基于SAM-CS和SOFM的胃上皮肿瘤细胞图像识别

针对胃上皮肿瘤细胞图像(以下简称肿瘤细胞图像)黏结严重和信息冗余的特点,提出了一种将自适应观测矩阵的压缩感知(SAM-CS)和自组织特征映射(SOFM)神经网络相结合的算法。该算法将肿瘤细胞图像拉成列向量,然后利用通过自适应过程产生的观测矩阵,基于压缩感知理论对图像信息进行观测,产生线性观测向量,最后利用SOFM神经网络的学习算法对观测向量进行训练和分类,实现对肿瘤细胞图像的识别。实验表明,相比常用算法,该算法至少提高了4.2%的识别准确率和5.7%的运算速度。     

Novel neural network modeling method and applications

Neural networks play an important role for designing the parametric model of electromagnetic structures. The current neural network methods are unfit for a circuit model with many input variables because it is costly to extract a large number of the training data and test data to complete the highly nonlinear mapping approximation. This article proposes a new neural network modeling method—the multidimensional neural network model, which can be used to solve the issue of multivariable radiofrequency and microwave passive device modeling. The entire multidimensional neural network modeling problem is simplified into a set of neural network submodels through decomposition method. Then the submodels are combined into an equivalent model, and the final entire model is produced through the neural‐network mapping model developed with the submodels and equivalent model. A microstrip hairpin filter model is developed using the proposed method. The simulation results show the correctness and the effectivity of the proposed method. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:769–779, 2015.     

Modeling of microstrip antennas using neural networks techniques: A review

Artificial neural networks modeling have recently acquired enormous importance in microwave community especially in analyzing and synthesizing of microstrip antennas (MSAs) due to their generalization and adaptability features. A trained neural model estimates response very fast, which is nearly equal to its measured and/or simulated counterpart. Thus, it completely bypasses the repetitive use of conventional models as these models need rediscretization for every minor changes in the geometry, which itself is a time‐consuming exercise. The purpose of this article is to review this emerging area comprehensively for both analyzing and synthesizing of the MSAs. During reviewing process, some untouched cases are also observed, which are essentially required to be resolved for antenna designers. Unique and efficient neural networks‐based solutions are suggested for these cases. The proposed neural approaches are validated by fabricating and characterizing of the prototypes too. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:747–757, 2015.     

Compact Keccak Hardware Architecture for Data Integrity and Authentication on FPGAs

ABSTRACT

Cryptographic hash functions play a crucial role in networking and communication security, including their use for data integrity and message authentication. Keccak hash algorithm is one of the finalists in the next generation SHA-3 hash algorithm competition. It is based on the sponge construction whose hardware performance is worth investigation. We developed an efficient hardware architecture for the Keccak hash algorithm on Field-Programmable Gate Array (FPGA). Due to the serialization exploited in the proposed architecture, the area needed for its implementation is reduced significantly accompanied by higher efficiency rate. In addition, low latency is attained so that higher operating frequencies can be accessed. We use the coprocessor approach which exploits the use of RAM blocks that exist in most FPGA platforms. For this coprocessor, a new datapath structure allowing parallel execution of multiple instructions is designed. Implementation results prove that our Keccak coprocessor achieves high performance in a small area.     

一种高增益宽频带圆极化微带阵列天线的研制

针对微带天线阻抗匹配带宽一般较窄的自身缺陷,基于相控阵雷达天线的应用背景,设计了一种工作在X波段的双层圆极化微带天线结构,且优化发现,其各电磁参数良好。为提高其增益,还在此基础上设计并最终制作了双层2×2结构的微带天线阵列,其实测性能与设计值相符,增益达到10.7dB,带宽1.2GHz,相应轴比为4dB,符合圆极化要求。     

Optical complexities of living cytoplasm--implications for live cell imaging and photo-micromanipulation techniques

The ability to manipulate the intracellular environment within living cells and to monitor the cytosolic chemical changes which occur during cell stimulation has lead to major advances in our understanding of how cells read and respond to their environment. Perhaps the most powerful suite of techniques for achieving these dual objectives is based on the use of light (photons). Because cells are 'transparent', light has been used to both interrogate and manipulate the chemistry inside living cells, exploiting technical advances in both the physical and biochemical sciences. However, cells are neither transparent nor homogeneous with respect to their optical properties. The interface between light and the living cell cytoplasm thus represent an important, yet largely ignored, interface. There has been no review of the optical properties of cytoplasm and little discussion about how the optical properties of living cytoplasm influence the outcome of such measurements and manipulations. In this short review, we discuss the importance of understanding the optical properties of cytoplasm for such techniques and how imperfections in experimental interpretation can arise.     

Novel slotted stepped‐impedance resonator and its application to compact and high performance bandpass filter and diplexer design

By etching slots in the low‐impedance section of the conventional stepped‐impedance resonator, a novel slotted stepped‐impedance resonator (SSIR) is proposed. As two examples, a fourth‐order bandpass filter (BPF) operating at 1 GHz with a size of 0.078 λ_g × 0.062 λ_g and a miniaturized diplexer operating at 0.9/1.57 GHz with a size of 0.054 λ₀ × 0.086 λ₀ are designed based on the proposed SSIR. The fabricated BPF exhibits a high selectivity and a wide ?30 dB rejection upper stopband from 1.13 f₀ to 6.52 f₀, while the fabricated diplexer has up to ?60 dB output isolation. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

求解泊松方程的紧致修正法

将紧致格式与低阶格式结合,构造紧致格式的修正项,并将修正项加入到源项中进行求解,得到了一种基于非均分网格求解泊松方程的紧致修正法,且将该方法应用于二维和三维泊松方程的数值求解中。数值计算结果表明:紧致修正方法的精度高于经典方法的精度,但四阶紧致修正方法比二阶经典方法对网格的依赖性强。     

结合水平集方法和PL主曲线分析的重叠细胞检测

提出了一种重叠细胞检测的新方法,该方法针对细胞图像处理中因细胞重叠而引起的过分割与欠分割现象这一难点,引入基于Mumford-Shah模型的水平集方法得到细胞的初始轮廓,采用EP（erosion-propagation）和PL主曲线相结合的图像分析方法得到目标的精确轮廓线。使用基于目标形状的图像配准方法和基于目标轮廓的分段特征分类器完成重叠细胞的检测。应用该方法对600张随机抽取的细胞图像中9374个细胞进行检测,实验正确率为91.83%,结果表明该方法能够有效地判断细胞是否重叠和重叠类型,为后续分割工作提供依据。     

悬液细胞图象识别的一种有效新算法

为进一步提高细胞识别的速度和识别率,本文研究一种细胞识别新算法。不同于其它的只对整个细胞形态进行一次图象处理的细胞识别方法,该算法在图象处理过程中,先后以细胞形态为重点和以细胞核形态为重点对原始图象进行两次图象处理,分别得到图象清晰度很高的整个细胞的二值图和细胞核的二值图。然后提取各项特征参数,对细胞图象进行识别。最后应用该算法对多幅细胞图象进行识别,结果表明该算法不但识别速度快,平均每幅图象的时间在3秒左右,而且识别率高达85%以上。