A CMOS spectrum analyzer frontend for cognitive radio achieving +25dBm IIP3 and −169 dBm/Hz DANL

A dual RF-receiver preceded by discrete-step attenuators is implemented in 65nm CMOS and operates from 0.3– 1.0 GHz. The noise of the receivers is reduced by cross-correlating the two receiver outputs in the digital baseband, allowing attenuation of the RF input signal to increase linearity. With this technique a displayed average noise level below -169 dBm/Hz is obtained with +25 dBm IIP3, giving a spurious-free dynamic range of 89 dB in 1 MHz resolution bandwidth

Semantic Interpretation of Mobile Laser Scanner Point Clouds in Indoor Scenes Using Trajectories

The data acquisition with Indoor Mobile Laser Scanners (IMLS) is quick, low-cost and accurate for indoor 3D modeling. Besides a point cloud, an IMLS also provides the trajectory of the mobile scanner. We analyze this trajectory jointly with the point cloud to support the labeling of noisy, highly reflected and cluttered points in indoor scenes. An adjacency-graph-based method is presented for detecting and labeling of permanent structures, such as walls, floors, ceilings, and stairs. Through occlusion reasoning and the use of the trajectory as a set of scanner positions, gaps are discriminated from real openings in the data. Furthermore, a voxel-based method is applied for labeling of navigable space and separating them from obstacles. The results show that 80% of the doors and 85% of the rooms are correctly detected, and most of the walls and openings are reconstructed. The experimental outcomes indicate that the trajectory of MLS systems plays an essential role in the understanding of indoor scene

Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases