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Parallel magnetic resonance (MR) imaging is an important acceleration technique based on the spatial sensitivities of array receivers. The recently proposed Parallel low-rank modeling of local k-space neighborhoods (PLORAKS) approach uses the low-rank matrix model based on local neighborhoods of undersampled multichannel k-space data for reconstruction purposes. The joint total variation (JTV) regularization term was then combined with the PLORAKS model to improve the quality of reconstructed images. To further improve the quality of parallel MR imaging, we propose combining adaptive transform learning and joint sparsity with the PLORAKS model to obtain two algorithms, and reconstruction problems are solved by using the alternating direction method of multipliers (ADMM) and conjugate gradient techniques. The experimental results show that the two proposed algorithms can achieve higher performance than the PLORAKS algorithm and the PLORAKS-JTV algorithm with the JTV regularization term in terms of the signal-to-noise ratio (SNR), normalized root mean square error (NRMSE), high-frequency error norm (HFEN), and structural similarity index measure (SSIM).

Iterative self-consistent parallel imaging reconstruction (SPIRiT) is an effective self-calibrated reconstruction model for parallel magnetic resonance imaging (PMRI). The joint L1 norm of wavelet or tight frame coefficients and joint total variation (TV) regularization terms are incorporated into the SPIRiT model to improve the reconstruction performance. The simultaneous two-directional low-rankness (STDLR) in k-space data is incorporated into SPIRiT to realize improved reconstruction. Recent methods have exploited the nonlocal self-similarity (NSS) of images by imposing nonlocal low-rankness of similar patches to achieve a superior performance. To fully utilize both the NSS in Magnetic resonance (MR) images and calibration consistency in the k-space domain, we propose a nonlocal low-rank (NLR)-SPIRiT model by incorporating NLR regularization into the SPIRiT model. We apply the weighted nuclear norm (WNN) as a surrogate of the rank and employ the Nash equilibrium (NE) formulation and alternating direction method of multipliers (ADMM) to efficiently solve the NLR-SPIRiT model. The experimental results demonstrate the superior performance of NLR-SPIRiT over the state-of-the-art methods via three objective metrics and visual comparison.

Investigating the effect of common allergens on allergic disease is very important for future human health. In this paper, we firstly propose the models for predicting the relationship between 39 common allergens and the joint level of total IgE and ECP. The joint level of total IgE and ECP is utilized to evaluate the order of severity for allergic disease. In particular, we employ the linear fitting method and deep learning method to obtain the models with high prediction accuracy, respectively. The validity of the proposed models can be verified by testing two other independent data sets from hospital diagnosis record. Additionally, we obtain some useful medical conclusions accordingly.

Self-consistent parallel imaging (SPIRiT) is an auto-calibrating model for the reconstruction of parallel magnetic resonance imaging, which can be formulated as a regularized SPIRiT problem. The Projection Over Convex Sets (POCS) method was used to solve the formulated regularized SPIRiT problem. However, the quality of the reconstructed image still needs to be improved. Though methods such as NonLinear Conjugate Gradients (NLCG) can achieve higher spatial resolution, these methods always demand very complex computation and converge slowly. In this paper, we propose a new algorithm to solve the formulated Cartesian SPIRiT problem with the JTV and JL1 regularization terms. The proposed algorithm uses the operator splitting (OS) technique to decompose the problem into a gradient problem and a denoising problem with two regularization terms, which is solved by our proposed split Bregman based denoising algorithm, and adopts the Barzilai and Borwein method to update step size. Simulation experiments on two in vivo data sets demonstrate that the proposed algorithm is 1.3 times faster than ADMM for datasets with 8 channels. Especially, our proposal is 2 times faster than ADMM for the dataset with 32 channels.

In recent years, as Cloud Computing and Internet of Things (IoT) technologies develop rapidly, smart home technology has entered a new stage: smart electric power usage, which will break the traditional extensive management situation for electric power loads, particularly under the background of an energy crisis. People will gradually gravitate towards the demands for more energy-related and intelligent services in smart home systems such as energy saving, improved understanding of electrical consumption and electrical safety of appliances. When the power consumption is considered by a smart grid, detailed electrical information of power loads will be perceived for demand management and optimization. To address these issues, measuring and monitoring the power of residential individual appliances are highly demanded by many applications.

Parallel Magnetic Resonance (MR) imaging is a well-established acceleration technique based on the spatial sensitivities of array receivers. Eigenvector-based SPIRiT (ESPIRiT) is a new parallel MR imaging reconstruction method that combines the advantages of the SENSE and GRAPPA methods. It estimates multiple sets of the sensitivity maps from the calibration matrix that is constructed from the auto-calibration data. To improve the quality of the reconstructed image, we introduced the Total Variation (TV) and ℓ

To solve the problems of time delay and different frequency offset between different transmit antennas and the same receive antennas for distributed multiple input multiple output orthogonal frequency division multiplexing systems. In this paper, a new timing and frequency synchronization algorithm is proposed. The search range of the fine timing synchronization is set with coarse time synchronization position and cyclic prefix, and the precise timing synchronization is obtained by utilizing the correlation between data information and training sequence. In the integer frequency offset estimation algorithm, the real component of the training sequence is taken as the real sequence, and then the differential relationship between the first and last samples is estimated accordingly. Theoretical analysis and simulation show that: (1) under multipath fading channel, the timing offset and frequency offset between different transmit and receive antennas can be effectively estimated with the synchronization algorithm proposed in this paper; (2) with signal noise ratio $$-$$-5Â dB, the probability of the correct synchronization is 95Â % and the precision of the frequency offset estimation can be as high as $$10^{-2}$$10-2, which is over the similar synchronization algorithm.

In practical multimedia systems, the content of coded video streams often needs to be re-edited at the nodes of transmitting networks. For example, logo insertion is always required for copyright protection at different local transmitting nodes. This kind of video stream editing is denoted as video content editing transcoding (VCET) in this paper. Though some techniques have been suggested for VCET, these methods cannot meet the requirement of dynamic transmitting bandwidth in practical applications. In this paper, we proposed an interface macroblock-based transcoding scheme for VCET, which can reuse the variable length codes of the original video streams as much as possible to achieve the best VCET quality. In order to ensure that the edited video streams can be transmitted by the original bandwidth, we also proposed a rate control algorithm for VCET, which can accurately control the bitrate of edited video streams according to the frame level coding bits of the original video streams. Experimental results showed that the proposed scheme achieved substantially better results in bitrate accuracy, computational complexities, and video quality than many other existing schemes.

In order to guarantee pay-TV services, data of digital television (DTV) programs are scrambled by conditional access systems (CAS). In practical applications, some DTV transmitting nodes need descramble the scrambled DTV programs for editing purposes. After editing, how to re-scramble these edited programs is a challenging task since building CAS at transmitting nodes is expensive and insecure. In this paper, we proposed a novel scheme to solve this problem. Together with the recommended scheme, we also proposed techniques regarding video key data selection and extraction, synchronization of scrambled and descrambled transport stream (TS) packets, and scrambled/unscrambled interleaving multiplexer. The proposed scheme requires much lower complexity than existing methods while maintaining enough security for practical applications. Neither professional CAS equipment nor real-time common key (CK) transmitting is required by our re-scrambling scheme. Various experimental results demonstrated that the proposed re-scrambling scheme achieved superior performance in practical DTV systems, and it also obtained good compatibility with different CAS algorithms.

It is difficult to solve Magnetic Resonance (MR) image reconstruction problems with linear combinations of total variation and l1 norm regularization terms. In order to solve these compound regularization problems, we propose an efficient algorithm in this letter. The proposed algorithm adopts the Bregman iteration technique to convert the original constrained problem to a sequence of unconstrained problems, which are then solved using operator splitting and variable splitting techniques. Simulation experiments demonstrate that significant improvement of the quality of reconstructed images is achieved by the proposed algorithm when compared to previous methods.

A novel adaptive variable step-size constant module medical CT image blind equalization algorithm was proposed. The process of image restoration transformed by a linear operation is equivalent to one dimensional blind equalization. The constant modulus blind equalization cost function applied to medical CT image was founded. The kurtosis of error signal was utilized as step-size control factor to speed up the convergence and improve the performance of the algorithm. Computer simulations show that new algorithm improves peak signal to noise ratio, restoration effects and efficiency of operations, and decrease state residual error.

Compressed Sensing (CS) is an emerging theory in signal processing. The CS framework suggests that the signal can be reconstructed from the sub-samples by some recovery algorithm when the signal is sparse. We propose an improved video coding scheme to compress the depth map by Total Variation (TV) minimization in the pixel domain from the quantized/dequantized transform coefficients due to the extremely sparse gradient of depth maps. We keep all the frequency coefficients to avoid introducing noise to the reconstruction results. The TV minimization solver is integrated into the standard encoder, and the proposed coding scheme chooses the optimal recovery method by using the Rate-distortion Optimization (RDO) method. The experimental results show that our proposed scheme is highly efficient for the depth map sequences.

The AVS video coding standard employs a robust Rate-Distortion Optimization (RDO) technique to improve compression performance. Although RDO technique can achieve better compression efficiency, it also increases tremendous computational complexity. In order to reduce complexity, this paper proposes a fast intra mode decision algorithm for AVS based on the ranking of Sum of Absolute Transformed Difference (SATD) cost. The proposed algorithm makes use of the high correlation among the most probable mode, the mode with the minimum SATD cost and the best mode in the Rate-Distortion sense, to filter out the useless modes. The experimental results show that the proposed algorithm can save the total encoding time significantly with negligible loss of Peak Signal-to-Noise Ratio.

A blind equalization algorithm based on fuzzy neural network was proposed. Blind channel estimation and fuzzy neural network classifier were utilized to realize blind equalization. Firstly Blind channel estimation was used to identify the character of the channel. Signals were rebuilt by de-convolution, and the original judgment equipment was replaced by fuzzy neural network classifier. Simulations indicate that the novel algorithm improves convergence and bit error rate and so on.