物理不可克隆函数(PUF:Physical Unclonable Function)是一种新型的加密组件,具有防伪、不可克隆及不可预测等特性.本文提出了一种新型的低成本PUF,与传统PUF相比更适用于无线射频识别(Radio Frequency Identification,RFID)系统.该PUF...物理不可克隆函数(PUF:Physical Unclonable Function)是一种新型的加密组件,具有防伪、不可克隆及不可预测等特性.本文提出了一种新型的低成本PUF,与传统PUF相比更适用于无线射频识别(Radio Frequency Identification,RFID)系统.该PUF结构主要由上电密钥生成器和混合函数两部分构成.上电密钥生成器由比特生成器阵列构成,混合函数则由低成本流加密算法构成,其作用是隐藏密钥生成器,以提高安全性.此外,本文还提出了择多模块和多寻认证协议来改善PUF响应及其在RFID系统中的稳定性.实验表明,该PUF的硬件成本低并且具有很好的稳定性,非常适用于RFID系统等资源受限的应用场合.展开更多
Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair clas...Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair classification ability under constrained conditions, in which face images are acquired under similar illumination with similar poses. The performances of these methods may deteriorate when face images show drastic variances in poses and occlusion as routinely encountered in real-world data. The reduction in the performances of current gender classification methods may be attributed to the sensitiveness of features to image translations. This work proposes to alleviate this sensitivity by introducing a majority voting procedure that involves multiple face patches.Specifically, this work utilizes a deep learning method based on multiple large patches. Several Convolutional Neural Networks(CNN) are trained on individual, predefined patches that reflect various image resolutions and partial cropping. The decisions of each CNN are aggregated through majority voting to obtain the final gender classification accurately. Extensive experiments are conducted on four gender classification databases, including Labeled Face in-the-Wild(LFW), CelebA, ColorFeret, and All-Age Faces database, a novel database collected by our group. Each individual patch is evaluated, and complementary patches are selected for voting. We show that the classification accuracy of our method is comparable with that of state-of-the-art systems. This characteristic validates the effectiveness of our proposed method.展开更多
There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms ...There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets.展开更多
文摘物理不可克隆函数(PUF:Physical Unclonable Function)是一种新型的加密组件,具有防伪、不可克隆及不可预测等特性.本文提出了一种新型的低成本PUF,与传统PUF相比更适用于无线射频识别(Radio Frequency Identification,RFID)系统.该PUF结构主要由上电密钥生成器和混合函数两部分构成.上电密钥生成器由比特生成器阵列构成,混合函数则由低成本流加密算法构成,其作用是隐藏密钥生成器,以提高安全性.此外,本文还提出了择多模块和多寻认证协议来改善PUF响应及其在RFID系统中的稳定性.实验表明,该PUF的硬件成本低并且具有很好的稳定性,非常适用于RFID系统等资源受限的应用场合.
基金supported by the National HighTech Research and Development (863) Program of China (No. 2012AA011004)the National Science and Technology Support Program (No. 2013BAK02B04)the National Key Research and Development Plan (No. 2016YFB0801301)
文摘Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair classification ability under constrained conditions, in which face images are acquired under similar illumination with similar poses. The performances of these methods may deteriorate when face images show drastic variances in poses and occlusion as routinely encountered in real-world data. The reduction in the performances of current gender classification methods may be attributed to the sensitiveness of features to image translations. This work proposes to alleviate this sensitivity by introducing a majority voting procedure that involves multiple face patches.Specifically, this work utilizes a deep learning method based on multiple large patches. Several Convolutional Neural Networks(CNN) are trained on individual, predefined patches that reflect various image resolutions and partial cropping. The decisions of each CNN are aggregated through majority voting to obtain the final gender classification accurately. Extensive experiments are conducted on four gender classification databases, including Labeled Face in-the-Wild(LFW), CelebA, ColorFeret, and All-Age Faces database, a novel database collected by our group. Each individual patch is evaluated, and complementary patches are selected for voting. We show that the classification accuracy of our method is comparable with that of state-of-the-art systems. This characteristic validates the effectiveness of our proposed method.
基金Supported by the National Natural Science Foundation of China (No.61105057)the Ph.D Foundation of Jiangsu University of Science and Technology (Nos.35301002 and 35211104)
文摘There have been many skewed cancer gene expression datasets in the post-genomic era. Extraction of differential expression genes or construction of decision rules using these skewed datasets by traditional algorithms will seriously underestimate the performance of the minority class, leading to inaccurate diagnosis in clinical trails. This paper presents a skewed gene selection algorithm that introduces a weighted metric into the gene selection procedure. The extracted genes are paired as decision rules to distinguish both classes, with these decision rules then integrated into an ensemble learning framework by majority voting to recognize test examples; thus avoiding tedious data normalization and classifier construction. The mining and integrating of a few reliable decision rules gave higher or at least comparable classification performance than many traditional class imbalance learning algorithms on four benchmark imbalanced cancer gene expression datasets.
