MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Multi-scale Attention Dilated Residual Image Denoising Network Based on Skip Connection

In the field of image denoising, deep learning technology holds a dominance. However, the current network model tends to lose fine-grained information with the depth of the network. To address this issue, this paper proposes a Multi-scale Attention Dilated Residual Image Denoising Network(MADRNet) based on skip connection, which consists of Dense Interval Transmission Block(DTB), Sparse Residual Block(SRB), Dilated Residual Attention Reconstruction Block(DRAB) and Noise Extraction Block(NEB). The DTB enhances the classical dense layer by reducing information redundancy and extracting more accurate feature information. Meanwhile, SRB improves feature information exchange and model generalization through the use of sparse mechanism and skip connection strategy with different expansion factors. The NEB is primarily responsible for extracting and estimating noise. Its output, together with that of the sparse residual module, acts on the DRAB to effectively prevent loss of shallow feature information and improve denoising effect. Furthermore, the DRAB integrates an dilated residual block into an attention mechanism to extract hidden noise information while using residual learning technology to reconstruct clear images. We respectively examined the performance of MADRNet in gray image denoising, color image denoising and real image denoising. The experiment results demonstrate that proposed network outperforms some excellent image denoising network in terms of peak signal-to-noise ratio, structural similarity index measurement and denoising time. The proposed network effectively addresses issues associated with the loss of detail information.

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks(GANs)are paid more attention to dealing with the end-to-end speech enhancement in recent years.Various GANbased enhancement methods are presented to improve the quality of reconstructed speech.However,the performance of these GAN-based methods is worse than those of masking-based methods.To tackle this problem,we propose speech enhancement method with a residual dense generative adversarial network(RDGAN)contributing to map the log-power spectrum(LPS)of degraded speech to the clean one.In detail,a residual dense block(RDB)architecture is designed to better estimate the LPS of clean speech,which can extract rich local features of LPS through densely connected convolution layers.Meanwhile,sequential RDB connections are incorporated on various scales of LPS.It significantly increases the feature learning flexibility and robustness in the time-frequency domain.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes.It indicates that our method is more generalized in untrained conditions.

An Experimental Study on the Use of Fonio Straw and Shea Butter Residue for Improving the Thermophysical and Mechanical Properties of Compressed Earth Blocks

The efficient use of building materials is one of the responses to increasing urbanization and building energy consumption. Soil as a building material has been used for several thousand years due to its availability and its usual properties improving and stabilization techniques used. Thus, fonio straws and shea butter residues are incorporated into tow soil matrix. The objective of this study is to develop a construction eco-material by recycling agricultural and biopolymer by-products in compressed earth blocks (CEB) stabilization and analyze these by-products’ influence on CEB usual properties. To do this, compressed stabilized earth blocks (CSEB) composed of clay and varying proportion (3% to 10%) of fonio straw and shea butter residue incorporated were subjected to thermophysical, flexural, compressive, and durability tests. The results obtained show that the addition of fonio straw and shea butter residues as stabilizers improves compressed stabilized earth blocks thermophysical and mechanical performance and durability. Two different clay materials were studied. Indeed, for these CEB incorporating 3% fonio straw and 3% - 10% shea butter residue, the average compressive strength and three-point bending strength values after 28 days old are respectively 3.478 MPa and 1.062 MPa. In terms of CSEB thermal properties, the average thermal conductivity is 0.549 W/m·K with 3% fonio straw and from 0.667 to 0.798 W/m. K is with 3% - 10% shea butter residue and the average thermal diffusivity is 1.665.10-7 m2/s with 3% FF and 2.24.10-7 m2/s with 3.055.10-7 m2/s with 3% - 10% shea butter residue, while the average specific heat mass is between 1.508 and 1.584 kJ/kg·K. In addition, the shea butter residue incorporated at 3% - 10% improves CSEB water repellency, with capillary coefficient values between 31 and 68 [g/m2·s]1/2 and a contact angle between 43.63°C and 86.4°C. Analysis of the results shows that, it is possible to use these CSEB for single-storey housing construction.

Speech Enhancement via Mask-Mapping Based Residual Dense Network

Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network(DNN).But the mapping-based methods only utilizes the phase of noisy speech,which limits the upper bound of speech enhancement performance.Maskingbased methods need to accurately estimate the masking which is still the key problem.Combining the advantages of above two types of methods,this paper proposes the speech enhancement algorithm MM-RDN(maskingmapping residual dense network)based on masking-mapping(MM)and residual dense network(RDN).Using the logarithmic power spectrogram(LPS)of consecutive frames,MM estimates the ideal ratio masking(IRM)matrix of consecutive frames.RDN can make full use of feature maps of all layers.Meanwhile,using the global residual learning to combine the shallow features and deep features,RDN obtains the global dense features from the LPS,thereby improves estimated accuracy of the IRM matrix.Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments.Specifically,in the untrained acoustic test with limited priors,e.g.,unmatched signal-to-noise ratio(SNR)and unmatched noise category,MM-RDN can still outperform the existing convolutional recurrent network(CRN)method in themeasures of perceptual evaluation of speech quality(PESQ)and other evaluation indexes.It indicates that the proposed algorithm is more generalized in untrained conditions.

Analysis of incidence of residue neuromuscular blockade for rocuronium and cisatracurium

Objective:To observe the incidence of residual neuromuscular blockade at the end of operation and during tracheal extubation, and analyze the risk factors causing residual neuromuscular blockade by judging the degree of muscle relaxation according to clinical signs when after using rocuronium or cis-atracurium in general anesthesia.Methods: 500 adults were implemented with propofol-remifentanil intravenous anesthesia or sevoflurane inhalation anesthesia. Rocuronium and cis-atracurium were given, respectively. The TOFr was observed with blind method by TOF Watch SX monitor during anesthesia.Results: The mean TOFr=0.53±0.38 at the end of operation,including 275 cases of 0<TOFr<0.9 and 112 cases of TOFr=0. The mean TOFr=0.97±0.12 at extubation, including 60 cases of TOFr<0.9. The incidence of residual neuromuscular blockade at extubation showed an increasing trend with the increase of age or body mass index. The average TOFr value at extubation, which interval time over 10 min after neostigmine administration to extubation was significant higher than that of interval time less than 10 min.Conclusions:There has 12% patients with TOFr<0.9 when extubation by estimating rocuronium and cis-atracurium effect with clinical signs and experience, it has a hidden danger of residual neuromuscular blockade. The main risk factors to increasing the incidence of residual neuromuscular blockade are growing old and the short time of administrating muscle relaxants or neostigmine to extubation.

Portland Cement-Residues-Polymers Composites and Its Application to the Hollow Blocks Manufacturing

Agricultural wastes and sawdust combined with cement matrix in the manufacture of building elements has been practiced with success in developed countries. In this study, sawdust from wood species (Pinus caribaea and Eucalyptus grandis) and an agricultural waste—rice husk (Oriza sativa) were combined with Portland cement type V (high initial strength), modified by polymer styrene-butadiene (SBR) addition. Hollow blocks produced with Eucalyptus grandis and rice husk residues showed better compressive strength;however, those produced with residues derived from Pinus caribaea presented non-satisfactory results, due to the particle size that was used.

Comparative Study of Thermal Comfort Induced from Masonry Made of Stabilized Compressed Earth Block vs Conventional Cementitious Material

This paper investigates the stabilization effect on compressed earth blocks (CEB) produced from quartz-kaolinite rich earthen material stabilized with 0% - 25% calcium carbide residue (CCR). The paper evaluated various physico-thermal properties of the stabilized CEB and thermal comfort in the model building made of CEB masonry. The optical properties of CEB were evaluated from the mineral composition of the earthen material and CCR and apparent density of the CEB. A simulation was carried out on naturally ventilated model building whose masonry is made of CCR stabilized CEB comparing to the so-called conventional cementitious materials such as cement blocks and concrete. The results showed a decrease of the apparent density of the CEB from 2100 kg·m&ndash;3 for unstabilized CEB (0% CCR) to 1600 kg·m&ndash;3 for 25% CCR stabilized CEB. The thermal conductivity and depth of penetration of the heat flux on a 24 hours period of CEB respectively decreased from 1 W·m&ndash;1·K&ndash;1 and 12.7 cm for 0% CCR-CEB to 0.5 W·m&ndash;1·K&ndash;1 and 10.2 cm for 25% CCR-CEB. The emissivity, solar absorptivity and visible absorptivity of the CEB respectively decreased from 0.82, 0.82 and 0.82 for 0% CCR-CEB to 0.80, 0.64 and 0.64 for 25% CCR-CEB. The number of hours of warm and humid thermal discomfort was impacted for stabilized CEB based masonry in comparison with cement based masonry. The warm discomfort in building made of 20% CCR-CEB masonry was 400 hours lesser than that in building made of hollow cement blocks masonry. If air conditioning system is used to keep the indoor temperature below 28°C, the economy of 310,000 CFA francs (535 USD) is made every year on energy consumption for cooling in the model building made of 20% CCR-CEB masonry, corresponding to 9.6% less, with respect to that made of hollow cement blocks masonry.

Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

In block ciphers,the nonlinear components,also known as sub-stitution boxes(S-boxes),are used with the purpose of inducing confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers(GI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.But the prime field dependent on the Elliptic curve(EC)provides one S-box at a time by fixing three parameters a,b,and p.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.

CIRBlock:融合低代价卷积的轻量反向残差模块

针对轻量级卷积神经网络MobileNet采用的反向残差结构仍具有较多的冗余计算的问题,构建了一种更为轻量的反向残差模块(cheap inverted residuals block,CIRBlock),并设计了一种新的轻量级卷积神经网络CIRNet。通过低代价卷积操作,简化逐点卷积,并构建旁路分支进行特征复用,减少反向残差的输出通道;利用通道注意力机制和通道混洗,增强通道间信息交流;在下采样时利用旁路分支信息构建和主分支相同的拓扑结构,提高特征冗余结构的通道多样性;完成轻量化网络模块CIRBlock的设计,并通过人工堆叠CIRBlock构建不同复杂度的轻量级卷积神经网络CIRNet。在目标分类上的实验表明:在CIFAR数据集上,基于相同的VGG16架构,使用CIRBlock比使用MobileNetV2的反向残差结构FLOPs降低58.1%,参数量减少55.5%,分类精度损失小于0.4%。在Mini-ImageNet目标分类数据集上,CIRNet分类精度比MobileNetV2高0.35%,FLOPs降低69%,参数量减少77.4%。

Two-Parameter Block Triangular Splitting Preconditioner for Block Two-by-Two Linear Systems

This paper proposes a two-parameter block triangular splitting(TPTS)preconditioner for the general block two-by-two linear systems.The eigenvalues of the corresponding preconditioned matrix are proved to cluster around 0 or 1 under mild conditions.The limited numerical results show that the TPTS preconditioner is more efficient than the classic block-diagonal and block-triangular preconditioners when applied to the flexible generalized minimal residual(FGMRES)method.

Nonlinear Components of a Block Cipher over Eisenstein Integers

In block ciphers,the nonlinear components,also known as substitution boxes(S-boxes),are used with the purpose to induce confusion in cryptosystems.For the last decade,most of the work on designing S-boxes over the points of elliptic curves,chaotic maps,and Gaussian integers has been published.The main purpose of these studies is to hide data and improve the security levels of crypto algorithms.In this work,we design pair of nonlinear components of a block cipher over the residue class of Eisenstein integers(EI).The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters.However,in the same way,by taking three fixed parameters only one S-box is obtained through a prime field-dependent Elliptic curve(EC),chaotic maps,and Gaussian integers.The newly designed pair of S-boxes are assessed by various tests like nonlinearity,bit independence criterion,strict avalanche criterion,linear approximation probability,and differential approximation probability.

Temperature-Triggered Hardware Trojan Based Algebraic Fault Analysis of SKINNY-64-64 Lightweight Block Cipher

SKINNY-64-64 is a lightweight block cipher with a 64-bit block length and key length,and it is mainly used on the Internet of Things(IoT).Currently,faults can be injected into cryptographic devices by attackers in a variety of ways,but it is still difficult to achieve a precisely located fault attacks at a low cost,whereas a Hardware Trojan(HT)can realize this.Temperature,as a physical quantity incidental to the operation of a cryptographic device,is easily overlooked.In this paper,a temperature-triggered HT(THT)is designed,which,when activated,causes a specific bit of the intermediate state of the SKINNY-64-64 to be flipped.Further,in this paper,a THT-based algebraic fault analysis(THT-AFA)method is proposed.To demonstrate the effectiveness of the method,experiments on algebraic fault analysis(AFA)and THT-AFA have been carried out on SKINNY-64-64.In the THT-AFA for SKINNY-64-64,it is only required to activate the THT 3 times to obtain the master key with a 100%success rate,and the average time for the attack is 64.57 s.However,when performing AFA on this cipher,we provide a relation-ship between the number of different faults and the residual entropy of the key.In comparison,our proposed THT-AFA method has better performance in terms of attack efficiency.To the best of our knowledge,this is the first HT attack on SKINNY-64-64.

基于改进U-Net的水下图像增强算法

针对水下退化图像存在颜色失真、模糊雾化、对比度低等问题,提出了一种新的基于改进U-Net的水下图像增强算法。设计一种新的残差注意力结构和边缘检测模块并将其引入到U-Net网络中,构建改进后的水下图像增强算法。实验结果表明,本文提出的算法在校正水下色偏和增强对比度方面均得到了很好的效果,IE值较原始图像平均提高了14.2%,UCIQE值较原始图像平均提高了24%。消融实验结果表明,本文提出的残差注意力结构、边缘检测模块和损失函数均对水下图像增强起到了积极的效果。

融合Inception V1-CBAM-CNN的轴承剩余寿命预测模型

针对现有的滚动轴承剩余寿命(RUL)预测方法精度低、轴承健康指标(HI)构建困难等问题,提出了一种基于卷积神经网络(CNN)并融合Inception V1模块和卷积注意力机制模块(CBAM)的滚动轴承RUL预测模型。首先,在CNN中添加了CBAM机制,并进行了加权处理,在通道和空间维度对重要特征进行了强化,对次要特征进行了抑制,通过添加改进的InceptionV1模块,提高了CNN通道间信息交互水平,全面提取了退化特征;然后,进行了网络优化,采用全局最大池化(GMP)方法对模型进行了简化,采用Dropout和批量归一化(BN)方法,避免了过拟合,提高了精度,且克服了训练时出现的梯度消失问题;最后,对数据进行了处理,将降噪后的信号重组为三维张量,将其作为HI,构建了退化标签,引入了评价指标,采用PHM2012轴承数据集进行了实验验证,在3种工况下将其与深度神经网络(DNN)、CNN方法、结合注意力机制的残差网络方法(ResNet)进行了对比。研究结果表明:该方法在变负载条件下的平均RMSE为0.033,较其他方法的RMSE值分别降低了86%、78%和69%,在预测精度和泛化能力方面具有明显优势。

GPR图像的数据集构建及其DRDU-Net去噪算法

为了解决生成对抗网络(Generative Adversarial Network,GAN)在生成探地雷达(Ground Penetrating Radar,GPR)图像时存在训练不稳定的问题,提出利用带有梯度惩罚的Wasserstein距离生成对抗网络(WGAN-GP)生成GPR图像,并结合时域有限差分法和实地采集图像提出了一种构建GPR图像数据集的方法.相较于原始GAN与Wasserstein GAN等方法,WGAN-GP具有更好的稳定性,而且生成的GPR图像更接近真实图像.在此基础之上,将密集残差块和U-Net相结合提出了一种适合于GPR图像的密集残差去噪U-Net方法.该方法利用U-Net中编码-解码结构提高了GPR图像的去噪性能;同时,密集残差块的引入加强了GPR图像的特征复用,且使U-Net训练更加稳定.最后,利用仿真实验验证了所提去噪方法的性能,并与三维块匹配(BM3D)和U-Net方法进行了对比.结果表明:所提方法与BM3D以及U-Net去噪方法相比,具有更好的去噪效果.当σ等于20时,在模拟和实测数据上取平均值,其峰值信噪比分别提升了约6.5 dB和2.4 dB;结构相似性分别提升了约0.09和0.04.

岩块尺度对采空区煤自燃区域的影响研究

为探究综放采空区岩块尺度对煤自然发火的影响,采用Fluent软件数值模拟不同直径岩块条件下采空区的煤自燃规律,分析采空区氧浓度场和风速场,分别以氧气体积分数和风速为指标,确定采空区氧化带面积并以二场叠加场作为采空区煤自燃危险区划分标准,研究煤自燃危险区域与岩块直径的关系。研究结果表明:当岩块直径由0增加到10 m时,以氧气体积分数为指标,氧化带宽度变小,面积由5563.84 m^(2)减至2602.69 m^(2);以风速为指标,氧化带面积由3376.60 m^(2)减至1262.95 m^(2);将2种指标确定的氧化带进行叠加得到煤自燃危险区,随着岩块直径的增加,其面积由1854.04 m^(2)减至552.91 m^(2),煤自燃危险区最大宽度由20.40 m缩减至9.06 m。研究结果可为采空区煤自燃发火区域的确定提供参考。

MBRNet:融合残差连接的多分支手写字符识别网络

脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基于此,结合DW卷积和残差连接设计了一种多分支残差模块,该模块通过DW卷积以较小的内存和参数量为代价来加深网络深度,增强网络的特征提取能力;再通过残差连接抑制网络梯度问题和退化问题;另外,提出了一种多分支权重算法,来改善多分支残差模块中各分支的权重分配问题;并将六个以多分支残差模块为主的结构线性连接,组成HCCR识别网络。该模型在CASIA-HWDB1.0、CASIA-HWDB1.1、ICDAR2013数据集上的识别准确率分别达到了97.77%、97.30%、97.64%,表现出高精度的识别效果。