Compound four-cavity single-longitudinal mode fiber laser.

A compound resonant cavity-type single longitudinal mode erbium-doped fiber laser is proposed and verified in this paper. We use a compound four-cavity to expand the longitudinal mode spacing of the laser and a homemade grating F-P filter and a saturable absorber to narrow the gain spectral bandwidth, enabling the laser to operate in a single-frequency state. Through theoretical analysis and experimental verification of the important roles of the main constructed fiber laser parts, the center wavelength of the laser output of 1550.06 nm is obtained, and the optical signal-to-noise ratio is 65 dB. In a 50 minute period, the wavelength fluctuation is exhibited along with the maximum power fluctuation of 0.28 dB. The value of the laser output linewidth is ∼250 Hz, measured by using the delayed self-heterodyne method.

Locating Target Regions for Image Retrieval in an Unsupervised Manner.

Image retrieval performance can be improved by training a convolutional neural network (CNN) model with annotated data to facilitate accurate localization of target regions. However, obtaining sufficiently annotated data is expensive and impractical in real settings. It is challenging to achieve accurate localization of target regions in an unsupervised manner. To address this problem, we propose a new unsupervised image retrieval method named unsupervised target region localization (UTRL) descriptors. It can precisely locate target regions without supervisory information or learning. Our method contains three highlights: 1) we propose a novel zero-label transfer learning method to address the problem of co-localization in target regions. This enhances the potential localization ability of pretrained CNN models through a zero-label data-driven approach; 2) we propose a multiscale attention accumulation method to accurately extract distinguishable target features. It distinguishes the importance of features by using local Gaussian weights; and 3) we propose a simple yet effective method to reduce vector dimensionality, named twice-PCA-whitening (TPW), which reduces the performance degradation caused by feature compression. Notably, TPW is a robust and general method that can be widely applied to image retrieval tasks to improve retrieval performance. This work also facilitates the development of image retrieval based on short vector features. Extensive experiments on six popular benchmark datasets demonstrate that our method achieves about 7% greater mean average precision (mAP) compared to existing state-of-the-art unsupervised methods.

Low-Temperature Growth of InGaAs Quantum Wells Using Migration-Enhanced Epitaxy.

The growth of InGaAs quantum wells (QWs) epitaxially on InP substrates is of great interest due to their wide application in optoelectronic devices. However, conventional molecular beam epitaxy requires substrate temperatures between 400 and 500 °C, which can lead to disorder scattering, dopant diffusion, and interface roughening, adversely affecting device performance. Lower growth temperatures enable the fabrication of high-speed optoelectronic devices by increasing arsenic antisite defects and reducing carrier lifetimes. This work investigates the low-temperature epitaxial growth of InAs/GaAs short-period superlattices as an ordered replacement for InGaAs quantum wells, using migration-enhanced epitaxy (MEE) with low growth temperatures down to 200-250 °C. The InAs/GaAs multi-quantum wells with InAlAs barriers using MEE grown at 230 °C show good single crystals with sharp interfaces, without mismatch dislocations found. The Raman results reveal that the MEE mode enables the growth of (InAs)4(GaAs)3/InAlAs QWs with excellent periodicity, effectively reducing alloy scattering. The room temperature (RT) photoluminescence (PL) measurement shows the strong PL responses with narrow peaks, revealing the good quality of the MEE-grown QWs. The RT electron mobility of the sample grown in low-temperature MEE mode is as high as 2100 cm2/V∗s. In addition, the photoexcited band-edge carrier lifetime was about 3.3 ps at RT. The high-quality superlattices obtained confirm MEE's effectiveness for enabling advanced III-V device structures at reduced temperatures. This promises improved performance for applications in areas such as high-speed transistors, terahertz imaging, and optical communications.

Migration-Enhanced Epitaxial Growth of InAs/GaAs Short-Period Superlattices for THz Generation.

The low-temperature-grown InGaAs (LT-InGaAs) photoconductive antenna has received great attention for the development of highly compact and integrated cheap THz sources. However, the performance of the LT-InGaAs photoconductive antenna is limited by its low resistivity and mobility. The generated radiated power is much weaker compared to the low-temperature-grown GaAs-based photoconductive antennas. This is mainly caused by the low abundance of excess As in LT-InGaAs with the conventional growth mode, which inevitably gives rise to the formation of As precipitate and alloy scattering after annealing. In this paper, the migration-enhanced molecular beam epitaxy technique is developed to grow high-quality (InAs)m/(GaAs)n short-period superlattices with a sharp interface instead of InGaAs on InP substrate. The improved electron mobility and resistivity at room temperature (RT) are found to be 843 cm2/(V·s) and 1648 ohm/sq, respectively, for the (InAs)m/(GaAs)n short-period superlattice. The band-edge photo-excited carrier lifetime is determined to be ~1.2 ps at RT. The calculated photocurrent intensity, obtained by solving the Maxwell wave equation and the coupled drift-diffusion/Poisson equation using the finite element method, is in good agreement with previously reported results. This work may provide a new approach for the material growth towards high-performance THz photoconductive antennas with high radiation power.

Multi-scale multi-attention network for diabetic retinopathy grading.

Objective.Diabetic retinopathy (DR) grading plays an important role in clinical diagnosis. However, automatic grading of DR is challenging due to the presence of intra-class variation and small lesions. On the one hand, deep features learned by convolutional neural networks often lose valid information about these small lesions. On the other hand, the great variability of lesion features, including differences in type and quantity, can exhibit considerable divergence even among fundus images of the same grade. To address these issues, we propose a novel multi-scale multi-attention network (MMNet).Approach.Firstly, to focus on different lesion features of fundus images, we propose a lesion attention module, which aims to encode multiple different lesion attention feature maps by combining channel attention and spatial attention, thus extracting global feature information and preserving diverse lesion features. Secondly, we propose a multi-scale feature fusion module to learn more feature information for small lesion regions, which combines complementary relationships between different convolutional layers to capture more detailed feature information. Furthermore, we introduce a Cross-layer Consistency Constraint Loss to overcome semantic differences between multi-scale features.Main results.The proposed MMNet obtains a high accuracy of 86.4% and a high kappa score of 88.4% for multi-class DR grading tasks on the EyePACS dataset, while 98.6% AUC, 95.3% accuracy, 92.7% recall, 95.0% precision, and 93.3% F1-score for referral and non-referral classification on the Messidor-1 dataset. Extensive experiments on two challenging benchmarks demonstrate that our MMNet achieves significant improvements and outperforms other state-of-the-art DR grading methods.Significance.MMNet has improved the diagnostic efficiency and accuracy of diabetes retinopathy and promoted the application of computer-aided medical diagnosis in DR screening.

Multilevel 2-D Quantum Wavelet Transforms.

Wavelet transform is being widely used in classical image processing. One-dimension quantum wavelet transforms (QWTs) have been proposed. Generalizations of the 1-D QWT into multilevel and multidimension have been investigated but restricted to the quantum wavelet packet transform (QWPTs), which is the direct product of 1-D QWPTs, and there is no transform between the packets in different dimensions. A 2-D QWT is vital for image processing. We construct the multilevel 2-D QWT's general theory. Explicitly, we built multilevel 2-D Haar QWT and the multilevel Daubechies D4 QWT, respectively. We have given the complete quantum circuits for these wavelet transforms, using both noniterative and iterative methods. Compared to the 1-D QWT and wavelet packet transform, the multilevel 2-D QWT involves the entanglement between components in different degrees. Complexity analysis reveals that the proposed transforms offer exponential speedup over their classical counterparts. Also, the proposed wavelet transforms are used to realize quantum image compression. Simulation results demonstrate that the proposed wavelet transforms are significant and obtain the same results as their classical counterparts with an exponential speedup.

Segmentation-Based Color Channel Registration for Disparity Estimation of Dual Color-Filtered Aperture Camera.

Single-lens-based optical range finding systems were developed as an efficient, compact alternative for conventional stereo camera systems. Among various single-lens-based approaches, a multiple color-filtered aperture (MCA) system can generate disparity information among color channels, as well as normal color information. In this paper, we consider a dual color-filtered aperture (DCA) system as the most minimal version of the MCA system and present a novel inter-color image registration algorithm for disparity estimation. This proposed registration algorithm consists of three steps: (i) color channel independent feature extraction; (ii) feature-based adaptive weight disparity estimation; and (iii) color mapping matrix (CMM)-based cross-channel image registration. Experimental results show that the proposed method can not only generate an accurate disparity map, but also realize high quality cross-channel registration with a disparity prior for DCA-based range finding and color image enhancement.

The multi-level and multi-dimensional quantum wavelet packet transforms.

The classical wavelet packet transform has been widely applied in the information processing field. It implies that the quantum wavelet packet transform (QWPT) can play an important role in quantum information processing. In this paper, we design quantum circuits of a generalized tensor product (GTP) and a perfect shuffle permutation (PSP). Next, we propose multi-level and multi-dimensional (1D, 2D and 3D) QWPTs, including a Haar QWPT (HQWPT), a D4 QWPT (DQWPT) based on the periodization extension and their inverse transforms for the first time, and prove the correctness based on the GTP and PSP. Furthermore, we analyze the quantum costs and the time complexities of our proposed QWPTs and obtain precise results. The time complexities of HQWPTs is at most 6 on 2n elements, which illustrates high-efficiency of the proposed QWPTs. Simulation experiments demonstrate that the proposed QWPTs are correct and effective.

Resistance sintering under ultra high pressure: a new approach to produce bulk nanocrystalline refractory metal.

Several different types of commercially available grades of W and Mo powders with nano size (50 nm) and micro size (0.2-3 microm) were sintered by a novel sintering method named as resistance sintering under ultra high pressure in the absence of additive. The sintering effect was characterized through microstructure observation and testing the mechanical properties. It is finds that as very short sintering time (less than 1 min.) is needed, this novel method is an effective technology to fabricate nanocrystalline refractory metal. Density greater than 90% theoretical density was achieved, while the grain size still remained about 50 nm, this grain size is substantially smaller than previously reported. The sintering mechanism of this sintering method is very different from the conventional sintering methods and was primarily analyzed in this paper.