Signal Filtering Research Articles

Continuous finite-dimensional locally optimal filtering of diffusion-hopping signals

Continuous finite-dimensional locally optimal filtering of diffusion-hopping signals

High Efficiency Tunable Graphene-Based Plasmonic Filter in the THz Frequency Range

A tunable plasmonic filter waveguide with indium antimonide activated by graphene layer configuration is proposed and numerically investigated. We demonstrate that the proposed tunable single-stub plasmonic filter using a thin layer of graphene can operate in the terahertz (THz) region as a notch filter. To investigate the transmission response of the structure, finite element method (FEM) calculations are utilized. The designed filter has precise minimum of zero at the notch frequency and also it improves the maximum transmitting light. Moreover, by applying the gate voltage between the graphene sheet and the InSb substrate, it can be seen that the central frequency of the filter is shifted by 32 GHz, and also the maximum of the transmission has been improved by 64% from 0.56 to 0.92 which shows less power loss. Furthermore, a band-stop plasmonic filter is proposed by increasing the number of stubs, which improves the bandwidth of the filter by 33% in compare to a single-stub structure. With such advantages, this structure is promising for future integrated plasmonic devices for applications such as communications, signal filtering, and switching.

TiN/Al2O3 Interface Study through ALD Process Modulations

Integration and miniaturization of highly reliable electronic devices are the basis of advanced technologies used for the manufacturing of implantable medical systems such as pacemakers or neuronal stimulators. Among these components, passive devices and more particularly capacitors have a key role in signal filtering and power energy management. In this context, the capacitance density improvement is a way to enable smaller components, thus increasing the final device miniaturization. Currently, Murata Integrated Passive Solutions is running in production a technology called “PICS™” enabling the 3D structuring of the silicon substrate that dramatically enhances its specific area (i.e. capacitance density) when combined with highly reliable low-k dielectric stacks such as silicon dioxide/nitride/dioxide (ONO). In order to significantly increase the capacitance density, two points are critical: first the 3D structures need to evolve toward a more aggressive template with a higher aspect ratio, so as to increase much more the dielectric specific area. The second point is the change of dielectric toward materials with higher dielectric constant such as Al2O3 or HfO2. Atomic Layer Deposition (ALD) technique is a key requirement here, as it is a method enabling conformal deposition of various materials, dielectrics or conductors, such as TiN, on high aspect ratio structures. In parallel of the integration and the optimization of such materials within 3D structures, planar ALD based Metal/Insulator(s)/Metal (MIM) capacitors need to be studied to serve as a reference point. The aim of our investigations is to link the electrical properties of MIM capacitors with the parameters applied during a batch ALD process such as temperature, pulse and purge times, with the support of physico-chemical characterizations (X-ray Photoelectron Spectrometry (XPS), Secondary Ion Mass Spectrometry (SIMS), Scanning Transmission Electron Microscopy (STEM)…). We will focus on the dielectric/electrode interfaces analysis through leakage current mechanism modeling and interpretations. Especially, the amount of O3 used in the Al2O3 deposition process has an important impact on the electrical properties of the MIM by impacting both TiN/Al2O3 interfaces in different ways. An optimization of the O3 dose is proposed.

Kalman Filter Algorithm Design for HC-SR04 Ultrasonic Sensor Data Acquisition System

In the control system application, the existence of noise measurement may impact on the performance degradation. The noise measurement of the sensor is produced due to several reasons, such as the low specification, external signal disturbances, and the complexity of measured state. Therefore, it should be avoided to achieve the good control performance. One of the solutions is by designing a signal filter. In this paper, the design of Kalman Filter (KF) algorithm for ultrasonic range sensor is presented. KF algorithm is designed to overcome the existence of noise measurement on the sensor. The type of ultrasonic range sensor used is HC-SR04 which is capable to detect the distance from 2 cm to 400 cm. The discrete KF algorithm is implemented using ATMega 328p microcontroller on Arduino Uno board. The algorithm is then tested with different three covariance values of process noise. The test result shows that the KF algorithm is able to reduce the measurement noise of the ultrasonic sensor. The analysis of variance conducted shows that the smaller value of covariance matrix of the process and measured noises, the better filtering process performed. However, this results in a longer generated response time. Thus, an optimization is required to obtain the best filtering performance.

Fast and straightforward analysis approach of charge transport data in single molecule junctions

In this study, we introduce an efficient data sorting algorithm, including filters for noisy signals, conductance mapping for analyzing the most dominant conductance group and sub-population groups. The capacity of our data analysis process has also been corroborated on real experimental data sets of Au-1,6-hexanedithiol-Au and Au-1,8-octanedithiol-Au molecular junctions. The fully automated and unsupervised program requires less than one minute on a standard PC to sort the data and generate histograms. The resulting one-dimensional and two-dimensional log histograms give conductance values in good agreement with previous studies. Our algorithm is a straightforward, fast and user-friendly tool for single molecule charge transport data analysis. We also analyze the data in a form of a conductance map which can offer evidence for diversity in molecular conductance. The code for automatic data analysis is openly available, well-documented and ready to use, thereby offering a useful new tool for single molecule electronics.

An adaptive spatio-temporal Gaussian filter for processing cardiac optical mapping data

Optical mapping is widely used as a tool to investigate cardiac electrophysiology in ex vivo preparations. Digital filtering of fluorescence-optical data is an important requirement for robust subsequent data analysis and still a challenge when processing data acquired from thin mammalian myocardium. Therefore, we propose and investigate the use of an adaptive spatio-temporal Gaussian filter for processing optical mapping signals from these kinds of tissue usually having low signal-to-noise ratio (SNR). We demonstrate how filtering parameters can be chosen automatically without additional user input. For systematic comparison of this filter with standard filtering methods from the literature, we generated synthetic signals representing optical recordings from atrial myocardium of a rat heart with varying SNR. Furthermore, all filter methods were applied to experimental data from an ex vivo setup. Our developed filter outperformed the other filter methods regarding local activation time detection at SNRs smaller than 3 dB which are typical noise ratios expected in these signals. At higher SNRs, the proposed filter performed slightly worse than the methods from literature. In conclusion, the proposed adaptive spatio-temporal Gaussian filter is an appropriate tool for investigating fluorescence-optical data with low SNR. The spatio-temporal filter parameters were automatically adapted in contrast to the other investigated filters.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography.

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented. Instead of linear filters, a singular value decomposition filter is employed as the pre-stage filtering module prior to PSD, which has advantages of zero phase shift, little distortion, and a high signal-to-noise ratio (SNR) in digital signal processing. Simulation and experimental results demonstrated that the proposed method can effectively eliminate the influence of impulsive noise in multi-frequency PSD, and it was capable of achieving a higher SNR and smaller demodulation error.

A fW- and kHz-Class Feedforward Leakage Self-Suppression Logic Requiring No External Sleep Signal to Enter the Leakage Suppression Mode

In this letter, we present a novel logic family for nanowatt and subnanowatt always-on circuits. This logic family achieves the ultralow leakage of 5 fW per gate without requiring external sleep control signals to put the circuits in the leakage suppression mode while addressing the switching speed problem of such ultralow leakage logic families. It achieves the fan-out-of-4 delay of 10.2 $\mu \text{s}$ , marking an improvement of $148\times $ over the state-of-the-art. With the proposed logic family, we prototyped a finite impulse response filter for physical signal sensing systems, in which sampled signals are often sparse (predominantly constant or slowly changing) and their frequency varies with time in a burst fashion. During the sparse period, the filter consumes only 109 pW by autonomously suppressing leakage. It can still process signals whose bandwidth can be as high as half of the filter’s maximum operating frequency, 1.03 kHz. The proposed logic improves leakage-delay-product by $200 \times $ over the prior art.

Robust real-time extraction of respiratory signals from PET list-mode data

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions’ detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting (‘binning’) of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method ‘combined local motion detection’ (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources.CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability.Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

Filtering Sinyal Menggunakan Band Pass Filter

In our daily lives we find many filters, filters from the word itself are filters. Filters vary, there are air filters to filter dirty air to be clean, filters / filters for coffee and tea to filter coffee or tea pulp, and so on. All of them try to filter something to get what we want. The filter here is a frequency filter, from which the filter will filter the frequency. The existing frequency we filter so get the frequency as we want. In this research, signal filtering will be carried out using Band Pass. The band-pass filter will pass signals with frequencies in a certain range and reduce the signal with other frequencies. The sound sample used is the sound of a cat with just one sample. In this sample signal screening is carried out with 5 different frequencies, namely at frequencies of 8000, 16000, 32000, 48000 and 96000. Data retrieval and processing of voice signals are carried out with the help of audacity and Matlab R2010b software. Sound data in the .wav format is filtered to remove noise and filter using a band pass filter. Sounds issued that are received are destructive which eliminate the sound information that is carried. This of course requires that the sound quality received is not good, so it requires sound signal processing to eliminate noise. Then the voice data is processed by voice signal using the FFT (Fast Fourier Transform) method. The results of this study are the output of sound signals that are clean from noise.

Application of a zero-latency whitening filter to compact binary coalescence gravitational-wave searches

Joint electromagnetic and gravitational-wave (GW) observation is a major goal of both the GW astronomy and electromagnetic astronomy communities for the coming decade. One way to accomplish this goal is to direct follow-up of GW candidates. Prompt electromagnetic emission may fade quickly, therefore it is desirable to have GW detection happen as quickly as possible. A leading source of latency in GW detection is the whitening of the data. We examine the performance of a zero-latency whitening filter in a detection pipeline for compact binary coalescence (CBC) GW signals. We find that the filter reproduces signal-to-noise ratio (SNR) sufficiently consistent with the results of the original high-latency and phase-preserving filter for both noise and artificial GW signals (called "injections"). Additionally, we demonstrate that these two whitening filters show excellent agreement in $\chi^2$ value, a discriminator for GW signals.

An optimal filter for short photoplethysmogram signals

A photoplethysmogram (PPG) contains a wealth of cardiovascular system information, and with the development of wearable technology, it has become the basic technique for evaluating cardiovascular health and detecting diseases. However, due to the varying environments in which wearable devices are used and, consequently, their varying susceptibility to noise interference, effective processing of PPG signals is challenging. Thus, the aim of this study was to determine the optimal filter and filter order to be used for PPG signal processing to make the systolic and diastolic waves more salient in the filtered PPG signal using the skewness quality index. Nine types of filters with 10 different orders were used to filter 219 (2.1s) short PPG signals. The signals were divided into three categories by PPG experts according to their noise levels: excellent, acceptable, or unfit. Results show that the Chebyshev II filter can improve the PPG signal quality more effectively than other types of filters and that the optimal order for the Chebyshev II filter is the 4th order.

Development of the Strategy of Active Control of Instability Waves in Unexcited Turbulent Jets

The possibility of controlling instability waves in the mixing layer of a subsonic unexcited jet is studied. These waves can be noise sources in both free jets and jets as parts of configurations. In the study the method of experimental diagnostics of the instability waves in the near field of a jet using an azimuthal multimicrophone array is realized. The data on the near field fluctuations are used for testing the control strategy proposed by the authors. The strategy consists in narrowband sliding filtration of the original signal and the formation of a narrowband controlling action on the basis of the linear principle of signal superposition. The results of the study represent the next step toward the realization of an active control system suppressing natural instability waves in turbulent jets.

Adaptive Filtration of Physiological Artifacts in EEG Signals in Humans Using Empirical Mode Decomposition

A new method for adaptive filtration of experimental EEG signals in humans and for removal of different physiological artifacts has been proposed. The algorithm of the method includes empirical mode decomposition of EEG, determination of the number of empirical modes that are considered, analysis of the empirical modes and search for modes that contains artifacts, removal of these modes, and reconstruction of the EEG signal. The method was tested on experimental human EEG signals and demonstrated high efficiency in the removal of different types of physiological EEG artifacts.

Improving predictability of stimulated reservoir volume from different geological perspectives

A major challenge in the development of unconventional resources is to comprehensively characterize and evaluate hydraulic fracturing outcomes. Microseismic (MS) data are a fundamental element of interpretations and estimates of stimulated reservoir volume (SRV). However, an interpretation based solely on MS data has its limitations because of many disagreements between MS signals and hydraulic fracturing. In this study, detailed core and well log data are applied to generate a rock fracability index, based on a formation's proneness to form hydraulic fractures from different geological perspectives. Near-wellbore zones are first evaluated by the rock fracability index. Then a pixel-based reservoir modeling method is implemented to develop a rock mechanical model of a whole shale layer. Employing a comparison between a rock mechanical model and the distribution of MS events, some mismatched areas are located, and the corresponding MS signal filter threshold criteria are adjusted. As a result, a new MS dataset is generated to develop an improved SRV. This approach is applied in the Biyang shale oil reservoir, China, which was deposited in a continental lacustrine environment with complex laminations and high heterogeneous rock mineral compositions. As compared to the original SRV, the SRV generated by the application of this approach clearly illustrates three major hydraulic fracture networks and can be further applied to model the well production. The proposed approach provides a good and valuable guide to increase the reliability of a SRV.

Integrating gravity and magnetic field data to delineate structurally controlled gold mineralization in the Sefwi Belt of Ghana

Gravity and magnetic surveys were used to delineate potential gold mineralization zones in the Sefwi belt of Ghana. The study area is an intrusive dominated area that hosts pockets of small scale mining operations locally referred to as Galamsey. These Galamsey operations are not guided by a scientific approach to back the trend of gold mineralization which is conventionally mined. The study aimed at mapping lithological units, structural setting and relating Galamsey sites to delineate potential zones of gold mineralization. A Scintrex CG5 gravimeter and GEM's Overhauser magnetometer were used for gravity and magnetic data acquisition respectively. The magnetic data were corrected and enhancing filters such as reduction to the pole (RTP), analytical signal and first vertical derivative were applied using Oasis montaj 7.1. Gravity data were also reduced to the geoid using the Oasis montaj software to produce a complete Bouguer anomaly map. The regional/residual separation technique produced a residual gravity map. The RTP and analytical signal filters from the magnetic data and residual gravity anomaly map from the gravity data helped in mapping belt type (Dixcove) Birimian granitoids and mafic intrusive unit, interpreted as gabbro. The first vertical derivative filter was useful in mapping NE/SW minor faults and crosscutting dykes largely concentrated in the belt type Birimian granitoids. All the three mapped Galamsey sites fell on a minor fault and are associated with the belt type granitoids which were used in delineating four potential zones of gold mineralization.

Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Transformations on graphs can provide compact representations of signals with many applications in denoising, feature extraction, or compression. In particular, lifting transforms have the advantage of being critically sampled and invertible by construction, but the efficiency of the transform depends on the choice of a good bipartition of the graph into update ( $\mathcal{U}$ ) and prediction ( $\mathcal{P}$ ) nodes. This is the update/prediction ( $\mathcal{U}/\mathcal{P}$ ) assignment problem, which is the focus of this paper. We analyze this problem theoretically and derive an optimal $\mathcal{U}/\mathcal{P}$ assignment under assumptions about signal model and filters. Furthermore, we prove that the best $\mathcal{U}/\mathcal{P}$ partition is related to the correlation between nodes on the graph and is not the one that minimizes the number of conflicts (connections between nodes of same label) or maximizes the weight of the cut. We also provide experimental results in randomly generated graph signals and real data from image and video signals that validate our theoretical conclusions, demonstrating improved performance over state-of-the-art solutions for this problem.

A Novel Design and FPGA Implementation of Filters Adapted Using LMS Variants

Adaptive filters have wide range of applications in areas such as echo or interference cancellation, prediction and system identification. Due to high computational complexity of adaptive filters, their hardware implementation is not an easy task. However, it becomes essential in many cases where real-time execution is needed. This paper presents the design and hardware implementation of a variable step size 40 order adaptive filter for de-noising acoustic signals. To ensure an area efficient implementation, a novel structure is being proposed. The proposed structure eliminates the requirement of extra registers for storage of delayed inputs thereby reducing the silicon area. The structure is compared with direct-form and transposed-form structures by adapting the filter coefficients using four different variants of the least means square (LMS) algorithm. Subsequently, the filters are implemented on three different field programmable gate arrays (FPGAs) viz. Spartan 6, Virtex 6 and Virtex 7 to find out the best device family that can be used to implement an Adaptive noise canceller (ANC) by comparing speed, power and area utilization. The synthesis results clearly reveal that ANC designed using the proposed structure has resulted in a reduction in silicon area without incurring any significant overhead in terms of power or delay.

The Capability Assessment of the Spectrum Decomposition Technique for Measurements of the Group Velocity of Lamb Waves

The ultrasonic guided waves are dispersive waves characterized by the phase and group velocities dispersion curves. In order to use guided waves in various industrial applications, their parameters must be known. Since these guided wave velocities depend on the frequency and thickness of the material, they propagate differently comparing to bulk ultrasonic waves. Therefore, to analyze the parameters of such waves, new measurement techniques should be proposed and possibilities of their application have to be investigated. In this paper possibilities to measure the group velocity based on the spectrum decomposition approach are presented. The investigations are carried out using the simulated and experimental signals of Lamb wave propagating in a 2 mm thickness aluminium plate. The two fundamental modes $$\hbox {A}_{0}$$ and $$\hbox {S}_{0}$$ are selected. Using the proposed technique, segments of the group velocity dispersion curves have been reconstructed and compared with the dispersion curves calculated by the SAFE method to estimate errors. Accordingly to the obtained lower absolute and relative errors, an optimal set of narrowband filters with bandwidth from 20 kHz up to 100 kHz for the $$\hbox {A}_{0}$$ mode and from 80 kHz up to 160 kHz for the $$\hbox {S}_{0}$$ mode are proposed. Applying the proposed optimal frequency sets of narrowband filters for the experimental signals, segments of the group velocity dispersion curves for both modes are reconstructed. The average relative error for the $$\hbox {A}_{0}$$ mode is 1.7–2.2% (expanded relative uncertainty ± (2.2–2.8)%) and 0.78–1.2% (expanded relative uncertainty ± (0.5–0.8)%) for the $$\hbox {S}_{0}$$ mode.

Microstructures and Dielectric Constants of Ba0.05SrxCa0.95-xTiO3 (x=0, 0.225, 0.475, 0.725 and 0.95) Synthesized by the Solution Combustion Technique

Barium strontium calcium titanate is a dielectric material exploited in fabrication of electronic devices such as capacitors, signal filters and satellite components. Dielectric properties can be enhanced through compositional and microstructural control. This study, therefore, aimed at synthesizing barium strontium calcium titanate (Ba0.05SrxCa0.95-xTiO3, where x = 0, 0.225, 0.475, 0.725 and 0.95) powders by a solution combustion technique. The powders were pressed, sintered at 1450°C and tested for their properties. Experimental results revealed that strontium content did not significantly influence chemical composition, particle sizes and density. All powders exhibited a single phase corresponding to Ba0.05SrxCa0.95-xTiO3with fine particles with the average size smaller than 0.4 micrometer. All sintered samples had density higher than 95% of theoretical density. On the contrary, the results indicated that strontium content affected grain size, grain morphology and dielectric constant of the sintered samples. The highest dielectric constant of 531 (at 1 MHz) was achieved in the Ba0.05Sr0.225Ca0.725TiO3. Dielectric constant was discussed with respect to microstructure.