装备固有测试性评价方法研究

固有测试性设计是装备测试性设计的重要内容,在对装备测试性进行验证和评价时,也需要评价其固有测试性。为了解决固有测试性评价方法适用性不强的问题,提出了一种新的装备固有测试性评价方法。在分析固有测试性设计要求的基础上,从检测隔离性能、功能结构划分的合理性、可控性和可观测性四个方面提出了6项评价指标,并利用模糊综合评价模型作为定量分析工具,最终达到量化评价装备固有测试性的目的。为进一步进行装备测试性综合评价或验证奠定了基础。最后通过某型液位传感系统的固有测试性评价实例,验证了该方法的可行性。     

基于TEAMS的测试性建模与分析

随着测试性技术的快速发展，设备的测试性受到越来越多的重视。在研究测试性设计的主要技术和参数基础上，基于 TEAMS 软件建立了典型通信设备的测试性设计模型，进行了测试性仿真与分析，为提高设备的测试性提供定性定量的支持。     

基于雷达装备的测试性评估方法研究

雷达装备在作战中具有重要的作用,而雷达装备的测试性设计对雷达装备起关键作用,对雷达装备的测试性设计进行分析,及时将测试性设计和试验相结合,使雷达装备的测试性水平得到提高。首先对雷达装备的测试性设计进行了分析和阐述,之后基于设计和试验提出了一种测试性评估方法,并提出一套测试性评估程序,来帮助雷达装备进行测试性评估。     

线性无耗零状态网络的互易性和幺正性

线性无耗网络不一定同时具备互易性和幺正性,但在零状态条件下,线性无耗网络同时具有互易性和幺正性。本文从互能定理出发,证明了标量媒质和双各向异性张量媒质的线性无耗零状态网络的互易性和幺正性。     

飞行器系统级可测试性设计方法研究

从系统测试性设计的角度,分析了国内外测试性设计技术的发展状况、存在的问题;讨论了开展系统测试性设计并制定测试性工作规范在飞行器设计中的必要性;总结了系统测试性设计的一般工作流程;并根据航天产品特点,对测试性设计方法进行探索研究.     

一种基于模型的测试性分析评估方法

产品可测试性设计是否满足测试性要求需要进行测试性分析和评估,基于模型的测试性分析评估方法因为它独特的优势被广泛用于产品测试性辅助分析之中。针对多层次系统产品的结构功能特点,提出一种基于相关性数学模型和多信号流图模型的测试性建模分析评估方法。该方法分析目标系统的测试性模型要素,建立了两测试性模型,以测试性工程和维修系统软件(TEAMS)为平台,通过软件仿真评估对模型进行校验,使其符合真实系统的故障传播关系和故障定位过程,在此基础上改进测试性设计,使其达到测试性定量指标。运用该方法对某装备电子系统部分进行实例分析,仿真结果验证了该方法的有效性和可行性,结论表明:基于相关性数学模型和多信号流图模型的测试性建模方法能够满足装备电子系统的测试性分析评估需求。     

论系统的叠加性与齐次性的关系

本文讨论了系统的叠加性和齐次性之间的关系,论证了一个系统如果满足叠加性,那么它一定满足实齐次性,进而一定是一个实线性系统;同时,通过一个反例说明：对于处理复数信号的复系统而言,满足叠加性不一定意味着它同时满足齐次性。这个结论修正了一些教科书中的不当之处,弥补了这方面理论分析上的不足。     

装备测试性综合验证技术研究

介绍了测试性对于电子装备的重要性,分析了目前已有的测试性评价方法存在的问题,并在测试性建模仿真的基础上,提出了一种测试性综合验证方法,该方法克服了目前已有的测试性评价方法的缺陷,能够全面、有效地对产品的测试性水平进行评估.     

基于最短路径数的WSN抗毁性评价方法

从已有的基于全网的等效最短路径数的抗毁性出发,应用到以数据为中心的无线传感器网络各个簇的抗毁性衡量中。进而用簇的抗毁性来衡量整个无线传感器网络的抗毁性。并对基于小世界模型的无线传感器网络抗毁性进行了抗毁性分析。仿真结果表明,该评估模型能更客观、准确地评估以数据为中心的无线传感器网络的抗毁性。     

基于多信号相关性模型的TMAS测试性建模仿真分析技术

介绍了基于多信号相关性图示模型的TMAS软件测试性建模仿真技术与应用研究过程.研究了测试性设计技术、测试性主要参数,重点研究了测试性建模的相关性模型基础、测试性建模仿真分析技术.基于相关性模型中典型的多信号模型,应用TMAS软件对某装备进行了测试性建模仿真分析,为装备的测试性设计提高提供了一种定性定量分析支持方法,也有...     

基于单电子晶体管图像信号处理应用研究

研究了单电子晶体管的特性，文章提出一种基于单电子晶体管阵列的传输特性实现CNN方法，设计构成CNN。仿真结果表明，所设计的硬件电路具有结构简单、功耗低、频率特性好．将其应用于图像处理具有一定的灵活性和通用性。     

Detail retaining convolutional neural network for image denoising

Compared with the traditional image denoising method, although the convolutional neural network (CNN) has better denoising performance, there is an important issue that has not been well resolved: the residual image obtained by learning the difference between noisy image and clean image pairs contains abundant image detail information, resulting in the serious loss of detail in the denoised image. In this paper, in order to relearn the lost image detail information, a mathematical model is deducted from a minimization problem and an end-to-end detail retaining CNN (DRCNN) is proposed. Unlike most denoising methods based on CNN, DRCNN is not only focus to image denoising, but also the integrity of high frequency image content. DRCNN needs less parameters and storage space, therefore it has better generalization ability. Moreover, DRCNN can also adapt to different image restoration tasks such as blind image denoising, single image superresolution (SISR), blind deburring and image inpainting. Extensive experiments show that DRCNN has a better effect than some classic and novel methods.     

Robust image retrieval by cascading a deep quality assessment network

The performance of computer vision algorithms can severely degrade in the presence of a variety of distortions. While image enhancement algorithms have evolved to optimize image quality as measured according to human visual perception, their relevance in maximizing the success of computer vision algorithms operating on the enhanced image has been much less investigated. We consider the problem of image enhancement to combat Gaussian noise and low resolution with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising or super resolution, allowing for optimization of the enhancement CNN to maximize retrieval performance. This framework allows us to couple enhancement to the retrieval problem. We also consider the problem of adapting image features for robust retrieval performance in the presence of distortions. We show through experiments on distorted images of the Oxford and Paris buildings datasets that our algorithms yield improved mean average precision when compared to using enhancement methods that are oblivious to the task of image retrieval. ¹     

Research on image feature extraction and retrieval algorithms based on convolutional neural network

With the rapid development of mobile Internet and digital technology, people are more and more keen to share pictures on social networks, and online pictures have exploded. How to retrieve similar images from large-scale images has always been a hot issue in the field of image retrieval, and the selection of image features largely affects the performance of image retrieval. The Convolutional Neural Networks (CNN), which contains more hidden layers, has more complex network structure and stronger ability of feature learning and expression compared with traditional feature extraction methods. By analyzing the disadvantage that global CNN features cannot effectively describe local details when they act on image retrieval tasks, a strategy of aggregating low-level CNN feature maps to generate local features is proposed. The high-level features of CNN model pay more attention to semantic information, but the low-level features pay more attention to local details. Using the increasingly abstract characteristics of CNN model from low to high. This paper presents a probabilistic semantic retrieval algorithm, proposes a probabilistic semantic hash retrieval method based on CNN, and designs a new end-to-end supervised learning framework, which can simultaneously learn semantic features and hash features to achieve fast image retrieval. Using convolution network, the error rate is reduced to 14.41% in this test set. In three open image libraries, namely Oxford, Holidays and ImageNet, the performance of traditional SIFT-based retrieval algorithms and other CNN-based image retrieval algorithms in tasks are compared and analyzed. The experimental results show that the proposed algorithm is superior to other contrast algorithms in terms of comprehensive retrieval effect and retrieval time.     

On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the state-of-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity.     

Stereoscopic image quality assessment considering visual mechanism and multi-loss constraints

In this paper, a convolutional neural network (CNN) with multi-loss constraints is designed for stereoscopic image quality assessment (SIQA). A stereoscopic image not only contains monocular information, but also provides binocular information which is as identically crucial as the former. So we take the image patches of left-view images, right-view images and the difference images as the inputs of the network to utilize monocular information and binocular information. Moreover, we propose a method to obtain proxy label of each image patch. It preserves the quality difference between different regions and views. In addition, the multiple loss functions with adaptive loss weights are introduced in the network, which consider both local features and global features and constrain the feature learning from multiple perspectives. And the adaptive loss weights also make the multi-loss CNN more flexible. The experimental results on four public SIQA databases show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.     

用细胞神经网络实现图像恢复的一种新方法

文中提出并讨论了用细胞神经网络实现图象最大熵恢复的可能性,并基于对最大熵方法的物理实质分析推出了相应细胞神经网络模板的新设计方法,针对二值图象的恢复问题进行了计算机仿真,结果证明了这一方法是可行的。     

Screen content image quality assessment based on convolutional neural networks

Screen content image (SCI) is a composite image including textual and pictorial regions resulting in many difficulties in image quality assessment (IQA). Large SCIs are divided into image patches to increase training samples for CNN training of IQA model, and this brings two problems: (1) local quality of each image patch is not equal to subjective differential mean opinion score (DMOS) of an entire image; (2) importance of different image patches is not same for quality assessment. In this paper, we propose a novel no-reference (NR) IQA model based on the convolutional neural network (CNN) for assessing the perceptual quality of SCIs. Our model conducts two designs solving problems which benefits from two strategies. For the first strategy, to imitate full-reference (FR) CNN-based model behavior, a CNN-based model is designed for both FR and NR IQA, and performance of NR-IQA part improves when the image patch scores predicted by FR-IQA part are adopted as the ground-truth to train NR-IQA part. For the second strategy, image patch qualities of one entire SCI are fused to obtain the SCI quality with an adaptive weighting method taking account the effect of the different image patch contents. Experimental results verify that our model outperforms all test NR IQA methods and most FR IQA methods on the screen content image quality assessment database (SIQAD). On the cross-database evaluation, the proposed method outperforms the existing NR IQA method in terms of at least 2.4 percent in PLCC and 2.8 percent in SRCC, which shows high generalization ability and high effectiveness of our model.     

我国固定网本地环路分拆问题的探讨

我国电信业新的竞争格局和宽带接入需求的增长推动了固定本地网领域的竞争。本地环路分拆有助于促进竞争同时减少重复建设。本文介绍了本地环路分拆的概念，对我国实施本地环路分拆的必要性进行了论述，并借鉴其他国家的实施经验，对我国实施本地环路分拆的监管政策提出了建议。     

SensIR: Towards privacy-sensitive image retrieval in the cloud

With the advance in content-based image retrieval and the popularity of Data-as-a-Service, enterprises can outsource their image retrieval systems on cloud platforms to reduce heavy storage, computation, and communication burdens. However, this brings many privacy problems. Although several privacy-preserving image retrieval schemes have been proposed to protect users’ privacy, they have two major drawbacks: i) the outsourced images are fully encrypted and thus cannot be used for other applications, which makes them impractical; ii) they mainly focus on traditional image retrieval systems and do not use new techniques such as convolutional neural network (CNN) to improve the accuracy. To address the above problems, we propose a novel privacy-sensitive image retrieval scheme, named SensIR, to search for similar images from an outsourced image database. In particular, we propose a privacy region detection, PRDet, to prevent private regions of images from exposing. We also propose a partial CNN (PCNN) to reduce the impact of the encrypted pseudorandom pixels. Further, we use similarity-preserving hash encoding and propose a systematic methodology to improve the accuracy of PCNN-based image retrieval when the privacy regions are large. Extensive experiments are conducted to illustrate the efficiency of privacy protection and the superior of the proposed scheme.