scholarly journals Distributed Fusion Filtering in Networked Systems with Random Measurement Matrices and Correlated Noises

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Raquel Caballero-Águila ◽  
Irene García-Garrido ◽  
Josefa Linares-Pérez
Keyword(s):  
Mean Squared Error ◽  
Current System ◽  
Unified Framework ◽  
Missing Measurements ◽  
Network Systems ◽  
Process Noise ◽  
Random Measurement ◽  
One Step ◽  
Sensor Measurement ◽  
Distributed Fusion

The distributed fusion state estimation problem is addressed for sensor network systems with random state transition matrix and random measurement matrices, which provide a unified framework to consider some network-induced random phenomena. The process noise and all the sensor measurement noises are assumed to be one-step autocorrelated and different sensor noises are one-step cross-correlated; also, the process noise and each sensor measurement noise are two-step cross-correlated. These correlation assumptions cover many practical situations, where the classical independence hypothesis is not realistic. Using an innovation methodology, local least-squares linear filtering estimators are recursively obtained at each sensor. The distributed fusion method is then used to form the optimal matrix-weighted sum of these local filters according to the mean squared error criterion. A numerical simulation example shows the accuracy of the proposed distributed fusion filtering algorithm and illustrates some of the network-induced stochastic uncertainties that can be dealt with in the current system model, such as sensor gain degradation, missing measurements, and multiplicative noise.

scholarly journals Optimal Fusion Filtering in Multisensor Stochastic Systems with Missing Measurements and Correlated Noises

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
R. Caballero-Águila ◽  
I. García-Garrido ◽  
J. Linares-Pérez
Keyword(s):  
Stochastic Systems ◽  
Linear Filter ◽  
Missing Measurements ◽  
Process Noise ◽  
Estimation Algorithms ◽  
Optimal Linear ◽  
One Step ◽  
Optimal Fusion ◽  
Innovation Approach ◽  
Correlated Noises

The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems with missing measurements and autocorrelated and cross-correlated noises. The stochastic uncertainties in the measurements coming from each sensor (missing measurements) are described by scalar random variables with arbitrary discrete probability distribution over the interval[0,1]; hence, at each single sensor the information might be partially missed and the different sensors may have different missing probabilities. The noise correlation assumptions considered are (i) the process noise and all the sensor noises are one-step autocorrelated; (ii) different sensor noises are one-step cross-correlated; and (iii) the process noise and each sensor noise are two-step cross-correlated. Under these assumptions and by an innovation approach, recursive algorithms for the optimal linear filter are derived by using the two basic estimation fusion structures; more specifically, both centralized and distributed fusion estimation algorithms are proposed. The accuracy of these estimators is measured by their error covariance matrices, which allow us to compare their performance in a numerical simulation example that illustrates the feasibility of the proposed filtering algorithms and shows a comparison with other existing filters.

Controllable Electrochemical Synthesis of Ag Hierarchical Micro/Nanostructures with High SERS-Activity

NANO ◽  
2020 ◽  
Vol 15 (04) ◽  
pp. 2050043
Author(s):  
Huayu Zhou ◽  
Jingjing Wang ◽  
Qiong Yang ◽  
Menglei Chen ◽  
Changsheng Song ◽  
...  
Keyword(s):  
Indium Tin Oxide ◽  
Current System ◽  
Three Dimensional ◽  
Constant Current ◽  
Localized Surface Plasmon ◽  
Excitation Wavelength ◽  
Raman Signal ◽  
Mixed Solution ◽  
Raman Enhancement ◽  
One Step

We report a one-step electrochemical deposition technique to prepare three-dimensional (3D) Ag hierarchical micro/nanostructured film consisting of well-crystallized Ag nanosheets grown on an indium tin oxide (ITO) conductive substrate. The Ag hierarchical micro/nanostructures were fabricated in the mixed solution of AgNO3 and sodium citrate in a constant current system at room temperature. Through reduction of Ag[Formula: see text] electrodeposited on the surface of ITO substrate, nanoparticles were grown to form nanosheets which further combined into 3D sphere-like microstructures. The 3D Ag micro/nanostructures have many sharp edges and nanoscale gaps which can give rise to good Raman-enhanced effect. Due to localized surface plasmon resonance (LSPR) effects, these special Ag micro/nanostructures exhibited good Raman-enhanced performance. Using Rhodamine 6G (R6G) molecules as probe molecule, we studied the influence of excitation wavelength on Raman enhancement. The results showed that the 532[Formula: see text]nm excitation wavelength is the best to obtain the strongest Raman signal and to reduce the influence of other impurity peaks. Using the as-synthesized Ag hierarchical micro/nanostructures, we can detect the 10[Formula: see text][Formula: see text]mol/L R6G aqueous solution, exhibiting great Raman-enhanced effect.

scholarly journals Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3235
Author(s):  
Koichi Fujiwara ◽  
Shota Miyatani ◽  
Asuka Goda ◽  
Miho Miyajima ◽  
Tetsuo Sasano ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mean Squared Error ◽  
False Positive Rate ◽  
Premature Ventricular Contraction ◽  
Heart Rate Variability Analysis ◽  
Unified Framework ◽  
Atrial Contraction ◽  
Premature Atrial Contraction ◽  
Positive Rate

Heart rate variability, which is the fluctuation of the R-R interval (RRI) in electrocardiograms (ECG), has been widely adopted for autonomous evaluation. Since the HRV features that are extracted from RRI data easily fluctuate when arrhythmia occurs, RRI data with arrhythmia need to be modified appropriately before HRV analysis. In this study, we consider two types of extrasystoles—premature ventricular contraction (PVC) and premature atrial contraction (PAC)—which are types of extrasystoles that occur every day, even in healthy persons who have no cardiovascular diseases. A unified framework for ectopic RRI detection and a modification algorithm that utilizes an autoencoder (AE) type of neural network is proposed. The proposed framework consists of extrasystole occurrence detection from the RRI data and modification, whose targets are PVC and PAC. The RRI data are monitored by means of the AE in real time in the detection phase, and a denoising autoencoder (DAE) modifies the ectopic RRI caused by the detected extrasystole. These are referred to as AE-based extrasystole detection (AED) and DAE-based extrasystole modification (DAEM), respectively. The proposed framework was applied to real RRI data with PVC and PAC. The result showed that AED achieved a sensitivity of 93% and a false positive rate of 0.08 times per hour. The root mean squared error of the modified RRI decreased to 31% in PVC and 73% in PAC from the original RRI data by DAEM. In addition, the proposed framework was validated through application to a clinical epileptic seizure problem, which showed that it correctly suppressed the false positives caused by PVC. Thus, the proposed framework can contribute to realizing accurate HRV-based health monitoring and medical sensing systems.

Econometrics of Fair Values

2008 ◽  
Vol 22 (1) ◽  
pp. 111-125 ◽  
Author(s):  
Shyam Sunder
Keyword(s):  
Measurement Errors ◽  
Mean Squared Error ◽  
Price Volatility ◽  
Policy Decisions ◽  
Unified Framework ◽  
Minimum Mean Squared Error ◽  
Squared Error

The properties of many important valuation rules can be quantified, examined, and compared in a unified framework to assist policy decisions. Valuation rules can be viewed as econometric estimators of unobserved values of aggregates. Which valuation rule has minimum mean squared error (relative to the unobserved value of bundles of resources) is a matter of econometrics, not of theory or principle; it depends in a known fashion on the relative magnitudes of the parameters—price volatility and measurement errors—of the economy, industry, or firm. In general, no valuation rule, fair or not, dominates the others. Given the parameters of an environment, this framework can help identify efficient valuation rules.

scholarly journals Learning to Make Document Context-Aware Recommendation with Joint Convolutional Matrix Factorization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Lei Guo ◽  
Yu Han ◽  
Haoran Jiang ◽  
Xinxin Yang ◽  
Xinhua Wang ◽  
...  
Keyword(s):  
Social Network ◽  
Social Influence ◽  
Matrix Factorization ◽  
Mean Squared Error ◽  
Contextual Information ◽  
Average Error ◽  
Final Decision ◽  
Context Aware ◽  
Unified Framework ◽  
Latent Factor

Context-aware recommendation (CR) is the task of recommending relevant items by exploring the context information in online systems to alleviate the data sparsity issue of the user-item data. Prior methods mainly studied CR by document-based modeling approaches, that is, making recommendations by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they cannot effectively utilize contextual information of the documents, which results in a shallow understanding of the documents. Recent works argued that the understanding of document context can be improved by the convolutional neural network (CNN) and proposed the convolutional matrix factorization (ConvMF) to leverage the contextual information of documents to enhance the rating prediction accuracy. However, ConvMF only models the document content context from an item view and assumes users are independent and identically distributed (i.i.d). But in reality, as we often turn to our friends for recommendations, the social relationship and social reviews are two important factors that can change our mind most. Moreover, users are more inclined to interact (buy or click) with the items that they have bought (or clicked). The relationships among items are also important factors that can impact the user’s final decision. Based on the above observations, in this work, we target CR and propose a joint convolutional matrix factorization (JCMF) method to tackle the encountered challenges, which jointly considers the item’s reviews, item’s relationships, user’s social influence, and user’s reviews in a unified framework. More specifically, to explore items’ relationships, we introduce a predefined item relation network into ConvMF by a shared item latent factor and propose a method called convolutional matrix factorization with item relations (CMF-I). To consider user’s social influence, we further integrate the user’s social network into CMF-I by sharing the user latent factor between user’s social network and user-item rating matrix, which can be treated as a regularization term to constrain the recommendation process. Finally, to model the document contextual information of user’s reviews, we exploit another CNN to learn user’s content representations and achieve our final model JCMF. We conduct extensive experiments on the real-world dataset from Yelp. The experimental results demonstrate the superiority of JCMF compared to several state-of-the-art methods in terms of root mean squared error (RMSE) and mean average error (MAE).

scholarly journals A Pipeline for Adaptive Filtering and Transformation of Noisy Left-Arm ECG to Its Surrogate Chest Signal

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 866
Author(s):  
Farzad Mohaddes ◽  
Rafael da Silva ◽  
Fatma Akbulut ◽  
Yilu Zhou ◽  
Akhilesh Tanneeru ◽  
...  
Keyword(s):  
Adaptive Filtering ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Recursive Least Squares ◽  
Step Size ◽  
Ecg Signals ◽  
Squared Error ◽  
One Step ◽  
Power Line Interference ◽  
Electrocardiogram Ecg

The performance of a low-power single-lead armband in generating electrocardiogram (ECG) signals from the chest and left arm was validated against a BIOPAC MP160 benchtop system in real-time. The filtering performance of three adaptive filtering algorithms, namely least mean squares (LMS), recursive least squares (RLS), and extended kernel RLS (EKRLS) in removing white (W), power line interference (PLI), electrode movement (EM), muscle artifact (MA), and baseline wandering (BLW) noises from the chest and left-arm ECG was evaluated with respect to the mean squared error (MSE). Filter parameters of the used algorithms were adjusted to ensure optimal filtering performance. LMS was found to be the most effective adaptive filtering algorithm in removing all noises with minimum MSE. However, for removing PLI with a maximal signal-to-noise ratio (SNR), RLS showed lower MSE values than LMS when the step size was set to 1 × 10−5. We proposed a transformation framework to convert the denoised left-arm and chest ECG signals to their low-MSE and high-SNR surrogate chest signals. With wide applications in wearable technologies, the proposed pipeline was found to be capable of establishing a baseline for comparing left-arm signals with original chest signals, getting one step closer to making use of the left-arm ECG in clinical cardiac evaluations.

Sign in / Sign up

Export Citation Format

Share Document