There is a need for an automated road status classification system considering the vast number of weather-related accidents that occur every winter. Previous research has shown that it is possible to detect hazardous road conditions, including, for example, icy pavements, using single point infrared illumination and infrared detectors. In this paper, we extend this research into camera surveillance of a road section allowing for classification of area segments of weather-related road surface conditions such as wet, snow covered, or icy. Infrared images have been obtained using an infrared camera equipped with a set of optical wavelength filters. The images have primarily been used to develop multivariate data models and also for the classification of road conditions in each pixel. This system is a vast improvement on existing single spot road status classification systems. The resulting imaging system can reliably distinguish between dry, wet, icy, or snow covered sections on road surfaces.

In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.

Speckle noise, dynamic range of light intensity, and spurious reflections are major challenges when laser scanners are used for 3D surface acquisition. In this work, a series of image processing operations, that is, Spatial Compound Imaging, High Dynamic Range Extension, Gray Level Transformation, and Most Similar Nearest Neighbor are proposed to overcome the challenges coming from the target surface. A prototype scanner for metallic surfaces is designed to explore combinations of these image processing operations. The main goal is to find the combination of operations that will lead to the highest possible robustness and measurement precision at the lowest possible computational load. Inspection of metallic tools where the surface of its edge must be measured at micrometer precision is our test case. Precision of heights measured without using the proposed image processing is firstly analyzed to be ±7.6 μm at 68% confidence level. The best achieved height precision was ±4.2 μm. This improvement comes at 24 times longer processing time and five times longer scanning time. Dynamic range extension of the image capture improves robustness since the numbers of saturated or underexposed pixels are substantially reduced. Using a high dynamic range (HDR) camera offers a compromise between processing time, robustness, and precision.

When there is a demand larger than the corresponding number of components in stock, obsolescence problems may occur for those systems with a life cycle longer than that of one or more of their components, such as automotive, avionics, military application, etc. This paper discusses the electronic component obsolescence problem and presents a formal mathematical strategic proactive obsolescence management model for long life-cycle systems. The model presented in this paper utilizes redesign and last-time-buy (LTB) as two management methods. The LTB cost is estimated by unit cost, demand quantities, buffer, discount rate, and holding cost. Redesign cost is associated with component type and quantities. This model can estimate the minimum management costs for a system with different architectures. It consists of two parts. The first is to generate a graph, which is in the form of an obsolescence management diagram. A segments table containing the data of this diagram is calculated and prepared for optimization at a second step. The second part is to find the minimum cost for system obsolescence management. Mixed integer linear programming is used to calculate the minimum management cost and schedule. The model is open sourced allowing other research groups to freely download and modify it. A display and control system case study is shown to apply this model practically. A reactive manner is presented as a comparison. The result of the strategic proactive management model shows significant cost avoidance compared to the reactive manner.

This paper shows that the liquid water content (LWC) and the median volume diameter (MVD) can be derived from the images of water droplets using a shadowgraph imaging system with incoherent LED illumination. Icing on structures, such as a wind turbine, is the result of a combination of LWC and MVD, and other parameters, such as temperature, humidity, and wind speed. Today, LWC and MVD are not commonly measured for wind turbines. Systems for measuring these properties are often expensive or impractical in terms of location or remote reading. The aim of this paper is to gain knowledge about how to design a single instrument based on imaging that has the ability to measure these properties with enough precision and accuracy to detect icing conditions for wind turbines. A method to calculate both the LWC and the MVD from the same images is described in this paper. The size of one droplet is determined by measuring the shadow created by the droplet in background illumination. The concentration is calculated by counting the measured droplets and estimating the volumes in which these droplets can be observed. In this paper, the observation volume is shown to be dependent on the particle size and the signal-to-noise ratio for each measured particle. An expected coefficient of variation of the LWC depending on the droplet size is shown to be 2.4% for droplets 10 μm in diameter and 1.6% for 25-μm droplets. This is based on an error estimation of the laboratory measurements calibrated using a micrometer dot scale.

In this paper we have explored different possibilities for partitioning the tasks between hardware, software and locality for the implementation of the vision sensor node, used in wireless vision sensor network. Wireless vision sensor network is an emerging field which combines image sensor, on board computation and communication links. Compared to the traditional wireless sensor networks which operate on one dimensional data, wireless vision sensor networks operate on two dimensional data which requires higher processing power and communication bandwidth. The research focus within the field of wireless vision sensor networks have been on two different assumptions involving either sending raw data to the central base station without local processing or conducting all processing locally at the sensor node and transmitting only the final results. Our research work focus on determining an optimal point of hardware/software partitioning as well as partitioning between local and central processing, based on minimum energy consumption for vision processing operation. The lifetime of the vision sensor node is predicted by evaluating the energy requirement of the embedded platform with a combination of FPGA and micro controller for the implementation of the vision sensor node. Our results show that sending compressed images after pixel based tasks will result in a longer battery life time with reasonable hardware cost for the vision sensor node.