energy per information bit must be minimized. Therefore, in this article, suitable processing efficient transmission schemes together with energy efficient architectures and their implementations are investigated in a true cross-layer approach. Target use cases are short range wireless transmitters working at carrier frequencies around 60 GHz and bandwidths between 1 GHz and 10 GHz.
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Cao, Xuanyu, Junshan Zhang, and H. Vincent Poor. "A Virtual-Queue-Based Algorithm for Constrained Online Convex Optimization With Applications to Data Center Resource Allocation." IEEE Journal of Selected Topics in Signal Processing 12, no. 4 (August 2018): 703–16. http://dx.doi.org/10.1109/jstsp.2018.2827302.

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Ren, Zhimin. "Data Processing Platform of Cloud Computing and Its Performance Analysis Based on Photoelectric Hybrid Interconnection Architecture." Journal of Nanoelectronics and Optoelectronics 15, no. 6 (June 1, 2020): 743–52. http://dx.doi.org/10.1166/jno.2020.2805.

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Data processing platform is the core support platform of cloud computing. The use of electric interconnection architecture will increase the complexity of network topology, while optical interconnection architecture is ideal, so cloud computing platform based on optical interconnection has become a research hotspot. The distributed optical interconnection architecture of cloud computing data processing platform is focused on. Combining the hybrid mechanism of optical circuit switching and electric packet switching, it can meet a variety of traffic requirements. Meanwhile, it improves the switching mechanism, communication strategy, and router structure. Moreover, considering that the hybrid optoelectronic interconnection architecture can improve the network delay and throughput, but there is still a problem of network consumption. Combined with the network characteristics of the data processing platform (wireless mesh structure) of cloud computing, the network topology algorithm is studied, and the relationship between the topology and the maximum number of allocable channels is analyzed. Furthermore, the equation of topological reliability calculation is defined, and the optimization model of topological design is proposed, according to which the data processing platform of cloud computing is further optimized under the photoelectric hybrid interconnection architecture. During the experiment, before topology optimization, by changing the message length, it is found that adding optical circuit switching can help achieving large capacity and new type of transmission, and can effectively reduce the time delay. After topology optimization structure is adopted, the photoelectric hybrid-data processing platform of cloud computing without topology optimization is compared. It is found that under different reliability constraints, the throughput and end-to-end delay of the network are significantly improved, which proves that the data processing platform of cloud computing based on the photoelectric hybrid interconnection architecture is a feasible cloud computing platform.
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Zhong, Jianying, Jibin Zhu, Yonghao Guo, Yunxin Chang, and Chaofeng Zhu. "A Customer Clustering Algorithm for Power Logistics Distribution Network Structure and Distribution Volume Constraints." International Journal of Circuits, Systems and Signal Processing 15 (August 25, 2021): 1051–56. http://dx.doi.org/10.46300/9106.2021.15.113.

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Customer clustering technology for distribution process is widely used in location selection, distribution route optimization and vehicle scheduling optimization of power logistics distribution center. Aiming at the problem of customer clustering with unknown distribution center location, this paper proposes a clustering algorithm considering distribution network structure and distribution volume constraint, which makes up for the defect that the classical Euclidean distance does not consider the distribution road information. This paper proposes a logistics distribution customer clustering algorithm, which improves CLARANS algorithm to make the clustering results meet the constraints of customer distribution volume. By using the single vehicle load rate, the sufficient conditions for logistics distribution customer clustering to be solvable under the condition of considering the ubiquitous and constraints are given, which effectively solves the problem of logistics distribution customer clustering with sum constraints. The results state clearly that the clustering algorithm can effectively deal with large-scale spatial data sets, and the clustering process is not affected by isolated customers, The clustering results can be effectively applied to the distribution center location, distribution cost optimization, distribution route optimization and distribution area division of vehicle scheduling optimization.
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Salim, Ibrahim, and A. Hamza. "Fast Feature-Preserving Approach to Carpal Bone Surface Denoising." Sensors 18, no. 7 (July 21, 2018): 2379. http://dx.doi.org/10.3390/s18072379.

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We present a geometric framework for surface denoising using graph signal processing, which is an emerging field that aims to develop new tools for processing and analyzing graph-structured data. The proposed approach is formulated as a constrained optimization problem whose objective function consists of a fidelity term specified by a noise model and a regularization term associated with prior data. Both terms are weighted by a normalized mesh Laplacian, which is defined in terms of a data-adaptive kernel similarity matrix in conjunction with matrix balancing. Minimizing the objective function reduces it to iteratively solve a sparse system of linear equations via the conjugate gradient method. Extensive experiments on noisy carpal bone surfaces demonstrate the effectiveness of our approach in comparison with existing methods. We perform both qualitative and quantitative comparisons using various evaluation metrics.
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Gupta, Saurav, Sachin N. Kapgate, and Ajit Kumar Sahoo. "In-Network Distributed Least-Mean-Square Identification of Nonlinear Systems Using Volterra–Laguerre Model." Journal of Circuits, Systems and Computers 29, no. 02 (May 24, 2019): 2050030. http://dx.doi.org/10.1142/s0218126620500309.

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It is of great importance to model the behavior of nonlinear systems in a distributed fashion using wireless sensor networks (WSNs) because of its computation and energy-efficient data processing. However, least squares methods have been previously employed to estimate the parameters of Volterra model for modeling nonlinear systems. Still, it is more convenient and advantageous to use in-network distributed identification strategy for real-time modeling and control. In this context, a black-box model with generalized structure and remarkable modeling ability called Volterra–Laguerre model is considered in which distributed signal processing is employed to identify the nonlinear systems in a distributed manner. The model cost function is expressed as a separable constrained minimization problem which is decomposed into augmented Lagrangian form to facilitate the distributed optimization. Then, alternating direction method of multipliers is employed to estimate the optimal parameters of the model. Convergence of the algorithm is guaranteed by providing its mean stability analysis. Simulation results for a nonlinear system are obtained under the noisy environment. These results are plotted against the results of noncooperative and centralized methods, demonstrating the effectiveness and superior performance of the proposed algorithm.
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Akhatov, A. R., and F. M. Nazarov. "METHODS OF IMPLEMENTATION OF BLOCKCHAIN TECHNOLOGIES ON THE BASIS OF CRYPTOGRAPHIC PROTECTION FOR THE DATA PROCESSING SYSTEM WITH CONSTRAINT AND LAGGING INTO ELECTRONIC DOCUMENT MANAGEMEN." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 184 (October 2019): 3–12. http://dx.doi.org/10.14489/vkit.2019.10.pp.003-012.

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The problem of application design by restriction and delay in ED (Electronic Document) management based on blockchain technologies to ensure a new level of security, reliability, transparency of data processing is considered. Increasing the reliability of information in systems by limiting and delaying ED management of enterprises and organizations during collecting, transmitting, storing and processing ED based on new, little-studied optimization technologies for processing blockchain-type data is a relevant and promising research topic. Important advantages of the potential use of transaction blocks built according to certain rules in systems by limiting and delaying ED are ensuring security by encrypting transactions for subsequent confirmation, the inability to make unauthorized changes due to the dependence of the current blockchain state on previous transactions, transparency and reliability of procedures due to public and distributed storage, as well as the interaction of a large number of users between without the use of “trusted intermediaries”. Studies show that when using existing algorithms for adding blocks in any system, it is possible to achieve the requirements of decentralization, openness of the entered data, the inability to change the data once entered into the system. However, mathematics-cryptographic information protection must be developed for each designed system separately. The task of providing and formulating the rules of data reliability control by limiting and delaying in ED circulation based on cryptographic methods of encrypting transaction blocks constituting the blockchain has been formulated. The approaches have been adopted as a methodology of support for systems by limiting and delaying electronic documents based on a new database architecture.
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RECINE, GREG, and DWIGHT L. WOOLARD. "PREDICTING THE PATH OF ELECTRONIC TRANSPORT THROUGH A MOLECULAR DEVICE VIA A MOUNTAIN-PASS ALGORITHM." International Journal of High Speed Electronics and Systems 18, no. 01 (March 2008): 223–28. http://dx.doi.org/10.1142/s0129156408005291.

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The so-called “mountain-pass” theorem allows for the finding of a critical point on the path between two points on a multidimensional contour where the maximal elevation is minimal. By implementing the “elastic string algorithm”, it is possible to not only find the critical point, but to compute the mountain-pass itself on a finite-dimensional contour. For a given molecule that sits between two probes making up a nanostructure device, we propose that the mountain-pass will be a likely path of electron transport through the molecule, the contour being the electronic potential of the molecule. This potential along this path will be used as the input potential for SETraNS, a 1D Wigner-Poisson electron transport solver in order to explore the current-bias characteristics of such the molecule in such a device. In order to calculate the mountain pass, the elastic string algorithm is used to set up a constrained non-linear optimization problem which is, in turn, solved via a Monte Carlo method. We will compute the mountain-pass for a well-known test contour in order to show the validity of this approach. The procedure developed here is to be combined with conformational analysis via the molecular modeling program AMBER and the quantum transport program SETraNS in order to predict molecular function. When achieved, this combined procedure will allow for the better design and implementation of nanoscale molecular devices for application such as sensing, switching and data processing.
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Dissertations / Theses on the topic "Constrained optimization. Electronic data processing"

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Atlas, James. "Efficient coordination techniques for non-deterministic multi-agent systems using distributed constraint optimization." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 168 p, 2009. http://proquest.umi.com/pqdweb?did=1885755811&sid=3&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Jeon, Woojay. "Pitch detection of polyphonic music using constrained optimization." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/15802.

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Mailhe, Maxime. "Batch processing task optimization." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/11893.

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Zhang, Yue. "Detection copy number variants profile by multiple constrained optimization." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/439.

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Copy number variation, causing by the genome rearrangement, generally refers to the copy numbers increased or decreased of large genome segments whose lengths are more than 1kb. Such copy number variations mainly appeared as the sub-microscopic level of deletion and duplication. Copy number variation is an important component of genome structural variation, and is one of pathogenic factors of human diseases. Next generation sequencing technology is a popular CNV detection method and it has been widely used in various fields of life science research. It possesses the advantages of high throughput and low cost. By tailoring NGS technology, it is plausible to sequence individual cells. Such single cell sequencing can reveal the gene expression status and genomic variation profile of a single-cell. Single cell sequencing is promising in the study of tumor, developmental biology, neuroscience and other fields. However, there are two challenging problems encountered in CNV detection for NGS data. The first one is that since single-cell sequencing requires a special genome amplification step to accumulate enough samples, a large number of bias is introduced, making the calling of copy number variants rather challenging. The performances of many popular copy number calling methods, designed for bulk sequencings, are not consistent and cannot be applied on single-cell sequenced data directly. The second one is to simultaneously analyze genome data for multiple samples, thus achieving assembling and subgrouping similar cells accurately and efficiently. The high level of noises in single-cell-sequencing data negatively affects the reliability of sequence reads and leads to inaccurate patterns of variations. To handle the problem of reliably finding CNVs in NGS data, in this thesis, we firstly establish a workflow for analyzing NGS and single-cell sequencing data. The CNVs identification is formulated as a quadratic optimization problem with both constraints of sparsity and smoothness. Tailored from alternating direction minimization (ADM) framework, an efficient numerical solution is designed accordingly. The proposed model was tested extensively to demonstrate its superior performances. It is shown that the proposed approach can successfully reconstruct CNVs especially somatic copy number alteration patterns from raw data. By comparing with existing counterparts, it achieved superior or comparable performances in detection of the CNVs. To tackle this issue of recovering the hidden blocks within multiple single-cell DNA-sequencing samples, we present an permutation based model to rearrange the samples such that similar ones are positioned adjacently. The permutation is guided by the total variational (TV) norm of the recovered copy number profiles, and is continued until the TV-norm is minimized when similar samples are stacked together to reveal block patterns. Accordingly, an efficient numerical scheme for finding this permutation is designed, tailored from the alternating direction method of multipliers. Application of this method to both simulated and real data demonstrates its ability to recover the hidden structures of single-cell DNA sequences.
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D'Souza, Sammy Raymond. "Parallelizing a nondeterministic optimization algorithm." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3084.

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This research explores the idea that for certain optimization problems there is a way to parallelize the algorithm such that the parallel efficiency can exceed one hundred percent. Specifically, a parallel compiler, PC, is used to apply shortcutting techniquest to a metaheuristic Ant Colony Optimization (ACO), to solve the well-known Traveling Salesman Problem (TSP) on a cluster running Message Passing Interface (MPI). The results of both serial and parallel execution are compared using test datasets from the TSPLIB.
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Liu, Bin. "Optimization strategies for data warehouse maintenance in distributed environments." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0430102-133814.

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Wang, Fei, and 王緋. "Complex stock trading strategy based on parallel particle swarm optimization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49858889.

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Trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this thesis, a complex stock trading strategy, namely Performance-based Reward Strategy (PRS), is proposed. PRS combines the seven most popular classes of trading rules in financial markets, and for each class of trading rule, PRS includes various combinations of the rule parameters to produce a universe of 1059 component trading rules in all. Each component rule is assigned a starting weight and a reward/penalty mechanism based on profit is proposed to update these rules’ weights over time. To determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS. Due to the large number of component rules and swarm size, the optimization time is significant. A parallel PSO based on Hadoop, an open source parallel programming model of MapReduce, is employed to optimize PRS more efficiently. By omitting the traditional reduce phase of MapReduce, the proposed parallel PSO avoids the I/O cost of intermediate data and gets higher speedup ratio than previous parallel PSO based on MapReduce. After being optimized in an eight years training period, PRS is tested on an out-of-sample data set. The experimental results show that PRS outperforms all of the component rules in the testing period.
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Wang, Mianyu Kam Moshe Kandasamy Nagarajan. "A decentralized control and optimization framework for autonomic performance management of web-server systems /." Philadelphia, Pa. : Drexel University, 2007. http://hdl.handle.net/1860/2643.

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Alemany, Kristina. "Design space pruning heuristics and global optimization method for conceptual design of low-thrust asteroid tour missions." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31821.

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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Braun, Robert; Committee Member: Clarke, John-Paul; Committee Member: Russell, Ryan; Committee Member: Sims, Jon; Committee Member: Tsiotras, Panagiotis. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Jung, Gueyoung. "Multi-dimensional optimization for cloud based multi-tier applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37267.

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Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these applications at a very fine granularity. Meanwhile, resource virtualization has recently gained considerable attention in the design of computer systems and become a key ingredient for cloud computing. It provides significant improvement of aggregated power efficiency and high resource utilization by enabling resource consolidation. It also allows infrastructure providers to manage their resources in an agile way under highly dynamic conditions. However, these trends also raise significant challenges to researchers and practitioners to successfully achieve agile resource management in consolidated environments. First, they must deal with very different responsiveness of different applications, while handling dynamic changes in resource demands as applications' workloads change over time. Second, when provisioning resources, they must consider management costs such as power consumption and adaptation overheads (i.e., overheads incurred by dynamically reconfiguring resources). Dynamic provisioning of virtual resources entails the inherent performance-power tradeoff. Moreover, indiscriminate adaptations can result in significant overheads on power consumption and end-to-end performance. Hence, to achieve agile resource management, it is important to thoroughly investigate various performance characteristics of deployed applications, precisely integrate costs caused by adaptations, and then balance benefits and costs. Fundamentally, the research question is how to dynamically provision available resources for all deployed applications to maximize overall utility under time-varying workloads, while considering such management costs. Given the scope of the problem space, this dissertation aims to develop an optimization system that not only meets performance requirements of deployed applications, but also addresses tradeoffs between performance, power consumption, and adaptation overheads. To this end, this dissertation makes two distinct contributions. First, I show that adaptations applied to cloud infrastructures can cause significant overheads on not only end-to-end response time, but also server power consumption. Moreover, I show that such costs can vary in intensity and time scale against workload, adaptation types, and performance characteristics of hosted applications. Second, I address multi-dimensional optimization between server power consumption, performance benefit, and transient costs incurred by various adaptations. Additionally, I incorporate the overhead of the optimization procedure itself into the problem formulation. Typically, system optimization approaches entail intensive computations and potentially have a long delay to deal with a huge search space in cloud computing infrastructures. Therefore, this type of cost cannot be ignored when adaptation plans are designed. In this multi-dimensional optimization work, scalable optimization algorithm and hierarchical adaptation architecture are developed to handle many applications, hosting servers, and various adaptations to support various time-scale adaptation decisions.
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Books on the topic "Constrained optimization. Electronic data processing"

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Uri͡asʹev, S. P. Probabilistic Constrained Optimization: Methodology and Applications. Boston, MA: Springer US, 2000.

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Achterberg, Tobias. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 8th International Conference, CPAIOR 2011, Berlin, Germany, May 23-27, 2011. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimisation Problems (7th 2010 Bologna, Italy). Integration of AI and OR techniques in constraint programming for combinatorial optimization problems: 7th international conference, CPAIOR 2010, Bologna, Italy, June 14-18, 2010 : proceedings. Berlin: Springer, 2010.

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Censor, Yair. Parallel optimization: Theory, algorithms, and applications. New York: Oxford University Press, 1997.

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Talbi, El-Ghazali. Parallel Combinatorial Optimization. New York: John Wiley & Sons, Ltd., 2006.

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Gomes, Carla. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 10th International Conference, CPAIOR 2013, Yorktown Heights, NY, USA, May 18-22, 2013. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Uryasev, Stanislav. Stochastic Optimization: Algorithms and Applications. Boston, MA: Springer US, 2001.

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Mockus, Jonas. Bayesian Approach to Global Optimization: Theory and Applications. Dordrecht: Springer Netherlands, 1989.

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Petersen, E. R. PROPS+: Proabilistic and optimization spreadsheets plus what-if-solver. Reading, MA: Addison-Wesley, 1994.

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M, Pardalos Panos, and Värbrand Peter, eds. From Local to Global Optimization. Boston, MA: Springer US, 2001.

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Book chapters on the topic "Constrained optimization. Electronic data processing"

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Irshad, Misbah, Muhammad Sarfraz, and Malik Zawwar Hussain. "Outline Capture of Planar Objects by Detecting Corner Features." In Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 280–98. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6030-4.ch016.

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This chapter proposes a scheme that helps digitizing hand printed and electronic planar objects or vectorizing the generic shapes. An evolutionary optimization technique, namely Genetic Algorithm (GA), is used to solve the problem of curve fitting with cubic and rational cubic spline functions. The underlying scheme is comprised of various phases including data of the image outlines, detection of corner points, using GA for optimal values of shape parameters in the description of spline functions, and fitting curve using spline functions to the detected corner points.
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Ramadan, Rabie A., and Fatma H. Elfouly. "Multi-Object Tracking in Wireless Sensor Networks Using Swarm Intelligence." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 293–313. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5788-4.ch012.

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Wireless sensor networks (WSNs) may be described as a self-configured wireless networks that can be used to track physical objects or monitor environmental features, such as temperature or motion. The sensed data is then passed across the network to the main location or sink node, where the data can be processed and analyzed. Sensor nodes in WSN are fundamentally resource-constrained: they have restricted processing power, computing, space, and transmission bandwidth. Object tracking is considered as one of the major applications. However, many of the recent articles focused on object localization. In this chapter, the authors suggest an effective approach for tracking objects in WSNs. The aim is to achieve both minimal energy consumption in reporting activity and balanced energy consumption across the WSN lifetime extension of sensor nodes. Furthermore, data reliability is considered in our model. The chapter starts by formulating the multi-object tracking problem using 0/1 Integer Linear programming. In addition, the authors adopted the swarm intelligence technique to solve the optimization problem.
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Prusty, Alok R., Srinivas Sethi, and Ajit Kumar Nayak. "Energy Aware Optimized Routing Protocols for Wireless Ad Hoc Sensor Network." In Sensor Technology, 1494–521. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch070.

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Advancement in wireless technology made human life become simple and easy going. Wireless Ad Hoc Sensor Network (WASN) is one of the friendly wireless networks used to monitor the most unfriendly and ever changing dynamic environment that restricts continuous human attention. WASN has drawn significant attentions due to its unique capabilities to deal with complex network operation in highly resource constrained network construct. This ad hoc and unstructured deployment of tiny sensor nodes operate with controlled transmission range, processing capabilities, as well as very limited battery backup. The severe power depletion affects the existence of active nodes. Hence, data forwarding and reliable packet routing in such phenomenon oriented network becoming a challenge. In this chapter the clustering and hierarchical routing approaches for WASN environment has been briefly presented followed by some optimization strategies applicable to cluster routing process. This chapter can help researchers to think fresh dimensions of ongoing research in WASN cluster routing.
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Prusty, Alok R., Srinivas Sethi, and Ajit Kumar Nayak. "Energy Aware Optimized Routing Protocols for Wireless Ad Hoc Sensor Network." In Advances in Wireless Technologies and Telecommunication, 156–83. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0486-3.ch006.

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Advancement in wireless technology made human life become simple and easy going. Wireless Ad Hoc Sensor Network (WASN) is one of the friendly wireless networks used to monitor the most unfriendly and ever changing dynamic environment that restricts continuous human attention. WASN has drawn significant attentions due to its unique capabilities to deal with complex network operation in highly resource constrained network construct. This ad hoc and unstructured deployment of tiny sensor nodes operate with controlled transmission range, processing capabilities, as well as very limited battery backup. The severe power depletion affects the existence of active nodes. Hence, data forwarding and reliable packet routing in such phenomenon oriented network becoming a challenge. In this chapter the clustering and hierarchical routing approaches for WASN environment has been briefly presented followed by some optimization strategies applicable to cluster routing process. This chapter can help researchers to think fresh dimensions of ongoing research in WASN cluster routing.
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Conference papers on the topic "Constrained optimization. Electronic data processing"

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Tian, Bing, and Kun Liu. "Electronic Map Data Processing and Optimization." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5661625.

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Al-Qawasmeh, Abdulla M., Sudeep Pasricha, Anthony M. Maciejewski, and Howard Jay Siegel. "Thermal-Aware Performance Optimization in Power Constrained Heterogenous Data Centers." In 2012 26th IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2012. http://dx.doi.org/10.1109/ipdpsw.2012.19.

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Pham, Mai Quyen, Caroline Chaux, Laurent Duval, and Jean-Christophe Pesquet. "A constrained-based optimization approach for seismic data recovery problems." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854025.

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Zhang, Chi, YuXin Wang, Zhen Feng, and He Guo. "Power Consumption Optimization for Deadline-Constrained Workflows in Cloud Data Center." In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). IEEE, 2017. http://dx.doi.org/10.1109/ispa/iucc.2017.00038.

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Feier, Flaviu, Ileana Enatescu, Constantin Ilie, and Ioan Silea. "Newborns' cry analysis classification using signal processing and data mining." In 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2014. http://dx.doi.org/10.1109/optim.2014.6850990.

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Xu, Mingming, Liangpei Zhang, Bo Du, and Lefei Zhang. "The linear mixed model constrained particle swarm optimization for hyperspectral endmember extraction from highly mixed data." In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016. http://dx.doi.org/10.1109/whispers.2016.8071763.

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Grigore-Muler, Octavian, Mihai Barbelian, Janel Arhip, and Mihai Jurba. "Intelligent image data processing for acquiring target in a multisensor platform." In 2010 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2010. http://dx.doi.org/10.1109/optim.2010.5510452.

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Atzori, Luigi, Tiziana Dessi, and Vlad Popescu. "Indoor navigation system using image and sensor data processing on a smartphone." In 2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2012. http://dx.doi.org/10.1109/optim.2012.6231975.

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Bhopte, Siddharth, Dereje Agonafer, Roger Schmidt, and Bahgat Sammakia. "Optimization of Data Center Room Layout to Minimize Rack Inlet Air Temperature." In ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems collocated with the ASME 2005 Heat Transfer Summer Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/ipack2005-73027.

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Abstract:
In a typical raised floor data center with alternating hot and cold aisles, air enters the front of each rack over the entire height of the rack. Since the heat loads of data processing equipment continues to increase at a rapid rate, it is a challenge to maintain the temperature within the requirements as stated for all the racks within the data center. A facility manager has discretion in deciding the data center room layout, but a wrong decision will eventually lead to equipment failure. There are many complex decisions to be made early in the design as the data center evolves. Challenges occur such as optimizing the raised floor plenum, floor tile placement, minimizing the data center local hot spots etc. These adjustments in configuration affects rack inlet air temperatures which is one of the important key to effective thermal management. In this paper, a raised floor data center with 4.5 kW racks is considered. There are four rows of racks with alternating hot and cold aisle arrangement. Each row has six racks installed. Two CRAC units supply chilled air to the data center through the pressurized plenum. Effect of plenum depth, floor tile placement and ceiling height on the rack inlet air temperature is discussed. Plots will be presented over the defined range. Now a multi-variable approach to optimize data center room layout to minimize the rack inlet air temperature is proposed. Significant improvement over the initial model is shown by using multi-variable design optimization approach. The results of multi-variable design optimization are used to present guidelines for optimal data center performance.
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Gupta, Aditya, Ananthavijayan Sridhar, and Dereje Agonafer. "CFD Optimization of the Cooling of Yosemite Open Compute Server." In ASME 2017 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2017 Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ipack2017-74254.

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Over the past few years, there has been an ever increasing rise in energy consumption by IT equipment in Data Centers. Thus, the need to minimize the environmental impact of Data Centers by optimizing energy consumption and material use is increasing. In 2011, the Open Compute Project was started which was aimed at sharing specifications and best practices with the community for highly energy efficient and economical data centers. The first Open Compute Server was the ‘ Freedom’ Server. It was a vanity free design and was completely custom designed using minimum number of components and was deployed in a data center in Prineville, Oregon. Within the first few months of operation, considerable amount of energy and cost savings were observed. Since then, progressive generations of Open Compute servers have been introduced. Initially, the servers used for compute purposes mainly had a 2 socket architecture. In 2015, the Yosemite Open Compute Server was introduced which was suited for higher compute capacity. Yosemite has a system on a chip architecture having four CPUs per sled providing a significant improvement in performance per watt over the previous generations. This study mainly focuses on air flow optimization in Yosemite platform to improve its overall cooling performance. Commercially available CFD tools have made it possible to do the thermal modeling of these servers and predict their efficiency. A detailed server model is generated using a CFD tool and its optimization has been done to improve the air flow characteristics in the server. Thermal model of the improved design is compared to the existing design to show the impact of air flow optimization on flow rates and flow speeds which in turn affects CPU die temperatures and cooling power consumption and thus, impacting the overall cooling performance of the Yosemite platform. Emphasis is given on effective utilization of fans in the server as compared to the original design and improving air flow characteristics inside the server via improved ducting.
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