Signal Filtering Research Articles

A Performance Evaluation of Signal Filtering with different Adaptive Algorithms on Doppler-Angular Adaptive Generalized Sidelobe Canceler (GSC)

A Performance Evaluation of Signal Filtering with different Adaptive Algorithms on Doppler-Angular Adaptive Generalized Sidelobe Canceler (GSC)

Practical Approach for Controlling Optical Image Stabilization System

This paper proposes a practical methodology for designing an optical image stabilization system controller that consists of a gyro signal filter and feedback controller for a smart phone camera module called miniature camera module. Since the advent of smartphone, miniature camera modules have become much more compact and sophisticated than the modules in the digital single-lens reflex camera. Therefore, a greater consideration on designing controller for the miniature camera module is necessary to obtain higher performance. This paper proposes 1) the OIS system’s block diagram connecting the physical part and the controller part and 2) the methodology for designing feedback controller according to the performance index from the block diagram. A linear feedback controller and gyro signal filter are designed using the proposed methodology and its performance is verified via experimentation.

The control system elements of the new generation optical switching cell

In this paper calculation of new optical switch parameters that allows us to create next generation all-optical non-blocking switching system without external control devices is carried out for the first time. In particular, switching cell control device is studied in detail. The presented one includes Bragg filter, frequency detector, optical isolator, and former of a control signal. Here we also present the detail description and the numerical calculations of these devices for third transparent window (1550nm). The reflection and transmission coefficients are obtained, the passbands of the Bragg filter are presented, and the amplitude characteristic of the frequency detector is calculated.

An improved peak extraction method for heart rate estimation

In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.

Parallel wideband digital up‐conversion architecture with efficiency

Owing to the limited processing speed of current digital signal processing devices, digital generated signal frequency and bandwidth based on digital up-conversion technique has long been a bottleneck. Interpolation on the baseband signal puts great pressure on the following filtering and mixing operation. In this study, an efficient parallel architecture is presented, which moves the interpolation behind the filtering and mixing, and adopts parallel numerically controlled oscillator (NCO) arrays and poly-phase low-pass filter arrays to achieve the anticipated orthogonal mixing signal and prototype filter. Parallel NCO decomposition principle and mathematical derivation are elaborated, and realisation of the poly-phase filter arrays is also discussed. The proposed post-interpolation architecture can effectively relieve the filtering and mixing pressure including operation rate and computational complexity, which will benefit the hardware implementation. In the end, verification test of signal up-conversion with 400 MHz bandwidth is launched to certify the architecture validity.

Noise detection and image denoising based on fractional calculus

Abstract Fractional-order integral can weaken high-frequency signal and greatly preserve low-frequency signal nonlinearly, that is, the high-frequency noise can be removed while retaining the information of low-frequency image itself, thus the fractional calculus can achieve good a denoising effect and the application of fractional calculus theory in the digital image processing field has also been favored by more and more scholars. On the basis of summarizing and analyzing previous research works, this paper proposes a new method for detecting noise points in images using fractional differential gradient and an improved image denoising algorithm based on fractional integration. The noise detection method determines the noise position by the fractional differential gradient, and achieves to detect the noise, snowflake and stripe anomaly though utilizing the neighborhood information feature of the image and the contour and direction distribution of various noise anomalies in spatial domain; the image denoising algorithm firstly regards the appearance of noise points in image as a small probability event and divides it, then applies the fractional calculus to subtly transform and adjust signal filter and meanwhile utilizes iterative idea to control image denoising effect for accurately distinguishing between noise and high-frequency signals, so that the original image feature information is more preserved while the image is denoised. The simulation results show that this algorithm model can effectively remove the noise while maintaining the details of image edge and texture, and have the characteristics of simple algorithm and good stability. The model can effectively remove the noise and meanwhile preserve the details of image edges and textures, and has the characteristics of simple algorithm and high stability. The study results of this paper provide a reference for further research on the noise detection and image denoising based on fractional calculus.

Enabling Cognitive Pyroelectric Infrared Sensing: From Reconfigurable Signal Conditioning to Sensor Mask Design

Poor signal-to-noise ratios (SNRs) and low spatial resolutions have impeded low-cost pyroelectric infrared (PIR) sensors from many intelligent applications for thermal target detection/recognition. This article presents a cognitive signal conditioning and modulation learning framework for PIR sensing with the following two innovations to solve these problems: 1) a reconfigurable signal conditioning circuit design to achieve high SNRs and 2) an optimal sensor mask design to achieve high recognition performance. By using a programmable system on chip, the PIR signal amplifier gain and filter bandwidth can be adjusted automatically according to working conditions. Based on the modeling between PIR physics and thermal images, sensor masks can be optimized through training convolution neural networks with large thermal image datasets for feature extraction of specific thermal targets. The experimental results verify the improved performance of PIR sensors in various working conditions and applications by using the developed reconfigurable circuit and application-specific masks.

Multi-Criteria Synthesis of Frequency-Modulated Discrete Control of Semi-Active Vehicle Suspension - Part 1 Analysis and Control Strategies

The paper discusses the contradiction influence of system damping coefficient in the different excitation’s frequency diapasons on the raid comfort and the vehicles stability on road. It is analyzing the discrete “on-off” control laws for the semi-active suspension included in a model reduced to the vehicle wheel and accounting the vertical dynamics of the vehicle so-called “Quarter car model”. Based on analysis of the frequency characteristics it is propose a frequency modulation of the control realized by a signals filtration.

Prediction-error filters for signal and noise separation within ultrasound recordings

Ultrasonic image quality can be compromised by several mechanisms such as reverberation, off-axis scattering, and sound-speed inhomogeneities. These mechanisms can cause a loss of coherence of signals across the aperture and clutter in a B-mode image potentially leading to inadequate visualization and misdiagnosis. We develop a processing technique based on prediction-error filters (PEFs) for isolating tissue signal and several types of noise in the individual channel data of ultrasound scans. We first estimate a set of PEFs that allows for prediction of the 2-D spectra of signal and noise in the channel data domain and then use these filters to separate the signal and noise components within the recorded echoes. We use the Van Cittert-Zernike theorem as a priori knowledge of the signal spectrum to develop the signal-PEF, while the noise-PEFs are estimated directly from the channel data. We apply our PEFs to channel data from fullwave simulations of abdominal scans and to data acquired on a lesion phantom through a layer of bovine tissue. The proposed technique successfully separates signal from noise and improves lag-one coherence of the speckle signal from 0.47 to 0.97.Ultrasonic image quality can be compromised by several mechanisms such as reverberation, off-axis scattering, and sound-speed inhomogeneities. These mechanisms can cause a loss of coherence of signals across the aperture and clutter in a B-mode image potentially leading to inadequate visualization and misdiagnosis. We develop a processing technique based on prediction-error filters (PEFs) for isolating tissue signal and several types of noise in the individual channel data of ultrasound scans. We first estimate a set of PEFs that allows for prediction of the 2-D spectra of signal and noise in the channel data domain and then use these filters to separate the signal and noise components within the recorded echoes. We use the Van Cittert-Zernike theorem as a priori knowledge of the signal spectrum to develop the signal-PEF, while the noise-PEFs are estimated directly from the channel data. We apply our PEFs to channel data from fullwave simulations of abdominal scans and to data acquired on a lesion phantom...

Utilizing Precipitation and Spring Discharge Data to Identify Groundwater Quick Flow Belts in a Karst Spring Catchment

Abstract In karst terrains, fractures and conduits often occur in clusters, forming groundwater quick flow belts, which are the major passages of groundwater and solute transport. We propose a cost-effective method that utilizes precipitation and spring discharge data to identify groundwater quick flow belts by the multitaper method (MTM). In this paper, hydrological processes were regarded as the transformation of precipitation signals to spring discharge signals in a karst spring catchment. During the processes, karst aquifers played the role of signal filters. Only those signals with high energy could penetrate through aquifers and reflect in the spring discharge, while other weak signals were filtered out or altered by aquifers. Hence, MTM was applied to detect and reconstruct the signals that penetrate through aquifers. Subsequently, by analyzing the reconstructed signals of precipitation with those of spring discharge, we acquired the hydraulic response time and identified the quick flow belts. Finally, the methods were applied to the Niangziguan Spring (NS) catchment, China. Results showed that the hydraulic response time of the spring discharge to precipitation was 3 months at Pingding County; 4 months at Yuxian County, Yangquan City, Xiyang County, and Heshun County; and 27 months at Shouyang County and Zouquan County. These results suggested that Pingding County is located at a groundwater quick flow belt, which is a major passage of groundwater and contaminants, in the NS catchment. This is important since Pingding County is not only the key development area of karst groundwater but also the key conservation area for sustainable development of karst groundwater resources in NS catchment.

DIAGNOSTIC FACTORS FOR OPENED AND CLOSED KINEMATIC CHAIN OF VIBROARTHROGRAPHY SIGNALS

The paper presents results of preliminary research of vibroarthrography signals recorded from one healthy volunteer. The tests were carried out for the open and closed kinematic chain in the range of motion 90° - 0° - 90°. Analysis included initial signal filtration using the EMD algorithm. The aim was to investigate the occurrence of differences in the values of selected energy and statistical parameters for the cases studied.

Забезпечення завадозахищеності приймачів сигналів GNSS шляхом використання алгоритмів просторової фільтрації

Здійснюється оцінка потрібного рівня радіо-електронного захисту приймачів сигналів супутникових радіонавігаційних систем (СРНС), що забезпечують застосування наземних платформ озброєння та військової техніки, з урахуванням можливого радіоелектронного подавлення з боку противника. Розглядається адаптивний алгоритм просторової фільтрації сигналів СРНС в умовах впливу спеціально організованих завад з боку противника, побудований на основі рекурентного методу формування оцінки зворотної кореляційної матриці. На основі даного алгоритму створюється модель функціонування завадозахищеного приймача сигналів СРНС в умовах впливу активних завад. За допомогою створеної моделі оцінюється ефективність функціонування завадозахищеного приймача сигналів СРНС в умовах радіоелектронного подавлення.

Font Design—Shape Processing of Text Information Structures in the Process of Non-Invasive Data Acquisition

Computer fonts can be a solution that supports the protection of information against electromagnetic penetration; however, not every font has features that counteract this process. The distinctive features of a font’s characters define the font. This article presents two new sets of computer fonts. These fonts are fully usable in everyday work. Additionally, they make it impossible to obtain information using non-invasive methods. The names of these fonts are directly related to the shapes of their characters. Each character in these fonts is built using only vertical and horizontal lines. The differences between the fonts lie in the widths of the vertical lines. The Safe Symmetrical font is built from vertical lines with the same width. The Safe Asymmetrical font is built from vertical lines with two different line widths. However, the appropriate proportions of the widths of the lines and clearances of each character need to be met for the safe fonts. The structures of the characters of the safe fonts ensure a high level of similarity between the characters. Additionally, these fonts do not make it difficult to read text in its primary form. However, sensitive transmissions are free from distinctive features, and the recognition of each character in reconstructed images is very difficult in contrast to traditional fonts, such as the Sang Mun font and Null Pointer font, which have many distinctive features. The usefulness of the computer fonts was assessed by the character error rate (CER); an analysis of this parameter was conducted in this work. The CER obtained very high values for the safe fonts; the values for traditional fonts were much lower. This article aims to presentat of a new solution in the area of protecting information against electromagnetic penetration. This is a new approach that could replace old solutions by incorporating heavy shielding, power and signal filters, and electromagnetic gaskets. Additionally, the application of these new fonts is very easy, as a user only needs to ensure that either the Safe Asymmetrical font or the Safe Symmetrical font is installed on the computer station that processes the text data.

Optimization algorithms, an effective tool for the design of digital filters; a review

Nowadays, optimal and intelligent design approaches are vital in almost all areas of engineering. Scientists and engineers are attempting to make frameworks and models more proficient and intelligent. This paper deals with a detailed investigation on design of various digital filters using optimization algorithms. Generally digital filters are classified into two types which are FIR and IIR filters and are again classified into one dimensional, two dimensional and three dimensional filters for signal, image and video respectively. The design of a digital filter that satisfies all the required conditions perfectly is a challenging factor. So, apart from the conventional mathematical methods, optimization algorithms can be used to design optimal digital filters. IIR Filters are infinite impulse response filter; they have impulse response of infinite duration. FIR Filters are finite impulse response filters; they have impulse response of finite duration. In this paper we have discussed the design of various optimal digital filters based on various optimization algorithms, for processing of signal, image and video. The design of digital filters based on Evolutionary algorithms and swarm intelligence algorithms like Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Cuckoo Search Algorithm, Differential Evolution, Gravitational Search, Harmony Search, Spiral Optimization, teaching–learning based optimization, wind driven optimization, hybridization of optimization algorithm are presented.

Construcción de un prototipo de monitoreo de señales cardíacas aplicando tecnología inalámbrica Zigbee e internet

En este trabajo se presenta el desarrollo de un sistema que permite monitorear la actividad cardíaca a personas que necesitan observación continua. Dichos registros son observables con mínimo retardo respecto al momento en que suceden, siendo almacenados y enviados por medio de una conexión a internet al implementar la tecnología inalámbrica Zigbee. Se hizo uso del software Labview para procesar los datos a partir de la señal electrocardiográfica y se desarrolló una interfaz gráfica para visualizar las señales adquiridas. Luego delproceso de adquisición, aislamiento, filtrado análogo en el dispositivo portátil, transmisión inalámbrica y recepción por parte de un PC, se realiza el filtrado digital a la señal cardiaca adquirida para obtener la señal ECG filtrada en el PC local.

An Optimized De-Noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions

Empirical mode decomposition (EMD) offers a data-driven approach for signal filtering. In this letter, a new de-noising scheme is proposed, which estimates a pertinent probability distribution parameter with the Newton-Raphson root-finding method. The parameter estimation reduces the computational cost of relevant mode selection by reducing the range of possible mode distribution shapes. Computational complexity is assessed, revealing significant reduction in the number of operations required in comparison to a previous de-noising scheme which utilized the same form of statistical filtering. Furthermore, simulation results of the proposed EMD filter show superior performance over other EMD de-noising techniques with real biomedical recordings corrupted by colored noise.

On-Line Learning of Linear Dynamical Systems: Exponential Forgetting in Kalman Filters

The Kalman filter is a key tool for time-series forecasting and analysis. We show that the dependence of a prediction of Kalman filter on the past is decaying exponentially, whenever the process noise is non-degenerate. Therefore, Kalman filter may be approximated by regression on a few recent observations. Surprisingly, we also show that having some process noise is essential for the exponential decay. With no process noise, it may happen that the forecast depends on all of the past uniformly, which makes forecasting more difficult.Based on this insight, we devise an on-line algorithm for improper learning of a linear dynamical system (LDS), which considers only a few most recent observations. We use our decay results to provide the first regret bounds w.r.t. to Kalman filters within learning an LDS. That is, we compare the results of our algorithm to the best, in hindsight, Kalman filter for a given signal. Also, the algorithm is practical: its per-update run-time is linear in the regression depth.

Visual and kinesthetic modes affect motor imagery classification in untrained subjects

The understanding of neurophysiological mechanisms responsible for motor imagery (MI) is essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery, kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas in motor-related α- and β-frequency regions. Although the brain activity corresponding to MI is usually observed in specially trained subjects or athletes, we show that it is also possible to identify particular features of MI in untrained subjects. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of α- and β-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. In particular, the analysis of evoked responses shows that in all KI subjects the activity in the frontal cortex is suppressed during MI, while in the VI subjects the frontal cortex is always active. The accuracy in classification of left-arm and right-arm MI using artificial intelligence is similar for KI and VI. Since untrained subjects usually demonstrate the VI imagery mode, the possibility to increase the accuracy for VI is in demand for BCIs. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.

Stepwise fuzzy correction of the algorithm filters of random signals

The task of estimating the information contained in random signals from various sources – meters. It is assumed that the gauges are discrete and are described, like the original process assessed, by a discrete mathematical model in the form of difference equations. As an estimation algorithm, we consider a discrete Kalman filter, which, in the general case, when mathematical models are inadequate to real processes, can give distorted information. To improve the accuracy of estimation, it is proposed to apply the integration of all possible meters with the introduction of additional a priori information using a fuzzy logic system. At the same time, it is proposed to make a transition from the obtained probability characteristics of the estimated process to the membership functions of fuzzy logic based on the output filter parameters using the normalization of the posterior probability density. This approach allows to increase the accuracy of estimation, as it takes into account additional information and its complex processing.

Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China

This article presents the use of the Normalized Differences Vegetation Index (NDVI) time series based change detection method for agriculture phenology monitoring. NDVI make use of the multi-spectral remote sensing data band combinations techniques to find out landscape such as agriculture, vegetation, land use/cover, water bodies and forest. Geographic Information System (GIS) technology is becoming an essential tool to combing multiple maps and information from different sources as satellite, field and socio-economic data. Landsat 8 and Sentinel-2 satellite data were used to generate NDVI time series from Sep. 2017 to Nov. 2018. This research work was the procedure by pre-processing, signal filtering and interpolation of monthly NDVI time series that represent a complete crop phonological cycle. NDVI method is applied according to its specialty range from –1 to +1. We divided whole agriculture area into five part according to NDVI Values such as no agriculture, low agriculture, medium agriculture, high agriculture and very high agriculture area. The simulation results show that the NDVI is highly useful in detecting the surface feature of the area, which is extremely beneficial for sustainable development of agriculture and decision making. The methodology of reform NDVI time series had been providing feasible to improve crop phenology mapping.