目录：

智能控制窗帘

智能与电子工程学院

智能控制窗帘

1、摘要：随着当前人们生活水平的提高，人们对家居的要求也越来越高，而当前大部分的家庭使用的窗帘依旧为手动控制开关的窗帘，其自动程度较低，不能满足当前的需求。此次设计一款红外遥控窗帘，采用STC89C52作为核心控制模块，DS1302时钟模块，红外传感发射XXX、步进电机等作为此次设计主要模块，

2、用户可以通过定时、红外遥控等控制窗帘的开关，窗帘通过步进电机正传何反转开控制其打开或者关闭。用户可以通过液晶显示屏LCD1602观看时间及其当前窗帘的情况。此次设计的窗帘可以解决用户只能手动控制窗帘的打开或者关闭的问题，对当前对窗帘的研究具有一定的研究价值和实际研究意义。

3、Abstract:?With the improvement of current people’s living standard, people’s requirements for household are also higher and higher, and the curtain that most fXXilies use at present is still the curtain that controls the switch XXnually, its autoXXtic degree is lower, can’t meet the current deXXnd. The design of an infrared remote control curtain, using STC89C52 as the core control module, DS1302 clock module, infrared sensor transmitter receiver, stepper motor as the XXin module of the design, can realize the timing of the curtain control, can also use the remote control of the curtain control, Through the stepper motor forward and reverse control of curtain opening or closing, the user can watch the time and the current situation of the curtain through the LCD screen LCD160 The curtain design can solve the problem that users can only XXnually control the opening or closing of the curtain, which has certain research value and practical significance for the current research on the curtain.

4、日常生活在不断改善，时代在发展，人们正在朝着文明迈进。不同时期对室内空间和自然环境有不同的需求[1]。这是社会发展中不可避免的时尚趋势[1]。由单片机设计和运行的自动窗帘遥控系统软件，不仅可以处理日常用手开闭窗帘布的麻烦，而且可以提高日常生活水平，另外，它可以根据光源的明暗自动控制窗帘布。以此来调节最适合我们生活的房间的光线，进一步满足每个人的娱乐法规。因此，可以大规模生产该产品，这将迅速在全国范围内推广，从而产生巨大的经济效益；除了许多住宅应用外，遥控窗帘布还可以广泛用于单户XX和XX大楼，酒店，饭店，办公楼，电影院，大型会议室，金融机构，学院，医院和门诊病人诊所等公共场所，因此该产品具有广阔的行业前景[2]。

智能与电子工程学院

1、Wu, Zhoujie; Guo, Wenbo; Lu, Lilian; Zhang, Qican. Generalized phase unwrapping method that avoids jump errors for fringe projection profilometry[J]. Optics EXXress, 2021, 29(17): 27181-2719

2、Liu, Yunkun; Zhang, Chongyang; Kou, Tingdong; Li, Yueyang; Junfei Shen. End-to-end computational optics with a singlet lens for large depth-of-field iXXging[J]. Optics EXXress, 2021, 29(18): 28530-2854

3、Li, Yueyang; Shen, Junfei; Wu, Zhoujie; Zhang, Qican. Passive binary defocusing for large depth 3D measurement based on deep learning[J]. Applied Optics, 2021, 60(24): 7243-725

4、He, Tianyue; Zhang, Qican; Zhou, Mingwei; Shen, Junfei. CVNet: confidence voting convolutional neural network for cXXera spectral sensitivity estiXXtion[J]. Optics EXXress, 2021, 29(13): 19655-1967