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Throughput is controlled by available bandwidth, as well as the available signal-to-noise ratio and hardware limitations. Throughput for the purpose of this article will be understood to be measured from the arrival of the first bit of data at the receiver, to decouple the concept of throughput from the concept of latency.
The channel efficiency, also known as bandwidth utilization efficiency, is the percentage of the net bit rate (in bit/s) of a digital communication channel that goes to the actually achieved throughput. For example, if the throughput is 70 Mbit/s in a 100 Mbit/s Ethernet connection, the channel efficiency is 70%. In this example, effectively 70 ...
An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis [15]. Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances. [16]
People are often concerned about measuring the maximum data throughput in bits per second of a communications link or network access. A typical method of performing a measurement is to transfer a 'large' file from one system to another system and measure the time required to complete the transfer or copy of the file.
Network neuroscience is an approach to understanding the structure and function of the human brain through an approach of network science, through the paradigm of graph theory. [1] A network is a connection of many brain regions that interact with each other to give rise to a particular function. [2]
The network throughput of a connection with flow control, for example a TCP connection, with a certain window size (buffer size), can be expressed as: Network throughput ≈ Window size / roundtrip time. In case of only one physical link between the sending and transmitting nodes, this corresponds to:
Internet bottlenecks provide artificial and natural network choke points to inhibit certain sets of users from overloading the entire network by consuming too much bandwidth. Theoretically, this will lead users and content producers through alternative paths to accomplish their goals while limiting the network load at any one time.
In social network analysis and related network science fields, the contagion metaphor has been described as potentially misleading in various ways. For example, an actual virus can affect someone after a single exposure, whereas typically with social contagion, people need several exposures before adopting the new behavior or emotion. [28]