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Weibull mixture model to characterise end-to-end Internet delay at coarse time-scales

IEE PROCEEDINGS-COMMUNICATIONS 2006年第2期153卷 295-304页

作者： Hernández, JA Phillips, IW Univ Loughborough Loughborough Univ Technol Comp Sci Loughborough LE11 3TU Leics England

Traces collected at monitored points around the Internet contain representative performance information about the paths their probes traverse. If processed appropriately, basic measurement attributes, such as delay and loss, can be used to output conclusions about the performance status of the network, with subsequent applications in fault and performance management, network provisioning, traffic engineering and performance prediction. However, the task of analysis and extracting such valuable information from measurements only remains challenging. The Weibull mixture model, a method to characterise end-to-end network delay measurements within a few simple, accurate, representative and handleable parameters using a finite combination of Weibull distributions is presented. The model parameters are related to meaningful delay characteristics, such as average peak and tail behaviour in a daily profile, and can be optimally found using an iterative algorithm known as expectation maximisation. Studies on such parameter evolution can reflect current workload status and all possible network events impacting packet dynamics, with further applications in network management. The model is further tested and validated with real GPS synchronised measurements taken across the Internet, donated by RIPE NCC.

关键词： Weibull distribution Computer communications Global Positioning System Internet delays traffic engineering GPS synchronised measurement iterative algorithm information extraction RIPE NCC network management expectation-maximisation algorithm computer network management end-to-end network delay measurement packet dynamics Other computer networks performance prediction network provisioning expectation maximisation Weibull mixture model Other topics in statistics Network management fault management

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Modified student's t-hidden Markov model for pattern recognition and classification

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IET SIGNAL PROCESSING 2013年第3期7卷 219-227页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications;however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixture distribution as its hidden state is the Student's t-hidden Markov model (SHMM). The authors propose a novel Student's t-hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation-maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.

关键词： modified student t-hidden Markov model latent components expectation-maximisation algorithm optimal weight parameters t-mixture distribution Optimisation techniques pattern classification applications gradient method standard SHMM pattern classification synthetic data gradient methods emission densities Gaussian mixture models pattern recognition applications real data Gaussian processes hidden Markov models Pattern recognition scalar exponent regularization component densities Markov processes

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Sparse channel estimation algorithms for OTFS system

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IET COMMUNICATIONS 2022年第18期16卷 2158-2170页

作者： Ouchikh, Rabah Aissa-El-Bey, Abdeldjalil Chonavel, Thierry Djeddou, Mustapha Ecole Mil Polytech Lab Telecommun Bordj El Bahri Algeria IMT Atlantique UMR CNRS 6285 Lab STICC F-29238 Brest France

Orthogonal time-frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of the promising techniques designed in the 2D Delay-Doppler domain adapted to combat high Doppler fading channels. However, channel estimation in high Doppler scenarios in advanced mobile-communication systems is still a challenging task. In this paper, the problem of channel estimation in the Delay-Doppler domain of the OTFS is focused on. First, a simple adaptation of the generalized orthogonal matching pursuit procedure, which will serve as a baseline method in this work, is proposed. Then, iterative algorithms are derived beneficiating from the sparsity of the channel. The unknown channel vector is separated into an unknown sparse support vector corresponding to the delay and Doppler taps, and an unknown vector of channel gains. These algorithms involve l(1)-norm minimization and a two-stage iterative procedure to recover alternatively the channel support and its coefficients. The estimation problem is also addressed from a Bayesian point of view. The sparse representation is reformulated as a specific marginalization of the maximum a posteriori problem on the support of the channel. To deal with the intractability of this problem, two existing techniques are adapted to this context, namely: The Monte Carlo Markov chain with the Gibbs sampler and variational mean-field approximation with the variational Bayesian expectation-maximization procedure. Finally, to assess the performance of the proposed algorithms, their complexity and performance are compared against existing methods. Experimental tests, conducted in high-mobility scenarios and low-latency applications, show that the proposed schemes are slightly more expensive in terms of complexity load but perform significantly better in terms of normalized mean square error and bit error rate.

关键词： Monte Carlo methods high-mobility scenarios sparse representation unknown channel vector fading channels two-stage iterative procedure mean square error methods Bayes methods generalized orthogonal matching pursuit procedure estimation problem maximum likelihood estimation high Doppler scenarios time-frequency analysis sparse channel estimation algorithms channel estimation advanced mobile-communication systems channel support orthogonal time-frequency space modulation minimisation error statistics high Doppler fading channels simple adaptation variational Bayesian expectation-maximization procedure iterative methods Delay-Doppler domain OTFS system posteriori problem unknown sparse support vector unknown vector channel gains expectation-maximisation algorithm Markov processes

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Fully automated scheme for computer-aided detection and breast cancer diagnosis using digitised mammograms

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IET IMAGE PROCESSING 2020年第3期14卷 495-505页

作者： Eltrass, Ahmed S. Salama, Mohamed S. Alexandria Univ Dept Elect Engn Fac Engn Alexandria Egypt

Breast cancer becomes a significant public health problem in the world. During the early detection of breast cancer, it is a very challenging task to classify accurately the benign-malignant patterns in digital mammograms. This study proposes a new fully automated computer-aided diagnosis (CAD) system for breast cancer diagnosis with high-accuracy and low-computational requirements. The expectation-maximisation algorithm is investigated to extract automatically the region of interests (ROIs) within mammograms. The standard shape, statistical, and textural features of ROIs are extracted and combined with multi-resolution and multi-orientation features derived from a new feature extraction technique based on wavelet-based contourlet transform. A hybrid feature selection approach based on combining the support vector machine recursive feature elimination with correlation bias reduction algorithm is proposed. Also, the authors investigate a new similarity-based learning algorithm, called Q, for benign-malignant classification. The proposed CAD system is applied to real clinical mammograms, and the experimental results demonstrate the superior performance of the proposed CAD system over other existing CAD systems in terms of accuracy 98.16%, sensitivity 98.63%, specificity 97.80%, and computational time 2.2 s. This reveals the effectiveness of the proposed CAD system in improving the accuracy of breast cancer diagnosis in real-time systems.

关键词： medical image processing image texture support vector machines learning (artificial intelligence) wavelet transforms mammography image classification cancer feature extraction object detection expectation-maximisation algorithm image resolution statistical analysis feature selection correlation methods computer-aided detection breast cancer diagnosis public health problem benign-malignant patterns digital mammograms fully automated computer-aided diagnosis system low-computational requirements textural features multiorientation features feature extraction technique hybrid feature selection approach support vector machine recursive feature elimination CAD system breast cance early detection expectation-maximisation algorithm region of interest extraction ROIs multiresolution features wavelet-based contourlet transform correlation bias reduction algorithm Q similarity-based learning algorithm benign-malignant classification real-time systems real clinical mammograms time 2 2 s

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Multi-phase initial ranging process for OFDMA systems: improved dynamic threshold-based multi-user detection approach

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IET COMMUNICATIONS 2020年第13期14卷 2130-2140页

作者： Naveen, Kolihalli Narasaiah Ramesha, Kenchappa Dr Ambedkar Inst Technol Dept Elect & Commun Engn Bengaluru Karnataka India Dr Ambedkar Inst Technol Dept Elect & Instrumentat Engn Bengaluru Karnataka India

Initial ranging (IR) is a process of performing power adjustment, timing offset estimation, and synchronisation between the base station (BS) and all users. It plays a vital role in mobile WiMAX standard, which establishes a communication link between BS and ranging subscriber stations (RSSs). Since RSS is located far away from the BS and uplink signal is corrupted by noise, the IR process becomes inferior. Thus, the authors propose an improved dynamic threshold-based IR method comprising three phases: (i) code detection phase, (ii) filtering phase, and (iii) parameter estimation phase. In the first two phases, the received signal is pre-processed to eliminate noise and unselected codes from the codeset by employing adaptive filter and weight-based code detection method. Then, the noise-free signal is considered for the final phase, which improves accuracy. The iterative expectation maximisation algorithm is involved in the estimation of channel coefficient and timing offset. All possible active paths are detected by setting a dynamic threshold value for each channel by the enhanced dynamic threshold method. For all active channels, round trip delay, and power level are estimated by considering particular channel coefficient and timing offset. Extensive simulation of proposed work shows considerable improvements in performance.

关键词： multiuser detection frequency division multiple access adaptive filters expectation-maximisation algorithm OFDM modulation parameter estimation phase coding signal denoising channel estimation OFDMA systems base station mobile WiMAX standard ranging subscriber stations filtering phase parameter estimation phase received signal noise-free signal weight-based code detection method enhanced dynamic threshold method channel coefficient unselected code detection phase multiphase initial ranging process RSS timing offset estimation synchronisation communication link iterative expectation maximisation algorithm dynamic threshold-based IR method dynamic threshold-based multiuser detection approach

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Coherent compensation and high-resolution technology of multi-band inverse synthetic aperture radar fusion imaging

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IET RADAR SONAR AND NAVIGATION 2021年第2期15卷 167-180页

作者： Zhu, Xiaoxiu Liu, Limin Guo, Baofeng Hu, Wenhua Ma, Juntao Shi, Lin Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Campus Shijiazhuang Hebei Peoples R China

Multi-band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub-bands is a prerequisite to achieve multi-band ISAR fusion imaging. Here, a joint approach of coherent compensation and high-resolution imaging is proposed to compensate the incoherent phase and obtain high-resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off-grid. Then, a multi-band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off-grid, but also obtain high-quality ISAR fusion images under low signal-to-noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data.

关键词： high-resolution imaging modified coherent dictionary high-quality ISAR fusion images phase estimation accuracy coherent phase phase estimation multiband inverse synthetic aperture radar fusion imaging technology sparse representation incoherent phase compensation high-resolution ISAR fusion images radar imaging range resolution multiband ISAR fusion imaging model high hardware cost coherent compensation complex Gaussian scale mixture priors radar resolution image fusion incoherent phase estimation model high-resolution technology synthetic aperture radar expectation-maximisation algorithm

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EM-based SNR estimator for faster-than-Nyquist signalling system

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ELECTRONICS LETTERS 2015年第24期51卷 2051-2052页

作者： Liang, Xiaohu Liu, Aijun Pan, Xiaofei Wu, Gang PLA Univ Sci & Technol Inst Commun Engn Nanjing 210007 Jiangsu Peoples R China

Considering the intentionally introduced inter-symbol interference and coloured noise, proposed is a new signal-to-noise ratio (SNR) estimator based on the expectation-maximisation algorithm for the faster-than-Nyquist signalling system. The proposed SNR estimator can maximise the likelihood function by iteratively using the soft information from the channel decoder. Moreover, the new SNR estimator can be applied into high-order modulation. The performance of the proposed SNR estimator is verified and tested.

关键词： intersymbol interference expectation-maximisation algorithm channel coding decoding mobile radio telecommunication signalling electromagnetic based SNR estimator faster-than-Nyquist signalling system intersymbol interference coloured noise signal-to-noise ratio estimator expectation-maximisation algorithm maximum likelihood function channel decoder soft information

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Low-complexity joint data detection and channel equalisation for highly mobile orthogonal frequency division multiplexing systems

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IET COMMUNICATIONS 2010年第8期4卷 1000-1011页

作者： Dogan, H. Panayirci, E. Poor, H. Vincent Istanbul Univ Dept Elect & Elect Engn TR-34320 Istanbul Turkey Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

This study is concerned with the challenging and timely problem of channel equalisation and data detection for orthogonal frequency division multiplexing (OFDM) systems in the presence of frequency-selective and very rapidly time-varying channels. The algorithm is based on the space alternating generalised expectation-maximisation (SAGE) technique which is particularly well suited to multicarrier signal formats and can be easily extended to multi-input multi-output-OFDM systems. In fast fading channels, the orthogonality between subcarriers is destroyed by the time variation of a fading channel over an OFDM symbol duration which causes severe inter-carrier interference (ICI) and, in conventional frequency-domain approaches, results in an irreducible error floor. The proposed joint data detection and equalisation algorithm updates the data sequences in series leading to a receiver structure that also incorporates ICI cancellation, enabling the system to operate at high vehicle speeds. A computational complexity investigation as well as detailed computer simulations indicate that this algorithm has significant performance and complexity advantages over existing suboptimal detection and equalisation algorithms proposed earlier in the literature.

关键词： Electromagnetic compatibility and interference expectation-maximisation algorithm channel equalisation frequency selective channels fading channels Modulation and coding methods Mobile radio systems ICI cancellation Other topics in statistics OFDM modulation time-varying channels channel estimation space alternating generalised expectation-maximisation technique low complexity joint data detection highly mobile orthogonal frequency division multiplexing systems mobile radio intercarrier interference Communication channel equalisation and identification multicarrier signal suboptimal detection multiinput multioutput OFDM systems MIMO communication interference suppression

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Shrinkage-based biased signal-to-noise ratio estimator using pilot and data symbols for linearly modulated signals

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IET COMMUNICATIONS 2015年第11期9卷 1388-1395页

作者： Park, Chee-Hyun Lee, Soojeong Chang, Joon-Hyuk Hanyang Univ Sch Engn Seoul 133791 South Korea

In diverse engineering problems including wireless communications, the estimate of the signal-to-noise ratio (SNR) is required. In this study, the authors develop a shrinkage-based SNR estimator in the data-aided and non-data-aided schemes for higher M-ary phase-shift-keying (M >= 8) and quadrature amplitude modulations. The observed Cramer-Rao lower bound is used as the variance of the expectation maximisation estimator to determine the optimal shrinkage factor. Simulation results show that the normalised mean-squared error of the proposed method is lower than that of the expectation maximisation method for low and moderate SNR conditions.

关键词： data symbol signal-to-noise ratio estimation expectation-maximisation algorithm shrinkage-based SNR estimator linearly modulated signal higher M-ary phase shift keying nondata-aided scheme Signal processing and detection Modulation and coding methods Cramer-Rao lower bound data-aided scheme quadrature amplitude modulation optimal shrinkage factor wireless communication mean square error methods engineering problems quadrature amplitude modulations phase shift keying normalised mean squared error Interpolation and function approximation (numerical analysis) signal processing pilot symbol expectation maximisation estimator Other topics in statistics

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Human-level face verification with intra-personal factor analysis and deep face representation

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IET BIOMETRICS 2018年第5期7卷 467-473页

作者： Munasinghe, Sarasi Fookes, Clinton Sridharan, Sridha Queensland Univ Technol Image & Video Res Lab 2 George StGPO Box 2434 Brisbane Qld 4001 Australia

The last two decades have seen an escalating interest in methods for large-scale unconstrained face recognition. While the promise of computer vision systems to efficiently and accurately verify and identify faces in naturally occurring circumstances still remains elusive, recent advances in deep learning are taking us closer to human-level recognition. In this study, the authors propose a new paradigm which employs deep features in a feature extractor and intra-personal factor analysis as a recogniser. The proposed new strategy represents the face changes of a person using identity specific components and the intra-personal variation through reinterpretation of a Bayesian generative factor analysis model. The authors employ the expectation-maximisation algorithm to calculate model parameters which cannot be observed directly. Recognition outcomes achieved through benchmarking on large-scale wild databases, Labeled Faces in the Wild (LFW) and Youtube Face (YTF), clearly prove that the proposed approach provides remarkable face verification performance improvement over state-of-the-art approaches.

关键词： image representation computer vision face recognition learning (artificial intelligence) expectation-maximisation algorithm feature extraction Bayes methods deep face representation large-scale unconstrained face recognition computer vision systems deep learning human-level recognition deep features feature extractor intra-personal variation recognition outcomes remarkable face verification performance improvement human-level face verification intrapersonal factor analysis Labeled face recognition Youtube face recognition

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