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Constructing composite electrodes is considered to be a feasible way to realize high-specific-capacity Li-ion batteries. The core-double-shell-structured Si@C@TiO2 would be an ideal design for such batteries, considering that carbon (C) can buffer the volume change and TiO2 can constrain the structural deformation of Si. Although the electrochemical performance of the shells themselves is relatively clear, the complexity of the multishell heterointerface always results in an ambiguous understanding about the influence of the heterointerface on the electrochemical properties of the core material. In this work, a multilayer film model that can simplify and simultaneously expand the area of the heterointerface is used to study the heterointerfacial behavior. First, a multilayer film TiO2/C with different numbers of TiO2/C heterointerfaces is studied. It shows that the electrochemical performance is enhanced apparently by increasing the number of TiO2/C heterointerfaces. On the one hand, the TiO2/C heterointerface exhibits a strong lithium-ion storage capacity. On the other hand, the TiO2/C heterointerface appears to effectively promote the local Li-ion concentration gradient and thus boost the Li-ion transport kinetics. Then, TiO2/C is combined with Si to construct a composite anode Si/C/TiO2. An obvious advantage of TiO2/C over single TiO2 and C is observed. The utilization rate of Si is greatly improved in the first cycle and reaches up to 98% in Si/C/TiO2. The results suggest that the electrochemical performance of Si can be greatly manipulated by the heterointerface between the multishells.

Lithium-ion battery (LIB) is the dominant energy storage device for the application on electric vehicles and portable electronic devices. Up to now, the enhancement of LIBs’ energy density mainly depends on the improvement of anode’s capacity. Due to the obstacle of large volume effect and low rate performance, the use of intrinsic high-capacity anodes, such as silicon, is still in the laboratory stage. Combining battery and capacitor to improve the electrochemical performance of the commercial carbon anode is an alternative and effective strategy in practical application. In this paper, a high nitrogen (N) content (8.2%) carbon is obtained by pyrolyzing the melamine formaldehyde (MF) foam resin at low temperature (600 °C). It delivers a stable specific capacity of around 600 mAh g−1 at 0.1C. No significant capacity degradation appears after 200 cycles, and the coulombic efficiency maintained over 99.5%. Even at 1.2 C, a specific capacity of 350 mAh g−1 is delivered and so a Li-ions diffusion coefficient of 1.8×10–13 cm2s-1. The reason of the superior electrochemical performance of the high N-doped carbon anode is the synergistic effect of battery and capacitor. The calculated pseudocapacitance proportion is from 32 to 61% at scan rate from 0.1 to 1mV s−1.

Titanium dioxide (TiO2) is proposed as a promising anode material for lithium-ion batteries (LIBs) due to its highly stable structure and slight side reaction at the electrode/electrolyte interface...

Plug-and-play (PnP) is a powerful framework that applies off-the-shelf denoisers to regularize imaging inverse problems in an iterative style. Remarkably, in several restoration applications, this kind of regularization exhibits promising behaviors. Among the algorithms derived from the framework, the ISTA-based (PnP-ISTA) has attracted much attentions due to the effectiveness and simple update rule in iterations. And its convergence analysis has become a fundamental topic. Most recently, the theoretical convergence of PnP-ISTA with kernel denoisers has been established, where the kernel is generic (relaxing the reliance on special properties) and thus beneficial for wider applications. In there, the convergence proof focuses on fixed kernels. Note that, the denoisers with adaptive kernels usually achieve more powerful performance than those with fixed ones. Inspired by the preliminary observation, we extend the fixed kernels to adaptive ones for the denoisers in PnP-ISTA. Under a mild assumption, we prove the convergence theoretically. Meanwhile, for inpainting, an important scenario, we broaden the interval of the step size from $({0,1})$ to $({0,2})$ , which still guarantees the convergence. Experimental results agree with our theoretical conclusions.

Due to its local property, guided image filter (GIF) generally suffers from halo artifacts near edges. To make up for the deficiency, a weighted guided image filter (WGIF) was proposed recently by incorporating an edge-aware weighting into the filtering process. It takes the advantages of local and global operations, and achieves better performance in edge-preserving. However, edge direction, a vital property of the guidance image, is not considered fully in these guided filters. In order to overcome the drawback, we propose a novel version of GIF, which can leverage the edge direction more sufficiently. In particular, we utilize the steering kernel to adaptively learn the direction and incorporate the learning results into the filtering process to improve the filter’s behavior. Theoretical analysis shows that the proposed method can get more powerful performance with preserving edges and reducing halo artifacts effectively. Similar conclusions are also reached through the thorough experiments including edge-aware smoothing, detail enhancement, denoising, and dehazing.

In nanocomposite electrodes, besides the synergistic effect that takes advantage of the merits of each component, phase interfaces between the components would contribute significantly to the overall electrochemical properties. However, the knowledge of such effects is far from being well developed up to now. The present work aims at a mechanistic understanding of the phase interface effect in C@TiO

Guided (joint) image filters play an important role in many computer vision and image processing applications. The main principle behind these filters is transferring the structural information from a guidance image to an input one. However, in practice, the structures between the two images are not always consistent. As a result, the filtering outputs become sensitive to outliers, which easily leads to texture-copying artifacts. Most recently, by relaxing the dependence on the guidance, static/dynamic (SD) filter overcomes the drawback effectively. With the SD strategy, this filter can jointly leverage structural information from the guidance and input. However, due to the locality of its regularizer, SD is prone to another deficiency, i.e., edge blurring. To tackle this problem, in our work, we extend SD filter to a nonlocal version [nonlocal static/dynamic (NSD)]. Particularly, a nonlocal regularizer is first established in a subspace transformed by partial least squares, which can better respect the unequal roles of the images. Then, to efficiently formulate the structural consistency between the two images (guidance and input), a novel joint term is plugged into the regularizer. Finally, an acceleration approach is designed to reduce the computational complexity induced by the nonlocal extension, which makes NSD achieve a comparable running time in practice. Thorough experimental results demonstrate that the proposed filter not only can avoid texture copy effectively but also can preserve edges powerfully.

Tin/carbon (Sn/C) nanocomposite is considered as a promising anode material for high-performance Li-ion batteries (LIBs). However, since the carbon matrix is always derived from high-temperature carbonization of polymers and Sn has a low melting point (232 °C), the Sn nanoparticles in the Sn/C tend to be heavily aggregated during the carbonization process. It is thus challenging to synthesize well-dispersed Sn nanoparticles in a carbon matrix. Here, we report a facile templating method to encapsulate uniform well-dispersed Sn nanoparticles in amorphous carbon tube (Sn@aCT). The electrode fabricated with the hierarchical Sn@aCT exhibits excellent cycle performance. A stable specific capacity of 870 mAh g−1 after 350 cycles and a Li-ion diffusion coefficient as high as 1.2 × 10 − 12 cm 2 s − 1 are obtained. Meanwhile, the intermediate structure of SnO2@aCT and a carbon-coated Sn yolk-shell nanostructure (Sn@C-YS) are investigated for comparison. The results further manifest the advantage of the architecture of the Sn@aCT. Our strategy provides a feasible way to optimize Sn/C nanocomposite as a high-performance anode material for LIBs.

As an active tool for image segmentation, the classical fuzzy c-means (FCM) clustering algorithm is sensitive to noises. To overcome the weakness, several improved versions of FCM have been developed. Compared with the original one, these methods are more robust to noises. However, they are still not suitable for complex noises. To get better image segmentation results, especially in complex noisy scenarios, we provide a universal nonlocal FCM algorithm (UNFCM) by regularizing the objection function with a universal nonlocal spatial term. The experimental results on synthetic and real images suggest that the proposed UNFCM can yield higher quality segmentation results both in classification accuracy and visual effects.

In nanocomposite electrodes, besides the synergistic effect that takes advantage of the merits of each component, phase interfaces between the components would contribute significantly to the overall electrochemical properties. However, the knowledge of such effects is far from being well developed up to now. The present work aims at a mechanistic understanding of the phase interface effect in C@TiO2core-shell nanocomposite anode which is both scientifically and industrially important. Firstly, amorphous C, anatase TiO2and C@anatse-TiO2electrodes are compared. The C@anatase-TiO2shows an obvious higher specific capacity (316.5 mAh g-1at a current density of 37 mA g-1after 100 cycles) and Li-ion diffusion coefficient (4.0 × 10-14cm2s-1) than the amorphous C (178 mAh g-1and 2.9 × 10-15cm2s-1) and anatase TiO2(120 mAh g-1and 1.6 × 10-15cm2s-1) owing to the C/TiO2phase interface effect. Then, C@anatase/rutile-TiO2is obtained by a heat treatment of the C@anatase-TiO2. Due to an anatase-to-rutile phase transformation and diffusion of C along the anatase/rutile phase interface, additional abundant C/TiO2phase interfaces are created. This endows the C@anatase/rutile-TiO2with further boosted specific capacity (409.4 mAh g-1at 37 mA g-1after 100 cycles) and Li-ion diffusion coefficient (3.2 × 10-13cm2s-1), and excellent rate capability (368.6 mAh g-1at 444 mA g-1). These greatly enhanced electrochemical properties explicitly reveal phase interface engineering as a feasible way to boost the electrochemical performance of nanocomposite anodes for Li-ion batteries.

Which kind of edges should be preserved in texture smoothing, that is a major concern for experts. More recently, co-occurrence filter (CoF) gives a refreshing answer: in contrast to ordinary edges, the prominent structure edges (i.e., boundaries) ought to be given a prior consideration. In texture smoothing, CoF employs co-occurrence matrix to separate different edges in an image to preserve structures. However, the collection of co-occurrence information in the filter is pixel-wise, which is fragile to noises. To overcome the drawback, we extend CoF to a novel version, named patch-based co-occurrence filter with fast adaptive kernel (FAKPCoF). Different from acquiring pixel-wise co-occurrence matrix, FAKPCoF collects the co-occurrence information based on patches, which is more robust to noises. And rather than spatial Gaussian in CoF, an adaptive kernel is employed to keep the sharpness of edges. Meanwhile, to break through the computational bottleneck induced by the adaptive operation, a fast variant of the adaptive kernel is designed. Thorough experimental results demonstrate that the proposed FAKPCoF reveals more powerful performance both in texture smoothing and structure-preserving.

An instance is often represented from different aspects (views or modalities), which leads to high-dimensional features and even multiple labels. In this paper, we focus on the feature selection problem in multi-label classification, for which a trivial solution is handling the labels dividedly. Obviously, such a scheme may not work well by leaving the label relationship out of consideration. Recently, several research works conduct feature selection directly under a multi-label framework by implicitly or explicitly modeling label relationship. However, these works assume that all labels share the same feature subset or subspace, which is not reasonable enough for some scenarios since different labels tend to convey different semantics. To address this problem, we develop a novel approach in this paper to select label-dependent features for multi-label classification. Specifically, we (1) formulate a convex model based on a more general and practical assumption that different labels convey different semantics with specific features; (2) design an alternating optimization algorithm based on Nesterov's method and L1-ball projection for efficiently finding the optimal solution, which can realize multi-label classification, feature selection, and label relationship estimation simultaneously. Finally, experiments on publicly available datasets show that the proposed algorithm achieves better performance than several related methods.

Using coal tar pitch (CTP), a byproduct from coal tar distillation, as precursor, with the assistance of a common polymer (ammonium polyphosphate), we develop a one-step green synthesis route to prepare nitrogen and phosphorus co-doped porous graphene-like carbon (designated as N,P-PGC). The obtained products possess a high surface area and micropore attributing to the activation of the generated H3PO4. We have also demonstrated the potential applications of the resultant N,P-PGC as high-performance electrode material for supercapacitors. The materials possess a high capacitance of 219 F/g in 6 M KOH electrolyte at the current density of 0.5 A/g and excellent cycle stability with 95.6% capacitance retention after 10000 cycles, which can be attributed to the synergetic effect of (1) the electric double layer capacitance that came from the uniform porosities developed by in situ activation; and (2) pseudo-capacitance that originated from rich and tunable surface group by co-doping. Thus this work provides a low-cost and simple synthetic strategy to complete the transformation of byproduct to high value-added products.

Growing cupric oxide (CuO) nanowires (NWs) through thermal oxidation of copper (Cu) in air is a facile and low-cost strategy. In a temperature range of 673–1073 K, single-crystal, bi-crystal NWs and cone-shaped nanostructure usually coexist. As far as the underlying mechanism is concerned, the stress-driven grain-boundary (GB) diffusion mechanism has been the most successful theory up to now. However, it did not include the influence of oxygen partial pressure and cannot explain the growth of long single-crystal CuO NWs of uniform diameters. Thus, the CuO NWs growth mechanism is yet to be fully understood. In this study, we present that synergistic effects of the Kirkendall effect, stress-driven GB diffusion, large surface free energy gap between Cu2O and CuO facets and an effective electric field along the CuO NWs originating from oxygen gradient are mainly involved in the thermal growth of CuO NWs.

A nonlocal operator model for morphological image processing is proposed in this paper. Compared with the previous nonlocal morphological operators, the derivations of the proposed model are easier to inherit useful properties and physical interpretations from the traditional morphology. Two important properties of the model have been clarified in theory. And for instances, based on the model, two basic morphological operators (i.e., erosion and dilatation) are developed with convincing experimental results. In addition, the generality and usability of the model are also discussed.

As an effective patch-based denoising method, non-local means (NLM) method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The implementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the framework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results.

Noises are inevitably introduced in digital image acquisition processes, and thus image denoising is still a hot research problem. Different from local methods operating on local regions of images, the non-local methods utilize non-local information (even the whole image) to accomplish image denoising. Due to their superior performance, the non-local methods have recently drawn more and more attention in the image denoising community. However, these methods generally do not work well in handling complicated noises with different levels and types. Inspired by the fact in machine learning field that multi-kernel methods are more robust and effective in tackling complex problems than single-kernel ones, we establish a general non-local denoising model based on multi-kernel-induced measures (GNLMKIM for short), which provides us a platform to analyze some existing and design new filters. With the help of GNLMKIM, we reinterpret two well-known non-local filters in the united view and extend them to their novel multi-kernel counterparts. The comprehensive experiments indicate that these novel filters achieve encouraging denoising results in both visual effect and PSNR index.

Non-Local Euclidean Medians (NLEM) has recently been proposed and shows more effective than Non-Local Means (NLM) in removing heavy noise. In this letter, we find the inconsistency between the two dissimilarity measures in NLEM can affect its robustness, thus develop an improved version (INLEM) to compensate such an inconsistency. Further, we provide a concise convergence proof for the iterative algorithm used in both NLEM and INLEM. Finally, our experiments on synthetic and natural images show that INLEM achieves encouraging results.

Despite a state-of-the-art filter for removing Gaussian noise, non-local means filter (NLM), like its local counterpart (the mean filter), is no longer so effective in removing salt–pepper noise which is common in real world as well. By contrast, adaptive median filter (AMF) is concise and can remove this type of noise effectively. Inspired by the AMF filtering strategies, in this paper, we modify NLM to a novel non-local universal filter (UNLM) which can remove not only either of Gaussian noise and salt–pepper noise but also their mixture. Experiments on artificial and benchmark images validate its feasibility and effectiveness.

Large amount of information is random in image processing. To some extent, type-1 fuzzy sets can reduce the effect of uncertainties. Fuzzy sets theory can provide us with knowledge-based and robust tools for image processing. But there are many situations where it needs to be extended to type-2 fuzzy sets because it can also be difficult to determine the crisp membership function itself. By computing the fuzziness of the pixels' corrupted degree, a new image filter based on type-2 fuzzy sets for impulse noise is presented in this paper. Comparing with some other filters, this new filter is more effective. In the end, simulation results show that the new algorithm is feasible.