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Busy-waiting itself can be made much less wasteful by using a delay function (e.g., sleep()) found in most operating systems. This puts a thread to sleep for a specified time, during which the thread will waste no CPU time. If the loop is checking something simple then it will spend most of its time asleep and will waste very little CPU time.
Time delay neural network (TDNN) [1] is a multilayer artificial neural network architecture whose purpose is to 1) classify patterns with shift-invariance, and 2) model context at each layer of the network. Shift-invariant classification means that the classifier does not require explicit segmentation prior to classification.
In data communications, the bandwidth-delay product is the product of a data link's capacity (in bits per second) and its round-trip delay time (in seconds). [1] The result, an amount of data measured in bits (or bytes), is equivalent to the maximum amount of data on the network circuit at any given time, i.e., data that has been transmitted but not yet acknowledged.
Methods that make use of await must be declared with the async keyword. In methods that have a return value of type Task<T>, methods declared with async must have a return statement of type assignable to T instead of Task<T>; the compiler wraps the value in the Task<T> generic.
This excitation is output and simultaneously fed back into a delay line L samples long. The output of the delay line is fed through a filter. The gain of the filter must be less than 1 at all frequencies, to maintain a stable positive feedback loop. The filter can be a first-order lowpass filter (as pictured).
A traditional snickerdoodle recipe includes unsalted butter, granulated sugar, eggs, all-purpose flour, cream of tartar, baking soda and salt.
H. Buchner, J. Benesty, W. Kellermann, "An Extended Multidelay Filter: Fast Low-Delay Algorithms for Very High-Order Adaptive Systems". Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003. A free implementation of the MDF algorithm is available in Speex (main source file)
A maximum length sequence (MLS) is a type of pseudorandom binary sequence.. They are bit sequences generated using maximal linear-feedback shift registers and are so called because they are periodic and reproduce every binary sequence (except the zero vector) that can be represented by the shift registers (i.e., for length-m registers they produce a sequence of length 2 m − 1).