The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a broad bandwidth (DC to ∼50MHz) current sensor. It is inserted in the current conducting path and it has a low insertion inductance of about 1 nH due to a coaxial housing. The measurement procedure is based on HOKA principle using TMR sensors for the low frequency capture and a Rogowski coil for the high frequency capture. The HOKA principle combines both by analog signal processing.
This paper presents an Kalman Filter Data Fusion methodology and investigation for high dynamics and high precision multi-head angular position encoder. The proposed algorithm based on measurement of four dependent Read Heads and encoder ring for one mechanical shaft of high precision system with electric drive. The global fusion of proposed estimation provide computed value of position and additional...
This paper presents a galvanically isolated two channel measurement system for voltage and current measurement in medium-voltage dc-dc converters with more than 1 kV operating voltage. The system uses delta-sigma modulation and signal multiplexing to transmit the digitalized signals over fiber optical cables. In combination with a current-loop power supply, high noise immunity and excellent electrical...
This paper deals with the method of elimination of the DC offset impact on the speed estimation system which uses sine/cosine incremental position encoder. The impact of the DC offset in the input signals to the PLL-based speed estimation is shown. The problem can be solved by means of the highpass filter which should be implemented in the angle domain instead of time domain. The simulation and experimental...
Pulse photopletysmographic signal (PPG) is modulated by the respiratory rate, so there are some algorithms capable to extract respiratory information from the derived PPG signals, as the Pulse Amplitude Variability (PAV). Previous works have shown that the use of the PPG leads to different results depending on the PPG sensor location (finger and forehead). Therefore, a database recording finger and...
In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed...
Many optimization problems in communications and signal processing can be formulated as rank-one constrained optimization problems. This has motivated the development of methods to solve such problem in specific scenarios. However, due to the non-convex nature of the rank-one constraint, limited progress has been made in solving generic rank-one constrained optimization problems. In particular, the...
This paper proposes a new algorithm for image inpainting algorithm based on the matrix rank minimization with nonlinear mapping function. Assuming that each intensity value of a nonlinear mapped image can be modeled by the autoregressive (AR) model, the image inpainting problem is formulated as a kind of the matrix rank minimization problem, and this paper modifies the iterative partial matrix shrinkage...
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, i.e., in cases where the spectrum of the graph is accessible, we exhibit a DPP sampling scheme that enables perfect recovery of bandlimited signals. For large graphs, i.e., in cases where the graph's spectrum is not accessible, we investigate, both...
Linear structural equation models (SEMs) have been very successful in identifying the topology of complex graphs, such as those representing social and brain networks. In many cases however, the presence of highly correlated nodes hinders performance of the available SEM estimators that rely on the least-absolute shrinkage and selection operator (LASSO). To this end, an elastic net based SEM is put...
Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph is not given, but must be learned from the problem and available data. Often it is desirable to learn sparse graphs. However, making a graph highly sparse can split the graph into several disconnected components, leading...
In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed micro-controllers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case...
Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that...
Sampling from multi-dimensional and complex distributions is still a challenging issue for the signal processing community. In this research area, Hamiltonian Monte Carlo (HMC) schemes have been proposed several years ago, using the target distribution geometry to perform efficient sampling. More recently, a non-smooth HMC (ns-HMC) scheme has been proposed to generalize HMC for distributions having...
Acoustic monitoring of bird species is an increasingly important field in signal processing. Many available bird sound datasets do not contain exact timestamp of the bird call but have a coarse weak label instead. Traditional Non-negative Matrix Factorization (NMF) models are not well designed to deal with weakly labeled data. In this paper we propose a novel Masked Non-negative Matrix Factorization...
When identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique,...
We consider the task of automatically predicting spirometry readings from cough and wheeze audio signals for asthma severity monitoring. Spirometry is a pulmonary function test used to measure forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) when a subject exhales in the spirometry sensor after taking a deep breath. FEV1%, FVC% and their ratio are typically used to determine...
In Global Navigation Satellite Systems (GNSS), ionospheric scintillation is one of the more challenging propagation scenarios, particularly affecting high-precision receivers based on carrier phase measurements. In this contribution, we propose a new digital carrier synchronization state-space formulation for the mitigation of strong scintillation. It takes into account multi-frequency GNSS observations,...
In a recent work, we addressed the identification poblem of bilinear forms with the Wiener filter. In this context, a different approach was introduced, by defining the bilinear term with respect to the impulse responses of a spatiotemporal model, which resembles a multiple-input/single-output (MISO) system. However, in practice, the Wiener filter may not be always very efficient or convenient to...
This paper proposes a new Newton-based adaptive filtering algorithm, namely the Quasi-Newton Least-Mean Fourth (QNLMF) algorithm. The main goal is to have a higher order adaptive filter that usually fits the non-Gaussian signals with an improved performance behavior, which is achieved using the Newton numerical method. Both the convergence analysis and the steady-state performance analysis are derived...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.