Sentence examples for quantization data from inspiring English sources

During the last three decades, the theory of frames, which generalize the notion of bases by allowing redundancy yet still providing a reconstruction formula, has been growing rapidly, since several new applications such as nonlinear sparse approximation (e.g., image compression), coarse quantization, data transmission with erasures, and wireless communication, have been developed [1 7].

The processing of image quantization data was performed by removing (1) all "empty" and "control" spots (696 spots) and all spots flagged by the GenePix software package or manually as "not found" or "bad" and (2) all spots with a signal to noise ratio (reported by GenePix) below 3.0 in both channels before a global lowess normalization [ 56] was applied to all remaining probes.

Figure 4 presents the empirical distortion-quantization data for the Football sequence and the corresponding DQ model, using the estimated model parameters, this means,, and. Figure 4 Empirical DQ data and DQ model for the Football sequence (average over 130 frames, QCIF@15Hz).

However, since there exists dependency between quantization and encoding of quantized data which can be exploited to obtain better performance gain, it would be worth considering a joint design of quantizers and encoders.

Quantization of measurements arises in practical estimation systems where estimation algorithms are conducted on quantized data.

This limitation necessitates the quantization of data transmitted over such networks.

This paper investigates the control problem for nonlinear networked control systems with global Lipschitz nonlinearities subject to output quantization and data packet dropout.

The communication links between the plant and filter are unreliable network channels, and the effects of output logarithmic quantization and data packet losses are considered together.

Experiments show that the system has a bandwidth of 85 Hz with a minimum resolution of 50 nm, which is dominated by the quantization of data acquisition devices.

In this paper, the output-feedback control problem is considered for networked systems involving in signal quantization and data packet dropout.

The resulting algorithm is named VQ_fk_nps (Vector Quantization for data clustering in a Feature space using the Gaussian Kernel function and the Nearest Prototype Strategy).