Novel architectures for 1-D and 2-D discrete wavelet transform (DWT) by using lifting schemes are presented in this paper. An embedded decimation technique is exploited to optimize the architecture ...for 1-D DWT, which is designed to receive an input and generate an output with the low- and high-frequency components of original data being available alternately. Based on this 1-D DWT architecture, an efficient line-based architecture for 2-D DWT is further proposed by employing parallel and pipeline techniques, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. This 2-D architecture is called fast architecture (FA) that can perform J levels of decomposition for N*N image in approximately 2N 2 (1-4 -J )/3 internal clock cycles. Moreover, another efficient generic line-based 2-D architecture is proposed by exploiting the parallelism among four subband transforms in lifting-based 2-D DWT, which can perform J levels of decomposition for N*N image in approximately N 2 (1-4 -J )/3 internal clock cycles; hence, it is called high-speed architecture. The throughput rate of the latter is increased by two times when comparing with the former 2-D architecture, but only less additional hardware cost is added. Compared with the works reported in previous literature, the proposed architectures for 2-D DWT are efficient alternatives in tradeoff among hardware cost, throughput rate, output latency and control complexity, etc
Multi-scale convolution can be used in a deep neural network (DNN) to obtain a set of features in parallel with different perceptive fields, which is beneficial to reduce network depth and lower ...training difficulty. Also, the attention mechanism has great advantages to strengthen representation power of a DNN. In this paper, we propose an attention augmented multi-scale network (AAMN) for single image super-resolution (SISR), in which deep features from different scales are discriminatively aggregated to improve performance. Specifically, the statistics of features at different scales are first computed by global average pooling operation, and then used as a guidance to learn the optimal weight allocation for the subsequent feature recalibration and aggregation. Meanwhile, we adopt feature fusion at two levels to further boost reconstruction power, one of which is intra-group local hierarchical feature fusion (LHFF), and the other is inter-group global hierarchical feature fusion (GHFF). Extensive experiments on public standard datasets indicate the superiority of our AAMN over the state-of-the-art models, in terms of not only quantitative and qualitative evaluation but also model complexity and efficiency.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Few-shot segmentation (FSS) is a challenging task because the same class of targets in the support and query images may have different scales, textures and background information. Prototype learning ...(PL) is a current mainstream FSS method, which characterizes the interaction between the prototype vector and query feature. However, the prototype vector commonly based on global average pooling only contains first-order feature information, which is vulnerable to varying appearance of similar target and the diversity of background. Moreover, the auxiliary information of the query image is not fully explored in previous prototype learning methods. In this paper, we propose a dual prototype learning (DPL) based on a second-order prototype (SOP) and self-support first-order prototype with a constraint mechanism (SSFPC) to improve the FSS performance. The SOP can capture higher-order statistical information by averaging the covariance matrix of the feature map. The similarity between the first-order support prototype and the first-order self-support query prototype is introduced to boost the adaptability of the first-order prototype to the query image. The remarkable performance gains on the benchmarks (PASCAL-Formula Omitted and COCO-Formula Omitted) manifest the effectiveness of our method. Our source code will be available at https://github.com/13ww/DPL.git .
The successful synthesis is reported of Mn, Fe, Co, Ni, Cu-doped g-C
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nanoflakes via a simple one-step pyrolysis method, respectively. Among them, the Fe-doped g-C
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nanoflakes exhibited the ...highest peroxidase-like activity, which can be used for colorimetric detection of hydrogen peroxide (H
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nanoflake was also explored and verified the generation of hydroxyl radical (•OH) through fluorescence method. It is believed that the Fe-doped g-C
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nanoflakes as enzyme mimics will greatly promote the practical applications in a variety of fields in the future including biomedical science, environmental governance, antibacterial agent, and bioimaging due to their extraordinary catalytic performance and stability.
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This work delivers a report on development of atomic mobility description with quantified uncertainties directly from concentration profiles via the Markov chain Monte Carlo algorithm as implemented ...in HitDIC software, which was demonstrated in fcc Cu–Ni–Sn ternary system. Different from the traditional mobility evaluation, the present new strategy started from the collection and review of all the concentration profiles available in the literature. Based on the collected concentration profiles, the atomic mobility descriptions and the corresponding quantified uncertainties in fcc Cu–Ni–Sn ternary and its sub-binary systems were directly evaluated. The reliability of the obtained mobility descriptions was firstly validated by the good agreement between the simulated and measured composition profiles, as well as by the calculated and reported inter-, tracer and intrinsic diffusivities. The fit of diffusion quantities to the experimental results as predicted by the present mobilities was also found to be generally better than previous traditional assessments, further verifying the accuracy of the present mobility descriptions. Moreover, the estimated mobility uncertainties suggest that the stability and reliability of mobility descriptions highly depend on the quantity and quality of the input concentration profiles. The successful demonstration of the present work indicates that the high-quality atomic mobility database in various alloys can be efficiently established by combining the HitDIC software and reliable concentration profiles.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•A new image representation scheme using block compressive sensing (CS) for encrypted image compression is proposed.•A coefficient randomly permutation (CRP) technique in DCT domain is proposed.•A ...new BCS adaptive sampling (AS) scheme is proposed.
The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Non-local similarity-based group sparse representation (GSR) has shown great potential in image restoration. Considering the universal existing non-stationarity of natural images and the statistic ...characteristic differences of different components in the sparse domain of image patch group, this paper proposes a new image compressive sensing reconstruction (ICSR) algorithm based on z-score standardized group sparse representation (ZSGSR). Specifically, the image is first partitioned into overlapping patches, and the similar patch groups are further generated to be decomposed by adaptive PCA dictionary; then, the resulting sparse coefficients are performed component-wise on z-score standardization; finally, the <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> norm of the standardized sparse coefficients are used to regularize the ICSR. The reconstruction model is solved by splitting Bregman iteration (SBI) and soft threshold shrinking algorithms. The z-score standardization could enhance sparse representation ability, which reflects the importance of different sparse coefficients well; this is beneficial to effectively preserve the crucial small coefficients and to better recovery, the edges and texture details of images, thus improving the reconstructed image quality. Using objective and subjective quality evaluation, the extensive experiments show that the proposed method can obtain a better performance than the existing state-of-the-art algorithms.
A fast and efficient hardware implementation for computing the Singular value decomposition（SVD） and Eigenvalue decomposition（EVD） is presented.Considering that the SVD and EVD are complex and ...expensive operations, to achieve high performance with low computing complexity, our approach takes full advantage of the combination of parallel and sequential computation, which can increase efficiently the hardware utilization. Besides, regarding to EVD, we propose a hardware solution of a simplified Coordinate rotation digital computer（CORDIC）-like algorithm which can obtain higher speed. The performance analysis and comparison results show that the proposed methods can be realized on Filedprogrammable gate arrays（FPGAs） with less computation time by using systolic array. It will be shown that the proposed implementation could be an efficient alternative for real-time applications.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
MicroRNAs (miRNAs) as a well-known kind of cancer marker are closely associated with the formation and metastasis of tumors. Here, a novel tetraphenylethylene (TPE)-doped covalent organic frameworks ...(TPE-COFs) with strong aggregation-induced electrochemiluminescence (AIECL) response was synthesized and introduced to construct an ultrasensitive biosensor for the detection of miRNA-21. The strong aggregation-induced emission (AIE) response was obtained because the molecular motion of TPE was restricted by COFs which had the porosity and highly ordered topological structure. Meanwhile, the porous structure of COFs allowed TPE to react with electrochemiluminescence (ECL) coreactants more effectively. Furthermore, COFs significantly improved the electron transport efficiency of the entire ECL system. All of these endowed the TPE-COFs with superior AIECL performance. Then, a TPE-COFs based ECL resonance energy transfer (ECL-RET) system was constructed for ultrasensitive miRNA-21 biosensing with differential signal readout. The proposed assays exhibited excellent sensitivity with a wide dynamic range from 10 aM to 1 pM and a low detection limit of 2.18 aM. Therefore, these indicated that doping TPE in COFs was a creative way to develop functional COFs and provided an effective way for enhancing AIECL. Furthermore, this work boarded the application of AIECL in analytical chemistry.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Most patients with lung cancer still die from their disease, necessitating additional options to improve treatment. Here, we provide evidence for targeting CD22, a cell adhesion protein known to ...influence B-cell survival that we found is also widely expressed in lung cancer cells. In characterizing the antitumor activity of an established anti-CD22 monoclonal antibody (mAb), HB22.7, we showed CD22 expression by multiple approaches in various lung cancer subtypes, including 7 of 8 cell lines and a panel of primary patient specimens. HB22.7 displayed in vitro and in vivo cytotoxicity against CD22-positive human lung cancer cells and tumor xenografts. In a model of metastatic lung cancer, HB22.7 inhibited the development of pulmonary metastasis and extended overall survival. The finding that CD22 is expressed on lung cancer cells is significant in revealing a heretofore unknown mechanism of tumorigenesis and metastasis. Our work suggests that anti-CD22 mAbs may be useful for targeted therapy of lung cancer, a malignancy that has few tumor-specific targets.
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