The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full s...
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The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full sub-band in compressed sensing framework. Firstly, we constructed a full sub-band coefficient sparse vector of mutil-level discrete wavelet transform. Secondly, we designed a weight matrix to improve measurement matrix. Finally, sparsity vector was processed by compressed sensing to get the measured value. Compared with the exiting algorithms, the experimental results of the proposed algorithm show that the PSNRs of reconstructed images is improve up to 1~2 dB under the same objective quality.
Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited...
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Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited due to various dependencies introduced in spatial and temporal prediction in HEVC. In this paper, through pixel correlation analysis, establishment of CTU node model, multi-core resource allocation strategy, we proposed an intra/inter-frame joint WPP coding algorithm for multi-core platform which can significantly improve the parallelism, while achieved good results in bit rate, PSNR, and acceleration ratio. Experiments on standard HD test sequence show that the proposed algorithm can lead to up to 2x, 1.3x speed up compared with the original WPP and IWF parallelism.
This paper presents a fast quality scalable video coding method based on compressed sensing(CS). The proposed method obtained the coding scheme of the enhancement MJU by using the interlayer and spatial correlation an...
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This paper presents a fast quality scalable video coding method based on compressed sensing(CS). The proposed method obtained the coding scheme of the enhancement MJU by using the interlayer and spatial correlation and kept the base layer's coding scheme unchanged. And the part in the enhancement layer which needed to be fine quantified was combined with the compressed sensing theory selectively which based on the sparsity of the signal and the complexity of the reconstruction. In order to satisfy the coding syntax structure of the reference software, the measurement value got by compressed sensing was complemented by 0 s and the flag bit was set to distinguish the special sub-blocks coded by CS. Experimental results show that the proposed algorithm can effectively improve the efficiency of scalable video coding and reduce the computational complexity.
Quantum random numbers are a fundamental resource to security and cryptography. Based on the intrinsically random processes, quantum random number generators(QRNGs) provides an opportunity to certify genuine random ...
Quantum random numbers are a fundamental resource to security and cryptography. Based on the intrinsically random processes, quantum random number generators(QRNGs) provides an opportunity to certify genuine random numbers. All implementations of QRNGs that invoke device-independent(DI), the violation of Bell inequality are necessary to guarantee that the outcomes of the measurements cannot be determined in advance. For the single Clauser-Horne-Shimony-Holt(CHSH) inequality, it is not optimal with respect to randomness certification. Since local projective measurement are performed, one random bit can be certified from the maximally entangled two-qubit states. Recently, Andersson et al. [Phys. Rev. A 97, 012314(2018)] had proven that the device-independent certification can obtain two random bits through the maximum quantum violation of Gisin’s elegant Bell inequality. Here, we report a proof-of-principle realization of optimal randomness certification with practical photonic apparatus. In our experiment, we implement two kinds of qubit measurement, mutually unbiased bases(MUBs) and symmetric informationally complete positive operator-valued measures(SIC-POVM) of four-outcomes, respectively. Through randomness extractor, including Toeplitz-hashing extractor and an improved linear feedback shift register(LFSR), we finally extracted random bit string which have good uniformity and pass all randomness tests of the NIST.
Improving Q-factor of analogy of electromagnetically induced transparency (EIT-like) based on the symmetric and asymmetric quasi-dark mode has been numerically demonstrated. The Q-factor of EIT-like based on the symme...
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ISBN:
(纸本)9781538653746
Improving Q-factor of analogy of electromagnetically induced transparency (EIT-like) based on the symmetric and asymmetric quasi-dark mode has been numerically demonstrated. The Q-factor of EIT-like based on the symmetric quasi-dark mode (mirrored metasurface) is an order of magnitude larger than the Q-factor of EIT-like based on the asymmetric quasi-dark mode. The low radiative loss of the symmetric quasi-dark mode causes the improved Q-factor. Furthermore, the proposed way of improving the Q-factor has important application in microwave, terahertz and optical metasurface.
The identity vector(i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the socal...
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The identity vector(i-vector) approach has been the state-of-the-art for text-independent speaker recognition, both identification and verification in recent years. An i-vector is a low-dimensional vector in the socalled total variability space represented with a thin and tall rectangular matrix. This paper introduces a novel algorithm to improve the computational and memory requirements for the application. In our method, the series of symmetric matrices can be represented by diagonal expression,sharing the same dictionary, which to some extent is analogous to eigen decomposition, and we name this algorithm Eigen decomposition like factorization(EDLF). Similar algorithms are listed for comparison, in the same condition,our method shows no disadvantages in identification accuracy.
A quality of service(QoS) guaranteed cross-layer resource allocation algorithm with physical layer, medium access control(MAC) layer and call admission control(CAC) considered simultaneously is proposed for the ...
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A quality of service(QoS) guaranteed cross-layer resource allocation algorithm with physical layer, medium access control(MAC) layer and call admission control(CAC) considered simultaneously is proposed for the full IP orthogonal frequency division multiple access(OFDMA) communication system, which can ensure the quality of multimedia services in full IP *** algorithm converts the physical layer resources such as subcarriers, transmission power, and the QoS metrics into equivalent bandwidth which can be distributed by the base station in all three layers. By this means, the QoS requirements in terms of bit error rate(BER), transmission delay and dropping probability can be guaranteed by the cross-layer optimal equivalent bandwidth allocation. The numerical results show that the proposed algorithm has higher spectrum efficiency compared to the existing systems.
The sparse signal recovery-based direction of arrival (DOA) estimation has received a great deal of attention over the past decade. From the sparse representation point of view, $$\ell _0$$ -norm is the best choice to...
The sparse signal recovery-based direction of arrival (DOA) estimation has received a great deal of attention over the past decade. From the sparse representation point of view, $$\ell _0$$ -norm is the best choice to evaluate the sparsity of a vector. However, solving an $$\ell _0$$ -norm minimization problem is non-deterministic polynomial hard (NP-hard). Thus, The common idea for many sparse DOA estimation methods is to use the $$\ell _1$$ -norm as the sparsity metric. However, its sparse solution may not coincide with the solution resulting from the $$\ell _0$$ -norm thus deteriorating the DOA estimation performance. In this paper, we propose a new sparse method based on $$\ell _p$$ ( $$0<1$$ ) regularization for DOA estimation to achieve a sparser solution than $$\ell _1$$ regularization. In particular, we use the Taylor expansion to convert the $$\ell _p$$ -norm minimization problem to a weighted $$\ell _1$$ -norm problem. Then, a two-step iterative method is employed to achieve the DOA estimate. The $$\ell _p$$ ( $$0<1$$ ) regularization is able to improve the angle resolution, leading to an improved performance in low SNR and correlated signal scenarios. Numerical results show that our proposed method has better estimation performance than many other methods do.
In this paper, an analytical framework is developed to evaluate the uplink outage and spatial blocking performance in a multichannel heterogeneous cellular network. A weighted uplink cell association criterion and ran...
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In this paper, a new method for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed. We first reconstruct the covariance matrix of the coarray with block-Toeplitz structure and then retrieve the DO...
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