一种半监督对抗鲁棒模型无关元学习方法

元学习期望训练所得的元模型在学习到的“元知识”基础上利用来自新任务的少量标注样本，仅通过较少的梯度下降步骤微调模型就能够快速适应该任务。但是，由于缺乏训练样本，元学习算法在元训练期间对现有任务过度训练时所得的分类器决策边界不够准确，不合理的决策边界使得元模型更容易受到微小对抗扰动的影响，导致元模型在新任务上的鲁棒性能降低。提出一种半监督对抗鲁棒模型无关元学习（semi-ARMAML）方法，在目标函数中分别引入半监督的对抗鲁棒正则项和基于信息熵的任务无偏正则项，以此优化决策边界，其中对抗鲁棒正则项的计算允许未标注样本包含未见过类样本，从而使得元模型能更好地适应真实应用场景，降低对输入扰动的敏感性，提高对抗鲁棒性。实验结果表明，相比ADML、R-MAML-TRADES等当下主流的对抗元学习方法，semi-ARMAML方法在干净样本上准确率较高，在MiniImageNet数据集的5-way 1-shot与5-way 5-shot任务上对抗鲁棒性能分别约提升1.8%和2.7%，在CIFAR-FS数据集上分别约提升5.2%和8.1%。     

Advances and perspectives on one-dimensional nanostructure electrode materials for potassium-ion batteries

Potassium-ion batteries (PIBs) have aroused considerable interest as a promising next-generation advanced large-scale energy storage system due to the abundant potassium resources and high safety. However, the K⁺ with large ionic radius brings restricted diffusion kinetics and severe volume expansion in electrode materials, resulting in inferior actual rate characteristics and rapid capacity fading. Designing electrode materials with one-dimensional (1D) nanostructure can effectively enhance various electrochemical properties due to the well-guided electron transfer pathways, short ionic diffusion channels and high specific surface areas. In this review, we summarize the recent research progress and achievements of 1D nanostructure electrode materials in PIBs, especially focusing on the development and application of cathode and anode materials. The nanostructure, synthetic methods, electrochemical performances and structure-performance correlation are discussed in detail. The advanced characterizations on the reaction mechanisms of 1D nanostructure electrode materials in PIBs are briefly summarized. Furthermore, the main future research directions of 1D nanostructure electrode materials are also predicted, hoping to accelerate their development into the practical PIBs market.     

Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm has been used to find optimal size of the block of the images for extraction of focus measure features. After finding optimal blocks, three focus measures Sum of Modified Laplacian, Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique. Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image. Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm (GA), Principal Component Analysis (PCA), Laplacian Pyramid discrete wavelet transform (DWT), and aDWT for image fusion. It has been found that proposed method performs well as compare to existing methods.

     

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.     

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the “p” value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.     

Evaluating autostereoscopic 3D baseball games using signal detection theory and receiver operating characteristic space

This study investigates the effects of display technique (2D, autostereoscopic 3D), ball speed (138, 140 km/h), and operation time (5, 10, 15 min) on the four outcomes of signal detection theory (SDT) (i.e., hit, miss, false alarm, correct rejection), β, d’, receiver operating characteristic (ROC) space, Simulator Sickness Questionnaire (SSQ), and the iGroup Presence Questionnaire (IPQ). The results indicated that the display technique was significant on the hit rate, SSQ, and IPQ, where a higher hit rate, visual fatigue, and IPQ were found in the 3D technique. The results also showed that the ball speed was significant on the miss rate and d’, where a low miss rate and high d’ were found in the higher speed of 138 km/h. The results further demonstrated that the operation time was significant on the false alarm rate, correct rejection rate, d’, SSQ, and IPQ, with a long operation time being associated with a better performance for every variable, except the SSQ.Relevance to industryFrom the results obtained regarding the ROC space, this study found that the possibility of participants’ misjudgements was low, and the accuracy of this research is considered reliable. The results of this study could serve as a reference for N3DS and game manufacturers in designing future products.     

Electrospun nanofiber composites with micro-/nano-particles for thermal insulation

Polyacrylonitrile (PAN) nanofiber and silica aerogel (SAG) laminated composites were prepared via electrospinning for thermal insulation. Conventional single nozzle and co-axial electrospinning were used to increase the fraction of aerogel particles in the composite sheets while maintaining the mechanical strength of the sheet. When the core-shell electrospinning technique with co-axial nozzle was applied, the proportion of aerogel particles increased two fold without a deterioration of the mechanical properties. The average thermal conductivity of the laminated composite sheet was reduced by approximately 12.5% as compared to the nanofiber composite prepared using the single-nozzle electrospinning technique. For additional reduction in thermal conductivity, hollow glass microspheres (HGM) was inserted between the interlayer spacing of the electrospun sheets to increase the interlayer spacing. When HGM particles were inserted, it was observed that the thermal conductivity decreased by approximately 20% compared to that of the specimen without particles.     

基于GL0816传感器的高速线阵CMOS相机系统设计

为了满足印刷等高端工业检测中物体快速运动,需要大幅面、高行频、高分辨率图像采集等要求,研发了一款微米级高分辨率、高速线阵工业相机。首先,介绍了高行频、高分辨率国产CMOS图像传感器GL0816的功能与特点。然后,基于该芯片设计了一套高速大幅面高分辨率线阵工业相机系统,该系统采用FPGA作为整个系统的控制核心,以DDR3LSDRAM作为图像缓存器,以GigE vision2.0协议为输出标准,以SFP+作为高速图像输出接口。最后,搭建相机系统测试环境,对所设计的相机进行系统测试。结果表明:该相机系统行分辨率为8 192,可连续采集2 000行作为一帧图像输出,行频为50kHz,动态范围为57.32dB,信噪比为40.95dB,具有实时图像采集功能。该相机系统具有大幅面、高帧频、高分辨率、高信噪比、宽动态范围等优点,适用于印刷检测行业快速运动目标捕获成像及图像实时显示。     

Reliability analysis of metal‐composite adhesive joints under debonding modes I,II, and I/II using the results of experimental and FEM analyses

In the present study, the experimental and finite element (FE) analyses are first carried out to investigate the deboning behavior of metal‐composite adhesive joints under modes of I and mode II loading. To conduct an FE on the debonding propagation, cohesive zone method (CZM), as well as maximum nominal stress and energy criteria, is applied. In the reliability analysis, to achieve the probability of debonding growth (PODG), limit state functions are formulated based on the energy release rate. To that end, the first‐order reliability method (FORM), the second‐order reliability method (SORM), and the Monte Carlo simulation (MCS) are used to calculate the PODG. The effect of initial debonding length on the PODG in all mentioned modes is investigated. Results obtained from reliability analysis reveal that the random variables including the initial debonding length, width, and thickness are the most sensitive variables to ascertain the PODG.