Deep learning-based image compressive sensing methods have received extensive attention in recent years due to their superior learning ability and fast processing speed. The majority of existing image compressive sensing neural networks use single-scale sampling, whereas multiscale sampling has demonstrated excellent performance compared to single-scale. We propose a multiscale deep network for compressive sensing image reconstruction that consists of a multiscale sampling network and a reconstruction network. First, we use convolution to mimic the linear decomposition of images, and the convolution is learned during the training process. Then a sampling network captures compressive measurements across multiple decomposed scales. The reconstruction network, which includes both the initial and enhanced reconstruction networks, learns an end-to-end mapping between the compressed sensing (CS) measurements and the recovered images of the network. Experimental results indicate that the proposed network framework outperforms the existing CS methods in terms of objective metrics, peak signal to noise ratio (PSNR), structural similarity index, and subjective visual quality. Specifically, at a 0.1 sampling rate, using 10 images for testing, and the average PSNR maximum (minimum) gain is 5.95 dB (0.25 dB).
Compressed sensing (CS) is a signal processing framework for effectively reconstructing signal from a small number of measurements obtained by linear projections of the signal. It is an ongoing challenge for the real-time image reconstruction of the computational imaging, including single pixel imaging based on CS. We built a block-based CS (BCS) image reconstruction framework via a deep learning network with smoothed projected Landweber (SPL). A fully connected network performs both BCS linear sensing and non-linear reconstruction stages, and SPL removes the blocking artifacts due to incorporate Wiener filtering into projected Landweber (PL) method at each iteration. The sensing matrix and nonlinear prediction operator are jointly optimized, and the smoothing filtering is coalesced into the PL framework for eliminating high-frequency oscillatory blocking artifact. Experimental results reveal that the optimized scheme outperforms the approach only based on deep neural network. The reconstruction quality can be improved while being only slightly slower, especially the gain of structural similarity is significantly better than peak signal-to-noise ratio, and the reconstruction image texture details are vivid and natural. At 10% sensing rate, the structural similarity maximum (minimum) gain reaches 0.098 (0.021). The proposed approach is not only far superior to other state-of-the-art CS algorithms in terms of reconstruction time and quality but also comparable with up-to-date deep learning methods.
We propose the fractional-order total variation (TV) algorithm with nonlocal self-similarity for image reconstruction in compressed sensing to alleviate texture details deterioration and eliminate staircase artifacts, which results from the TV algorithms. The Grünwald–Letnikov fractional-order differential operators, which consider more neighboring image pixels and use four different directions to handle fractional-order gradients, are used to replace the integer-order differential operators. To suppress the staircase artifacts, modified nonlocal means operators are introduced into our method, which can contain prior image structural information and update the Lagrangian multipliers. An efficient augmented Lagrangian algorithm is used to solve the TV problem. Numerical results show that the algorithm achieves remarkable performance improvements at various sampling ratios. Compared with fractional-order TV-based projections onto convex sets, the maximum gains of peak signal-to-noise ratio and structural similarity index with all images are up to 2.52 dB and 0.0178, respectively, and the algorithm performs the better for preserving details and eliminating the staircase effect at the cost of taking more time.
In the present study, a new Lieb lattice with five points(hereinafter referred to as Lieb-5 lattice) in the minimum periodic unit is used as a platform. The sites of Lieb-5 lattices are classified into two categories according to their spatial position, respectively, the center lattice and the edge lattices. We investigate the effect of two categories lattices with different intensity on the propagation of the out-of-phase octupole beam. According to simulation results, when the intensity of the center lattice is less than that of the edge lattices(the ratio of two lattice intensities is 2:3), eight-peak shape is always maintained during beam propagation, presenting “strong localization”. Otherwise, the energy between incident lattices is periodically coupled with the increase of propagation distance, presenting “weak localization.”
A two-stage phase noise compensation (PNC) algorithm is proposed based on the circular multilevel quadrature amplitude modulation (C-mQAM). In the first stage, a cost function to estimate the phase noise roughly is constructed, before which the received symbols are classified by their amplitudes and rotated. It can be approximated to a cosine function, and three test phases are required for the calculation of its parameters. Kalman filter (KF), utilized in the second stage, provides the final estimation of phase noise. The performance of the proposed algorithm is evaluated with two aspects of computational complexity and the combined linewidth symbol duration product (△v·Ts) tolerance. The results show that the proposed algorithm offers a low computational complexity and high △v·Ts tolerance compared to the blind phase search (BPS) algorithm and extemded Kalman filter (EKF) algorithm.
We investigate the features of the self-deflection of the screening-photorefractive spatial soliton and the influence of
first-order and higher-order space charge field on the propagation characteristics by considering the diffusion effect, The
results show that the center of the optical beam moves on a parabolic trajectory; As to the highter-order space charge
field, the self-bending process is further enhanced by a factor that varies cubically with the applied field. The numerical
study shows that the bending distance of the soliton beam center increase, reaching its maximum value at the
characteristic temperature. Numerical investigations show that the in-phase interaction will result in the separation of two
solitons from each other, As for the anti-phase interaction side, two solitons separated each other and delect simultaneously
to the same side.
We study numerically the dynamics of a beam in a focusing photorefractive nonlinear optical lattice with a longitudinal
potential barrier. Such kind of lattice with the refractive index modulation in both transverse and longitudinal directions
can be realized by induced optically in photorefractive crystals. Different soliton states are found with different position
of the input pulse, which exhibits compression or splitting during transmission. The results also indicate that the intensity
of a beam and the transverse modulation frequency of lattice can affect apparently the ability of tunneling. For the same
lattice depth, the smaller the transverse frequency of the lattice is and the higher peak intensity the soliton possesses, the
easier the soliton tunnels through. However, when we increase the frequency of optical lattice, the optical beam can
successfully pass through the barrier for the relatively small value of lattice depth as well. Otherwise, the beam splits into
some filaments when the lattice depth is large enough. In addition, we find that beam can exhibit the behavior of
oscillation during transmission and the oscillation frequency of spatial soliton is influenced by the biased field.
In the paper, we have proposed a structure with only one photonic crystal (PC) micro-cavity side-coupled to a PC
one-way waveguide to generate strong on-resonance optical delay. According to the coupled mode theory (CMT),
the resonator system can maintain a 100% transmission spectrum throughout the complete resonant bandwidth,
which is also demonstrated by the numerical results calculated by the finite element method (FEM). As a temporal
Gaussian pulse is launched, the simulation results show that the device introduces a strong pulse delay while
maintaining total transmission efficiency within the resonant bandwidth, and the resonator structure may be of great
significance for making delay lines in optical buffer applications.
We design a highly efficient channel drop filter (CDF) with only one channel drop micro-cavity based on photonic
crystal (PC) one-way waveguide. By means of the new nature of waveguide-cavity interaction, over 95% channel drop
efficiency can be realized in the structure. Some multichannel drop filters with high drop efficiencies are also engineered
based on such the structure. These numerical results are all calculated by using the finite element method (FEM), which
agrees well with the theoretical analysis result.
Abstract: We have explored the shared-layer integration fabrication of an resonant-cavity-enhanced
p-i-n photodector (RCE- p-i-n-PD) and a single heterojunction bipolar transistor (SHBT) with the
same epitaxy grown layer structure. MOCVD growth of the different layer structure for the GaAs
based RCE- p-i-n-PD/SHBT require compromises to obtain the best performance of the integrated
devices. The SHBT is proposed with super-lattice in the collector, and the structure of the base
and the collector of the SHBT is used for the RCE. Up to now, the DC characteristics of the
integrated device have been obtained.
KEYWORDS: Telecommunications, Received signal strength, Mathematical modeling, Signal attenuation, Astatine, Mobile communications, Detection and tracking algorithms, Control systems, Microwave radiation, Electronics engineering
Handoff rate is a fundamental index to evaluate the handoffperformance and as a result it afftcts the overall performance of cellular mobile communication system. Meanwhile, moving patterns of mobiles are critical to the handoff rate. In this paper the relation between user's mobility and system handoff rate is studied The analytic result shows the soft handoff rate increases with the mobile speed Furthermore, with a little simplijication of the analytical model, the handoffrate and the mobile speed display linear relation, which is confirmed by the simulation results. Based on it, the effect about combinations of users 'multiple velocities on the handoff rate is investigated. It turns out that the handoffrate ofa system with several user mobility patterns can be estimated with the handoff rates ofcorresponding single user mobility pattern systems.
Integrated-optic technology can provide compact and rugged fiber-optic sensors. In this paper, we first propose and demonstrate an optical integrated circuits (IC) device in the Ti:LiNbO3 for a fiber-optic accelerometer, including basic design concepts, fabrication techniques and waveguide components.
Fiber-optic sensors are maturing at a fast pace with multifunction integrated optics making many of these sensors practical. We first demonstrate hybrid integrated optical accelerometer system which combines the Ti:LiNbO3 chip and the polarization-maintaining fiber to pick up vibration signal. So this system has both advantages of integrated optics and fiber-optic sensors. This paper summarizes of architecture of hybrid integrated optical accelerometer.
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