3D Object Recognition and Localization with a Dense LiDAR Scanner

Dense scanning is an effective solution for refined geometrical modeling applications. The previous studies in dense environment modeling mostly focused on data acquisition techniques without emphasizing autonomous target recognition and accurate 3D localization. Therefore, they lacked the capability to output semantic information in the scenes. This article aims to make complementation in this aspect. The critical problems we solved are mainly in two aspects: (1) system calibration to ensure detail-fidelity for the 3D objects with fine structures, (2) fast outlier exclusion to improve 3D boxing accuracy. A lightweight fuzzy neural network is proposed to remove most background outliers, which was proven in experiments to be effective for various objects in different situations. With precise and clean data ensured by the two abovementioned techniques, our system can extract target objects from the original point clouds, and more importantly, accurately estimate their center locations and orientations.

Enterprise environment modeling for penetration testing on the OpenStack virtualization platform

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing the work environment simplified the organization’s security testing, increased resource efficiency, and reduced the total cost of ownership of certain devices.

Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM

<p>Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX- UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF- SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.</p>

Cryo-TEM simulations of amorphous radiation-sensitive samples using multislice wave propagation

Image simulation plays a central role in the development and practice of high-resolution electron microscopy, including transmission electron microscopy of frozen-hydrated specimens (cryo-EM). Simulating images with contrast that matches the contrast observed in experimental images remains challenging, especially for amorphous samples. Current state-of-the-art simulators apply post hoc scaling to approximate empirical solvent contrast, attenuated image intensity due to specimen thickness and amplitude contrast. This practice fails for images that require spatially variable scaling, e.g. simulations of a crowded or cellular environment. Modeling both the signal and the noise accurately is necessary to simulate images of biological specimens with contrast that is correct on an absolute scale. The `frozen plasmon' method is introduced to explicitly model spatially variable inelastic scattering processes in cryo-EM specimens. This approach produces amplitude contrast that depends on the atomic composition of the specimen, reproduces the total inelastic mean free path as observed experimentally and allows for the incorporation of radiation damage in the simulation. These improvements are quantified using the matched filter concept to compare simulation and experiment. The frozen plasmon method, in combination with a new mathematical formulation for accurately sampling the tabulated atomic scattering potentials onto a Cartesian grid, is implemented in the open-source software package cisTEM.