Fernand Meyer

By 2020, more than 50 billion devices will be connected through radio communications. In conjunction with the rapid growth of the Internet of Things (IoT) market, low power wide area networks (LPWAN) have become a popular low-rate long-range radio communication technology. Sigfox, LoRa, and NB-IoT are the three leading LPWAN technologies that compete for large-scale IoT deployment. This paper provides a comprehensive and comparative study of these technologies, which serve as efficient solutions to connect smart, autonomous, and heterogeneous devices. We show that Sigfox and LoRa are advantageous in terms of battery lifetime, capacity, and cost. Meanwhile, NB-IoT offers benefits in terms of latency and quality of service. In addition, we analyze the IoT success factors of these LPWAN technologies, and we consider application scenarios and explain which technology is the best fit for each of these scenarios.

This chapter presents the principles of morphological segmentation. Segmentation is one of the key problems in image processing. In fact, one should say segmentations because there exist as many techniques as there are specific situations. An original method of segmentation based on the use of watershed lines has been developed in the framework of mathematical morphology. The chapter describes some useful morphological tools for segmentation: gradient, top-hat transform, distance function, geodesic distance function, and geodesic reconstructions. The gradient image is used in the watershed transformation, because the main criterion for the segmentation in many applications is the homogeneity of the gray values of the objects present in the image. The problems encountered in the segmentation process will be best illustrated by presenting a complete and typical segmentation problem in the field of automated cytology. The oversegmentation produced by direct construction of the watershed line is due to the fact that every regional minimum becomes the center of a catchment basin.