Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned\nAircraft System) have penetrated into civil and general-purpose applications, due to advances\nin battery technology, control components, avionics and rapidly falling prices. This paper\ndescribes the conceptual design and the validation campaigns performed for an embedded\nprecision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which\nuses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit\n(10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560\nmicrocontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial\nsmall quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and\ndistance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground\nstation. The main design issues are presented, such as the necessary corrections of the IMU data\n(i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data\nfusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state\nestimation algorithm, developed for the PODA platform and implemented on the Arduino, has been\ntested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic\nlanding and obstacle detection. Experimental results and simulations of various missions show the\neffectiveness of the approach....
In this study, a nonlocal elastic rod model is applied to analytically evaluate the bond behavior between fiber-reinforced polymer\n(FRP) bars and engineered cementitious composites (ECCs). The second-order differential equation, which is based on nonlocal\nelasticity theory, governs the bond behavior of the FRP bars along the bond length. The classical elasticity model is a special case of\nthe nonlocal model. The solution of the second-order differential equation can be obtained by substituting three-stage linear bond\nstress-slip relationship of the FRP bars. The slip values (solution of the second-order differential equation) within the bond length\ncalculated by the nonlocal continuum rod model are affected by the nonlocal parameter e0a. The results from a case study show\nthat the maximum pullout force decreases when the nonlocal size effect is considered, thereby providing a closer approximation of\nthe experimental data than the existing local model....
The use of an appropriate sensor on an unmanned aerial vehicle (UAV) is vital to assess specific environmental conditions\nsuccessfully. In addition, technicians and scientists also appreciate a platform to carry the sensors with some advantages such as\nthe low costs or easy pilot management. However, extra requirements like a low-altitude flight are necessary for special\napplications such as plant density or rice yield. A rotary UAV matches this requirement, but the flight endurance is too short\nfor large areas. Therefore, in this article, a fixed-wing UAV is used, which is more appropriate because of its longer flight\nendurance. It is necessary to develop an own controller system to use special sensors such as Lidar or Radar on the platform as a\nmultifunctionality system. Thereby, these sensors are used to generate a digital elevation model and also as a collision avoidance\nsensor at the same time. To achieve this goal, a small UAV was equipped with a hardware platform including a microcontroller\nand sensors. After testing the system and simulation, the controller was converted into program code to implement it on the\nmicrocontroller. After that, several real flights were performed to validate the controller and sensors. We demonstrated that the\nsystem is able to work and match the high requirements for future research....
The development of computation technology and artificial intelligence (AI) field brings\nabout AI to be applied to various system. In addition, the research on hardware-based AI processors\nleads to the minimization of AI devices. By adapting the AI device to the edge of internet of things\n(IoT), the system can perform AI operation promptly on the edge and reduce the workload of the\nsystem core. As the edge is influenced by the characteristics of the embedded system, implementing\nhardware which operates with low power in restricted resources on a processor is necessary. In this\npaper, we propose the intellino, a processor for embedded artificial intelligence. Intellino ensures\nlow power operation based on optimized AI algorithms and reduces the workload of the system\ncore through the hardware implementation of a neural network. In addition, intellinoâ??s dedicated\nprotocol helps the embedded system to enhance the performance. We measure intellino performance,\nachieving over 95% accuracy, and verify our proposal with an field programmable gate array\n(FPGA) prototyping....
The paper presents an original methodology for the implementation of the Logarithmic Number System (LNS) arithmetic, which uses Reduced Instruction Set Computing (RISC). The core of the proposed method is a newly developed algorithm for conversion between LNS and the floating point (FLP) representations named â??looping in sectorsâ?, which brings about reduced memory consumption without a loss of accuracy. The resulting effective RISC conversions use only elementary computer operations without the need to employ multiplication, division, or other functions. Verification of the new concept and related developed algorithms for conversion between the LNS and the FLP representations was realized on Field Programmable Gate Arrays (FPGA), and the conversion accuracy was evaluated via simulation. Using the proposed method, a maximum relative conversion error of less than ±0.001% was achieved with a 22-ns delay and a total of 50 slices of FPGA consumed including memory cells. Promising applications of the proposed method are in embedded systems that are expanding into increasingly demanding applications, such as camera systems, lidars and 2D/3D image processing, neural networks, car control units, autonomous control systems that require more computing power, etc. In embedded systems for real-time control, the developed conversion algorithm can appear in two forms: as RISC conversions or as a simple RISC-based logarithmic addition....
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