To meet the requirements for lightweight, miniaturized dispersive optical systems for space applications, linear variable filters with a high transmittance and spatial dispersion coefficient are proposed. The filters were produced with dual ion beam...
Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registra...
This paper presents a low-cost hyperspectral measurement setup in a new application based on fluorescence detection in the visible (Vis) wavelength range. The aim of the setup is to take hyperspectral fluorescence images of viscous materials. Based o...
Acidity determines the maturity of fruits and is an important component of fruit taste. Hardness is a key indicator for judging ripening status, storage tolerance, and transportation quality. Therefore, detecting acidity and hardness is of great sign...
Nighttime light remote sensing has unique advantages on reflecting human activities, and thus has been used in many fields including estimating population and GDP, analyzing light pollution and monitoring disasters and conflict. However, the existing...
This paper presents a low power wideband dB-linear analog baseband (ABB) circuit for a millimeter-wave (mmW) wireless receiver in 40 nm CMOS technology. The proposed ABB system consists of a multi-stage variable gain amplifier (VGA) and a low-pass fi...
Concept, design and practical implementation of a miniaturized spectrophotometer, utilized as a mid-infrared-based multi gas sensor is described. The sensor covers an infrared absorption wavelength range of 2.9 to 4.8 um, providing detection capabili...
This study introduces a novel deposition technique capable of depositing thin films with any arbitrary thickness profile. The apparatus consists of a fixed shadow mask and a rotating sample carrier plate. The shadow mask features a specifically desig...
The design of compact hyperspectral cameras with high ground resolution and large field of view (FOV) is a challenging problem in the field of remote sensing. In this paper, the time-delayed integration (TDI) of the digital domain is applied to solve...
In this work, we present the recent results of designing and developing a miniaturized mid-IR spectrometer designed to detect and identify specific markers, which may provide crucial information on air/water/food quality, human health, production lin...
This paper is a study on the synthesis of digital filters used to control active or self-excited systems. The active two-terminal branch implements a nonconvolution-type immittance operator, which generates a current waveform depending on the given i...
This study determines an optimal spectral configuration for the CyanoSat imager for the discrimination and retrieval of cyanobacterial pigments using a simulated dataset with machine learning (ML). A minimum viable spectral configuration with as few...
Our study aimed to improve the poor performance of existing filters, such as EKF, UKF and CKF, that results from their weak approximation ability to nonlinear systems. This paper proposes a new extended Kalman filter bank focusing on a class of produ...
A high-order Kalman filter for full-dimensional variables is proposed for a class of dynamic systems whose state model and measurement model are both nonlinear. The filter requires Taylor expansion of the system equations, and then performs Kronecker...
The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an...
Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we pr...
In allusion to the phenomenon that the extended Kalman filter is easy to diverge in the mover position estimation of permanent magnet synchronous linear motor, a linear motor extended Kalman filter speed estimation method based on attenuation memory...
State variable and parameter estimations are important for signal sensing and feedback control in both traditional engineering systems and quantum systems. The Kalman filter, which is one of the most popular signal recovery techniques in classical sy...
The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is...
A highly reconfigurable open-loop analog baseband circuitry with programmable gain, bandwidth and filter order are proposed for integrated linear frequency modulated continuous wave (LFMCW) radar receivers in this paper. This analog baseband chain al...
In this study the feasibility and performance of time variable decorrelation (VADER) filters derived from covariance information on decadal Gravity Recovery and Climate Experiment (GRACE) time series are investigated. The VADER filter is based on pub...
This paper deals with robust state estimation for discrete-time, linear parameter varying (LPV) descriptor systems. It is assumed that all the system state-space matrices are affine functions of the uncertain parameters and both the parameters and th...
This paper presents a control strategy with a linear extended state observer (LESO) and Kalman filter to achieve a high performance of the motion control system. The moment of inertia of the system, which is variable with the robotic joint motion, is...
Kalman filtering is a linear quadratic estimation (LQE) algorithm that uses a time series of observed data to produce estimations of unknown variables. The Kalman filter (KF) concept is widely used in applied mathematics and signal processing. In thi...
This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system to create a nonlinear random dynamic system with modeling errors and unknown statistical p...
In this paper, a new variational Bayesian-based Kalman filter (KF) is presented to solve the filtering problem for a linear system with unknown time-varying measurement loss probability (UTVMLP) and non-stationary heavy-tailed measurement noise (NSHT...
This paper discusses the dissipative filtering problem for discrete-time nonlinear networked systems with dynamic quantization and data packet dropouts. The Takagi–Sugeno (T–S) fuzzy model is employed to approximate the considered nonline...
This paper is concerned with the distributed and centralized fusion filtering problems in sensor networked systems with random one-step delays in transmissions. The delays are described by Bernoulli variables correlated at consecutive sampling times,...
In networked sensing systems, nonlinear state monitoring and soft sensing are widely used to reconstruct key variables that cannot be directly measured in real time. For such nonlinear estimation tasks, the Extended Kalman Filter (EKF) is a commonly...
Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For...
In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic dynamic systems. Firstly, those augmenting systems are modeled with help of the Kronecker produ...
In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump...
“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable...
An Adaptive-Parameter Maximum Correntropy Kalman Filter (APMCKF) algorithm is proposed to address state estimation degradation in semi-active suspensions caused by non-linear coupling between time-varying physical parameters and non-Gaussian noise. F...
To solve the problem that the time-frequency resolution of Short-Time Fourier Transform (STFT) is constrained by the window length and the moving step of the short time window, and to utilize the merits of a widely linear method, a novel instantaneou...
This paper focuses on the distributed fusion estimation problem in which a signal transmitted over wireless sensor networks is subject to deception attacks and random delays. We assume that each sensor can suffer attacks that may corrupt and/or modif...
Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creatin...
A new numerical method for solving Volterra non-linear convolution integral equations (NLCVIEs) of the second kind is presented in this work. This new approach, named IIRFM-A, is based on the combined use of the Laplace transformation, a first-order...
To address the issue of decreased rotor position estimation accuracy in permanent magnet synchronous motors (PMSMs) caused by linearization rounding errors in the extended Kalman filter (EKF), this paper proposes a rotor position estimation method fo...
This paper investigates the problem of non-fragile dissipative filtering for discrete-time nonlinear networked systems with dynamic quantization, a dynamic event-triggered mechanism and stochastic cyber attacks. The nonlinear networked system under i...
This work proposes a nonlinear modeling of a permanent magnet synchronous motor (PMSM) based on state space neural networks. The state space neural network is trained and the state variables (currents in a direct–quadrature frame and the rotati...
The continuous wave interferences (CWIs) and the narrow-band interferences (NBIs) have significantly impacted the acquisition, tracking and positioning accuracy of Beidou Navigation Satellite System (BDS) receivers. As an interference suppression tec...
Understanding how climate variability is reflected in streamflow is essential for reservoir management and hydropower planning. This study investigated how temporal scale influences climate–streamflow relationships, persistence characteristics, and p...
To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficien...
The maximum correntropy Kalman filter (MCKF) is an effective algorithm that was proposed to solve the non-Gaussian filtering problem for linear systems. Compared with the original Kalman filter (KF), the MCKF is a sub-optimal filter with Gaussian cor...
We consider measures of nonlinearity (MoNs) of a polynomial curve in two-dimensions (2D), as previously studied in our Fusion 2010 and 2019 ICCAIS papers. Our previous work calculated curvature measures of nonlinearity (MoNs) using (i) extrinsic curv...
State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In thi...
This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured...
This paper investigates the event-triggered robust dissipative filtering problem for discrete-time networked nonlinear systems subject to dynamic quantization and stochastic cyber attacks. The networked nonlinear system under consideration is represe...
Volatility, which represents the magnitude of fluctuating asset prices or returns, is used in the problems of finance to design optimal asset allocations and to calculate the price of derivatives. Since volatility is unobservable, it is identified an...
of 7