This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

This study introduces three novel dynamic interval-valued principal component analysis (DIPCA) methods: dynamic centers PCA (D-CPCA), dynamic vertices PCA (D-VPCA), and dynamic complete information PCA (D-CIPCA). These methods advance traditional interval-valued PCA (IPCA) by integrating dynamic aspects of industrial processes, thus addressing both data uncertainties and temporal correlations. The DIPCA methods were validated using real-world data from the Ain El Kebira cement plant. Results indicate significant improvements in fault detection accuracy, achieving lower false alarm rates and higher reliability compared to classical IPCA methods. Furthermore, an enhanced combined index for interval-valued data was developed, providing a single, comprehensive statistical measure for streamlined process monitoring.

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring systems.The obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

Grid Connected Photovoltaic (GCPV) sys-tems has been a rising research area in the industry fields. Therefore the high reliability, performance and safety operation of GCPV systems has become a high priority. Thus, it is important of developing an intel-ligent fault detection and diagnosis method that aims at increasing the efficiency of these systems. Therefore, the present study proposes an enhanced intelligent fault diagnosis approach. In the developed procedure, the measured normal and faulty data are applied together to extract the relevant feature, reduce the impact of noise, and then special features are fed to a neural network classifiers. To do that, a Multiscale Principal Component Analysis (MSPCA)-based Artificial Neural Network (ANN) method is proposed to provide the relia-bility and safety of the GCPV systems. From the GCPV measurements, features are appropriately extracted and scaled through multiscale principal component analysis. The ANN classifier is used in classifying twenty-one faults that can occur in GCPV systems operating under different working conditions. The diagnosis results show that the developed MSPCA-based ANN method not only able to detect faults, but also can effectively distinguish between different kinds of faults.

Safe production is of great significance in the process industry like the wind energy conversion (WEC) systems. An unexpected fault in part of the WEC system can damage the entire mechanical system, resulting in huge economic losses and even catastrophic failures. Therefore, this paper proposes an effective neural networks-based ensemble approach for fault de-tection and diagnosis (FDD) of WEC systems. The main contributions are twofold: first, an ensemble learning technique based on the combination of different neural network (ANN, CFNN, and GFNN) into one optimal model are developed in order to distinguish between the different WEC systems operating modes. Then, in order to enhance the results in terms of computation time and storage cost, a reduced version of the proposed neural network-based ensemble technique is presented. The main idea behind this proposal is to use the Hierarchical K-means (H-K-means) clustering to extract only the most significant samples from raw data. Then, the reduced data are introduced as inputs to the proposed neural network-based ensemble technique method to deal with the problem of fault classification. The experimental results demonstrated the feasibility and effectiveness of the proposed FDD techniques.

In this paper, new fault detection and di-agnosis (FDD) techniques dealing with uncertainties in wind energy conversion (WEC) systems are proposed. The uncertainty is addressed by using the interval-valued data representation. The main contributions are twofold: first, to simplify the Recurrent Neural Network (RNN) model in terms of training and computation time and storage cost as well, a reduced version of RNN is proposed. Reduced RNN is established on the H-K-means algorithms to treat the correlations between samples and extract a reduced number of observations from the training data matrix. The main idea behind using H-K-means algorithms for dataset size reduction is to simplify the RNN model in terms of training and computation time. Second, two reduced RNN-based interval-valued data techniques are proposed to distinguish between the different WEC system operating modes. Therefore, two reduced RNN-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with the WEC system uncertainties. The presented results confirm the high feasibility and effectiveness of the proposed FDD techniques.

PV systems are prone to failure owing to aging and external/environmental factors. These failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, re-sulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diag-nosis (FDD) is an important issue in high-efficiency grid-connected PV systems. Deep learning techniques are the most well-known data-driven methodologies. The biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. This paper therefore presents a comparative study of FDD based deep learning. The techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. The diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.

PV systems are subject to failures during their operation due to the aging effects and exter-nal/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and fur-ther system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The most well-known data-driven methods are Deep Learning (DL) approaches. The biggest advantage of DL algorithms, in diagnosis, are that they try to learn high- level features from PV data in a high-order, non-linear and adaptive manners. Then, the fault is classified using soft-max activation function. This work therefore presents a comparative study of FDD based DL techniques. These techniques include Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The DL techniques-based fault diagnosis are implemented using an emulated Grid-Connected PV (GCPV) system. The classification results for the pretrained DL models is exhibited and performance of the models are evaluated.

This paper proposes an effective fault detection and diagnosis (FDD) paradigm in Wind Energy Converter (WEC) Systems. The developed FDD frame-work merges the benefits of kernel principal component analysis (KPCA) model and bidirectional long short-term memory (BiLSTM) feature classifier. KPCA is used to extract and select the most effective features. While, BiLSTM is used for classification purposes. The proposed KPCA-based BiLSTM approach involves two main steps; feature extraction and selection and fault classification. It is tackled in such a way that KPCA model is developed in order to select and extract the more efficient features where the final features are fed to BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performances of the developed technique when compared to the conventional FDD methods.

The objective of this paper is to extend the Maximum Adaptive Exponential Weighted Moving Average (Max-AEWMA) chart to the Max Double AEWMA (Max-DAEWMA) chart. The Max-DAEWMA statistic is based on the Max of the absolute values of the two DAEWMA statistics, one for controlling the variance and the other for the mean. The combined novel technique, is called particle filter (PF)-based Max-DAEWMA for detecting faults of wastewater treatment plants (WWTP). The statistical chart, Max-DAEWMA is applied to detect the fault in mean and/or drifts in WWTP systems where the state variables are estimated using PF technique. The results show the effectiveness of the Max-DAEWMA method over Max-DEWMA and EWMA charts.

In this paper, we develop an improved fault detection (FD) technique in order to enhance monitoring abilities of nonlinear chemical processes. Kernel principal component analysis (KPCA) is an effective data driven technique for monitoring nonlinear processes. However, it is well known that data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency. Thus, to enhance process monitoring abilities, we propose to combine advantages of KPCA and multiscale representation using wavelets by constructing a multiscale KPCA model and a new detection chart named multiscale kernel generalized likelihood ratio test (MS-KGLRT) is derived for fault detection. The detection performance of the new chart is studied using the Tennessee Eastman process (TEP).

It is well known that Exponentially Weighted Moving Average (EWMA) chart is designed to be optimal and efficient to quickly detect small faults. However, the classical EWMA can not perform well in the case of simultaneously large and small faults. To address this limitation, we propose to use an adaptive or a variable parameters control chart. Therefore, in this paper, we propose a novel approach, called particle filter (PF)-based adaptive EWMA (AEWMA) chart, with time-varying smoothing parameter lambda, to detect the fault in Wastewater Treatment Plant (WWTP) process. So that, the PF is applied to compute the residuals, and the AEWMA chart is used to detect the faults. The validation of the developed PF-based AEWMA technique is done using a simulated benchmark COST WWTP BSM1 model. The proposed PF-based AEWMA approach showed better detection abilities when compared to the classical EWMA and Shewhart charts.

Statistical control charts are essential to ensure both safety and efficient operation of many industrial processes. Many dimensionality reduction techniques such as principal component analysis (PCA) and Partial Least Squares (PLS) regression exist, and are often employed for modeling purposes as they are relatively easy to compute. However, these techniques are only effective for modeling and monitoring linear processes. The Kernel Principal Component Analysis (KPCA) method is an extension of PCA that helps deal with any nonlinearities in the process data. However, KPCA-based fault detection methods may result in a higher false alarm rate than the conventional method. In this paper, an improved KPCA method is developed in order to tackle the issue of high false alarm rates, by utilizing a mean filter to smoothen the detection statistics that are obtained from the KPCA method. The advantages presented by the developed method are illustrated using a simulated nonlinear model. The results clearly show that the improved KPCA method provides improved fault detection results with low missed detection and false alarm rates, and smaller ARL1 values compared to the conventional methods.

Statistical process monitoring charts are critical in ensuring safety for many chemical processes. Principal Component Analysis (PCA) is often used, due to its computational simplicity. However, many chemical processes may be inherently nonlinear, and this degrades the performance of the linear PCA method. Kernel Principal Component Analysis (KPCA) is an extension of the conventional PCA chart, which can help deal with nonlinearity in a given process. Additionally, PCA assumes that process data are Gaussian and uncorrelated, and only contain a moderate level of noise. These assumptions do not usually hold in practice. Multiscale wavelet-based data representation produces wavelet coefficients that possess characteristics that are able to handle violations in these assumptions. A multiscale kernel principal component analysis (MSKPCA) method has already been developed to tackle all of these issues, but it usually provides a high false alarm rate. In this paper, an improved MKSPCA chart is developed in order to deal with the false alarm rate issue, by smoothening the detection statistic using a mean filter. The advantages brought forward by the improved method are demonstrated through a simulated example in which the developed fault detection method is used to monitor a continuous stirred tank reactor (CSTR). The results clearly show that the improved MSKPCA method provides lower missed detection and false alarm rates as well as ARL1 values compared to those provided by the conventional methods.

Fault detection is important for effective and safe process operation. Partial least squares (PLS) has been used successfully in fault detection for multivariate processes with highly correlated variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T2and the Q statistics are not well suited to detect small faults because they only use information about the process in the most recent observation. Exponentially weighed moving average (EWMA), however, has been shown to be more sensitive to small shifts in the mean of process variables. In this paper, a PLS-based EWMA fault detection method is proposed for monitoring processes represented by PLS models. The performance of the proposed method is compared with that of the traditional PLS-based fault detection method through a simulated example involving various fault scenarios that could be encountered in real processes. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS method.

Monitoring charts play an important role in statistical quality control. Shewhart charts are among the most commonly used charts in process monitoring, and have seen many extensions for improved performance. Unfortunately, measured practical data are usually contaminated with noise, which degrade the detection abilities of the conventional Shewhart chart by increasing the rate of false alarms. Therefore, the effect of noise needs to be suppressed for enhanced process monitoring. Wavelet-based multiscale representation of data, which is a powerful feature extraction tool, has shown good abilities to efficiently separate deterministic and stochastic features. In this paper, the advantages of multiscale representation are exploited to enhance the fault detection performance of the conventional Shewhart chart by developing an integrated multiscale Shewhart algorithm. The performance of the developed algorithm is illustrated using two examples, one using synthetic data, and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional Shewhart chart and the conventional Shewhart chart applied on multiscale pre-filtered data.

One of the most popular multivariate statistical methods used for data-based process monitoring is Principal Component Analysis (PCA). In the absence of a process model, PCA has been successfully used as a data-based FD technique for highly correlated process variables. Some of the PCA detection indices include the T2 or Q statistics, which have their advantages and disadvantages. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abili ties. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The PCA-based GLR fault detection algorithm provides optimal properties by maximizing the detection probability of faults for a given false alarm rate. However, the presence of measurement noise and modeling errors increase the rate of false alarms. Therefore, to further improve the quality of fault detection, multiscale filtering is utilized to filter the residuals obtained from the PCA model, which helps suppress the effect on errors, and thus decrease the false alarm rate. The proposed fault detection methodology is demonstrated through its application to monitor the ozone level in the Upper Normandy region, France, and it is shown to effectively reduce the rate of false alarms whilst retaining the capability of detecting process faults.

Operating distillation columns under control requires inferring the compositions of the distillate and bottom streams (which are challenging to measure) from other more easily measured variables, such as temperatures at different trays of the column. Models that can be used in this regard are called inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction accuracy of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction ability of these models. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithm that integrates modeling and filtering. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using two examples, one using synthetic data and the other using simulated distillation column data. Both examples clearly demonstrate the effectiveness of the IMSLVR algorithm.

Many operations, such as monitoring and control, require the availability of some key process variables. When these variables are difficult to measure, it is usually relied on inferential models that can be used to estimate these variables from other easier-to-measure variables. Latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least square (PLS), and regularized canonical correlation analysis (RCCA), are commonly used as inferential models. In this paper, these linear LVR modeling techniques are first reviewed, and then a new algorithm that extends these LVR modeling techniques to nonlinear processes is presented. The developed nonlinear LVR (NLLVR) modeling algorithm utilizes nonlinear functions in the form of polynomials to capture the nonlinear relationships between the latent variables are the model output. The structures of these polynomials as well as the number of latent variables used are optimized using cross validation. The performances of the developed NLLVR modeling techniques are illustrated and compared with those the conventional linear LVR techniques (PCR, PLS, and RCCA). This comparison is performed using two examples, one using synthetic data and the other using simulated distillation column data. The results of both examples show that a significant improvement in model predictions can be achieved using the NLLVR modeling methods.

The main contribution of this work is the application of delay-dependent adaptive control techniques to control nonlinear continuous stirred tank reactor (CSTR) model with state delay. The delay-dependent adaptive control problem is first formulated and stabilizing adaptive control algorithms are developed and then applied to the CSTR process model. The CSTR model includes a nonlinear perturbation which is assumed to have a norm that is bounded by a scaled norm of the state vector. Here, we consider two cases where the weight of the state norm is assumed to be known and unknown. Simulation results show the efficacy of the delay-dependent adaptive control schemes in controlling the CSTR nonlinear process model.

Ozone is one of the lost serious air pollution problems. Monitoring abnormal changes in the concentration of ozone in the troposphere is of great interest because of its negative influence on human health, vegetation, and materials. Modeling ozone is very challenging because of the complexity of the ozone formation mechanisms in the troposphere and the uncertainty about the meteorological conditions in urban areas. In the absence of a process model, principal component analysis (PCA), which is a multivariate statistical technique, has been successfully used as a data-based fault detection (FD) method for highly correlated process variables. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abilities. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The developed PCA-based GLR FD algorithm is utilized to enhance monitoring the ozone concentrations in Upper Normandy, France. The performances of PCA and PCA-based GLR test are compared through two practical case studies, one involving a sensor fault and the other involving tropospheric ozone pollution in multiple measuring stations. The results show that the PCA-based GLR test can detect abnormal ozone levels with a smaller number of false alarms than the conventional PCA method.

The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, a simpler approach based on the Expectation-Maximization (EM) algorithm is proposed which iteratively estimates the clock parameters using a Kalman smoother in the E-step, and the location of the unknown node in the M-step. The convergence and the mean square error (MSE) performance of the proposed algorithm are evaluated using simulation studies which demonstrate the high fidelity of the proposed joint estimation approach.

MOTIVATION: Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. RESULTS: We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. AVAILABILITY: The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

Recent advances in high-throughput technologies for biological data acquisition have spurred a broad interest in the construction of mathematical models for biological phenomena. The development of such mathematical models relies on the estimation of unknown parameters of the system using the time-course profiles of different metabolites in the system. One of the main challenges in the parameter estimation of biological phenomena is the fact that the number of unknown parameters is much more than the number of metabolites in the system. Moreover, the available metabolite measurements are corrupted by noise. In this paper, a new parameter estimation algorithm is developed based on the stochastic estimation framework for nonlinear systems, namely the unscented Kalman filter (UKF). A new iterative UKF algorithm with covariance resetting is developed in which the UKF algorithm is applied iteratively to the available noisy time profiles of the metabolites. The proposed estimation algorithm is applied to noisy time-course data synthetically produced from a generic branched pathway as well as real time-course profile for the Cad system of E. coli. The simulation results demonstrate the effectiveness of the proposed scheme.

Clock synchronization has become an indispensable requirement in wireless sensor networks due to its central importance in vital network operations such as data fusion and duty cycling, and has attracted considerable research interest recently. Assuming exponentially distributed random delays in a two-way message exchange mechanism, this work proposes a network-wide clock synchronization algorithm using a factor graph representation of the network. Message passing using the max-product algorithm is adopted to derive the update rules for the proposed iterative procedure. A closed form solution is obtained for each node's belief about its clock offset at each iteration. Simulation results show that the application of the proposed message passing-based network-wide clock synchronization algorithm provides convergent estimates for both regular cycle-free and random topologies. Moreover, the mean square error (MSE) performance of the proposed algorithm is also compared with the Cramer-Rao bound (CRB) for small example networks, which further highlights the effectiveness of the proposed algorithm.

The problems of node localization and clock synchronization in wireless sensor networks are naturally tied from a statistical signal processing perspective. In this work, we consider the joint estimation of an unknown node's location and clock parameters by incorporating the effect of imperfections in node oscillators, which render a time varying nature to the clock parameters. The data exchange mechanism is based on a two-way message exchange with anchor nodes. In order to alleviate the computational complexity associated with the optimal maximum a-posteriori estimator, two iterative approaches are proposed as simpler alternatives. The first approach utilizes an Expectation-Maximization (EM) based algorithm which iteratively estimates the clock parameters and the location of the unknown node. The EM algorithm is further simplified by a non-linear processing of the data to obtain a closed form solution of the location estimation problem using least squares (LS). The performance of the estimation algorithms is benchmarked by deriving the Hybrid Cramer-Rao lower bound (HCRB) on the mean square error (MSE) of the estimators. The theoretical findings are corroborated by simulation studies which reveal that the LS estimator closely matches the performance of the EM algorithm for small time of arrival measurement noise, and is well suited for implementation in low cost sensor networks.

Measured biological data are a rich source of information about the biological phenomena they represent. For example, time-series genomic or metabolic micro array data can be used to construct dynamic genetic regulatory network models, which can be used to better understand the biological system and to design intervention strategies to cure or manage major diseases. Unfortunately, biological measurements are usually highly contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful data analysis and denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to filter biological data contaminated with white noise. The performances of these multiscale filtering techniques are demonstrated and compared to those of some conventional low pass filters using simulated time series metabolic data. The results of this comparative study show that significant improvement can be achieved using multiscale filtering over conventional filtering methods.

This paper reviews the information theoretic methods used for inferring gene regulatory networks. Mutual information has been widely used as a dependency measure to estimate the undirected interactions between genes using steady state data. However, employing time-series data results in a directed graph. Since two genes may be interacting with each other via an intermediate gene, their mutual information may show a direct dependency. To resolve this issue, data processing inequality and conditional mutual information have been employed. Mutual information, being a symmetric measure, is unable to predict directed edges using the steady-state data alone, while algorithms using time-series data can be computationally complex as more data is involved. Therefore, non-symmetric measures such as φ mixing coefficients have recently been proposed in the literature. The algorithms using these techniques are also discussed in this article. Estimation of information-theoretic metrics is explained which is a core component of all the methods. Performance metrics that are frequently used to test the robustness and accuracy of the algorithms are also described and some avenues of future research are proposed.

This paper considers the problem of inferring gene regulatory networks using time series data. A nonlinear model is assumed for the gene expression profiles, whereas the microarray data follows a linear Gaussian model. A particle filter based approach is proposed to estimate the gene expression profiles and the parameters are estimated online using Kalman filter. In order to capture the inherent sparsity of the gene networks, LASSO based least square optimization is performed. The performance of the proposed algorithm is compared with the extended Kalman filter (EKF) algorithm using Mean Square Error (MSE) as the fidelity criterion. The simulations are performed using the synthetic as well as real data and the proposed algorithm is observed to outperform the EKF in the scenarios considered.

We consider the problem of distributed and secure routing for target tracking in wireless sensor networks (WSN) based on quantized sensors measurements. We propose a new method for jointly selecting the optimal communication path between slave sensors and cluster head (CH), detecting the malicious sensors and estimating the target position. Firstly, we detect the malicious sensor nodes based on the information relevance of their measurements. Secondly, we select the optimal communication route in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Then, we estimate the target position using Quantized Variational Filtering (QVF) algorithm. The computation of these criteria is based on the target position predictive distribution provided by the QVF algorithm. The performance of the proposed method is validated by simulation results in target tracking for WSN.

This paper considers the problem of learning the structure of gene regulatory networks from gene expression time series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and sparse structure.

Measured microarray genomic and metabolic data are a rich source of information about the biological systems they represent. For example, time-series biological data can be used to construct dynamic genetic regulatory network models, which can be used to design intervention strategies to cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data contaminated with white or colored noise. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.

An important objective of modeling biological phenomena is to develop therapeutic intervention strategies to move an undesirable state of a diseased network toward a more desirable one. Such transitions can be achieved by the use of drugs to act on some genes/metabolites that affect the undesirable behavior. Due to the fact that biological phenomena are complex processes with nonlinear dynamics that are impossible to perfectly represent with a mathematical model, the need for model-free nonlinear intervention strategies that are capable of guiding the target variables to their desired values often arises. In many applications, fuzzy systems have been found to be very useful for parameter estimation, model development and control design of nonlinear processes. In this paper, a model-free fuzzy intervention strategy (that does not require a mathematical model of the biological phenomenon) is proposed to guide the target variables of biological systems to their desired values. The proposed fuzzy intervention strategy is applied to three different biological models: a glycolytic-glycogenolytic pathway model, a purine metabolism pathway model, and a generic pathway model. The simulation results for all models demonstrate the effectiveness of the proposed scheme.

This work presents a multi-objective algorithm for jointly selecting the appropriate group of candidate sensors and optimizing the quantization for target tracking inWireless Sensor Networks (WSN). We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering sensors selection problem. Firstly, we jointly optimize the quantization level and the group of candidate sensors selection in order to provide the required data of the target and to balance the energy dissipation in the WSN. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The quantization optimization and the sensors selection are based on multi-objective (MO) that define the main parameters that may influence the relevance of the participation in cooperation for target tracking. This optimization is also based on the transmitting power between one sensor and the CH. The best sensors selection and quantization optimization are designed to reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The simulation results show that the proposed method, outperforms the quantized variational filtering algorithm under sensing range constraint and the centralized quantized particle filter.

We consider the problem of Maximum Likelihood (ML) estimation of clock parameters in a two-way timing exchange scenario where the random delays assume a Weibull distribution, which represents a more generalized model. The ML estimate of the clock offset for the case of exponential distribution was obtained earlier. Moreover, it was reported that when the fixed delay is known, MLE is not unique. We determine the uniformly minimum variance unbiased (UMVU) estimators for exponential distribution under such a scenario and produce biased estimators having lower MSE than UMVU for all values of clock offset. We then consider the case when shape parameter is greater than one and reduce the corresponding optimization problems to their equivalent convex forms, thus guaranteeing convergence to a global minimum.