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Convex Optimization Algorithms

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7 documents available in our comprehensive collection of Convex Optimization Algorithms resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of Energy Efficient Beamforming
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Energy Efficient Beamforming (BejarZazoPalomar_TSP2014.pdf)

3.17 MBEN 15 pages
Collaborative beamforming in Wireless Sensor Networks (WSN) can help distribute communication load and reduce battery depletion, but existing techniques are...
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Preview of Nearest Correlation Matrix Algorithm
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Nearest Correlation Matrix Algorithm (thesis.pdf)

807 KBEN 152 pages
Rudiger Borsdorf's 2007 Master of Science dissertation presents a Newton algorithm for finding the nearest correlation matrix, a problem that arises in various...
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Preview of ASSR Technique
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ASSR Technique (14122cnc05.pdf)

830 KBAlmamoon Naife Alauthman, Wan Nor Shuhadah Wan Nik, Nor Aida Mahiddin, Universiti Sultan Zainal AbidEN 13 pages
Optimizing wireless sensor network lifetime by adapting sensor sensing range. Energy consumption is minimized by varying sensing range and reducing messages sent. This approach maximizes network lifetime.
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Preview of SOLO: Efficient 3D Rigid-Body Motion Registration
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SOLO: Efficient 3D Rigid-Body Motion Registration (icra12-slaugther-al-sentis.pdf)

1.9 MBAllen Yang, Chris Slaughter, Justin Bagwell, Costa Checkles, Luis Sentis, Sriram VishwanathEN 8 pages
Efficient, real-time 3D motion registration using Robust PCA initialization and sparse subspace projection for low-cost cameras.
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Preview of Algorithmic Methods: Optimal Group Assignment, MAX-SAT Approximation, and Task Scheduling
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Algorithmic Methods: Optimal Group Assignment, MAX-SAT Approximation, and Task Scheduling (methods-exam19.pdf)

140 KBEN 1 page
**Question 1 (a):** Prove that an optimal solution exists where at most *n* groups have positive weights, ensuring each agent's demand is met. **Question 1 (b):** Describe a polynomial-time algorithm to find the optimal solution value without constructing the...
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Preview of Comparing Traditional and Deep Learning Methods for Defect Detection in XCT Images
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Comparing Traditional and Deep Learning Methods for Defect Detection in XCT Images (contribution_117_final.pdf)

2.37 MBYosifov, Miroslav; Weinberger, Patrick; Reiter, Michael; Beenhouwer, Jan De; Sijbers, Jan; Kastner,EN 10 pages
Defect detectability analysis compares traditional and deep learning methods in numerical simulations for X-ray computed tomography (XCT) in non-destructive testing (NDT).
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Preview of SPHINX-II for WSJ CSR Task
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SPHINX-II for WSJ CSR Task (H92-1080.pdf)

596 KBF. Alleva; H. Hon; X. Huang; M. Hwang; R. Rosenfeld; R. WeideEN 6 pages
The configuration used includes sex-dependent, semi-continuous, shared-distribution hidden Markov models and left context dependent between-word triphones. Issues not previously addressed were tackled in this application.
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