【正文】 j) from equation (2). I(:) is the indicator function, from equation (5). We plot the channel capacity from equation (7), for an exemplary visual MIMO system, where the transmit elements of the LEA are light emitting diodes (LEDs) and the receiver is a machine vision camera (Basler Pilot piA640), over a range of distances d (Fig. 5) and over different viewing angles (Fig. 6). The underlying parameters used in our analysis are summarized in Table I. Inferences: From the analytical capacity plots we draw few notable inferences that relate to the multiplexing and diversity characterizations in visual MIMO. The visual MIMO system with no blur can achieve capacities of the order of Mbps even at long distances of about 90m. Blurring certainly reduces multiplexing range but still medium ranges of 3040m are achievable at high data rates. The data rate gains at these distances are attributed to multiplexing where each LED sends an independent stream of bits over parallel channels. The transitions in the plot (for the multi LED cases) indicate the switch from multiplexing to diversity mode. The capacity gains due to diversity at the long distances, though may not be significant parable to the multiplexing gains at shorter distances, are still close to an order of magnitude gain pared to the single LED system.Fig. 5. Visual MIMO channel Capacity versus distance ( = 0)Fig. 6. Visual MIMO channel Capacity versus angle (d constant) A visual MIMO system will have to switch between the multiplexing and diversity modes in discrete intervals based on distance and angle unlike RF MIMO where the gains in these modes could be achieved simultaneously but follow a continuous tradeoff in performance. Moreover, a visual MIMO system will have to switch autonomously between these modes depending on the orientation of the receiver with respect to the transmitter in order to leverage the gains. This suggests that the throughput of visual MIMO links can be significantly improved through rate adaptation techniques, which adapt the transmission scheme to the receiver perspective. In RF MIMO, in order to select the mode of operation (multiplexing and/or diversity) the channel state information (CSI) has to be known or estimated which either incurs a data overhead and/or plex receiver processing. But in visual MIMO, since the optical channel is deterministic in nature the overhead in selecting either of the modes of operations will be very less because the need to send preamble bits to determine the channel information (distance,angle etc.) may be obviated by the use of efficient puter vision techniques [6]. Since fading is negligible the plexity in estimating the CSI to exploit MIMO techniques is lesser than in RF but still leads to interesting challenges in puter vision and image processing. The visual MIMO channel capacity is consistent over a wide range of viewing angles (small or large depends on distance). We see that the system can achieve large multiplexing gains at short distances and at almost all viewing angles which implies that the system would be robust to any misalignment between the transmitter and receiver. Its cleat that at large distances (of the order of 75m), due to the effect of lens blur, the LEDs may not be resolved easily even at  = 0 and hence at such distances where multiplexing will fail but using diversity over all angles can still offer an order of gain in data rates. Such consistency in data rates over angular misalignment is important especially in mobile settings as the choice of multiplexing and/or diversity depends largely on the orientation of the mobile devices at each instance of time. This is in strong contrast to the RF systems (even MIMO) where the the signal can drop significantly with mobility especially when there is a deep fade in the channel or at high mobile velocities。 j) receives a signal from the transmit element k or not, and is referenced in terms of the distance from pixel at the center of the transmit element’s image (irefk 。j =  as the perpendicular distance and the angle between the transmitter array and image detector planes respectively. Hence the channel between each transmit element k and each pixel (i。j between each element of the transmitter array and every pixel is negligible. Therefore we refer to the distance dk。 j) respectively.Typically, since the pixel size is very small (order ofmicrons), the difference in distance dk。j , k。 j), the channel DC gain hk(i。 2。 j), xk 2 R represents the transmitted optical power from kth element of the LEA and Hk 2 RIJ is the channel matrix of the kth transmit element of the LEA, with elements hk(i。所以要配置內(nèi)核，使內(nèi)核支持 ，對(duì)內(nèi)核進(jìn)行相關(guān)的配置后，系統(tǒng)就完全支持 USB 接口的 WIFI 無(wú)線網(wǎng)卡了。這三部分需要寫到嵌入式系統(tǒng)的 NandFlash 中，不同的處理器，其燒寫方式有所不同。Linux 是完全免費(fèi)，與其它昂貴操作系統(tǒng)如 Vxworks 相比，容易普及。無(wú)線 AP（AP，Access Point，無(wú)線接入節(jié)點(diǎn)）是一個(gè)包含很廣的名稱，它包含無(wú)線接入點(diǎn)（無(wú)線 AP）和無(wú)線路由器（含無(wú)線網(wǎng)關(guān)、無(wú)線網(wǎng)橋）等類設(shè)備的統(tǒng)稱。在節(jié)點(diǎn)外接口與電源管理模塊中，電源管理芯片，選用 LM2596 和 LM1084，為系統(tǒng)提供 5V 和 電壓。在節(jié)點(diǎn)核心控制模塊硬件結(jié)構(gòu)中，ARM 作為一種嵌入式處理器，具有高性能、低功耗、低成本、體積小等優(yōu)點(diǎn)。它是一種短程無(wú)線傳輸技術(shù)，能夠在數(shù)百米范圍內(nèi)支持互聯(lián)網(wǎng)接入的無(wú)線電信號(hào)。 圖11 無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)結(jié)構(gòu)示意圖 無(wú)線傳感器網(wǎng)絡(luò)的傳感器節(jié)點(diǎn)內(nèi)部結(jié)構(gòu)示意圖如圖 11 所示，內(nèi)部分為四個(gè)模塊：電源模塊、傳感器模塊、信息處理模塊和無(wú)線通信模塊。無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)按照?qǐng)?zhí)行功能的不同又可劃分為傳感器節(jié)點(diǎn)和匯聚節(jié)點(diǎn)，傳感器節(jié)點(diǎn)完成數(shù)據(jù)的采集和通信鏈路的續(xù)傳，而匯聚節(jié)點(diǎn)只完成收發(fā)無(wú)線網(wǎng)絡(luò)數(shù)據(jù)和上傳給接收數(shù)據(jù)觀察者。第13周～第15周 數(shù)據(jù)整理，撰寫論文。第1周～第4周 資料收集，完成開(kāi)題報(bào)告的撰寫，英文資料的翻譯。無(wú)線傳感器網(wǎng)絡(luò)節(jié)點(diǎn)分為核心控制模塊、外圍接口及電源管理模塊、數(shù)據(jù)采集模塊，針對(duì)各個(gè)模塊的功能進(jìn)行硬件設(shè)計(jì)。④通信能力有限，傳輸距離不夠長(zhǎng)，受環(huán)境變化干擾。南加州大學(xué)研究了傳感器網(wǎng)絡(luò)上的聚集函數(shù)的計(jì)算方法,提出了節(jié)省能源的計(jì)算聚集的樹(shù)構(gòu)造算法,并通過(guò)實(shí)驗(yàn)證明了無(wú)線通信機(jī)制對(duì)聚集計(jì)算的性能有很大的影響。南加州大學(xué)提出了在生疏環(huán)境部署移動(dòng)傳感器的方法、傳感器網(wǎng)絡(luò)監(jiān)視結(jié)構(gòu)及其聚集函數(shù)計(jì)算方法、節(jié)省能源的計(jì)算、聚集的樹(shù)構(gòu)造算法等。比如中科院寧波軟件所和上海微系統(tǒng)所研究出自己的開(kāi)發(fā)平臺(tái),中國(guó)科技大學(xué),西北工業(yè)大學(xué)等院校都展 了路由層、數(shù)據(jù)鏈路層方面的研究。本文根據(jù)傳感器網(wǎng)絡(luò)發(fā)展?fàn)顩r，設(shè)計(jì)出基于WIFI的無(wú)線傳感器網(wǎng)絡(luò)，相比于傳統(tǒng)的無(wú)線傳感器網(wǎng)絡(luò)，能夠非常容易的與現(xiàn)有網(wǎng)絡(luò)進(jìn)行無(wú)縫的連接，相對(duì)降低組網(wǎng)成本和功耗。傳感器網(wǎng)絡(luò)具有自組織功能。微型化、多功能化、網(wǎng)絡(luò)化和智能化乃大勢(shì)所趨,無(wú)線傳感器網(wǎng)絡(luò)則詮釋了這些優(yōu)勢(shì)。長(zhǎng)春理工大學(xué)畢業(yè)設(shè)計(jì)任務(wù)書題目名稱：基于WIFI的無(wú)線傳感器采集系統(tǒng)設(shè)計(jì) 學(xué)生姓名：華丹陽(yáng) 起止日期：~題目要求(包括主要技術(shù)參數(shù)):1． 題目?jī)?nèi)容：設(shè)計(jì)基于WIFI技術(shù)的傳感器信息采集系統(tǒng)，實(shí)現(xiàn)數(shù)據(jù)信息的網(wǎng)絡(luò)發(fā)布2． 具體要求及技術(shù)參數(shù)： ； ； 。將來(lái)人們將通過(guò)遍布周圍的傳感器網(wǎng)絡(luò)直接感知客觀世界，極大的改變?nèi)藗冋J(rèn)識(shí)世界、改造世界的能力。組網(wǎng)不需要任何固定的網(wǎng)絡(luò)設(shè)備，傳感器節(jié)點(diǎn)通過(guò)分布式網(wǎng)絡(luò)協(xié)議形成自組織網(wǎng)絡(luò)，能夠自動(dòng)調(diào)整來(lái)適應(yīng)節(jié)點(diǎn)的變化，網(wǎng)絡(luò)中的節(jié)點(diǎn)可以快速、自動(dòng)的組成一個(gè)獨(dú)立的網(wǎng)絡(luò)。WIF