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It is important to enable deployment of time-sensitive functionalities on software, such as advocated by SDR and CR, i.e. some physical and link layer functionalities (e.g., signal processing, frame delineation, timeout control, and retransmission) require real-time support.
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In addition, elasticity imaging techniques use signal processing methods on consecutive ultrasound frames to detect tissue motion and strain, providing biomechanical as well as morphological information from ultrasound data [ 25].
Real time signal processing and storage requirements for entire frames, of rapidly acquired pixels impose severe demands on the space available for the electronics in a standard monolithic approach.
This section presents our contribution towards modifying critical blocks in the signal processing chain, namely the synchronization block [including frame synchronization, i.e., coarse time synchronization and coarse frequency offset (CFO) compensation], and the channel estimation block.
Due to the redundancy of frames, the frame has become an essential tool in signal processing such as wireless communication [5, 6], image processing [7], coding theory [8], and sampling theory [9].
It is only inferior compared to the method by Dong et al., yet with significant performance, under rotation, scaling, general affine transform and signal processing attacks, such as JPEG compression, H.264 intra-frame compression, lowpass filtering and noise addition.
In the current system, real time frame rate was limited by the signal processing, for instance FFT calculation.
Frames play a fundamental role in signal processing, image and data compression and sampling theory.
The remaining frames of signal are compiled for signal processing.
The overlapping SGP and single scaling SGP methods involve a dot product computation to calculate the clipped projections for each sample combined with additional signal processing to find the optimal scaling factor per symbol and per frame, respectively.
This procedure excluded the QD solution from the surrounding areas and allowed background signal to be collected in the same frame as the QD signal and separated using signal processing in Matlab (Fig. 2(D)).
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