ADC border effect and suppression of quantization error in the digital dynamic measurement

The digital measurement and processing is an important direction in the measurement and control field. The quantization error widely existing in the digital processing is always the decisive factor that restricts the development and applications of the digital technology. In this paper, we find that the stability of the digital quantization system is obviously better than the quantization resolution. The application of a border effect in the digital quantization can greatly improve the accuracy of digital processing. Its effective precision has nothing to do with the number of quantization bits, which is only related to the stability of the quantization system. The high precision measurement results obtained in the low level quantization system with high sampling rate have an important application value for the progress in the digital measurement and processing field.

Asymptotic uniformity of the quantization error for the Ahlfors-David probability measures

Let μ be an Ahlfors-David probability measure on Rq;therefore,there exist some constants s0> 0 and ε0,C1,C2> 0 such that C1εs0≤μ(B(x,ε))≤C2εs0 for all ε∈(0,ε0) and x ∈ supp(μ).For n≥ 1,let αn be an n-optimal set for μ of order r;furthermore,let {Pa(αn)}a∈αn be an arbitrary Voronoi partition with respect to αn.The n-th quantization error en,r(μ) for μ of order r can be defined as en,rr(μ):=∫ d(x,αn)r dμ(x).We define Ia(αn,μ):=∫Pa(αn) d(x,αn)r dμ(x),a ∈αn,and prove that,the three quantities ■ are of the same order as that of 1/nen,rr(μ).Thus,our result exhibits that,a weak version of Gersho’s conjecture holds true for the Ahlfors-David probability measures on Rq.

Fault Detection of Networked Control Systems with Uncertain Time-Varying Delay and Quantization Error

Problems related to fault detection of networked control systems (NCSs) with both uncertain time-varying delay and quantization error are studied in this paper. A novel model with the form of polytopic uncertainty is given to represent the influences of both the time-varying delay and the quantization error, and then the reference model based method is used to design the residual generator that is robust to both unknown network-induced delay and unknown inputs. A numerical example is also given to illustrate the merits of the presented method. The proposed method can be regarded as an extension of the authors' former work, which can only deal with time-varying delay.

The Effect of Quantization Error on Display Color Gamut Transformation

Researchers and designers who work with color displays often transform color gamut between two different display devices. This paper demonstrates the effect of quantization error on the transformation based on analyzing the color gamut deviation profoundly.

Quantization error in stereo imaging system with noise distributions

The problem of estimating quantization error in 2D images is an inherent problem in computer vision.The outcome of this problem is directly related to the error in reconstructed 3D position coordinates of an object.Thus estimation of quantization error has its own importance in stereo vision.Although the quantization error cannot be controlled fully,still statistical error analysis helps us to measure the performance of stereo systems that relies on the imaging parameters.Generally,it is assumed that the quantization error in 2D images is distributed uniformly that need not to be true from a practical aspect.In this paper,we have incorporated noise distributions(Triangular and Trapezoidal)for the stochastic error analysis of the quantization error in stereo imaging systems.For the validation of the theoretical analysis,the detailed simulation study is carried out by considering different cases.

Robust detector for range-spread targets in non-Gaussian background

Based on the target scatterer density, the range-spread target detection of high-resolution radar is addressed in additive non-Gaussian clutter, which is modeled as a spherically invariant random vector. Firstly, for sparse scatterer density, the detection of target scatterer in each range cell is derived, and then an M/K detector is proposed to detect the whole range-spread target. Se- condly, an integrating detector is devised to detect a range-spread target with dense scatterer density. Finally, to make the best of the advantages of M/K detector and integrating detector, a robust detector based on scatterer density （DBSD） is designed, which can reduce the probable collapsing loss or quantization error ef- fectively. Moreover, the density decision factor of DBSD is also determined. The formula of the false alarm probability is derived for DBSD. It is proved that the DBSD ensures a constant false alarm rate property. Furthermore, the computational results indi- cate that the DBSD is robust to different clutter one-lag correlations and target scatterer densities. It is also shown that the DBSD out- performs the existing scatterer-density-dependent detector.

Super-resolution DOA estimation for correlated off-grid signals via deep estimator

This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.