Lattice-reduction (LR) technique has been adopted to improve the performance and reduce the complexity in MIMO data detection. This paper presents an improved quantization scheme for LR aided MIMO detection based on Gram-Schmidt orthogonalization. For the LR aided detection, the quantization step applies the simple rounding operation, which often leads to the quantization errors. Meanwhile, these errors may result in the detection errors. Hence the purpose of the proposed detection is to further solve the problem of degrading the performance due to the quantization errors in the signal estimation. In this paper, the proposed quantization scheme decreases the quantization errors using a simple tree search with a threshold function. Through the analysis and the simulation results, we observe that the proposed detection can achieve the nearly optimal performance with very low complexity, and require a little additional complexity compared to the conventional LR-MMSE detection in the high Eb/N0 region. Furthermore, this quantization error reduction scheme is also efficient even for the high modulation order.

In this letter, a quantization error-aware Tomlinson-Harashinma Precoding (THP) is proposed based on the equivalent zero-forcing (ZF) criterion in Multiuser Multiple-Input Single-Output (MU-MISO) systems with limited feedback, where the transmitter has only quantized channel direction information (CDI). This precoding scheme is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). Our simulation results show that the new THP scheme outperforms the conventional precoding scheme in limited feedback systems with respect to Bit Error Ratio (BER).

This paper presents an error compensation method for fixed-width group canonic signed digit (GCSD) multipliers that receive a W-bit input and generate a W-bit product. To efficiently compensate for the truncation error, the encoded signals from the GCSD multiplier are used for the generation of the error compensation bias. By Synopsys simulations, it is shown that the proposed method leads to up to 84% reduction in power consumption and up to 78% reduction in area compared with the fixed-width modified Booth multipliers.

A reduced complexity quantization error correction method for lattice reduction aided (LRA) vector precoding is proposed. For LRA vector precoding,Babai's approximation procedure can generate quantization errors leading to performance loss. Instead of making a list to correct all possible errors as is done in the existing scheme, we propose a novel method in which only a subset of all possible errors are corrected. The size of the subset is determined by the probability distribution of the number of actual errors. Thus, the computation complexity of our correction procedure is reduced with little performance loss compared with the existing correction scheme.

The main idea in perceptual image compression is to remove the perceptual redundancy for representing images at the lowest possible bit rate without introducing perceivable distortion. A certain amount of perceptual redundancy is inherent in the color image since human eyes are not perfect sensors for discriminating small differences in color signals. Effectively exploiting the perceptual redundancy will help to improve the coding efficiency of compressing color images. In this paper, a locally adaptive perceptual compression scheme for color images is proposed. The scheme is based on the design of an adaptive quantizer for compressing color images with the nearly lossless visual quality at a low bit rate. An effective way to achieve the nearly lossless visual quality is to shape the quantization error as a part of perceptual redundancy while compressing color images. This method is to control the adaptive quantization stage by the perceptual redundancy of the color image. In this paper, the perceptual redundancy in the form of the noise detection threshold associated with each coefficient in each subband of three color components of the color image is derived based on the finding of perceptually indistinguishable regions of color stimuli in the uniform color space and various masking effects of human visual perception. The quantizer step size for the target coefficient in each color component is adaptively adjusted by the associated noise detection threshold to make sure that the resulting quantization error is not perceivable. Simulation results show that the compression performance of the proposed scheme using the adaptively coefficient-wise quantization is better than that using the band-wise quantization. The nearly lossless visual quality of the reconstructed image can be achieved by the proposed scheme at lower entropy.

The quantization error of phase delay in an ultrasonic annular arrays imaging system is analyzed which impairs image resolution, and proper sampling rate is considered to reduce system complexity.

This paper discusses the influence of the nonlinearity of analog-to-digital converters (ADCs) on the performance of orthogonal frequency division multiplexing (OFDM) receivers. We evaluate signal constellations and bit error rate performances while considering quantization errors and clippings. The optimum range for an ADC input amplitude is found as a result of the trade-off between quantization error and the effects of clipping. In addition, it is shown that the peak-to-average power ratio (PAPR) of the signal is not a good measure of the bit error rate (BER) performance, since the largest peaks occur only with very low probabilities. The relationship between the location of a subcarrier and its performance is studied. As a result, it is shown that the influence of the quantization error is identical for all subcarriers, while the effects of clipping depend on the subcarrier frequency. When clipping occurs, the BER performance of a subcarrier near the center frequency is worse than that near the edges.

Machine learning and data mining algorithms are increasingly being used in the intrusion detection systems (IDS), but their performances are laggard to some extent especially applied in network based intrusion detection: the larger load of network traffic monitoring requires more efficient algorithm in practice. In this paper, we propose and design an anomaly intrusion detection (AID) system based on the vector quantization (VQ) which is widely used for data compression and high-dimension multimedia data index. The design procedure optimizes the performance of intrusion detection by jointly accounting for accurate usage profile modeling by the VQ codebook and fast similarity measures between feature vectors to reduce the computational cost. The former is just the key of getting high detection rate and the later is the footstone of guaranteeing efficiency and real-time style of intrusion detection. Experiment comparisons to other related researches show that the performance of intrusion detection is improved greatly.

Digital Subtraction Angiography (DSA) is a technique used for enhancement of small details in angiogram imaging systems. In this approach, X-ray images of a subject, after injection, are subtracted from a reference X-ray image, taken from the same subject before injection. Due to the exponential absorption property of X-rays, effects of small details at different depth appear differently on X-ray images. Consequently, image subtraction cannot be employed on the original images without any adjustment or modification. Proper modification, in this case, is to use some form of logarithmic operation on images before subtraction. In medical imaging systems, the system designer has a choice to implement this logarithmic operation in the analog domain, before digitization of the video signal, or in the digital domain after analog-to-digital conversion (ADC) of the original video signal. In this paper, the difference between these two approaches is studied and upper bounds for quantization error in both cases are calculated. Based on this study, the best approach for utilization of the logarithmic function is proposed. The overall effects of these two approaches on the inherent signal noise are also addressed.

This paper proposes an Iterated Function System (IFS) which can reduce effects of quantization errors of the IFS parameters. The proposed method skips conventional analog-parameter search and directly selects optimum IFS parameters from pools of discrete IFS parameters. In conventional IFS-based image coding the IFS parameters are quantized after their analog optimum values are determined. The image reconstructed from the quantized parameters is degraded with errors that are traced back to quantization errors amplified in the iterated mappings. The effectiveness of this new realistic approach is demonstrated by simulation results over the conventional method.

This paper proposes an analysis method of scaling-factor-quantization error in fractal image coding using a state-space approach with the statistical analysis method. It is shown that the statistical analysis method is appropriate and leads to a simple result, whereas the deterministic analysis method is not appropriate and leads to a complex result for the analysis of fractal image coding. We derive the output error variance matrix for the measure of error and define the output error variance by scalar quantity as the mean of diagonal elements of the output error variance matrix. Examples are given to show that the scaling-factor-quantization error due to iterative computation with finite-wordlength scaling factors degrades the quality of decoded images. A quantitative comparison of experimental scaling-factor-quantization error with analytical result is made for the output error variance. The result shows that our analysis method is valid for the fractal image coding.