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Video Production and Post-Production
Published in Lionel Felix, Damien Stolarz, Jennifer Jurick, Hands-On Guide to Video Blogging and Podcasting, 2013
Lionel Felix, Damien Stolarz, Jennifer Jurick
Capturing video on a Windows-based computer comes with a little requirement that if not understood and taken care of, will leave you scratching your head, frustrated. Windows file systems have evolved over the years. Only a few years ago, a 4 GB disk drive was considered quite large. The older file systems such as FAT16 were designed in such a way that they could not recognize a partition larger than 4 GB. FAT16 could see a 4 GB file but the volume size was limited to 4 GB which didn’t help matters much. FAT32 was a large improvement with partition sizes up to 32 GB but still maintained a 4 GB maximum file size. NTFS on the other hand can handle a volume size of 256 terabytes, a substantial improvement. NTFS can handle a maximum file size of 16 terabytes. A terabyte is 1,000 gigabytes. Trying to edit 16 GB video files in not recommended.
An architecture for synchronising cloud file storage and organisation repositories
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Gil Andriani, Eduardo Godoy, Guilherme Koslovski, Rafael Obelheiro, Mauricio Pillon
Following previous work [49], the input and output operations were performed with different file sizes (1 MB, 50 MB, 100 MB and 200 MB), generated and submitted by the postmark tool [50]. For each experiment, 100 rounds were performed (10 files are manipulated per round), and the graphs show the median, maximum, minimum, first and third quartiles of total execution time. The tests were run with five storage scenarios (composing the main scenario depicted in Figure 1):Local storage: Read and write operations were submitted to the same storage device in Bob’s desktop (with NTFS) that the operating system is located on. This scenario is used as the baseline for performance analysis.Wired LAN: Operations were carried out on a remote repository with CIFS file system (over NTFS), on a Gigabit Ethernet LAN, termed organisation repository in Figure 1.Wifi LAN: Operations were performed on a organisation repository (CIFS over NTFS) accessed through an access point, configured in infrastructure mode with maximum bandwidth of 144.4 Mbps. For this scenario, LAN2 is controlled by the Wifi access point.USB storage: Read and write calls were performed on a flash drive connected to Bob’s desktop by a USB 2.0 interface (FAT32).Dokany filesystem [51]: Dokany is a C++ library for implementing FUSE file systems on Windows. Since our prototypes are built over Dokany, this scenario provides a baseline for their performance.
Removing feasibility conditions on practical preassigned finite-time tracking for state-constrained high-order nonlinear systems
Published in International Journal of Control, 2022
Ruiming Xie, You Wu, Xue-Jun Xie
To our knowledge, this problem has not been solved until now. Main contributions and difficulties of this paper are as follows: For more general state-constrained high-order nonlinear system (1), practical preassigned finite-time tracking control is investigated for the first time, and undesirable feasibility conditions in traditional BLF-based control methods are completely eliminated. Since the settling time can be preassigned, our proposed control ensures the faster tracking convergence rate than the newest work Wu and Xie (2019c) (see a comparative study in Example 4.1).Although feasibility conditions can be circumvented in some original results (Zhao & Song, 2019, 2020; Zhao et al., 2019), due to inherent nonlinearities caused by , the controllers in these papers can't be applied to high-order nonlinear system (1), and more complex nonlinear terms will be inevitably produced in control design, how to handle these terms is a tough work. To overcome these difficulties, a novel non-BLF-based control design and analysis method is given. First, nonlinear transformed functions (NTFs) are employed to convert the constrained system into an unconstrained one, and then full-state constraints aren't violated by ensuring the boundedness of transformed states in the closed-loop system. Next, for the transformed system, by introducing a key coordinate transformation and the preassigned finite-time stability theory, an adaptive fuzzy state-feedback controller is designed to ensure the properties of the closed-loop system.The approximation capacity holds when the states of fuzzy systems stay within a compact set. For the closed-loop system, how to construct this specific compact set and guarantee states remaining it are two fundamental and challenging problems in approximation-based control. Such two issues are thoroughly solved in Remark 3.1 and Theorem 3.1.