A Novel Adaptive Deskewing Algorithm for Document Images.

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image's layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC'2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm.

TransConver: transformer and convolution parallel network for developing automatic brain tumor segmentation in MRI images.

Background: Medical image segmentation plays a vital role in computer-aided diagnosis (CAD) systems. Both convolutional neural networks (CNNs) with strong local information extraction capacities and transformers with excellent global representation capacities have achieved remarkable performance in medical image segmentation. However, because of the semantic differences between local and global features, how to combine convolution and transformers effectively is an important challenge in medical image segmentation. Methods: In this paper, we proposed TransConver, a U-shaped segmentation network based on convolution and transformer for automatic and accurate brain tumor segmentation in MRI images. Unlike the recently proposed transformer and convolution based models, we proposed a parallel module named transformer-convolution inception (TC-inception), which extracts local and global information via convolution blocks and transformer blocks, respectively, and integrates them by a cross-attention fusion with global and local feature (CAFGL) mechanism. Meanwhile, the improved skip connection structure named skip connection with cross-attention fusion (SCCAF) mechanism can alleviate the semantic differences between encoder features and decoder features for better feature fusion. In addition, we designed 2D-TransConver and 3D-TransConver for 2D and 3D brain tumor segmentation tasks, respectively, and verified the performance and advantage of our model through brain tumor datasets. Results: We trained our model on 335 cases from the training dataset of MICCAI BraTS2019 and evaluated the model's performance based on 66 cases from MICCAI BraTS2018 and 125 cases from MICCAI BraTS2019. Our TransConver achieved the best average Dice score of 83.72% and 86.32% on BraTS2019 and BraTS2018, respectively. Conclusions: We proposed a transformer and convolution parallel network named TransConver for brain tumor segmentation. The TC-Inception module effectively extracts global information while retaining local details. The experimental results demonstrated that good segmentation requires the model to extract local fine-grained details and global semantic information simultaneously, and our TransConver effectively improves the accuracy of brain tumor segmentation.

Potential of ITO thin film for electrical probe memory applications.

Electrical probe memory has received considerable attention during the last decade due to its prospective potential for the future mass storage device. However, the electrical probe device with conventional diamond-like carbon capping and bottom layers encounters with large interfacial contact resistance and difficulty to match the experimentally measured properties, while its analog with titanium nitride capping and bottom layers also faces serious heat dissipation through either probe and silicon substrate. Therefore, the feasibility of using indium tin oxide (ITO) media for the capping and bottom layers of the electrical probe device is investigated by tailoring the thickness and electrothermal properties of the ITO capping and bottom layers within experimentally established range and subsequently calculating the resultant temperature at several predefined points based on a previously developed three-dimensional model. To meet the required temperature and to fit the experimentally reported values, the thickness, electrical conductivity, and thermal conductivity of the ITO capping and bottom layers are found to be 5 nm, 103 Ω-1 m-1, 0.84 W m-1 K-1, and 200 nm, 1.25 × 106 Ω-1 m-1, 0.84 W m-1 K-1, respectively. The practicality of using this optimized device to achieve ultrahigh density, ultralow energy consumption, ultrafast switching speed, low interfacial contact resistance, and high thermal reliability has also been demonstrated.

Amorphization Optimization of Ge2Sb2Te5 Media for Electrical Probe Memory Applications.

Electrical probe memory using Ge2Sb2Te5 media has been considered a promising candidate in the future archival storage market due to its potential for ultra-high density and long data retention time. However, most current research efforts have been devoted to the writing of crystalline bits using electrical probe memory while ignoring the viability of writing amorphous bits. Therefore, this paper proposes a physical, realistic, full three-dimensional model to optimize the practicable media stack by spatially and temporally calculating temperature distributions inside the active media during the writing of amorphous bits. It demonstrates the feasibility of using an optimized device that follows a Silicon/Titanium Nitride/Ge2Sb2Te5/Diamond-Like Carbon design with appropriate electro-thermal properties and thickness to achieve ultra-high density, low energy consumption, and a high data rate without inducing excessive temperature. The ability to realize multi-bit recording and rewritability using the designed device is also proven, making it attractive and suitable for practicable applications.

An Application of Chalcogenide Alloy Other than Storage Memory Field.

BACKGROUND: The necessity to handle mechanical functionality at nanoscale has recently motivated the prosperity of the nanoelectromechanical systems (NEMs). The fabrication of NEMS strongly depends on the so-called "topdown" techniques that are however limited by the resolution of electronbeam lithography. Meanwhile, the size of the NEMS needs to be shrunk continuously in order to further enhance the system performance. As a result, current research interest has been dedicated to "bottomup" techniques or even a hybridization of two aforementioned approaches, leading to the presence of the nanowire-based NEMs. Here, we presented some recent patent for nanowire-based NEMS. METHODS: We investigate the resonant frequency and the frequency tuneability of the nanowire-based nanoelectromechanical system using Ge2Sb2Te5 media. By varying the nanowire dimensions, corresponding resonant frequencies and frequency tuneability are calculated using an established mechanical model. RESULTS: We theoretically study the frequency tuneability of the nanowire-based NEMs using GST media. The resonant frequencies and the corresponding frequency tuneabilities for different nanowire dimensions are investigated using a developed mechanical model, and a previously established electrothermal model is performed to imitate the frequency tuning behavior of the system along with the phase-change phenomenon. By carefully controlling the amorphous fraction of the active region, a very high resonant frequency can be tuned within an ultra-high adjustable bandwidth. In addition, the merits of the phase-change memories including great scalability, low power consumption, fast transition time, and non-volatility can be also found on the proposed system. These results will open up a route for designing the next generation NEMs, and also pioneer a new application field for the GST media. CONCLUSIONS: Today phase-change materials have received a wide range of applications from nonvolatile memories to neuromorphic networks due to its unique combinations of structural, electrical, and thermal properties. However, as the mechanical properties of phase-change materials exhibits a remarkable difference between the amorphous and crystalline phases, the feasibility of continuously changing the resonant frequency of the nanowires based on phase-change materials becomes viable.

The Experimental Demonstration of the Optimized Electrical Probe Memory for Ultra-High Density Recording.

BACKGROUND: A theoretical model has been previously proposed to optimize the structure of the electrical probe memory system, whereby the optimal thickness and resistivity of DLC capping layer and TiN under layer are predicted to be 2 nm, 0.01 Ωm, and 40 nm, 2×10-7 Ωm,respectively However, there is no experimental evidence to show that such a media stack can be fabricated in reality by the time of writing and few patents regarding this intriguing topic have been reviewed and cited. METHODS: In order to realize this optimized design experimentally, the thickness dependent resistivity for both DLC and TiN film are assessed, from which it is not possible to obtain a media stack with exactly the same properties as the optimized design. Therefore, the previously proposed architecture is re-optimized using the measured properties values, and the capability of using the modified memory architecture to provide ultra-high density, high data rate, and low energy consumption is demonstrated. RESULTS: The results show that it is difficult to experimentally attain an electrical probe memory with exactly the same properties values as the optimized counterpart. CONCLUSIONS: An optimized electrical probe memory structure that includes a DLC capping layer and TiN under layer was previously proposed according to a parametric approach, while the practicality of realizing such a media stack experimentally has not bee investigated. In order to assess its practical feasibility, we first measured the electrical resistivities of DLC and TiN films for different thicknesses. In this case, for the purpose of optimizing the memory system with appropriate, but more physically realistic properties values, we re-designed the architecture using the measured properties, and the modified system is able to provide ultra-high density, large data rate, and low energy consumption.

The Oxidation Behaviour of Diamond Like Carbon for Phase-Change Probe Memory Application.

Phase-change probe memory, as a promising candidate for next-generation storage device, usually requires a capping layer to protect phase-change media from wear and corrosion. Diamond-like carbon film has been commonly used for capping layer due to its high mechanical hardness and easiness for tailoring physical properties. However, the possibility for such carbon thin film to react to surrounding oxygen when subjected to Joule heating during the recording process of phase-change probe memory is rarely investigated before from both experimental and simulation point of view. Therefore, a novel carbon oxidation model was developed to mimic the chemical reaction of carbon film to the surrounding oxygen in terms of the degradation of layer thickness. Results obtained from this model are in a good agreement with the experimental counterpart, indicating the physical reality of this proposed model.

Denoising 3D MR images by the enhanced non-local means filter for Rician noise.

The non-local means (NLM) filter removes noise by calculating the weighted average of the pixels in the global area and shows superiority over existing local filter methods that only consider local neighbor pixels. This filter has been successfully extended from 2D images to 3D images and has been applied to denoising 3D magnetic resonance (MR) images. In this article, a novel filter based on the NLM filter is proposed to improve the denoising effect. Considering the characteristics of Rician noise in the MR images, denoising by the NLM filter is first performed on the squared magnitude images. Then, unbiased correcting is carried out to eliminate the biased deviation. When performing the NLM filter, the weight is calculated based on the Gaussian-filtered image to reduce the disturbance of the noise. The performance of this filter is evaluated by carrying out a qualitative and quantitative comparison of this method with three other filters, namely, the original NLM filter, the unbiased NLM (UNLM) filter and the Rician NLM (RNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance over the other filters being compared.