Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.

Improving Pothole Recognition through Vision Tracking for Automated Pavement Assessment

Pavement condition assessment is essential when developing road network maintenance programs. In practice, pavement sensing is to a large extent automated when regarding highway networks. Municipal roads, however, are predominantly surveyed manually due to the limited amount of expensive inspection vehicles. As part of a research project that proposes an omnipresent passenger vehicle network for comprehensive and cheap condition surveying of municipal road networks this paper deals with pothole recognition. Existing methods either rely on expensive and high-maintenance range sensors, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In our previous work we created a pothole detection method for pavement images. In this paper we present an improved recognition method for pavement videos that incrementally updates the texture signature for intact pavement regions and uses vision tracking to track detected potholes. The method is tested and results demonstrate its reasonable efficiency.

Narrow stripe selective growth of oxide-free InGaAlAs/InGaAlAs MQWs by ultra-low pressure MOVPE

Narrow stripe selective growth of oxide-free InGaAlAs/InGaAlAs multiple quantum wells (MQWs) has been successfully performed on patterned InP substrates by ultra-low pressure MOVPE. Flat and clear interfaces were obtained for the narrow stripe selectively grown MQWs under optimized growth conditions. These selectively grown MQWs were covered by specific InP layers, which can keep the MQWs from being oxidized during the fabrication of the devices. The characteristics of selectively grown MQWs were strongly dependent on the mask stripe width. In particular, a PL peak wavelength shift of 73 nm, a PL intensity of more than 57% and a PL FWHM of less than 102 meV were observed simultaneously with a small mask stripe width varying from 0 to 40 mu m. The results were explained by considering the migration effect from the masked region (MMR) and the lateral vapour diffusion effect (LVD).

Narrow stripe selective growth of InGaAlAs waveguides used for buried heterostructure lasers - art. no. 63210I

The narrow stripe selective growth of the InGaAlAs bulk waveguides and InGaAlAs MQW waveguides was first investigated. Flat and clear interfaces were obtained for the selectively grown InGaAlAs waveguides under optimized growth conditions. These selectively grown InGaAlAs waveguides were covered by specific InP layers, which can keep the waveguides from being oxidized during the fabrication of devices. PL peak wavelength shifts of 70 nm for the InGaAlAs bulk waveguides and 73 nm for the InGaAlAs MQW waveguides were obtained with a small mask stripe width varying from 0 to 40 gm, and were interpreted in considering both the migration effect from the masked region (MMR) and the lateral vapor diffusion effect (LVD). The quality of the selectively grown InGaAlAs MQW waveguides was confirmed by the PL peak intensity and the PL FWHM. Using the narrow stripe selectively grown InGaAlAs MQW waveguides, then the buried heterostructure (BH) lasers were fabricated by a developed unselective regrowth method, instead of conventional selective regrowth. The InGaAlAs MQW BH lasers exhibit good performance characteristics, with a high internal differential quantum efficiency of about 85% and an internal loss of 6.7 cm(-1).

五种红树植物光合产量模型及对环境因子的响应

以木榄（Bruguiera gymnorriza）、白骨壤（Avicennia marina）、桐花树（Aegiceras corniculata）、秋茄（Kandelia candel）和海漆（Excoecaria agallocha）为对象，以光合作用对环境因子的响应为主线，建立了从叶片水平到群体冠层水平上的光合产量模型，探讨了从器官、个体到群体的光合产量对环境因子响应的定量关系。 将Farquhar提出的单叶片光合作用生理生化模型与气孔导度B-B模型相结合，建立了光合作用－气孔导度耦合模型。模型模拟结果与实际测量结果具有较好的一致性。在温度为25.0℃，光合有效辐射为1000μmol•m-2s-1 的条件下，当外部CO2浓度倍增到720μmol•mol-1时，白骨壤、木榄、桐花树、秋茄、海漆的光合速率分别提高22.56%，17.13%，18.43%，18.63%和18.41%。在大气CO2浓度和光合有效辐射通量密度不变的条件下，光合作用速率对温度的响应呈单峰型曲线，即有一个最适温度，5种红树植物的最适温度值均为26.5℃左右。大气CO2浓度和温度固定不变（分别为350μmol•mol-1和25.0 ℃）时，光合作用对光合有效辐射的响应符合Michaelis-Menten反应曲线，模型在PAR<1800μmol•m-2s-1时模拟精度较高（P<0.01）。 在典型晴天条件下，5种红树植物的光合速率日变化都出现两个极大值（分别在11时和15时左右），中午前后光合速率较低，模型模拟光合速率日变化与实测数值日变化趋势一致。本模型能较好地模拟5种红树植物光合产量以及对环境因子的响应，模拟预测精度较高（P<0.01）。 以Ross和Nilson叶倾角分布模型为基础，分别建立了直接辐射和散射辐射在冠层内传输的子模型。冠层内的消光系数均有明显的日变化，且上午8时之前和下午16时之后随时间变化较大。在典型晴天条件下，单位土地面积日合成干物质总量（折合为CH2O）白骨壤为15.840g•m-2d-1，对于木榄、桐花树、秋茄、海漆其相应的值分别为 22.254 g•m-2d-1， 23.610 g•m-2d-1，24.525 g•m-2d-1和25.996 g•m-2d-1 。

Synthesis of NaA zeolite membranes from clear solution

NaA zeolite membranes were successfully synthesized on a porous alpha -Al2O3 support from clear solution. The synthesis parameters, such as surface seeding, synthesis time, synthesis stages, etc. were investigated. Surface seeding can not only accelerate the formation of NaA zeolite on the support surface, but can also inhibit the transformation of NaA zeolite into other types of zeolites. A continuous NaA zeolite membrane formed on the seeded support after 2 h of synthesis. Gas permeation results showed that a synthesis time of 3 h produced the best NaA zeolite membrane. When the synthesis time was longer than 4 h, the NaA zeolite on the support surface began to transform into other types of zeolites, and the quality of the NaA zeolite membrane decreased. The quality of the NaA zeolite membrane can be improved by employing the multi-stage synthesis method. The NaA zeolite membrane with a synthesis time of 2 h after a two-stage synthesis showed the best gas permeation performance. The permeances of H-2, O-2, N-2, and n-C4H10 decreased as the molecular kinetic diameter of the gases increased. which showed the molecular sieving effect of the NaA zeolite membrane. The permselectivities of H-2/n-C4H10 and O-2/N-2 were 19.1 and 1.8, respectively. These values are higher than the Knudsen diffusion ratios of 5.39 and 0.94. However, the permeation of n-C4H10 also indicated that the NaA zeolite membrane had certain defects with diameters larger than the pore size of NaA zeolite. A synthesis model was proposed to clarify the effect of surface seeding. (C) 2001 Elsevier Science B.V. All rights reserved.