首页 > 最新文献

2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)最新文献

英文 中文
A subspace-based Manifold separation technique for array calibration 一种基于子空间的流形分离阵列标定技术
Minqiu Chen, Xi Chen, X. Mao
In this paper, we modify the classic manifold separation technique (MST), aiming to reduce its dependence on high signal-to-noise ratio (SNR) measuring environment. According to the analysis of the array response, it is demonstrated that to maintain a correct phase relationship between the received data at different calibration angles is indispensable for the application of MST. Thus, we slightly change the structure of the traditional calibration system, so that a phase reference for the measurements can be obtained. Besides, unlike the classic MST, where only a single snapshot measurement is utilized for calibration, multi-snapshot information is exploited in the novel method by using the subspace decomposition technique. Simulation results verify the superiorities of the proposed subspace-based calibration method in 1-D and 2-D scenarios.
本文对经典的流形分离技术(MST)进行了改进,旨在降低其对高信噪比(SNR)测量环境的依赖。通过对阵列响应的分析,证明了在不同校准角度下,保持接收数据之间正确的相位关系对于MST的应用是必不可少的。因此,我们稍微改变了传统校准系统的结构,从而可以获得测量的相位参考。此外,与传统的MST只利用单个快照测量值进行校准不同,该方法利用子空间分解技术利用了多快照信息。仿真结果验证了该方法在一维和二维场景下的优越性。
{"title":"A subspace-based Manifold separation technique for array calibration","authors":"Minqiu Chen, Xi Chen, X. Mao","doi":"10.1109/ISSPIT.2016.7886006","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886006","url":null,"abstract":"In this paper, we modify the classic manifold separation technique (MST), aiming to reduce its dependence on high signal-to-noise ratio (SNR) measuring environment. According to the analysis of the array response, it is demonstrated that to maintain a correct phase relationship between the received data at different calibration angles is indispensable for the application of MST. Thus, we slightly change the structure of the traditional calibration system, so that a phase reference for the measurements can be obtained. Besides, unlike the classic MST, where only a single snapshot measurement is utilized for calibration, multi-snapshot information is exploited in the novel method by using the subspace decomposition technique. Simulation results verify the superiorities of the proposed subspace-based calibration method in 1-D and 2-D scenarios.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116786264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Data mining using Probabilistic Grammars 使用概率语法的数据挖掘
Aljoharah Algwaiz, S. Rajasekaran, R. Ammar
Efficient and accurate data mining has become vital as technology advancements in data collection and storage soar. Researchers have proposed various valuable machine learning algorithms for data mining. However, not many have utilized formal methods. This paper proposes a data mining approach using Probabilistic Context Free Grammars (PCFGs). In this work we have employed PCFGs to mine from large heterogeneous datasets. The data mining problem of our interest is classification. To start with a probabilistic grammar is inferred from datasets for which classifications are known. The learnt model can then be used to classify any unknown data. Specifically, for each unknown data point, the model can be used to calculate probabilities that this point belongs to the various possible classes. A simple resolution strategy could be to associate the point with the class that corresponds to the maximum probability. To demonstrate the applicability of our approach we consider the problem of identifying splice junctions. Using a PCFG, an input DNA sequence is classified as donor, acceptor, or neither.
随着数据收集和存储技术的飞速发展,高效、准确的数据挖掘变得至关重要。研究人员已经为数据挖掘提出了各种有价值的机器学习算法。然而,使用正式方法的并不多。本文提出了一种基于概率上下文无关语法(pcfg)的数据挖掘方法。在这项工作中,我们使用pcfg从大型异构数据集中进行挖掘。我们感兴趣的数据挖掘问题是分类。首先,从已知分类的数据集推断概率语法。然后,学习到的模型可以用于对任何未知数据进行分类。具体来说,对于每一个未知的数据点,该模型可以用来计算该点属于各种可能的类的概率。一种简单的解析策略是将该点与最大概率对应的类关联起来。为了证明我们的方法的适用性,我们考虑了识别拼接连接的问题。使用PCFG,输入DNA序列被分类为供体、受体或两者都不是。
{"title":"Data mining using Probabilistic Grammars","authors":"Aljoharah Algwaiz, S. Rajasekaran, R. Ammar","doi":"10.1109/ISSPIT.2016.7886057","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886057","url":null,"abstract":"Efficient and accurate data mining has become vital as technology advancements in data collection and storage soar. Researchers have proposed various valuable machine learning algorithms for data mining. However, not many have utilized formal methods. This paper proposes a data mining approach using Probabilistic Context Free Grammars (PCFGs). In this work we have employed PCFGs to mine from large heterogeneous datasets. The data mining problem of our interest is classification. To start with a probabilistic grammar is inferred from datasets for which classifications are known. The learnt model can then be used to classify any unknown data. Specifically, for each unknown data point, the model can be used to calculate probabilities that this point belongs to the various possible classes. A simple resolution strategy could be to associate the point with the class that corresponds to the maximum probability. To demonstrate the applicability of our approach we consider the problem of identifying splice junctions. Using a PCFG, an input DNA sequence is classified as donor, acceptor, or neither.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121032275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Categorizing hardware failure in large scale cloud computing environment 大规模云计算环境下硬件故障分类
Moataz H. Khalil, W. Sheta, Adel Said Elmaghraby
Cloud computing environments are growing in complexity creating more challenges for improved resilience and availability. Cloud computing research can benefit from machine learning and data mining by using data from actual operational cloud systems. One aspect that needs in-depth analysis is the failure characteristics of cloud environments. Failure is the main contributor to reduced resiliency of applications and services in cloud computing. This work presents a categorizing method to identify machines removed from the system based on failure or due to maintenance. Our experiments are targeting large scale cloud computing environments and experimental data consists of 25 million submitted tasks on 12500 severs over a 29 day period. The parameters of categorizing are CPU and memory utilization. Also, this work developed a support vector machine (SVM) model for learning and prediction of machine failure. The devolved model achieved 99.04 % accuracy. Precision and Recall curves demonstrate that the model is consistent with varying data size. The model has very good consistency with max difference from theoretical data by only 0.008%.
云计算环境越来越复杂,为提高弹性和可用性带来了更多挑战。通过使用来自实际操作云系统的数据,云计算研究可以受益于机器学习和数据挖掘。需要深入分析的一个方面是云环境的故障特征。故障是云计算中应用程序和服务弹性降低的主要原因。这项工作提出了一种分类方法来识别基于故障或由于维护而从系统中移除的机器。我们的实验针对大规模云计算环境,实验数据由12500台服务器在29天内提交的2500万个任务组成。分类的参数包括CPU利用率和内存利用率。此外,本文还开发了一种用于机器故障学习和预测的支持向量机(SVM)模型。该模型的准确率达到99.04%。精度曲线和召回曲线表明该模型与不同的数据大小是一致的。模型与理论数据的最大差值仅为0.008%,具有很好的一致性。
{"title":"Categorizing hardware failure in large scale cloud computing environment","authors":"Moataz H. Khalil, W. Sheta, Adel Said Elmaghraby","doi":"10.1109/ISSPIT.2016.7886058","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886058","url":null,"abstract":"Cloud computing environments are growing in complexity creating more challenges for improved resilience and availability. Cloud computing research can benefit from machine learning and data mining by using data from actual operational cloud systems. One aspect that needs in-depth analysis is the failure characteristics of cloud environments. Failure is the main contributor to reduced resiliency of applications and services in cloud computing. This work presents a categorizing method to identify machines removed from the system based on failure or due to maintenance. Our experiments are targeting large scale cloud computing environments and experimental data consists of 25 million submitted tasks on 12500 severs over a 29 day period. The parameters of categorizing are CPU and memory utilization. Also, this work developed a support vector machine (SVM) model for learning and prediction of machine failure. The devolved model achieved 99.04 % accuracy. Precision and Recall curves demonstrate that the model is consistent with varying data size. The model has very good consistency with max difference from theoretical data by only 0.008%.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122636267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Combination of multiple detectors for credit card fraud detection 多检测器组合用于信用卡欺诈检测
A. Salazar, G. Safont, Alberto Rodríguez, L. Vergara
This paper presents a signal processing framework for the problem of automatic credit card fraud detection. This is a critical problem affecting large financial companies that has increased due to the rapid expansion of information and communication technologies. The framework establishes relationships between signal processing and pattern recognition issues around a detection problem with a very low ratio between fraudulent and legitimate transactions. Solutions are proposed using fusion of scores which are related to the familiar likelihood ratio statistic. Moreover, the classical detection problem analyzed by receiving operating characteristic curves is mapped to real-world business requirements based on key performance indicators. A strong practical case which combines real and surrogate data is approached, including comparison of the proposed methods with standard methods.
针对信用卡欺诈自动检测问题,提出了一种信号处理框架。这是随着信息通信技术(ict)的迅速发展而增加的大型金融公司面临的严重问题。该框架建立了信号处理和模式识别问题之间的关系,围绕检测问题,欺诈和合法交易之间的比例非常低。提出了解决方案,使用融合得分,其中涉及到熟悉的似然比统计量。此外,将接收工作特性曲线分析的经典检测问题映射到基于关键性能指标的实际业务需求。结合真实数据和替代数据的一个强大的实际案例进行了探讨,包括所提出的方法与标准方法的比较。
{"title":"Combination of multiple detectors for credit card fraud detection","authors":"A. Salazar, G. Safont, Alberto Rodríguez, L. Vergara","doi":"10.1109/ISSPIT.2016.7886023","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886023","url":null,"abstract":"This paper presents a signal processing framework for the problem of automatic credit card fraud detection. This is a critical problem affecting large financial companies that has increased due to the rapid expansion of information and communication technologies. The framework establishes relationships between signal processing and pattern recognition issues around a detection problem with a very low ratio between fraudulent and legitimate transactions. Solutions are proposed using fusion of scores which are related to the familiar likelihood ratio statistic. Moreover, the classical detection problem analyzed by receiving operating characteristic curves is mapped to real-world business requirements based on key performance indicators. A strong practical case which combines real and surrogate data is approached, including comparison of the proposed methods with standard methods.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123702714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Frequency-domain characterization of Singular Spectrum Analysis eigenvectors 奇异谱分析特征向量的频域表征
M. Leles, A. S. V. Cardoso, Mariana G. Moreira, H. N. Guimarães, C. M. Silva, A. Pitsillides
Singular Spectrum Analysis (SSA) is a nonparametric approach used to decompose a time series into meaningful components, related to trends, oscillations and noise. SSA can be seen as a spectral decomposition, where each term is related to an eigenvector derived from the trajectory matrix. In this context the eigenvectors can be viewed as eigenfilters. The frequency domain interpretation of SSA is a relatively recent subject. Although the analytic solution for the frequency-response of eigenfilters is already known, the periodogram is often applied for their frequency characterization. This paper presents a comparison of these methods, applied to eigenfilters' frequency characterization for time series components identification. To perform this evaluation, several tests were carried out, in both a synthetic and real data time series. In every situations the eigenfilters analytic frequency response method provided better results compared to the periodogram in terms of frequency estimates as well as their dispersion and sensitivity to variations in the SSA algorithm parameter.
奇异谱分析(SSA)是一种非参数方法,用于将时间序列分解为与趋势、振荡和噪声相关的有意义的分量。SSA可以看作是一个谱分解,其中每一项都与从轨迹矩阵导出的特征向量相关。在这种情况下,特征向量可以看作特征滤波器。SSA的频域解释是一个相对较新的课题。虽然特征滤波器频率响应的解析解已经已知,但周期图通常用于特征滤波器的频率表征。本文对这些方法进行了比较,并将其应用于时间序列分量识别的特征滤波器频率表征中。为了进行这一评估,在合成和真实数据时间序列中进行了几项测试。在每种情况下,与周期图相比,特征滤波器解析频响方法在频率估计以及对SSA算法参数变化的色散和灵敏度方面都提供了更好的结果。
{"title":"Frequency-domain characterization of Singular Spectrum Analysis eigenvectors","authors":"M. Leles, A. S. V. Cardoso, Mariana G. Moreira, H. N. Guimarães, C. M. Silva, A. Pitsillides","doi":"10.1109/ISSPIT.2016.7886003","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886003","url":null,"abstract":"Singular Spectrum Analysis (SSA) is a nonparametric approach used to decompose a time series into meaningful components, related to trends, oscillations and noise. SSA can be seen as a spectral decomposition, where each term is related to an eigenvector derived from the trajectory matrix. In this context the eigenvectors can be viewed as eigenfilters. The frequency domain interpretation of SSA is a relatively recent subject. Although the analytic solution for the frequency-response of eigenfilters is already known, the periodogram is often applied for their frequency characterization. This paper presents a comparison of these methods, applied to eigenfilters' frequency characterization for time series components identification. To perform this evaluation, several tests were carried out, in both a synthetic and real data time series. In every situations the eigenfilters analytic frequency response method provided better results compared to the periodogram in terms of frequency estimates as well as their dispersion and sensitivity to variations in the SSA algorithm parameter.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Respiratory rate monitoring by maximum likelihood video processing 基于最大似然视频处理的呼吸率监测
D. Alinovi, G. Ferrari, F. Pisani, R. Raheli
A novel video processing-based method for remote estimation of the respiratory rate (RR) is proposed. Relying on the fact that breathing involves quasi-periodic movements, this technique employs a generalized model of pixel-wise periodicity and applies a maximum likelihood (ML) criterion. The system first selects suitable regions of interest (ROI) mainly affected by respiratory movements. The obtained ROI are jointly analyzed for the estimation of the fundamental frequency, which is strictly related to the RR of the patient. A large motion detection algorithm is also applied, in order to exclude, from RR estimation, ROI possibly affected by unrelated large movements. The RRs estimated by the proposed system are compared with those extracted by a pneumograph and a previously proposed video processing algorithm. The results, albeit preliminary, show a good agreement with the pneumograph and a clear improvement over the previously proposed algorithm.
提出了一种基于视频处理的呼吸速率远程估计方法。基于呼吸涉及准周期性运动的事实,该技术采用了逐像素周期性的广义模型,并应用了最大似然(ML)标准。该系统首先选择受呼吸运动影响的感兴趣区域(ROI)。对获得的ROI进行联合分析,以估计基频,基频与患者的RR严格相关。为了从RR估计中排除可能受到不相关的大运动影响的ROI,还应用了大运动检测算法。将该系统估计的RRs与由气图和先前提出的视频处理算法提取的RRs进行比较。结果,虽然是初步的,但显示了与气图的良好一致,并且比先前提出的算法有了明显的改进。
{"title":"Respiratory rate monitoring by maximum likelihood video processing","authors":"D. Alinovi, G. Ferrari, F. Pisani, R. Raheli","doi":"10.1109/ISSPIT.2016.7886029","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886029","url":null,"abstract":"A novel video processing-based method for remote estimation of the respiratory rate (RR) is proposed. Relying on the fact that breathing involves quasi-periodic movements, this technique employs a generalized model of pixel-wise periodicity and applies a maximum likelihood (ML) criterion. The system first selects suitable regions of interest (ROI) mainly affected by respiratory movements. The obtained ROI are jointly analyzed for the estimation of the fundamental frequency, which is strictly related to the RR of the patient. A large motion detection algorithm is also applied, in order to exclude, from RR estimation, ROI possibly affected by unrelated large movements. The RRs estimated by the proposed system are compared with those extracted by a pneumograph and a previously proposed video processing algorithm. The results, albeit preliminary, show a good agreement with the pneumograph and a clear improvement over the previously proposed algorithm.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Spectrum Sensing enhancement using Principal Component Analysis 利用主成分分析增强频谱传感
A. Nasser, A. Mansour, K. Yao, H. Abdallah, M. Chaitou, H. Charara
In this paper, Principal Component Analysis (PCA) techniques are introduced in the context of Cognitive Radio to enhance the Spectrum Sensing performance. PCA step increases the SNR of the Primary User's signal and, consequently, enhances the Spectrum Sensing performance. We applied PCA as a combination scheme of a multi-antenna Cognitive Radio system. Analytic results will be presented to show the effectiveness of this technique by deriving the new SNR obtained after applying PCA, which can be considered a pre-processing step for a classical Spectrum Sensing algorithm. The effect of PCA is examined with well known detectors in Spectrum Sensing, where the proposed technique shows its efficiency. The performance of the proposed technique is corroborated through many simulations.
本文将主成分分析(PCA)技术引入到认知无线电中,以提高其频谱感知性能。主成分分析步骤提高了主用户信号的信噪比,从而提高了频谱感知性能。我们将主成分分析作为多天线认知无线电系统的组合方案。分析结果将显示该技术的有效性,通过推导应用主成分分析后获得的新信噪比,这可以被认为是经典频谱感知算法的预处理步骤。用光谱传感中已知的检测器检验了主成分分析的效果,其中提出的技术显示了它的有效性。仿真结果验证了该方法的有效性。
{"title":"Spectrum Sensing enhancement using Principal Component Analysis","authors":"A. Nasser, A. Mansour, K. Yao, H. Abdallah, M. Chaitou, H. Charara","doi":"10.1109/ISSPIT.2016.7886046","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886046","url":null,"abstract":"In this paper, Principal Component Analysis (PCA) techniques are introduced in the context of Cognitive Radio to enhance the Spectrum Sensing performance. PCA step increases the SNR of the Primary User's signal and, consequently, enhances the Spectrum Sensing performance. We applied PCA as a combination scheme of a multi-antenna Cognitive Radio system. Analytic results will be presented to show the effectiveness of this technique by deriving the new SNR obtained after applying PCA, which can be considered a pre-processing step for a classical Spectrum Sensing algorithm. The effect of PCA is examined with well known detectors in Spectrum Sensing, where the proposed technique shows its efficiency. The performance of the proposed technique is corroborated through many simulations.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130068248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
An FPGA design for the Two-Band Fast Discrete Hartley Transform 两波段快速离散哈特利变换的FPGA设计
Lambros Pyrgas, P. Kitsos, A. Skodras
The discrete Hartley transform finds numerous applications in signal and image processing. An efficient Field Programmable Gate Array implementation for the 64-point Two-Band Fast Discrete Hartley Transform is proposed in this communication. The architecture requires 57 clock cycles to compute the 64-point Two-Band Fast Discrete Hartley Transform and reaches a rate of up to 103.82 million samples per second at a 92 MHz clock frequency. The architecture has been implemented using VHDL and realized on a Cyclone IV FPGA of Altera.
离散哈特利变换在信号和图像处理中有许多应用。提出了一种有效的现场可编程门阵列实现64点两波段快速离散哈特利变换。该架构需要57个时钟周期来计算64点两频带快速离散哈特利变换,并在92 MHz时钟频率下达到每秒10382万个样本的速率。该体系结构采用VHDL实现,并在Altera公司的Cyclone IV FPGA上实现。
{"title":"An FPGA design for the Two-Band Fast Discrete Hartley Transform","authors":"Lambros Pyrgas, P. Kitsos, A. Skodras","doi":"10.1109/ISSPIT.2016.7886052","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886052","url":null,"abstract":"The discrete Hartley transform finds numerous applications in signal and image processing. An efficient Field Programmable Gate Array implementation for the 64-point Two-Band Fast Discrete Hartley Transform is proposed in this communication. The architecture requires 57 clock cycles to compute the 64-point Two-Band Fast Discrete Hartley Transform and reaches a rate of up to 103.82 million samples per second at a 92 MHz clock frequency. The architecture has been implemented using VHDL and realized on a Cyclone IV FPGA of Altera.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Artificial Neural Networks in WSNs design: Mobility prediction for barrier coverage 无线传感器网络设计中的人工神经网络:屏障覆盖的移动性预测
Zhilbert Tafa
Barrier coverage provides intrusion detection for various national security applications. If the network is randomly deployed, in moderately dense networks, the full end-to-end barrier line might not be provided. To fill the breaks and to assure the intrusion detection, additional nodes have to be introduced. The network should be designed in a way that enables the good (cost/benefit) balance between the number of initially deployed static nodes and the (added) mobile nodes. This research, for the first time introduces the artificial neural networks (ANNs) in predicting the number of the additionally supplied static nodes or simultaneously deployed mobile nodes for barrier coverage setup after the network's initial installation. The results show a high degree of predictability, with the R-factor of over 0.99 regarding the test data. Besides its primary results, the importance of the research relies also in fact that the approach can be extended to the prediction of k-barrier coverage, the mobility range, and to the other network design objectives.
屏障覆盖为各种国家安全应用提供入侵检测。如果网络是随机部署的,在中等密度的网络中,可能不会提供完整的端到端屏障。为了填补漏洞并确保入侵检测,必须引入额外的节点。网络的设计应使初始部署的静态节点和(增加的)移动节点之间的数量达到良好的(成本/效益)平衡。本研究首次引入人工神经网络(ann)来预测网络初始安装后额外提供的静态节点或同时部署的移动节点的数量。结果显示出高度的可预测性,测试数据的r因子超过0.99。除了其主要结果外,该研究的重要性还在于,该方法实际上可以扩展到k-屏障覆盖、移动范围和其他网络设计目标的预测。
{"title":"Artificial Neural Networks in WSNs design: Mobility prediction for barrier coverage","authors":"Zhilbert Tafa","doi":"10.1109/ISSPIT.2016.7886036","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886036","url":null,"abstract":"Barrier coverage provides intrusion detection for various national security applications. If the network is randomly deployed, in moderately dense networks, the full end-to-end barrier line might not be provided. To fill the breaks and to assure the intrusion detection, additional nodes have to be introduced. The network should be designed in a way that enables the good (cost/benefit) balance between the number of initially deployed static nodes and the (added) mobile nodes. This research, for the first time introduces the artificial neural networks (ANNs) in predicting the number of the additionally supplied static nodes or simultaneously deployed mobile nodes for barrier coverage setup after the network's initial installation. The results show a high degree of predictability, with the R-factor of over 0.99 regarding the test data. Besides its primary results, the importance of the research relies also in fact that the approach can be extended to the prediction of k-barrier coverage, the mobility range, and to the other network design objectives.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Asymptotically optimal search of unknown anomalies 未知异常的渐近最优搜索
Bar Hemo, Kobi Cohen, Qing Zhao
The problem of detecting an anomalous process over multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution parameterized by an unknown parameter (vector). The unknown parameter belongs to one of two disjoint parameter spaces, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search policy to solve the problem and prove its asymptotic optimality (as the error probability approaches zero) when the parameter under the null hypothesis is known. We further provide an explicit upper bound on the error probability for the finite sample regime.
考虑了在多个进程中检测一个异常进程的问题。我们考虑一种复合假设情况,其中观察过程时绘制的测量值遵循由未知参数(向量)参数化的共同分布。根据进程是否正常,未知参数属于两个不相交的参数空间之一。目标是一种顺序搜索策略,使受错误概率约束的预期检测时间最小化。我们开发了一种确定性搜索策略来解决这个问题,并证明了当零假设下的参数已知时,它的渐近最优性(误差概率趋于零)。我们进一步给出了有限样本区域误差概率的显式上界。
{"title":"Asymptotically optimal search of unknown anomalies","authors":"Bar Hemo, Kobi Cohen, Qing Zhao","doi":"10.1109/ISSPIT.2016.7886012","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886012","url":null,"abstract":"The problem of detecting an anomalous process over multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution parameterized by an unknown parameter (vector). The unknown parameter belongs to one of two disjoint parameter spaces, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search policy to solve the problem and prove its asymptotic optimality (as the error probability approaches zero) when the parameter under the null hypothesis is known. We further provide an explicit upper bound on the error probability for the finite sample regime.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134472264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
期刊
2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1