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A Cost-Effective Framework for the Optimal Placement of Drones in Smart Cities
Published in Fadi Al-Turjman, Drones in IoT-enabled Spaces, 2019
Fadi Al-Turjman, Reda Daboul, Semail Ulgen, Hadi Zahmatkesh
The script was executed on a device with Windows 8 Operating System (OS). The device has the following specifications: Intel(R) Atom(TM) CPU Z2760 @ 1.80 GHz, 1,800 MHz, 2 Core(s), 4 Logical Processor(s). The usage of RAM was low, and the computation time was from 4 to 6 s. There are a couple of assumptions that were made during the simulation phase. First, the battery capacity is not subjected to optimization, that is, the optimum value for battery capacity corresponding to the minimum number of drones are determined through trial and error. Second, each drone covers an area that is a square of 1 km2. Finally, the communication range between drones is considered as circular disks for simplicity purposes.
Real-time localization method for autonomous vehicle using 3D-LIDAR
Published in Maksym Spiryagin, Timothy Gordon, Colin Cole, Tim McSweeney, The Dynamics of Vehicles on Roads and Tracks, 2018
Yihuan Zhang, Liang Wang, Jun Wang, John M. Dolan
After the comparison of four localization methods, the proposed algorithm is implemented with C/C++ under Windows-8 Operating System. The online experiment is tested on our autonomous vehicle platform. The controller is the ADLINK Industrial Personal Computer (IPC) with 16GB of RAM and Intel Core i7–3610QE CPU clocked at 2.3GHz. The online experiment is carried out on our campus lasting 3.1 km with average speed 26.9 km/h. The real-time operating window is shown in Fig. 5.
Introduction to the IoT
Published in Anita Gehlot, Rajesh Singh, Praveen Kumar Malik, Lovi Raj Gupta, Bhupendra Singh, Internet of Things with 8051 and ESP8266, 2020
Anita Gehlot, Rajesh Singh, Praveen Kumar Malik, Lovi Raj Gupta, Bhupendra Singh
Compared to Classic Bluetooth, BLE is intended to provide considerably reduced power consumption and cost while maintaining a similar communication range. Mobile operating systems including iOS, Android, Windows Phone, and BlackBerry, as well as macOS, Linux, Windows 8, and Windows 10, natively support BLE.
A rollout algorithm for the resource constrained elementary shortest path problem
Published in Optimization Methods and Software, 2019
Francesca Guerriero, Luigi Di Puglia Pugliese, Giusy Macrina
Starting from a naive version of the rollout algorithm, named (), where the base-heuristic is composed of only the construction heuristic , we defined several versions by combining different local search-based heuristic. We compared the proposed solution approaches with the , and two optimal solution strategies, i.e. the general state space augmenting algorithm (, for short) [9], and the branch-and-cut (, for short) proposed in [29]. We coded the proposed solution approaches, the and the in Java language. We carried out the computational experiments by using an Intel PC CPU 1.80GHz, 4,00 GB RAM, under Microsoft Windows 8 operating system.
NomadicBTS: Evolving cellular communication networks with software-defined radio architecture and open-source technologies
Published in Cogent Engineering, 2018
Emmanuel Adetiba, Victor O. Matthews, Samuel N. John, Segun I. Popoola, Abdultaofeek Abayomi
The technical specifications of the device as related to this study are presented in Table 1. The device can stream up to 56 MHz of instantaneous data bandwidth over a high-speed USB 3.0 bus operating at 4.8 Gbps full duplex to the host PC. The RF/IF signal processing tasks on the device are carried out by the AD9364 Radio Frequency Integrated Circuit (RFIC), which is a direct conversion transceiver along with the Spartan 6 FGPA. The host PC shown in Figure 2 runs the SDR software back-end of the NomadicBTS architecture. It contains an Intel Core i5-3210M, 8.00 GB Random Access Memory (RAM), and operates at Central Processing Unit (CPU) speed of 2.50 GHz. This PC contains both the Microsoft Windows 8 and the open-source Ubuntu 16.04 LTS OSs. The USRP Hardware Driver (UHD) running on the Windows 8 OS was used in testing the adequacy of the USRP B200 device for this study while the Linux OS serves as the development and deployment platform.
Robotics in order picking: evaluating warehouse layouts for pick, place, and transport vehicle routing systems
Published in International Journal of Production Research, 2019
A series of numerical studies was conducted to (1) assess the impact of warehouse layout configurations on the performance of PPT-VRP systems, and (2) quantify the relative impacts of adding picker or transporter robots, increasing picker speeds, or increasing transporter capacities. All computational work was conducted on a PC with an Intel i5-2410m processor and 12 GB RAM running Microsoft Windows 8 in 64-bit mode. The PPT-VRP models were solved by Gurobi 6.0.3 (Gurobi Optimization 2016) via Python version 2.7.5.