基于CAN总线技术的客车远程监测系统设计

为保障营运车辆安全运行,提高运营企业的管理水平,避免财产损失和灾难性事故的发生,本设计整合了MC9S12DG128B微控制器及DS-8104HM-M嵌入式车载录像机,采用CAN总线技术及3G无线通信技术等,开发了营运客车运行状态远程监测系统。通过系统研究和多次试验表明:该系统能实时采集并上传CAN总线信息和视频数据流,监控管理平台可实时监测到营车辆状态参数及车内影像,完成了预期的各项功能。     

四川栗子坪国家级自然保护区野生鸟兽的红外相机初步监测

红外相机技术目前已成为监测大中型兽类和林下鸟类的一种重要手段,广泛用于生物多样性调查。2018年—2019年,采用红外相机监测技术在四川栗子坪国家级自然保护区布设了56个相机位点,布设密度为1台·hm-2,对常绿、落叶阔叶混交林野生鸟兽进行了初步调查。相机累计工作17 074个有效工作日,共获得野生动物独立有效照片15 746张,鉴定出隶属4目13科的18种兽类和2目6科的16种鸟类。相对丰富度指数较高的前3种兽类和3种鸟类分别为毛冠鹿(Elaphodus cephalophus)、藏酋猴(Macaca thibetana)、野猪(Sus scrofa)和血雉(Ithaginis cruentus)、红腹角雉(Tragopan temminckii)及白腹锦鸡(Chrysolophus amherstiae)。记录到2种国家I级和9种国家II级重点保护物种,被IUCN红色名录评估为濒危(EN)的1种、易危(VU)的5种、近危(NT)的2种。此外,红外相机还监测到了家畜活动(牛、狗和马)和人为活动,因此,建议保护区管理部门仍需采取相应管理措施,进一步提高周边居民的保护意识,促进保护区与社...     

基于图像技术的寒地水稻生长参数采集系统的研究

对寒地水稻生长状况进行实时采集是调节控制寒地水稻生育期用水需求并估算水稻产量的重要依据。以黑龙江省建三江地区洪河农场水稻科技园区的稻田为研究对象,通过植物反射光谱的特性,研究基于图像技术的寒地水稻叶面积指数、株高、叶绿素含量、叶长、叶宽等生长参数的采集估算。试验表明,用3CCD摄像机估算寒地水稻分蘖期参数不适合。近红外光谱分析技术作为一种高效无损的图像检测技术,对其应用前景进行了展望。     

Camera-trapping and seed-labelling reveals widespread granivory and scatter-hoarding of nuts by rodents in the Fynbos Biome

Many plant–animal interactions can be challenging to directly observe, due to species being small, cryptic and/or nocturnal. Previous research on seed predation and dispersal by rodents in the Fynbos Biome of South Africa has relied on indirect evidence, as methods for directly monitoring rodent–seed interactions were not available. The aims of the study were to determine which resident small mammals scatter-hoard nuts and the geographic, seasonal and taxonomic extent of scatter-hoarding in the Fynbos Biome. We used camera traps focused on seed stations at eight sites in the Fynbos Biome to determine the responses of small mammals to tagged nut-like fruits (nuts) of seven endemic plant species belonging to the Proteaceae (n = 3), Rosaceae (n = 2), Restionaceae (n = 1) and Cupressaceae (n = 1), as well as commercial sunflower seeds. We found Acomys subspinosus and Gerbilliscus paeba scatter-hoarded nuts, which they typically carried and buried individually. Rhabdomys pumilio and Micaelamys namaquensis only consumed nuts. Leucadendron pubescens and L. loranthifolium are added to the list of known plant species that are scatter-hoarded by rodents. Nuts of Cliffortia cuneata and C. phillipsii, and the critically endangered Widdringtonia cedarbergensis, were consumed but not dispersed by small mammals, whereas nuts of Ceratocaryum argenteum were neither consumed nor scatter-hoarded by rodents (within its native range). Gerbilliscus paeba and A. subspinosus scatter-hoarded nuts aseasonally, outside of seed-fall periods. Scatter-hoarding was widespread throughout the Fynbos Biome, although it was highly localised across and within sampled sites. The absence of scatter-hoarding rodents at sites with rodent-dispersed plants remains an important aspect for future investigation.     

基于红外相机技术的广东曲江沙溪省级自然保护区兽类资源分析

2019年7月,使用网格法布设了11台红外相机首次对广东曲江沙溪省级自然保护区大中型兽类资源进行了调查研究,调查累计102天,累计回收1 123个相机工作日的数据,共拍摄到兽类独立有效照片数103张,鉴定出兽类3目6科9种,分别为黄腹鼬Mustela kathiah、鼬獾Melogale moschata、斑林狸Prionodon pardicolor、豹猫Prionailurus bengalensis、野猪Sus scrofa、小麂Muntiacus reevesi、赤腹松鼠Callosciurus erythraeus、倭花鼠Tamiops maritimus和红腿长吻松鼠Dremomys pyrrhomer,其中,斑林狸和红腿长吻松鼠为沙溪自然保护区新纪录种;发现相对多度指数最高的兽类是野猪,为29.39,分布最广泛的地栖兽类是鼬獾;建立兽类累积曲线发现前31天兽类种数上升较快,后71天趋于平缓;研究进一步证实了红外相机技术可以作为保护区兽类补充调查的重要手段,可以在保护区兽类物种编目、相对多度指数、分布现状与凭证标本等收集上发挥重大作用。     

浅沟信息的数码照相识别方法

浅沟作为黄土区坡面重要侵蚀形式之一,其侵蚀量占粱峁侵蚀产沙量的46%。浅沟的发生发展在坡面侵蚀中占据重要地位,如果得不到控制和治理,浅沟发育成切沟后,将吞噬更多可利用土地资源。以黄土丘陵沟壑区野外拍摄的照片资料为研究对象,通过ERDAS软件运用AOI分析法处理照片资料,提出浅沟信息识别问题的解决方案。据此方法,获得识别浅沟信息的规律,即处于浅沟的AOI的RGB三基色构成遵循DNR>DNG的规律,处于峁坡的小斑三基色构成遵循DNR相似文献   

立体苗盘管理机器人的机械臂参数优化与试验

为使立体苗盘管理机器人的机械臂能够在植物工厂狭窄的作业环境下,灵活、高效地完成目标工作空间的所有搬运和喷洒动作任务需求,同时尽量减小机械臂的操纵空间和结构尺寸,采用理论与试验相结合的方法对机械臂参数进行了优化设计。首先采用D-H法建立了机器人的运动学模型,然后通过工作空间分析确定出优化参数的工作空间约束条件。在此基础上,以"距离最短"和"结构紧凑"为性能指标建立目标优化函数,并利用遗传算法求解出最优的大臂杆长648 mm、中臂杆长472 mm和小臂杆长396 mm,最优机械臂关节转角极限值为96°、68°和126°。最后进行机器人样机的搬运和喷洒运动规划试验,并借助高速摄像系统标记机械臂末端运动轨迹坐标。试验结果表明:优化后的机械臂能够到达目标工作空间的所有极限位置及其他特征位置点,最大绝对定位误差为9.8 mm,最大相对定位误差为0.98%,在允许的误差范围内,能够满足机械臂工作空间对目标工作空间的有效包容。     

基于视觉检测的苹果采摘机器人系统设计与实现

为提高苹果采摘的自动化与智能化水平,降低重复繁琐的人工劳动强度,减少对果实的损坏率,研制了一款用于苹果成熟自动检测并采摘的轮式机器人系统。系统由硬件平台和软件平台两部分组成。其中,硬件平台由四轮驱动越野小车、IPC-610L工控机、图像数据采集卡、四自由度机械臂和末端执行器组成;软件平台基于Visual C++6.0开发环境,使用双目立体视觉技术和图像处理技术实现对苹果的识别与定位,再通过机械臂的路径规划实现对苹果的采摘。通过仿真实验和数据分析表明:机器人在无人值守的情况下,能实现自动导航、自动识别、自动采摘苹果等功能,并且识别成功率大于94.00%,采摘成功率达到91.33%,平均采摘周期约为1 1 s,具有较高的准确性及稳定性。     

Anterior and posterior segment photography. An alternative approach using a dSLR camera adaptor

Purpose To describe a novel digital single lens reflex (dSLR) camera adaptor for anterior and posterior segment photography. Methods The adaptor was used to evaluate canine, feline, and equine patients presenting to Tufts Ophthalmology service. Anterior segment imaging was conducted with the adaptor mounted between a dSLR camera body (Canon 7D) and a macro lens (Canon EF‐S 60mm/f2.8). Posterior segment imaging was performed with the aid of an indirect ophthalmic lens mounted in front of the macro lens. Coaxial illumination during viewing was provided by a single white light‐emitting diode (LED) within the adaptor, while illumination during exposure was provided by the pop‐up flash or an accessory flash. Corneal and/or lens reflections were eliminated using a pair of linear polarizers, having their azimuths at right angles to one another. Results This dSLR camera adaptor provides quality high‐resolution, reflection‐free, images of both the anterior and posterior segments. It was easy to transport, assemble, and handle. The necessary adjustments, positioning, and focusing required for quality images were easily performed. Conclusion The described dSLR camera adaptor provides an alternative to existing imaging systems. High‐resolution image acquisition occurred at a fraction of the cost of established imaging system, particularly those devoted to the posterior segment.     

基于地表数码照相的林地总体植被覆盖度计算模型

Vegetation fractional coverage (VFC) is one of the key indicators of vegetation distribution. In the work a measurement-based model was developed to derive total forest VFC (TG) as well as the VFC of trees (T) and shrub-grasses (G) separately in a subtropical forest area in Nanjing, China. Both upward and downward photographs were taken with a digital camera in 72 quadrats (10 m × 10 m each). Fifteen models were established and validated. Models jointly using both T and G performed better than those using the T and G separately. The best model, TG = T + G- 1.134 × T × G- 0.025 (R2 = 0.9115, P 0.01, root mean squared error = 0.0789), is recommended for application. This model provides a good way to obtain total forest VFC values through taking tree and shrub-grass photos on ground below tree canopy rather than above tree canopy.