ESP8266 AT指令手冊示例，物聯(lián)網(wǎng)使用廣泛，參考此文檔可進行快速開發(fā)。
上傳時間： 2018-06-05
上傳用戶：dear920
在人們的生產(chǎn)實踐中，經(jīng)常會遇到如何利用現(xiàn)有資源來安排生產(chǎn)，以取得最大經(jīng)濟 效益的問題。此類問題構(gòu)成了運籌學(xué)的一個重要分支—數(shù)學(xué)規(guī)劃，而線性規(guī)劃(Linear Programming 簡記 LP)則是數(shù)學(xué)規(guī)劃的一個重要分支。自從 1947 年 G. B. Dantzig 提出 求解線性規(guī)劃的單純形方法以來，線性規(guī)劃在理論上趨向成熟，在實用中日益廣泛與深 入。特別是在計算機能處理成千上萬個約束條件和決策變量的線性規(guī)劃問題之后，線性 規(guī)劃的適用領(lǐng)域更為廣泛了，已成為現(xiàn)代管理中經(jīng)常采用的基本方法之一。 1.1 線性規(guī)劃的實
標簽： 實踐
上傳時間： 2018-09-17
上傳用戶：中國宏軍
Reconstruction- and example-based super-resolution (SR) methods are promising for restoring a high-resolution (HR) image from low-resolution (LR) image(s). Under large magnification, reconstruction-based methods usually fail to hallucinate visual details while example-based methods sometimes introduce unexpected details. Given a generic LR image, to reconstruct a photo-realistic SR image and to suppress artifacts in the reconstructed SR image, we introduce a multi-scale dictionary to a novel SR method that simultaneously integrates local and non-local priors. The local prior suppresses artifacts by using steering kernel regression to predict the target pixel from a small local area. The non-local prior enriches visual details by taking a weighted average of a large neighborhood as an estimate of the target pixel. Essentially, these two priors are complementary to each other. Experimental results demonstrate that the proposed method can produce high quality SR recovery both quantitatively and perceptually.
標簽： Super-resolution Multi-scale Dictionary Single Image for
上傳時間： 2019-03-28
上傳用戶：fullout
Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns.The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.
標簽： localization environments challenging Resilient mapping R-SLAM and in
上傳時間： 2019-09-15
上傳用戶：zhudx2007
atlab code for Non-negative Factorization of the Occurrence Tensor from Financial Contracts https://arxiv.org/pdf/1612.03350.pdf,
標簽： tensor_n
上傳時間： 2020-01-01
上傳用戶：dawn1818
atlab code for Non-negative Factorization of the Occurrence Tensor from Financial Contracts https://arxiv.org/pdf/1612.03350.pdf, which
標簽： tensor
上傳時間： 2020-01-01
上傳用戶：dawn1818
Abstract—In the future communication applications, users may obtain their messages that have different importance levels distributively from several available sources, such as distributed storage or even devices belonging to other users. This scenario is the best modeled by the multilevel diversity coding systems (MDCS). To achieve perfect (information-theoretic) secrecy against wiretap channels, this paper investigates the fundamental limits on the secure rate region of the asymmetric MDCS (AMDCS), which include the symmetric case as a special case. Threshold perfect secrecy is added to the AMDCS model. The eavesdropper may have access to any one but not more than one subset of the channels but know nothing about the sources, as long as the size of the subset is not above the security level. The question of whether superposition (source separation) coding is optimal for such an AMDCS with threshold perfect secrecy is answered. A class of secure AMDCS (S-AMDCS) with an arbitrary number of encoders is solved, and it is shown that linear codes are optimal for this class of instances. However, in contrast with the secure symmetric MDCS, superposition is shown to be not optimal for S-AMDCS in general. In addition, necessary conditions on the existence of a secrecy key are determined as a design guideline.
標簽： Fundamental Limits Secure Class on of
上傳時間： 2020-01-04
上傳用戶：kddlas
This book gives a comprehensive overview of the technologies for the advances of mobile radio access networks. The topics covered include linear transmitters, superconducting filters and cryogenic radio frequency (RF) front head, radio over fiber, software radio base stations, mobile terminal positioning, high speed downlink packet access (HSDPA), multiple antenna systems such as smart antennas and multiple input and multiple output (MIMO) systems, orthogonal frequency division multiplexing (OFDM) systems, IP-based radio access networks (RAN), autonomic networks, and ubiquitous networks.
標簽： Advances Networks Access Mobile Radio in
上傳時間： 2020-05-26
上傳用戶：shancjb
The family of recent wireless standards included the optional employment of Multiple-Input Multiple-Output(MIMO)techniques.This was motivatedby the observationaccordingto the classic Shannon–Hartley law that the achievable channel capacity increases logarithmically with the transmit power. In contrast, the MIMO capacity increases linearly with the number of transmit antennas, provided that the number of receive antennas is equal to the number of transmit antennas. With the further proviso that the total transmit power is increased in proportion to the number of transmit antennas, a linear capacity increase is achieved upon increasing the transmit power, which justifies the spectacular success of MIMO systems.
標簽： Multi-Functional Systems MIMO
上傳時間： 2020-05-31
上傳用戶：shancjb
Rapid growth of wireless communication services in recent decades has created a huge demand of radio spectrum. Spectrum scarcity and utilization inefficiency limit the development of wireless networks. Cognitive radio is a promising tech- nology that allows secondary users to reuse the underutilized licensed spectrum of primary users. The major challenge for spectrum sharing is to achieve high spectrum efficiency while making non-intrusive access to the licensed bands. This requires in- formation of availability and quality of channel resources at secondary transmitters, however, is difficult to be obtained perfectly in practice.
標簽： Opportunistic Spectrum Sharing
上傳時間： 2020-05-31
上傳用戶：shancjb
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