The ever increasing demand for fast mobile internet connectivity continues to set challenges for research in radio communications. On one hand the capacity demand can be served by offloading data traffic to local networks; on the other hand using more bandwidth, and possibly dynamically allocating spectrum in a flexible way, will improve the usage of the available spectrum. The future of wireless access continues to be defined by the 3GPP and IEEE standards setting bodies. Radios can also provide innovative features that offer new functionalities for consumers, such as ultra fast local connectivity, sensing and positioning. This talk will present examples of various radio innovations and the challenges related to commercializing them.

This paper demonstrates the use of a simple but effective color-based inspection method in parquet slab grading. The approach is to divide the image to small rectangular regions and calculate color percentile features from these areas. Classification is performed in two stages: defect detection and recognition. The recognition results are further used in determining the final grade for the parquet slabs. Comparative results are also presented.

The spectral stability of illumination is an important aspect in color machine vision. The physical properties of lamps may change the spectrum of the illumination with time. In this work we examine the robustness of different color spaces against spectrally varying illumination in industrial wood defect inspection, when the inspection system is not adapted to the variations of illumination. The spectral changes are accomplished by digitally transforming the color temperature of illumination of wood images. We conclude that the original RGB color space is the best color space under varying illumination, the XYZ and CIELuv spaces being also very promising. An interesting observation is that features from some of the color spaces perform worse than features obtained from grey-level images. However, the importance of color information is emphasized, since RGB gives better results than grey level even under strong spectral variation of illumination.

Automated grading of lumber is attractive for the sawmill industry. Achieving better accuracy of quality grading and control of quality variation in volume production easily improves the profit obtained from it. We describe a color vision based framework to grading softwood lumber. The proposed inspection principle is to recognize the sound wood regions early, as this reduces the computational requirements at later defect recognition stages. The recognition stages are ordered based on their estimated costs and implementational complexity. This is important because color increases the data volumes significantly over grey scale images. Finally, the description of the board and its defects is passed to the quality grader which assigns the class giving the best price for each board. In comparative tests, the computational solutions have turned out to be simpler than with grey level images, compensating for the higher cost of the color imaging system. The effects of changes in the spectrum of illumination have also been evaluated to identify robust color features and to produce the requirements for color calibration.

An experimental comparison of shape classification methods based on autoregressive modeling and Fourier descriptors of closed contours is carried out. The performance is evaluated using two independent sets of data: images of letters and airplanes. Silhouette contours are extracted from non-occluded 2D objects rotated, scaled, and translated in 3D space. Several versions of both types of methods are implemented and tested systematically. The comparison clearly shows better performance of Fourier-based methods, especially for images containing noise.>

In this paper we describe the outlines of a color vision solution for grading softwood lumber The proposed inspection principle is to recognize the sound wood regions early as this reduces the computational requirements at later stages. This is important because color increases the data volumes significantly over grey scale images. The computational solutions have turned out to be simpler than with grey scale data, compensating for the cost of the more sophisticated imaging system.