Frequency Domain Properties Research Articles

Frequency frame approach on tuning FOPI controller for TOPTD thermal processes

The frequency frame is used to tune fractional order proportional–integral​ controllers for stability, performance and robustness of third order plus time delay plants. Such plants are frequently used in describing thermal processes such as an air heater or a fired boiler. The aim is to tune the controller to meet some frequency domain properties. As robustness is an indispensable issue for thermal processes, main inspiration of the paper comes from flattening the phase curve in the Bode plot to provide improved robustness for the system. In spite of some existing studies, flattening is not realized by equalizing the phase derivative to zero at a given frequency value. Firstly, gain and phase crossover frequency points are enclosed with a rectangular frame. Then, lengths of the edges of this frame are changed to tune phase and gain margins. Curves inside the frame can be flattened by proper tuning of the edges. This will enhance the robustness and also ensure the iso-damping property. Equations to obtain the controller are given with two theorems. Demonstrations are made on two different thermal plants which are a novel electrical air heater and a bagasse fired boiler and the results are given on detailed illustrations. The results proved that preferred gain and phase properties are successfully obtained and improved performance and robustness are provided for related systems.

Topological Network Analysis of Early Alzheimer's Disease Based on Resting-State EEG.

Previous studies made progress in the early diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) without considering EEG connectivity. To fill this gap, we explored significant differences between early AD patients and controls based on frequency domain and spatial properties using functional connectivity in mild cognitive impairment (MCI) and mild AD datasets. Four global metrics, network resilience, connection-level metrics and node versatility were used to distinguish between controls and patients. The results show that the main frequency bands that are different between MCI patients and controls are the θ and low α bands, and the differently affected brain areas are the frontal, left temporal and parietal areas. Compared to MCI patients, in patients with mild AD, the main frequency bands that are different are the low and high α bands, and the main differently affected brain region is a larger right temporal area. Four LOFC bands were used as input to train the ResNet-18 model. For the MCI dataset, the average accuracy of 20 runs was 93.42% and the best accuracy was 98.33%, while for the mild AD dataset, the average accuracy was 98.54% and the best accuracy was 100%. To determine the timing of early treatment and discovering the susceptible patients, and to slow the progression of the disease, we assume that the occurrence of MCI and mild AD and their progression to more serious AD and dementia could be inferred by analyzing the topological structure of the brain network generated by EEG. Our findings provide a novel solution for connectome-based biomarker analysis to improve personalized medicine.

Lower-limb muscle responses evoked with noisy vibrotactile foot sole stimulation.

AimCutaneous feedback from the foot sole contributes to the control of standing balance in two ways: it provides perceptual awareness of tactile perturbations at the interface with the ground (e.g., shifts in the pressure distribution, slips, etc.) and it reflexively activates lower‐motor neurons to trigger stabilizing postural responses. Here we focus on the latter, cutaneous (or cutaneomotor) reflex coupling in the lower limb. These reflexes have been studied most‐frequently with electrical pulse trains that bypass natural cutaneous mechanotransduction, stimulating cutaneous afferents in a largely non‐physiological manner. Harnessing the mechanical filtering properties of cutaneous afferents, we take a novel mechanical approach by applying supra‐threshold continuous noisy vibrotactile stimulation (NVS) to the medial forefoot.MethodsUsing NVS, we characterized the time and frequency domain properties of cutaneomotor reflexes in the Tibialis Anterior. We additionally measured stimulus‐triggered average muscle responses to repeated discrete sinusoidal pulses for comparison. To investigate cutaneomotor reflex gain scaling, stimuli were delivered at 3‐ or 10‐times perceptual threshold (PT), while participants held 12.5% or 25% of maximum voluntary contraction (MVC).ResultsPeak responses in the time domain were observed at lags reflecting transmission delay through a polysynaptic reflex pathway (~90–100 ms). Increasing the stimulus amplitude enhanced cutaneomotor coupling, likely by increasing afferent firing rates. Although greater background muscle contraction increased the overall amplitude of the evoked responses, it did not increase the proportion of the muscle response attributable to cutaneous input.ConclusionTaken together, our findings support the use of NVS as a novel tool for probing the physiological properties of cutaneomotor reflex pathways.

Modular Model Predictive Control upon an Existing Controller

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

Simulating Motion Artifact Using an Autoregressive Model for Research in Biomedical Signal Quality Analysis.

Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.

Advances on odd-even high-harmonic spectroscopy

High harmonic generation (HHG) has important applications in attosecond science, such as generating attosecond pulses and probing the structure and the electron dynamics of atoms and molecules with attosecond resolution. Different from symmetric cases, because of symmetry breaking, asymmetric molecules can emit both odd-order and even-order harmonics in strong single-color laser fields. Ultrafast probing of polar molecules with HHG is a hot issue in recent years, as polar molecules are generally very active in chemical reactions. With dividing the HHG signal into odd and even parts, recent studies have shown that odd and even harmonics from asymmetric molecules have different properties in frequency domain. To explain this typical difference, a theoretical model has been developed to describe the generation mechanism of odd versus even HHG from asymmetric molecules. It is shown that during the process of tunneling, the gerade component of the asymmetric orbital plays a dominating role and the contributions of the ungerade component can be neglected. Thereafter, the recombination of the rescattering electron with the gerade (ungerade) component of the asymmetric orbital mainly contributes to the emission of odd (even) harmonics. As a result, odd and even harmonics carry different information of the asymmetric system. Recently, it has been shown that the contributions of the ungerade component of asymmetric orbital to ionization are strongly suppressed due to the destructive intramolecular interference. This suppressing effect is general for strong-field ionization of polar molecules, implying that the theoretical model of odd-even HHG holds for a wide parameter region. These results open a new perspective for probing polar molecules with odd-even HHG. Specifically, when it is relatively difficult to read the information of the molecular structure or the electron dynamics through the whole harmonic spectrum of asymmetric molecules, one can extract the characteristic information of odd or even harmonic separately, then this characteristic information can be integrated and inverted to get the information of the whole asymmetric system. These discussions suggest ultrafast probing of asymmetric molecules with odd-even high-harmonic spectroscopy (HHS), where odd and even harmonics are treated independently. This idea of odd-even HHS and the theoretical model of odd-even HHG mentioned above have been widely used in ultrafast probing of asymmetric molecules in theoretical studies. For example, it has been shown that odd-even HHS and this theoretical model can be used to reconstruct the asymmetric molecular orbital, probe the nuclear dynamics of polar molecules, calibrate the degree of orientation of asymmetric molecules, and probe the structure of multi-center molecules, etc. Experimentally, the idea of odd-even HHS has also been widely used in probing the structure and the electron dynamics of polar molecules. In particular, this theoretical model of odd-even HHG has also been used in probing the shape resonance and charge transport of asymmetric molecules in experiments. In addition, theoretical studies based on the first-principle calculations have also shown the applicability of this theoretical model of odd-even HHG, which provides the theoretical tool for the present odd-even HHS. The research and development of odd-even HHS plays an important role in promoting the application of HHG in attosecond science and chemical reactions.

Time varying mean extraction for stationary and nonstationary winds

This paper discusses different strategies for the extraction of the time-varying mean from wind speed time histories. Due to the advantage of allowing analytical evaluations, the attention is focused on kernel regression techniques, considering different weighting functions, namely a constant, a Gaussian and a cardinal sine weighting function. The problem is firstly treated analytically, and the frequency-domain properties of the filter associated to different kinds of weighting functions in the definition of the slowly varying mean through kernel regression are analysed. Then, different weighting functions are adopted for the analysis of digitally-simulated stationary wind speed time histories and for the time histories of thunderstorm outflows recorded by a tri-axial anemometer. The consequences of the adoption of different weighting functions on the harmonic content and statistical properties of turbulence are studied. The same features are found also for thunderstorm outflow records.

Advanced Embedded Control of Electrohydraulic Power Steering System

Abstract The article presents a developed embedded system for control of electrohydraulic power steering based on multivariable uncertain plant model and advanced control techniques. The plant model is obtained by identification procedure via “black box” system identification and takes into account the deviations of the parameters that characterize the way that the control signal acts on the state of the model. Three types of controller are designed: Linear-Quadratic Gaussian (LQG) controller, H∞ controller and μ-controller. The main result is a performed comparative analysis of time and frequency domain properties of control systems. The results show the better performance of systems based on µ-controllers. Also the robust stability and robust performance are investigated. All three systems achieved robust stability which guarantees their workability, but only the system with µ-controller has robust performance against prescribed uncertainties. The control algorithms are implemented in specialized 32-bit microcontroller. A number of real world experiments have been executed, which confirm the quality of the electrohydraulic power steering control system.

Implementation of High-Efficiency and Ultra-Low-Power Transceiver for the Design of Body Channel Communication Applications

Body channel communications (BCCs) were investigated, to enhance requirements for low power and high-reconfiguration power within permitting technology of wireless measurement systems for wireless communication applications. Standard options for BCC are targeted totally toward channel modeling with a potency measuring technique, a transmission technique, and a wireless transceiver style. Wireless digital transmission designed as a personalized methodology meant for the body channel offers significantly the necessary pathway to implement versatile and low-power BCC systems. In addition to the developing level of wearable communication protocol and applications, there is also an increasing reliability on a flexible BCC transmitter which helps to reconfigure power and meet power reduction conditions. This paper proposes an ultra-low-power, Hamming encoding-based, and body channel communication receiver for wireless body area network applications. Proposed BCC transmitter uses a channel of 1–100 MHz frequency to enhance the transmitter frequency and time domain properties. Hamming-based BCC transmitter makes use of a two-stage low-power analog processing circuit and digital information restoration circuit. Analog processing circuit consists of a dual op-amp adjustable preamplifier. Proposed BCC transceiver has higher data reliability because of the orthogonal characteristic of the Hamming codes. Moreover, the proposed Hamming code concatenated method strengthens the jitter tolerance and improves the code rate. Proposed BCC transceiver was verified using a field-programmable gate array board. Proposed data transceiver achieves a data speed rate of 20 Mbps. Bit error reduction values achieved were < 10−6 and < 10−5 at 60 Mbps and 100 Mbps, respectively. It uses a primary space of 0.14 mm × 0.2 mm when it is functioning below a 15 Mb/s data rate (low-power) condition. BCC transmitter utilizes the power of only 1.0 mW.

A New Perspective on the Initial Uplink Synchronization Problem

We explore the frequency domain properties of the signal model associated with the initial uplink synchronization (IUS) problem, and provide some new insights. We show that the adverse effects of carrier frequency offset (CFO) are confined in a small region in the frequency domain. We present a new code design approach that avoids the above mentioned region to cancel the adverse effects of CFO. In this design the codes of different users occupy mutually disjoint intervals on the frequency axis. In this way we minimize the interference between different codes, and thereby, allow considerably large number of simultaneous IUS users in the system. For the proposed codes, we design a simple energy detector. This detector relies on classical results from statistical detection theory. We also propose a simple timing offset estimation technique, which requires us to locate the maximum in a periodogram. The resulting detection-estimation algorithms are simple, non-iterative, fast and accurate. The utility of the proposed design is illustrated using numerical simulation results.

A Hybrid Method for the Diagnosis and Classifying Parkinson's Patients based on Time-frequency Domain Properties and K-nearest Neighbor.

The vibrations of hands and arms are the main symptoms of Parkinson's ailment. Nevertheless, the affection of the vocal cords leads to troubles and defects in the speech, which is another accurate symptom of the disease. This article presents a diagnostic model of Parkinson's disease (PD) and proposes the time–frequency transform (wavelet WT) and Mel-frequency cepstral coefficients (MFCC) treatment for this disease. The proposed treatment is centered on the vocal signal transformation by a method based on the WT and to extract the coefficients of the MFCC and eventually the categorization of the sick and healthy patients by the use of the classifier K-nearest neighbor (KNN). The analysis used in this article uses a database that contains 18 healthy patients and twenty patients. The Daubechies mother WT is used in treatments to compress the vocal signal and extract the MFCC cepstral coefficients. As far as, the diagnosis of Parkinson's ailment is concerned the KNN classifying performance gives 89% accuracy when applied to 52% of the database as training data, whereas when we increase this percentage from 52% to 73%, we reach 98.68% accuracy which is higher than using the support-vector machine classifier. The KNN is conclusive in the determination of the PD. Moreover, the higher the training data is, the more precise the results are.

A method of power system simulation model reduction for transmission grid frequency response analysis

Application of the frequency scan method for the determination of resonant conditions in a transmission power grid requires great effort since the harmonic power system simulation model needs to be developed. This process is rather complicated since the original model used for fundamental frequency load-flow analysis is built with respect to certain assumptions and, thus, intolerable errors are introduced when frequency-domain properties of the power system are investigated. To strike a balance between inputs needed for the development of such model (time, data amounts) and accuracy of the results, it is proposed to employ a method which makes it possible to represent dead-end, double-ended and tapped 110-220 kV substations as a single frequency-dependent equivalent so that the harmonic power system model is reduced. Such an element essentially is a series R-L or R-L-C shunt, parameters of which vary with frequency. The algorithm for the evaluation of its parameters is proposed and the test case for a real 110 kV grid area is discussed. Results of the method application show that it can be used in practice.

Spatial Compounding of Volumetric Data Enables Freehand Optoacoustic Angiography of Large-Scale Vascular Networks.

Optoacoustic tomography systems have attained unprecedented volumetric imaging speeds, thus enabling insights into rapid biological dynamics and marking a milestone in the clinical translation of this modality. Fast imaging performance often comes at the cost of limited field-of-view, which may hinder potential applications looking at larger tissue volumes. The imaged field-of-view can potentially be expanded via scanning and using additional hardware to track the position of the imaging probe. However, this approach turns impractical for high-resolution volumetric scans performed in a freehand mode along arbitrary trajectories. We have developed an accurate framework for spatial compounding of time-lapse optoacoustic data. The method exploits the frequency-domain properties of vascular networks in optoacoustic images and estimates the relative motion and orientation of the imaging probe. This allows rapidly combining sequential volumetric frames into large area scans without additional tracking hardware. The approach is universally applicable for compounding volumetric data acquired with calibrated scanning systems but also in a freehand mode with up to six degrees of freedom. Robust performance is demonstrated for whole-body mouse imaging with spiral volumetric optoacoustic tomography and for freehand visualization of vascular networks in humans using volumetric imaging probes. The newly introduced capability for angiographic observations at multiple spatial and temporal scales is expected to greatly facilitate the use of optoacoustic imaging technology in pre-clinical research and clinical diagnostics. The technique can equally benefit other biomedical imaging modalities, such as scanning fluorescence microscopy, optical coherence tomography or ultrasonography, thus optimizing their trade-offs between fast imaging performance and field-of-view.

Quantifying near fault pulses using generalized Morse wavelets

Recently, a family of analytic wavelets known as generalized Morse wavelets (GMW) has been established in the literature. This versatile wavelet family uses two parameters (γ and β) to adjust its frequency or time domain properties. By varying these two parameters, this wavelet family includes many commonly used analytic wavelets. In this paper, GMW are examined, along with the continuous wavelet transform (CWT), to determine their suitability for extraction of ground motion pulses from a suite of acceleration records. GMW are compared to a popular pulse model that has recently been adapted for wavelet analysis in the literature. It is shown, based on two different metrics, that GMW performs comparably with the other model for this suite of ground motions. Since CWT is repeated with different wavelet parameters for each ground motion, the parameter selection and pulse extraction process are examined. It is shown that using the maximum wavelet coefficient alone does not allow for the extraction of pulses that best match the time series or the spectral response. Alternative criteria are proposed in order to optimize pulse selection using performance-based metrics.

Multitaper Infinite Hidden Markov Model for EEG.

Electroencephalographam (EEG) monitoring of neural activity is widely used for identifying underlying brain states. For inference of brain states, researchers have often used Hidden Markov Models (HMM) with a fixed number of hidden states and an observation model linking the temporal dynamics embedded in EEG to the hidden states. The use of fixed states may be limiting, in that 1) pre-defined states might not capture the heterogeneous neural dynamics across individuals and 2) the oscillatory dynamics of the neural activity are not directly modeled. To this end, we use a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which discovers the set of hidden states that best describes the EEG data, without a-priori specification of state number. In addition, we introduce an observation model based on classical asymptotic results of frequency domain properties of stationary time series, along with the description of the conditional distributions for Gibbs sampler inference. We then combine this with multitaper spectral estimation to reduce the variance of the spectral estimates. By applying our method to simulated data inspired by sleep EEG, we arrive at two main results: 1) the algorithm faithfully recovers the spectral characteristics of the true states, as well as the right number of states and 2) the incorporation of the multitaper framework produces a more stable estimate than traditional periodogram spectral estimates.

Positive real and strictly positive real MIMO systems: theory and application

The paper is dealing with the concept of positive real and strictly positive real properties of linear time-invariant systems. Although strict positive realness is the most important subset of positive real systems and extensively used in stabilizing control systems, there are inconsistencies in the literature for its necessary and sufficient conditions. Also, many frequency properties of such systems are less attended by the scholars. In this paper, a comprehensive discussion on definitions, properties, lemmas, and theorems related to multi-input multi-output positive real and strictly positive real systems will be presented. Further, several frequency domain properties are presented to characterize these functions. Also, three equivalent high frequency condition presented in the literature for strictly positive real systems are compared. The main results have been supported via several illustrative examples.

Detecting Vehicle Anomaly in the Edge via Sensor Consistency and Frequency Characteristic

Autonomous vehicles are expected to significantly enhance the human mobility. However, recently researchers have discovered and demonstrated some attacks on vehicles, which have caused a panic among the public. Furthermore, these attacks have demonstrated that the security issue is still one of the major challenges of vehicles. In this paper, we propose a novel edge computing based anomaly detection, coined edge computing based vehicle anomaly detection (EVAD), which exploits edge based sensor data fusion to identify the anomaly events. The time domain property, i.e., the correlation between different intra-vehicle sensors, and the frequency domain property of sensor data are utilized to judge whether an anomaly has occurred within the vehicle. Especially, to reduce the computation overhead and improve the performance, multiple sensors will be organized as ring architecture, which is a tradeoff of detection accuracy and complexity. In addition, the major components (e.g., anomaly detection module) of EVAD are embedded in edge computing devices, which make the anomaly detection be more efficient and privacy preserving. Meanwhile, a more appropriate model is generated on the cloud server, of which computation overhead maybe heavy for edge computing devices. This paper evaluates the performance of EVAD under different scenarios, and the experimental results demonstrate its feasibility and efficiency. The average true positive rate achieves 99.5% with 1% false positive rate.

Delta-Ramp Encoder for Amplitude Sampling and Its Interpretation as Time Encoding

The theoretical basis for conventional acquisition of bandlimited signals typically relies on uniform time sampling and assumes infinite-precision amplitude values. In this paper, we explore signal representation and recovery based on uniform amplitude sampling with assumed infinite precision timing information. The approach is based on the delta-ramp encoder which consists of applying a one-level level-crossing detector to the result of adding an appropriate sawtooth-like waveform to the input signal. The output samples are the time instants of these level crossings, thus representing a time-encoded version of the input signal. For theoretical purposes, this system can be equivalently analyzed by reversibly transforming through ramp addition a nonmonotonic input signal into a monotonic one which is then uniformly sampled in amplitude. The monotonic function is then represented by the times at which the signal crosses a predefined and equally-spaced set of amplitude values. We refer to this technique as amplitude sampling. The time sequence generated can be interpreted alternatively as nonuniform time sampling of the original source signal. We derive duality and frequency-domain properties for the functions involved in the transformation. Iterative algorithms are proposed and implemented for recovery of the original source signal. As indicated in the simulations, the proposed iterative amplitude-sampling algorithm achieves a faster convergence rate than frame-based reconstruction for nonuniform sampling. The performance can also be improved by appropriate choice of the parameters while maintaining the same sampling density.

Your Table Can Be an Input Panel

This paper explores the possibility of extending the input and interactions beyond the small screen of the mobile device onto ad hoc adjacent surfaces, e.g., a wooden tabletop with acoustic signals. While the existing finger tracking approaches employ the active acoustic signal with a fixed frequency, our proposed system Ipanel employs the acoustic signals generated by sliding of fingers on the table for tracking. Different from active signal tracking, the frequency of the finger-table generated acoustic signals keeps changing, making accurate tracking much more challenging than the traditional approaches with fix frequency signal from the speaker. Unique features are extracted by exploiting the spatio-temporal and frequency domain properties of the generated acoustic signals. The features are transformed into images and then we employ the convolutional neural network (CNN) to recognize the finger movement on the table. Ipanel is able to support not only commonly used gesture (click, flip, scroll, zoom, etc.) recognition, but also handwriting (10 numbers and 26 alphabets) recognition at high accuracies. We implement Ipanel on smartphones, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of Ipanel, and show that it maintains high accuracies across different users with varying input behaviours (e.g., input strength, speed and region). Further, Ipanel's performance is robust against different levels of ambient noise and varying surface materials.

Frequency domain properties and fundamental limits of buffer–feedback regulation in biochemical systems

Feedback regulation in biochemical systems is fundamental to homeostasis with failure causing disease or death. Recent work has found that cooperation and synergy between feedback and buffering – the use of reservoirs of molecules to maintain molecular concentrations – are often critical for biochemical regulation, and that buffering can act as a derivative or lead controller. However, buffering differs from derivative feedback in important ways: it is not typically limited by stability constraints on the parallel feedback loop, for some signals it acts instead as a low-pass filter, and it can change the location of disturbances in the closed-loop system. Here, we propose a frequency-domain framework for studying the regulatory properties of buffer–feedback systems. We determine standard single-output closed-loop transfer functions and discuss loop-shaping properties. We also derive novel fundamental limits for buffer–feedback regulation, which show that buffering and removal processes can reduce the fundamental limits on feedback regulation. We apply the framework to study the regulation for glycolysis (anaerobic metabolism) with creatine phosphate buffering.