For the frequency-division-duplex (FDD)-based massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) feedback plays a critical role. Although deep learning has been used to compress the CSI feedback, some issues like truncation and noise still need further investigation. Facing these practical concerns, we propose an improved model (called CsiNet-Plus), which includes a truncation process and a channel noise process. Simulation results demonstrate that the CsiNet-Plus outperforms the existing CsiNet. The performance interchangeability between truncated decimal digits and the signal-to-noise-ratio helps support flexible configuration.

An efficient reciprocity and passivity preserving balanced truncation for RLC networks is presented in this paper. Reciprocity and passivity are fundamental principles of linear passive networks. Hence, reduction with preservation of reciprocity and passivity is necessary to simulate behavior of the circuits including the RLC networks accurately and stably. Moreover, the proposed method is more efficient than the previous balanced truncation methods, because sparsity patterns of the coefficient matrices for the circuit equations of the RLC networks are fully available. In the illustrative examples, we will show that the proposed method is compatible with PRIMA, which is known as a general reduction method of RLC networks, in efficiency and used memory, and is more accurate at high frequencies than PRIMA.

The overdrive technique is widely used to eliminate motion blur in liquid-crystal displays (LCDs). However, this technique requires a large frame memory to store the previous frame. A reduction in the frame memory requires an image compression algorithm suitable for real-time data processing. In this paper, we present an algorithm based on multimode-color-conversion block truncation coding (MCC-BTC) to obtain a constant output bit rate and high overdrive performance. The MCC-BTC algorithm uses four compression methods, one of which is selected. The four compression modes either use the single-bitmap-generation method or the subsampling method for chrominance. As shown in the simulation results, the proposed algorithm improves the performance of both coding (up to 2.73dB) and overdrive (up to 2.61dB), and the visual quality is improved in comparison to other competing algorithms in literature.

An efficient balanced truncation for RC and RLC networks is presented in this paper. To accelerate the balanced truncation, sparse structures of original networks are considered. As a result, Lyapunov equations, the solutions of which are necessary for making the transformation matrices, are efficiently solved, and the reduced order models are efficiently obtained. It is proven that reciprocity of original networks is preserved while applying the proposed method. Passivity of the reduced RC networks is also guaranteed. In the illustrative examples, we will show that the proposed method is compatible with PRIMA in efficiency and is more accurate than PRIMA.

The alternating direction implicit (ADI) method is proposed for low-rank solution of projected generalized continuous-time algebraic Lyapunov equations. The low-rank solution is expressed by Cholesky factor that is similar to that of Cholesky factorization for linear system of equations. The Cholesky factor is represented in a real form so that it is useful for balanced truncation of sparsely connected RLC networks. Moreover, we show how to determine the shift parameters which are required for the ADI iterations, where Krylov subspace method is used for finding the shift parameters that reduce the residual error quickly. In the illustrative examples, we confirm that the real Cholesky factor certainly provides low-rank solution of projected generalized continuous-time algebraic Lyapunov equations. Effectiveness of the shift parameters determined by Krylov subspace method is also demonstrated.

Reversible data hiding has been a hot research topic since both the host media and hidden data can be recovered without distortion. In the past several years, more and more attention has been paid to reversible data hiding schemes for images in compressed formats such as JPEG, JPEG2000, Vector Quantization (VQ) and Block Truncation Coding (BTC). Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have no ability to reduce the bit rate but may reduce the image quality. This paper presents a novel reversible data hiding scheme for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, the original BTC technique is performed on the original image to obtain the BTC-compressed data which can be represented by a high mean table, a low mean table and a bitplane sequence. Then, the proposed reversible data hiding scheme is performed on both the high mean table and low mean table. Our hiding scheme is a lossless joint hiding and compression method based on 22 blocks in mean tables, thus it can not only hide data in mean tables but also reduce the bit rate. Experiments show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity and efficiency.

The Liquid-crystal display (LCD) overdrive technique has been utilized to reduce motion blur on a display via a reduction in the response time. However, to measure the variation of the pixel amplitudes, it is necessary to store the previous frame using a large frame memory. To downscale the frame memory, block truncation coding (BTC) is commonly employed due to the simplicity of its implementation, even if some visual artifacts may occur for image blocks with high frequency components. In this paper, we present a multimode-multilevel BTC (MBTC) technique that improves performance while maintaining simplicity. To improve the visual quality, we uniquely determine the quantization level and coding mode of each block according to the distribution of the luminance and chrominance amplitudes. For a compression ratio of 6:1, the proposed method demonstrates higher coding efficiency and overdrive performance by up to 3.81 dB in the PSNR compared to other methods.

We find necessary and sufficient conditions for the (shifted) oversampling expansions to hold in wavelet subspaces. In particular, we characterize scaling functions with the (shifted) oversampling property. We also obtain L2 and L∞ norm estimates for the truncation and aliasing errors of the oversampling expansion.

Block truncation coding (BTC) is an efficient image compression algorithm that generates a constant output bit-rate. For color image compression, vector quantization (VQ) is exploited to improve the coding efficiency. In this letter, we propose an improved VQ based BTC (VQ-BTC) algorithm using template matching and Lloyd quantization (LQ). The experimental results show that the proposed method improves the PSNR by 0.9 dB in average compared to the conventional VQ-BTC algorithms.

As we scale toward nanometer technologies, the increase in interconnect parameter variations will bring significant performance variability. New design methodologies will emerge to facilitate construction of reliable systems from unreliable nanometer scale components. Such methodologies require new performance models which accurately capture the manufacturing realities. In this paper, we present a Linear Fractional Transform (LFT) based model for interconnect parametric uncertainty. The new model formulates the interconnect parametric uncertainty as a repeated scalar uncertainty structure. With the help of generalized Balanced Truncation Realization (BTR) and Linear Matrix Inequalities (LMI's), the porposed model reduces the order of the original interconnect network while preserves the stability. The LFT based new model even guarantees passivity if the BTR reduction is based on solutions to a pair of Linear Matrix Inequalities (LMI's) generated from Lur'e equations. In case of large number of uncertain parameters, the new model may be applied successively: the uncertain parameters are partitioned into groups, and with regard to each group, LFT based model is applied in turns.

Gamma correction is an essential function and is time consuming task in every display device such as CRT and LCD. And gray scale CCT reproduction in most LCD are quite different from those of standard CRT. An automated fast and accurate display adjusment method and system for gamma correction and for constant gray scale CCT calibration of mobile phone LCD is presented in this paper. We develop the test pattern disply and register control program in mobile phone and devleop automatic measure program in computer using spectroradimeter. The proposed system is maintain given gamma values and CCT values accuratly. In addition, This system is possible to fast mobile phone LCD adjusment within one hour.

Traditionally, it has been considered that the received signal to noise power ratio should be uniformly preserved to maximize system capacity for uncoded system with reliable feedback channel. However, once channel coding is employed as a building block, another power control scheme presents better performance. In this paper, we consider several power reallocation schemes for an effective use of limited power in a turbo coded system in lognormal shadowing channel. We show that the proposed power reallocation can reduce the decoding error probability by almost two orders of magnitude and provide a power gain of 0.87 dB at a target bit error rate of 10-4 over the equal power allocation among all code symbols. We also propose applying different power levels and cut-off thresholds on systematic and parity bits, and investigate the effect of channel estimation error.

The power reduction of display devices has become an important issue for extending battery life and running time when they are used in digital multimedia broadcasting (DMB) mobile phones. DMB mobile phones generally use 16-bit data per pixel to reduce power consumption even though a liquid crystal display (LCD) graphic controller can support 16-, 18-, and 24-bit data per pixel. Also, the total transmission time of 16-bit data per pixel is only half that for 18- and 24-bit data per pixel. Decoded 24-bit image data in the frame memory of a DMB decoder are asymmetrically truncated to 16-bit image data. This results in a lack of smoothness such as blocking effects and/or pseudo edge artifacts. To solve these problems, the author proposes and implements a new asymmetric pixel data truncation error compensation algorithm using 1-bit least significant bit (LSB) data expansion with correlated color information for the purpose of ensuring smoothness. In the experimental results, the proposed algorithm is able to correct various artifacts.

CORDIC (COordinate Rotation DIgital Computer) is a well known algorithm using simple adders and shifters to evaluate various elementary functions. Thus, CORDIC is suitable for the design of high performance chips using VLSI technology. In this paper, a complete analysis of the computation error of both the (conventional) CORDIC algorithm and the CORDIC algorithm with expanded convergence range is derived to facilitate the design task. The resulting formulas regarding the relative and absolute approximation errors and the truncation error are summarized in the tabular form. As the numerical accuracy of CORDIC processors is determined by the word length of operands and the number of iterations, three reference tables are constructed for the optimal choice of these numbers. These tables can be used to facilitate the design of cost-effective CORDIC processors in terms of areas and performances. In addition, two design examples: singular value decomposition (SVD) and lattice filter for digital signal processing systems are given to demonstrate the goal and benefit of the derived numerical analysis of CORDIC.

In this paper, we present a design method for fixed-width squarer that receives an n-bit input and produces an n-bit squared product. To efficiently compensate for the truncation error, modified Booth-folding encoder signals are used for the generation of error compensation bias. The truncated bits are divided into two groups (major and minor) depending upon their effects on the truncation error. Then, different error compensation methods are applied to each group. By simulations, it is shown that the proposed fixed-width squarers have lower error than other fixed-width squarers and are cost-effective.

We propose reallocating the power resource among the code symbols in such a way to minimize the post decoding error probability of turbo code. We consider several power reallocation policies and investigate their performance in slowly-varying Rayleigh flat fading channel. We show that the proposed scheme can reduce the post decoding error probability by two orders of magnitude and provide a power gain of 0.86 dB at BER=10-6 over the traditional equal power allocation among all code symbols. We also propose applying different power levels and cut-off thresholds on systematic and parity check bits depending on the channel gain, and investigate the effect of channel gain estimation error.

In this paper we discuss an adaptive process, which is based on the so-called self-tuning mechanism. We simplify this mechanism and apply it to a threshold system. From view points of information quantity and estimation accuracy we show this mechanism enhances information transmission through the threshold system. In addition we extend our theory so that it could be applied to a truncation coding.

In this letter, a new scheme of designing multilevel BTC coding is proposed. The optimal quantization can obtain by selecting the quantization threshold with an exhaustive search. However, it requires an enormous amount of computation and is thus impractical while we take an exhaustive search for the multilevel BTC. A two-steps searching method is applied to reduce the computational complexity. Comparisons of results with various methods have verified that the proposed method approach to the optimal quantization with little computation complexity.

In this paper, a new scheme for designing multilevel BTC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous amount of computation and is, thus impractical when we consider an exhaustive search for the multilevel BTC. In order to find a better threshold so that the average mean square error between the original and reconstructed images is a minimum, the genetic algorithm is applied. Comparison of the results of the proposed method with the exhaustive search reveal that the former method can almost achieve optimal quantization with much less computation than that required in the latter case.

Following our former works on regular sampling in wavelet subspaces, the paper provides two algorithms to estimate the truncation error and aliasing error respectively when the theorem is applied to calculate concrete signals. Furthermore the shift sampling case is also discussed. Finally some important examples are calculated to show the algorithm.