Improve Estimation Performance Research Articles

Drowsiness Estimation Using Electroencephalogram and Recurrent Support Vector Regression

As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able to improve their estimation ability as the length of time or number of trials increases. Thus, in this study, we aim to find an effective drowsiness estimation method that is also able to improve its prediction ability as the subject’s activity increases. We used electroencephalogram (EEG) data to estimate drowsiness, and the Karolinska sleepiness scale (KSS) for drowsiness evaluation. Five parameters (α, β/α, (θ+α)/β, activity, and mobility) from the O1 electrode site were selected. By combining these parameters and KSS, we demonstrate that a typical support vector regression (SVR) algorithm can estimate drowsiness with a correlation coefficient (R2) of up to 0.64 and a root mean square error (RMSE) of up to 0.56. We propose a “recurrent SVR” (RSVR) method with improved estimation performance, as highlighted by an R2 value of up to 0.83, and an RMSE of up to 0.15. These results suggest that in addition to being able to estimate drowsiness based on EEG data, RSVR is able to improve its drowsiness estimation performance.

Quasi-Proportional-Resonant Controller Based Adaptive Position Observer for Sensorless Control of PMSM Drives Under Low Carrier Ratio

The position and speed estimation performance of the sliding mode observer (SMO) for permanent magnet synchronous motor (PMSM) drives under low carrier ratio degrades, due to the inherent chattering and enlarged control delay. To improve estimation performance, this paper proposes a quasi-proportional-resonant (QPR) based adaptive observer to obtain back electromotive force with adaptive harmonics cancellation. In this observer, the QPR controller is employed to eliminate errors between the observed currents and the actual ones in the αβ reference frame, replacing sign function and low pass filter (LPF) of the SMO to cancel the chattering, and harmonics immune performance can also be achieved thanks to frequency selection characteristic of the QPR controller. Besides cancelling the chattering, the proposed observer can also eliminate phase shift produced by the LPF, since the QPR controller is a zero phase-shift amplifier at the resonant frequency. Comparative experiments are carried out in a 3-kW PMSM drive, and the results validate the effectiveness of the proposed adaptive position observer.

Location-Free Spectrum Cartography

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals, as occurs in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built upon this approach and offer a markedly improved estimation performance than existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios.

Low-Rank Structured Covariance Matrix Estimation

The covariance matrix estimation problem is posed in both the Bayesian and frequentist settings as the solution of a maximum a posteriori (MAP) or maximum likelihood (ML) optimization, respectively, when the true covariance consists of a known (or bounded) noise floor and a low-rank component. Persymmetric structure may also be assumed. The MAP and ML solutions with the non-convex rank constraint are shown to be a simple scalar thresholding of eigenvalues of a suitably translated and projected sample covariance matrix. No iterative optimization is required; therefore, the computation is suited to real-time applications. Our proof is short and elementary without resorting to the duality theory. Numerical results are presented to illustrate the improved estimation performance obtained by incorporating the structural constraints on the unknown covariance.

Performance Evaluation of Different Channel Estimation Techniques Using OFDM System on Large and Small Scale Fading Channel Model

The development of internet related applications and their increasing demands have led to advancement in high speed communication systems. For the accomplishment of these increasing demands MIMO channels with multiple transmitting and receiving antennas have been devised. MIMO systems enhance the capacity using spatial multiplexing and interference reduction through spatial dimensioning. Furthermore, MIMO systems mutual with orthogonal frequency division multiplexing (MIMO-OFDM) improves system capacity and mitigation against fading. These systems are the basis of 4G and beyond wireless communication standards. This research work focus on improvement in channel estimation performance using various techniques on standard communication models. Semi blind channel estimation technique is employed for channel estimation, which includes use of training sequence in form of pilots. Block type arrangement is being used, since it’s based on frequency selective channels. LSE and MMSE estimations are the two basic estimation techniques which can be efficiently used to estimate channels in OFDM systems. For MMSE implementation information of noise variance and channel covariance is required. Furthermore it is extremely complex than LSE estimator. DFT and DCT based channel estimation methods are employed to increase the performance and diminish the complexity. Different interpolation methods are been used and analyzed for better performance. Number of subcarriers which also have an impact on estimation performance has also been discussed. Overall channel estimation performance have been examined for both large and small scale fading channel models.

Networked H∞ filtering for Takagi–Sugeno fuzzy systems under multi-output multi-rate sampling

This paper studies networked H∞ filtering for Takagi–Sugeno fuzzy systems with multi-output multi-sensor asynchronous sampling. Different output variables in a dynamic system are sampled by multiple sensors with different sampling rates. To estimate the signals of such a system, a continuous multi-rate sampled-data fusion method is proposed to design a novel networked filter. By considering a class of decentralized event-triggered transmission schemes, multi-channel network-induced delays, and the updating modes of the MOMR sampled-data, a networked jumping fuzzy filter is proposed to estimate system signals based on the transmitted multi-rate sampled-data of fuzzy system and the multi-rate sampled states of filter, and the jumping among filter modes is governed by a Markov process which depends on the arrival times of sampled output sub-vectors. To deal with asynchronous membership functions, the networked fuzzy filtering system is modeled as an uncertain fuzzy stochastic system with membership function deviation bounds. Based on stability and H∞ performance analysis, several membership-function-dependent conditions are presented to co-design the event-triggered transmission schemes and the fuzzy filter such that the filtering error system is robustly mean-square exponentially stable with a prescribed H∞ attenuation level. Finally, the improvement in estimation performance and comparison with the existing filtering methods are discussed through simulation examples.

Spatial stock assessment methods: A viewpoint on current issues and assumptions

Spatial stock assessments have been developed to address violations of the dynamic pool assumption that the region to be assessed contains a single homogeneous stock. The possibility of such violation is often evident in data that suggest different trends in abundance or catch / survey age- / size-structure among areas that cannot be explained simply by the fishing history among areas. Currently, most stock assessments account for spatial structure using the ‘areas-as-fleets’ approach in which fishery or survey selectivity and catchability are assumed to differ spatially. However, several simulation studies suggest that adopting spatial approaches to stock assessment will improve estimation performance compared to the areas-as-fleets approach or ignoring spatial structure when conducting stock assessments, although at the cost of a larger number of estimable parameters. Spatial approaches to stock assessment and the provision of management advice have been available since the 1950s. However, spatial stock assessments only became adopted for management purposes in the 1990s, with the widespread adoption of the “integrated approach” to stock assessment, which allowed the use of multiple data sets for parameter estimation. The number of spatial stock assessments is now increasing rapidly. This paper outlines some of the key decisions that need to be made when conducting a spatial stock assessment (number of areas, how to model recruitment, movement, growth and dispersal, and model parameterization).

Estimation of wheat planting date using machine learning algorithms based on available climate data

Agricultural applications supported with information technologies increase plant production, protect soil and reduce labor, which is crucial for sustainable agriculture. Impacts of planting dates on production are very well known. In the current study machine learning algorithms have been used in determining planting date. The proposed method aims to help farmers to obtain higher yield providing them with accurate planting date. For this purpose, metereological data was used as an input. For each year, metereological information (Daily Maximum Air Temperature, Daily Relative Humudity, Daily Average Air Temperature, Daily Minimum Air Temperature and Daily Precipitation) in the first 300 days were used to determine three different planting dates; early, normal and late for wheat crop. For estimation of planting date, classification algorithms of k Nearest Neighbor (kNN), Support Vector Machine (SVM) and Decisions Trees were used. Performances of different algorithms were calculated with leave one out cross validation approach. In order to eleminate extremely high processing time because of high dimension of the data set and improve estimation performance, genetic algorithm was used to reduce the number of features. For the estimations performed using both all features and also the features selected with genetic algorithm the highest accuracies were obtained using kNN method with classification accuracy rates of 37% and 92%, respectively. Overall, the results showed that wheat planting date could be determined successfully from climate information obtained in the first 300 days with the help of machine learning techniques combined with feature selection using genetic algorithm, which will prevent low productivity, financial and labor loss as a result of inaccurate planting date.

Alternating Iteratively Reweighted Least Squares Minimization for Low-Rank Matrix Factorization

Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information. In that vein, low-rank representations (LRRs), which seek low-dimensional embeddings of data have naturally appeared. In an effort to reduce computational complexity and improve estimation performance, LRR has been viewed via a matrix factorization (MF) perspective. Recently, low-rank MF (LRMF) approaches have been proposed for tackling the inherent weakness of MF, i.e., the unawareness of the dimension of the low-dimensional space where data reside. Herein, inspired by the merits of iterative reweighted schemes for sparse recovery and rank minimization, we come up with a generic low-rank promoting regularization function. Then, focusing on a specific instance of it, we propose a regularizer that imposes column-sparsity jointly on the two matrix factors that result from MF, thus promoting low-rankness on the optimization problem. The low-rank promoting properties of the resulting regularization term are brought to light by mathematically showing that it is actually a tight upper bound of a specific version of the weighted nuclear norm. The problems of denoising and matrix completion are redefined according to the new LRMF formulation and solved via efficient alternating iteratively reweighted least squares type algorithms. Theoretical analysis of the algorithms regarding the convergence and the rates of convergence to stationary points is provided. The effectiveness of the proposed algorithms is verified in diverse simulated and real data experiments.

Performance Improvement of Finite Time Parameter Estimation with Relaxed Persistence of Excitation Condition

In this paper, a novel finite time parameter estimation method is proposed to solve the parameter estimation problem for a class of linearly parameterized nonlinear systems. The main feature of the proposed method is that the existing method is modified via concurrent learning technique such that the strict persistence of excitation (PE) condition on the regression matrix is relaxed to a rank condition on the recorded data. This makes the presented method more practical. Furthermore, the convergence rate is improved significantly by sliding mode technique in finite time sense. The simulation results of the existing general nonlinear system illustrate the aforementioned features. Comparison with existing methods from literature proves the effectiveness of the proposed method.

Combined Constrained Adaptive Sum and Difference Beamforming in Monopulse Angle Estimation

Motivated by beam distortions in multiple interferences environment, we propose a novel combined constrained method for adaptive sum and difference beamforming in monopulse angle estimation. By optimizing the adaptive weights of sum and difference beams jointly, the restriction on the degree of freedom to determine the adaptive sum or difference weight is evenly distributed to all weights. The constraints of array gain and monopulse ratio curve are utilized to avoid beam distortion while nulling the interferences. Simulations of the proposed algorithm have been provided, which show a significant improvement in beamforming and angle estimation performance compared with the conventional constrained adaptive monopulse algorithm.

Hierarchical fusion estimation for multi-sensor networked systems with transmission delays and packet dropouts

Abstract This paper focuses on hierarchical fusion estimation for a multi-sensor networked system subject to transmission delays and packet dropouts. In the hierarchical fusion estimation, an asynchronous sampling scheme is utilized to reduce networked transmission burdens and communication energy. A hierarchical fusion estimation method is proposed to improve estimation performance with transmission delays and packet dropouts. A numerical example is shown to illustrate effectiveness of the developed techniques.

Data-Driven Filtering for Nonlinear Systems With Bounded Noises and Quantized Measurements

This paper focuses on the data-driven filtering problem on nonlinear systems with bounded noises and quantized measurements which are subject to logarithmic quantization. Because the accurate system model is unknown, an almost-optimal data-driven filter is designed from an available data set within the nonlinear set membership framework. The set is composed of system inputs and quantized measurements, which can be more easily obtained than the mathematical model. The logarithmic quantization is dependent on the fact that its quantization error serves as a multiplicative noise which widely exists in many applications. For a further improvement of estimation performance, a preliminary approximation is obtained by neural network technique. Two numerical examples are given to illustrate the effectiveness of the proposed algorithm.

Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study.

BackgroundThe recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor–based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results.ObjectiveThis study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts.MethodsWe presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information.ResultsThe estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points).ConclusionsThe proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.

Reference-Free Calibration in Sensor Networks

Sensor calibration is one of the fundamental challenges in large-scale Internet of Things networks. In this article, we address the challenge of reference-free calibration of a densely deployed sensor network. Conventionally, to calibrate an in-place sensor network (or sensor array), a reference is arbitrarily chosen with or without prior information on sensor performance. However, an arbitrary selection of a reference could prove fatal, if an erroneous sensor is inadvertently chosen. To avert single point of dependence, and to improve estimator performance, we propose unbiased reference-free algorithms. Although our focus is on reference-free solutions, the proposed framework allows the incorporation of additional references, if available. We show, with the help of simulations, that the proposed solutions achieve the derived statistical lower bounds asymptotically. In addition, the proposed algorithms show improvements on real-life datasets, as compared to prevalent algorithms.

Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers

We consider the problem of estimating the unobserved amount of photovoltaic (PV) generation and demand in a power distribution network starting from measurements of the aggregated power flow at the point of common coupling (PCC) and local global horizontal irradiance (GHI). The estimation principle relies on modeling the PV generation as a function of the measured GHI, enabling the identification of PV production patterns in the aggregated power flow measurements. Four estimation algorithms are proposed: the first assumes that variability in the aggregated PV generation is given by variations of PV generation, the next two use a model of the demand to improve estimation performance, and the fourth assumes that, in a certain frequency range, the aggregated power flow is dominated by PV generation dynamics. These algorithms leverage irradiance transposition models to explore several azimuth/tilt configurations and explain PV generation patterns from multiple plants with non-uniform installation characteristics. Their estimation performance is compared and validated with measurements from a real-life setup including 4 houses with rooftop PV installations and battery systems for PV self-consumption.

Direct Instantaneous Frequency Rate Estimation to Improve the Carrier Estimation Performance in Mars Entry, Descent, and Landing Flight

This paper focuses on the carrier estimation performance improvement in Mars entry, descent, and landing (EDL) flights. Carrier reconstruction could be used for trajectory derivation and Martian atmosphere profile inversion, and is the critical information for mission operations, as it helps determine the flight status of the spacecraft, demodulate the downlink information. The current approach is maximum likelihood estimation based on a two-dimensional (2D) maximum energy search algorithm, which computes the grid energy over all the combinations of frequency cells and frequency rate cells among the search space. Although it has good performance on robust estimation, the frequency estimation accuracy is limited due to the short coherent integration. An instantaneous frequency rate tracking approach based on the cubic phase function (CPF) is proposed that directly estimates the instantaneous frequency rate over the frequency rate cells, followed by the frequency estimation among the frequency cells. A sequential estimation method is introduced to propose the sequential CPF statistics, which uses the a priori Doppler phase information to suppress the noise squaring loss inherent in the standard CPF statistics. Simulations have been made on the released Mars Science Laboratory EDL trajectory for the two approaches, which show that considerable estimation improvement has been achieved for aerobraking flight by the new algorithm.

Channel Characterization for Wideband Large-Scale Antenna Systems Based on a Low-Complexity Maximum Likelihood Estimator

Wideband large-scale array systems operating at millimeter-wave bands are expected to play a key role in future communication systems. It is recommended by standardization groups to use spherical-wave models (SWMs) to characterize the channel in near-field cases because of the large array apertures and the small cell size. However, this feature is not widely reflected in channel models yet, mainly due to the high computational complexity of SWMs compared to that of the conventional plane-wave model (PWM), especially when ultrawideband signals are considered. In this paper, a maximum likelihood estimator of low computational complexity is implemented with an SWM for ultrawideband signals. The measurement data obtained from an ultrawideband large-scale antenna array system at 28–30 GHz are processed with the proposed algorithm. The power azimuth-delay profiles estimated from the SWM and the PWM are compared to those obtained from rotational horn antenna measurement, respectively. It shows that the multipath components (MPCs) are well-estimated with the proposed algorithm, and significant improvement in estimation performance is achieved with the SWM compared to the PWM. Moreover, the physical interpretation of the estimated MPCs is also given along with the estimated scatterers.

Time delay estimation based on log-sum and lp-norm penalized minor component analysis

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320-327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation.

Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking

In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.