{Overleaf 模板库}LaTeX 模板和示例 — Recent

Um modelo, com capa, e artigos, desenvolvido para todos os alunos, ou não, que desejam um arquivo simples de se usar.

This is an example letter to illustrate the use of the brownletter.cls LaTeX class file. Copyright 2003, (tatbul@cs.brown.edu) For more Brown University LaTeX resources, please see: http://cs.brown.edu/about/system/managed/latex/

An example using the longtable environment to create tables that span multiple pages.

Exam Software is a Test Management Software, which offers a complete solution for Computer Based Test (CBT).They are used to set up multiple choice tests for set time periods that when submitted are automatically corrected and the results logged beside the student's reference number. User Roles: “Demo” user can only take part in quiz. “Admin” user can enter, modify the quiz questions and mark the correct choices. “Admin” can also take quiz .

For math proofs... Simple template so I don't have to copy and paste all the time!

The output of this table is used as Table 1 of the paper: Wangyan Li, Zidong Wang, Guoliang Wei, Lifeng Ma, Jun Hu, and Derui Ding, “A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks,” Discrete Dynamics in Nature and Society, vol. 2015, Article ID 683701, 12 pages, 2015. doi: 10.1155/2015/683701.

HoTT

Compressive Sensing is a Signal Processing technique, which gave a break through in 2004. The main idea of CS is, by exploiting the sparsity nature of the signal (in any domain), we can reconstruct the signal from very fewer samples than required by Shannon-Nyquist sampling theorem. Reconstructing a sparse signal from fewer samples is equivalent to solving a under-determined system with sparsity constraints. Least square solution to such a problem yield poor `results because sparse signals cannot be well approximated to a least norm solution. Instead we use l1 norm(convex) to solve this problem which is the best approximation to the exact solution given by l0 norm(non-convex). In this paper we plan to discuss three applications of CS in estimation theory. They are, CS based reliable Channel estimation assuming sparsity in the channel is known for TDS-OFDM systems[1]. Indoor location estimation from received signal strength (RSS) where CS is used to reconstruct the radio map from RSS measurements[2]. Identifying that subspace in which the signal of interest lies using ML estimation, assuming signal lies in a union of subspaces which is a standard sparsity assumption according to CS theory[3]. Index terms : Compressive Sensing, Indoor positioning, fingerprinting, radio map, Maximum likelihood estimation, union of linear subspaces, subspace recovery.

Cálculo de momento de inercia de un cilindro solido y una esfera hueca