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Based on the explicit formula of the pointwise error of Fourier projection approximation and by applying van der Corput-type Lemma, optimal convergence rates for periodic functions with different degrees of smoothness are established. It shows that the convergence rate enjoys a decay rate one order higher in the smooth parts than that at the singularities. In addition, it also depends on the distance from the singularities. Ample numerical experiments illustrate the perfect coincidence with the estimates. [ABSTRACT FROM AUTHOR]

This paper presents, for the first time, analytical solutions for the charge and flux as a function of the state variable in the case of the HP memristor, modeled as an ideal generic memristor with Joglekar and Prodromakis window functions for a general value of the control parameter. The solutions are in the closed form of finite sums for an arbitrary positive integer values of the control parameter. Our approach is based on the decomposition of original problems using appropriate pairs of smooth hinge functions. The generalization of this approach allows us to define a novel window function based on smooth hinge functions, which brings simpler analytical expressions for charge and flux as a function of the state variable, and faster simulations when the model is used.

Copyright of Informacije MIDEM: Journal of Microelectronics, Electronic Components & Materials is the property of MIDEM Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The mounted stationary sensor on the ring casing has become a widely accepted way to monitor the planetary gear condition and one challenging aspect of this method is the modulation effect caused by the moving planet gears. It is valuable to study the modulation effect caused by the moving planet gears. Method: Different window functions have been proposed to model this modulation phenomenon and there are several drawbacks: the selection of the window function parameters is often arbitrary and subjective instead of basing on the planetary gear physical parameter; the current window function is hard to reflect the impact from the ring gear boundary conditions. Therefore, this paper simulated this modulation phenomenon as moving load dynamic problem to overcome these drawbacks, where the ring casing has been modelled as an axisymmetric flexible structure and the internal meshing forces have been modelled as spatially varying moving loads. Based on the improved hamming window function, this paper related the window function parameters with the planetary gear physical properties and operation conditions and then, quantified the modulation effect with the consideration of moving load speed as well as the ring gear boundary condition. Results: For the planetary gear carrier arm rotation speed, the result suggested that the moving speed mainly affect the parameter β and the corresponding value showed a nonlinear decreasing trend when the moving speed increased. For the ring gear rim thickness, similarly, parameter β increased significantly with the increase of the rim value. For the radial support stiffness, parameter β has also been affected most and it shows a decreasing trend when the support stiffness increased. For the sensor location, both parameter α and β shows a periodical change around the ring casing. Conclusion: The results suggested that both parameters have been impacted by the rotation speed and boundary conditions, among which parameter β has been impacted most. [ABSTRACT FROM AUTHOR]

This paper reports the optimization of voltage controlled resistive switching devices in terms of device and circuit level parameters in a memristor-based circuit in order to demonstrate profound non-linear characteristics for high performance NVM applications. The pinched hysteresis loop has been correlated to the different stages of formation and rupture of conductive filaments in memristors. A qualitative study of the VTEAM model has also been presented. The memristor circuit has been simulated for varying device and circuit level parameters and options to optimize the circuit to achieve high degree of power-aware non-linearity in the characteristics of the memristor have been demonstrated in this paper. The effect of window functions on the non-linear behavior of memristors has been presented towards the end of the paper. [ABSTRACT FROM AUTHOR]

Abstract In view of the problem that the sensing characteristics of the multi-mode interferometric fiber sensors cannot be accurately analyzed, an analysis method based on the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) is proposed and demonstrated theoretically and experimentally. The suitabilities of the rectangular window function with the narrow main lobe (high spectrum resolution) and low side lobe (high main mode energy leakage) and the Hanning window function with the wide main lobe (low spectrum resolution) and high side lobe (high energy concentration) in this kind of sensor analysis are discussed, respectively. This method can not only realize the sensing performance analysis of the various modes, but also overcome the inconsistency of the different interference wavelength (dip) sensing characteristics in the conventional analysis methods. At the same time, this method is also beneficial to solve the repetitive problem of such sensors.

The critical characteristic temperatures, i.e. onset temperature and end temperature of chemical reactions, are hard to determine. Instead, the extrapolated ones by the tangent method are commonly used. However, problems such as large errors and uncertainty of the tangent method are restricting its application. In this paper, the noise power window function (nPWF®) method was firstly proposed and applied to determine the critical temperatures. The construction theory of nPWF® method and analysis process were comprehensively introduced. Three typical reaction processes including decomposition of calcium carbonate (CaCO3), melting/solidification of tin (Sn) and carbon gasification were conducted under controlled temperature programmes. The detection signals of DTG (derivative thermogravimetric analysis), DTA (differential thermal analysis) and gas component yield were interpreted to the nPWF signal to determine the critical temperatures, with a high accuracy improved by 1–2 orders of magnitude than the extrapolated ones. Finally, accuracy, applicability and superiority of the nPWF® method were analysed. [ABSTRACT FROM AUTHOR]

Copyright of Informacije MIDEM: Journal of Microelectronics, Electronic Components & Materials is the property of MIDEM Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to further reduce the errors arising in synchronous sampling, this paper designs a multi-channel hybrid sampling electrical parametric feature modeling method based on an AIOT sensing module in the process of design. The AIOT sensing module is used to process the received RF signals using a transconductance amplifier to improve the digitization and reconfigurability of the RF front-end circuit. The signal is transformed and calculated using the Fast Fourier Transform to obtain electrical parameters such as the amplitude of each harmonic and the power factor. The results show that the variation amplitude of the window function of the design method in this paper is generally maintained between -1.5 and 1.5, the amplitude accuracy of the active power rate is within 3.17555%, and the relative error accuracy is within 0.000951%. It can be seen that the AIOT-based sensing module can significantly improve the calculation accuracy of electrical parameters and can be applied to the process of sampling digital measurements of other power signal characteristics.

There are nonlinear drift memristor models utilizing window functions in the literature. The resistive memories can also be modeled using memristors. If the memristor’s resistance switches from its minimum value to its maximum value or from its maximum value to its minimum value, the transition phenomenon is called resistive or memristive switching. The value of the time required for this transition is especially important for resistive computer memory applications. The switching time is measured by experiments and should be calculatable from the parameters of the memristor model used. In the literature, to the best of our knowledge, the resistive switching times have not been calculated except for the HP memristor model and a piecewise linear memristor model. In this study, the memristive switching times of some of the well-known memristor models using a window function are calculated and found to be infinite. This is not feasible according to the experiments in which a finite memristive switching time is reported. Inspired by these results, a new memristor window function that results in a finite switching time is proposed. The results of this study and the criteria given here can be used to make more realistic memristor models in the future.

The problem of changes in the audio signal when changing the sampling frequency arose a long time ago, but interest in it has recently become more acute with an increase in the quality of signal formation and transmission. To some extent, this was also determined by the methods of assessing the quality of changes in the harmonic signal without taking into account changes in the actual broadcast signal. Algorithms for the formation of a new digital sequence after changing the sampling rate provided perfect quality in the conversion of the sinusoidal signal and large errors in the conversion of the real signal. Algorithms for changing the sampling rate Fd have been improved. The aim of this work is to study the features of changes in the sampling frequency in the paths of the broadcast channel and to develop algorithms for converting the sampling frequency in the frequency domain with an increase in conversion accuracy. It is shown that the algorithms for converting the sampling rate in the time domain have reached perfection and cannot be improved, so the transition to processing in the frequency domain is important. An algorithm for converting the sampling frequency in the frequency domain has been developed. The spectrum of the error signal for various types of converters is determined and the need for its compensation is determined. A method for introducing predistortions for converters with known and unknown sequence of sample rates used has been developed. The use of the results of this work makes it possible to improve the quality of broadcasting signals and information programs.

There are nonlinear drift memristor models utilizing window functions in the literature. The resistive memories can also be modeled using memristors. If the memristor 's resistance switches from its minimum value to its maximum value or from its maximum value to its minimum value, the transition phenomenon is called resistive or memristive switching. The value of the time required for this transition is especially important for resistive computer memory applications. The switching time is measured by experiments and should be calculatable from the parameters of the memristor model used. In the literature, to the best of our knowledge, the resistive switching times have not been calculated except for the HP memristor model and a piecewise linear memristor model. In this study, the memristive switching times of some of the well-known memristor models using a window function are calculated and found to be infinite. This is not feasible according to the experiments in which a finite memristive switching time is reported. Inspired by these results, a new memristor window function that results in a finite switching time is proposed. The results of this study and the criteria given here can be used to make more realistic memristor models in the future. [ABSTRACT FROM AUTHOR]

This paper proposes two video quality assessment methods based on deep neural network. (i)The first method uses the IQF-CNN (convolution neural network based on image quality features) to build image quality assessment method. The LIVE image database is used to test this method, the experiment show that it is effective. Therefore, this method is extended to the video quality assessment. At first every image frame of video is predicted, next the relationship between different image frames are analyzed by the hysteresis function and different window function to improve the accuracy of video quality assessment. (ii)The second method proposes a video quality assessment method based on convolution neural network (CNN) and gated circular unit network (GRU). First, the spatial features of video frames are extracted using CNN network, next the temporal features of the video frame using GRU network. Finally the extracted temporal and spatial features are analyzed by full connection layer of CNN network to obtain the video quality assessment score. All the above proposed methods are verified on the video databases, and compared with other methods. [ABSTRACT FROM AUTHOR]

Different memristive devices have different characteristic curves; how to describe and simulate various kinds of memristive devices with a unified model is still a significant work. In this work, a new memristor model is presented—DSAM, drift speed adaptive memristor model. This model is composed of a linear i–v relation function and a speed adaptive state function. A detailed analysis of model parameters' effect is proposed. It is shown that different parameters perform different drift speed curves, which can be adjusted to describe various memristive devices. The proposed model can also adapt to various voltage inputs. Finally, the model is tested in fitting different memristor devices with an average error of less than 5.5 % . [ABSTRACT FROM AUTHOR]

Window functions are required to achieve excellent frequency resolution, peak sidelobe ratio, and sidelobe decay. The cosine-sum window function satisfies these requirements. However, conventional cosine-sum windows limit the number of terms and coefficient values. Therefore, this study proposes a new class of cosine-sum windows that has more terms than those in the conventional cosine-sum window and considers positive and negative coefficients. For such windows, we present three design examples. The first is a window function with low sidelobe decay, which reduce the sidelobes by adding correction terms to the conventional windows without damaging the sidelobe decay. The second is a window function that improves the frequency resolution by emphasizing the discontinuities of the extracted signal. The third is a window function that reduces sidelobes further without deteriorating the frequency resolution by adding correction terms to the window functions, which are designed in advance. We confirmed the effectiveness of these examples of the proposed window function.

Crosshole seismic testing is commonly used to determine detailed and reliable wave velocity profiles. Material damping measurements can also be performed in crosshole seismic testing, but these measurements require a window function to maintain similar amplitude shapes over the frequency range of interest for the calculation. In-situ seismic testing, including downhole and seismic cone penetration testing (SCPT), also requires a window function for the material damping calculations. However, the length of the window function has the largest influence on the material damping calculation. Therefore, the half-power bandwidth method is introduced to determine the material damping ratio without the window function in crosshole testing by replicating the unconfined, free-free, resonant column (Fr-Fr) test. To demonstrate the influence in the length of the window function, the first cycle of the signal is used in the material damping calculation. The half- power bandwidth method is verified using synthetic signals and field data. Two sets of crosshole and downhole tests were performed, one at a backfill test pad and the second at a Hornsby Bend research site operated by the University of Texas at Austin. The in-situ material damping ratio calculated from these two sets of crosshole tests using the half-power bandwidth method are compare with the Spectral Ratio Slope (SRS) method applied to the downhole testing at these sites. The material damping calculated from the half-power bandwidth method using the full signal results in a reliable and precise damping value compared to the SRS method applied to the downhole data.

Eliminating the Gibbs oscillations that occur during the Finite Impulse Response (FIR) digital filter design with the Fourier Series method will ensure correct filtering. For this reason, the development of the window improves the performance of the filter and, therefore, the system. In this study, the cosh window function is designed using Particle Swarm Optimization, which is a preferred optimization method in many areas. Thus, alternatives to the standard results obtained from the existing traditional calculations will be produced, and different windows that perform the same function will be obtained. In addition, exponential and cosh window functions were designed in LabVIEW environment, which is a graphical programming language-based program, and the designed windows were analyzed at different parameter values. LabVIEW provides a fast and easy programming environment, and it provides the opportunity to realize real-time applications with its external hardware. Utilizing this feature, the amplitude spectrum of cosh window designed in LabVIEW is displayed in real time for different window parameter values. As a result, FIR digital filters were designed using cosh window based on optimization and the cosh window designed in LabVIEW, and the distorted EEG signal was filtered using these filters and displayed in real time. [ABSTRACT FROM AUTHOR]

A multithreaded convolution implementation based on block algorithms is considered. Convolution is essentially the basis of many methods that solve the problem of determining the degree of similarity or independence of two processes, in other words, when it is necessary to determine the degree of correlation. The algorithm itself is executed with a significant delay, because for its execution it is necessary to accumulate the entire signal and then process it. The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. The article shows the main features of the convolution implementation by the overlap method with addition and the overlap method with addition, as well as numerical examples. The results obtained show that the application of these methods without the use of a window function leads to significant distortions in the signal spectrum. Based on the results of the analysis, a universal scheme for performing convolution based on multithreaded processing of an input data block is proposed. This allows to achieve a good compromise between computational complexity, system architecture, and time costs.

In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors. [ABSTRACT FROM AUTHOR]

The design of memristor-based electronic circuits and devices gives researchers opportunities for the engineering of CMOS-memristor-based electronic integrated chips with ultra-high density and various applications. Metal-oxide memristors have good compatibility with the present CMOS integrated circuits technologies. The analysis of new electronic circuits requires suitable software and fast-functioning models. The main purpose of this paper is to propose the application of several modified, simplified, and improved metal-oxide memristor models in electronic devices and provide a comparison of their behavior, basic characteristics, and properties. According to this, LTSPICE is utilized in this paper because it is a free software product with good convergence. Several memristor-based electronic circuits, such as non-volatile passive and hybrid memory crossbars, a neural network, and different reconfigurable devices–filters, an amplifier, and a generator are analyzed in the LTSPICE environment, applying several standards and modified metal-oxide memristor models. After a comparison of the operation of the considered schemes, the main advantages of the modified metal-oxide memristor models, according to their standard analogs, are expressed, including fast operation, good accuracy, respectable convergence, switching properties, and successful applicability in complex electronic circuits. [ABSTRACT FROM AUTHOR]

Hardware validation and verification studies for emulator circuits of the memristor element usually include either analog or digital realization results. In this study, the implementation stages of a memristor element using the Joglekar window function with programmable and reconfigurable analog and digital platforms are given together in order to observe the characteristic current–voltage relationship, experimentally. Herein, a Joglekar window function-based voltage-controlled memristor has been implemented with the field-programmable analog array (FPAA) platform for the first time. In fact, the FPAA platform has attracted attention thanks to its analog nature, rapid prototyping and low hardware cost specialties, and because different nonlinear functions can be easily constructed on this device. Similarly, the field-programmable gate array (FPGA) platform has recently been recognized for its parallel processing feature in digital signal processing studies. The FPGA-based digital implementation stages of the Joglekar window function-based memristor element are also addressed in this study. Thus, the FPAA and FPGA based memristor emulator designs are presented together in order to contribute the memristor-based analog and digital applications that is required to obtain real-time signals. [ABSTRACT FROM AUTHOR]

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present article, we propose a directionally tuned variant of the continuous Stockwell transform in the context of multi-dimensional signal analysis. Owing to the directional selectivity coupled with a frequency adaptable window, the proposed transform serves as a bridge between the directional short-time Fourier and wavelet transforms. Besides the formulation of all the fundamental results, including the orthogonality relation and reconstruction formula, we also obtain several uncertainty inequalities associated with the directional Stockwell transform. [ABSTRACT FROM AUTHOR]

In the present article, we propose a directionally tuned variant of the continuous Stockwell transform in the context of multi-dimensional signal analysis. Owing to the directional selectivity coupled with a frequency adaptable window, the proposed transform serves as a bridge between the directional short-time Fourier and wavelet transforms. Besides the formulation of all the fundamental results, including the orthogonality relation and reconstruction formula, we also obtain several uncertainty inequalities associated with the directional Stockwell transform. [ABSTRACT FROM AUTHOR]

A memristor is a passive circuit element that has numerous applications ranging from storage to processing. The attributes of memristor, such as low power, fast switching speed, and non-volatility, make it a promising candidate for computing applications. To deploy the memristors at the circuit level, a versatile, computationally efficient, and generic model is required to analyze the circuit performance. This article presents a computationally inexpensive, generic, and accurate phenomenological model of the memristor. The proposed model predicts an accurate current–voltage ($I$ – $V$) relationship based on the current conduction mechanism. The presented modeling technique can be incorporated into any practical memristor behavior by optimizing the fitting parameters. The model has the capability to optimize its accuracy and computational efficiency by calibrating the model parameters. The results are compared with the characterization data of numerous memristors and noteworthy models of memristors to validate the proposed approach. The results depict that the proposed model is more flexible, computationally efficient, versatile, and generic. It improves the simulation run time up to 24.47% with the relative root mean squared error of 0.4142%. The model exhibits remarkable results when compared with titanium dioxide-based devices. The proposed model can be deployed to various applications, such as logic design, memory design, and neuromorphic computing. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at $3.01{\pm }2.22$ mmHg with the correlation coefficient at 0.926 and $4.43{\pm }3.37$ mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $5.34{\pm }4.08$ mmHg with the correlation coefficient at 0.740 and $5.89{\pm }4.98$ mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $7.91{\pm }8.06$ mmHg with the correlation coefficient at 0.876 and $3.47{\pm }5.59$ mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $9.77{\pm }9.01$ mmHg with the correlation coefficient at 0.805 and $7.08{\pm }5.55$ mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) communication is an essential technology that supports underwater surveys by transmitting images and movies wirelessly. However, UWA communication is still challenging due to the large delay and Doppler spreads in UWA channels. Doppler-resilient orthogonal signal division multiplexing (D-OSDM) under water has been found effective in such channels, but Doppler modeling errors prevent further improvement in communication quality. In this paper, we clarify the effect of Doppler modeling errors on the communication quality of D-OSDM and propose the use of a window function in D-OSDM to address the issues that such errors raise. We also evaluate the communication quality of D-OSDM using the window function through simulations and experiments. The experimental results show a 56% increase in the number of error-free data blocks, indicating that windowing can improve communication quality. [ABSTRACT FROM AUTHOR]

Over the years, a fixed window for smoothing radiometer cold-space and warm-load counts and processing brightness temperature in calibration has been used for all microwave sounders at European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and NOAA. Although this practice is based on ground tests and legacy satellites, it remains unclear if this empirical parameter is optimal for in-orbit radiometers, as the space environment is different from the ground and radiometers may drift. We found that the fixed window is not optimal and leads to large noise. We have developed an adaptive window that accommodates channel differences and temporal changes in hardware. Our method has reduced noise by as much as 50% for 183 GHz channels of MetOp-C microwave humidity sounder (MHS). We observed temporal jumps and shifts in counts, gain, and noise of 89 and 190 GHz, and accordingly, the adaptive window can adjust to reduce such an impact. Further analyses reveal that $1/f$ noise plays an important role for determining the adaptive window. $1/f$ noise is nonstationary and gives rise to the fluctuation of counts and gain. As a result, for channels with large $1/f$ noise a short window should be used to mitigate the fluctuation. Our study suggests that an adaptive method has advantages over the fixed method for considering channel differences and time-varying noise. [ABSTRACT FROM AUTHOR]

A robust low power Electrocardiogram (ECG) pre-processor is proposed to extract the useful information from biomedical signal. The methodology uses window-based efficient design that consumes less power and resources of Field Programmable Gate Array (FPGA). The comparison has been done among different window functions and filter architectures (Symmetric and Anti-symmetric). The entire hardware implementation is carried out on ZedBoard Zynq-7000 FPGA board. The inference has been drawn that symmetric Bartlett window consumes only 0.88%, 5.13%, and 9% of LUTs, slice registers and DSPs units respectively. 35mW of dynamic and 105 mW of static power is consumed by the pre-processor module. The proposed design is recommended to find applications in wearable and portable biomedical equipments. [ABSTRACT FROM AUTHOR]

In the paper, the systematic (bias) error of digital processing of the biharmonic signal of two multiplexed fiber-optic interferometers under spectral interrogation, when the registered spectral transfer function being processed through a discrete Fourier transform has been analyzed. The synthesis of a special weighted window function was put forward; this made it possible to reduce the systematic errors in determining the frequencies of a polyharmonic signal without a significant increase in the random (noise) error. The effectiveness of this approach was proven through numerical simulation and experimentally by comparison with the error when using standard Dolph – Chebyshev window. The proposed approach can be applied in any problems related to the estimation of frequencies and phases of polyharmonic signals.

As complementary to Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR), SAR Offset Tracking (OT) is an efficient tool for large ground deformation monitoring in situations when DInSAR cannot work. However, SAR images are affected by speckle noise and some strong point-like scatters which can cause what is known as Patch Like (PL), a kind of errors that can be seen as homogeneous patches of almost constant deformation in the results. These errors are clearly visible in the results as non-consistent deformations along time, but they are difficult to detect with the traditional metrics that evaluate the cross-correlation results, like the Signal to Noise Ratio (SNR). This paper addresses this problem and proposes a simple amplitude filter to reduce PL named as Patch Like Reduction (PLR). The main idea is to find a sensor and scene independent threshold to remove the high amplitude pixels prone to cause PL. Five different SAR data sets and in-field GPS measurements are used to determine the optimal threshold and evaluate the performance of the proposed method. The results show that PL effects can be reduced with the proposed amplitude filter. The processing parameters of the improved OT processing chain are optimized as well to preserve the results resolution as much as possible.

The heart wall has a multilayered structure and moves rapidly during ejection and rapid filling periods. Local strain rate (SR) measurements of each myocardial layer can contribute to accurate and sensitive evaluations of myocardial function. However, ultrasound-based velocity estimators using a single-frequency phase difference cannot realize these measurements owing to insufficient maximum detectable velocity, which is limited by a quadrature frequency. We previously proposed a velocity estimator using multifrequency phase differences to improve the maximum detectable velocity. However, the improvement is affected by a spatial discrete Fourier transform (DFT) window length that represents the locality of the velocity estimation. In this article, we theoretically describe that shortening the window increases the interference between different frequency components and decreases the maximum detectable velocity. The tradeoff between the maximum detectable velocity and the window length was confirmed through simulations and a water-tank experiment. Under the tradeoff, the Hanning window, which was used in previous studies, is not always appropriate for the local measurement of the velocity, which sometimes exceeds 100 mm $/\text{s}$ depending on the subject, direction of the ultrasound beam to the heart wall, and cardiac periods. In the in vivo measurement with the short window, the Tukey window with a large flat part that has a high-frequency resolution and ameliorates the discontinuity at both edges of the windowed signal was appropriate to measure the maximum velocity. This study offers the potential for local measurements of each myocardial layer using the multifrequency velocity estimator with the appropriate window function and window length. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Proposed method combines Raman preprocessing, feature selection, ML for origin identification. • Innovative Raman spectral preprocessing techniques improve data quality and reduce dimensionality. • Optimized window function with binning width of 5 achieves highest accuracy in preprocessing. • Information gain feature selection extracts discriminative spectral features effectively. • LinearSVC, MLPClassifier, LDA, and RVFLClassifier provide robust performances. In this study, a simple and accurate approach is proposed for enhancing the origin identification of raspberry samples using a combination of innovative Raman spectral preprocessing techniques, feature selection, and machine learning algorithms. Window function was creatively introduced and combined with baseline removal technique to preprocess the Raman spectral data, reducing the dimensionality of the raw data and ensuring the quality of the processed data. An optimization process was conducted to determine the optimal parameter for the window function, resulting in a binning window width of 5 that yielded the highest accuracy. After applying three feature selection techniques, it was found that the information gain model had the best performance in extracting discriminative spectral features. Finally, ten different machine learning algorithms were employed to construct predictive models, and the optimal models were selected. Linear Support Vector Classifier (LinearSVC), Multi-Layer Perceptron Classifier (MLPClassifier), and Linear Discriminant Analysis (LDA) achieve accuracy, precision, recall, and F1 values above 0.96, while the Random Vector Functional Link Network Classifier (RVFLClassifier) surpasses 0.93 for these performance metrics. These results demonstrate the effectiveness of the proposed approach in identifying the origin of raspberry samples with high accuracy and robustness, providing a valuable tool for agricultural product authentication and quality control. [ABSTRACT FROM AUTHOR]

The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM (Voltage ThrEshold Adaptive Memristor Model). A number of improvements are made towards the stick effect elimination and other numerical refinements to make the analysis of large networks fast and accurate. A set of models are proposed that utilize the synergy of several techniques such as window asymmetrization, integration with saturation, state equation preprocessing, scaling, and smoothing. The performance of models is tested in Cadence PSPICE 17.2 and particularly in HSPICE v2017, the latter on a large-scale CNN (Cellular Nonlinear Network) for detecting edges of binary images. The simulations manifest the usability of developed models for fast and reliable operation in networks containing more than one million nodes.

In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.

The design of memristor-based electronic circuits and devices gives researchers opportunities for the engineering of CMOS-memristor-based electronic integrated chips with ultra-high density and various applications. Metal-oxide memristors have good compatibility with the present CMOS integrated circuits technologies. The analysis of new electronic circuits requires suitable software and fast-functioning models. The main purpose of this paper is to propose the application of several modified, simplified, and improved metal-oxide memristor models in electronic devices and provide a comparison of their behavior, basic characteristics, and properties. According to this, LTSPICE is utilized in this paper because it is a free software product with good convergence. Several memristor-based electronic circuits, such as non-volatile passive and hybrid memory crossbars, a neural network, and different reconfigurable devices–filters, an amplifier, and a generator are analyzed in the LTSPICE environment, applying several standards and modified metal-oxide memristor models. After a comparison of the operation of the considered schemes, the main advantages of the modified metal-oxide memristor models, according to their standard analogs, are expressed, including fast operation, good accuracy, respectable convergence, switching properties, and successful applicability in complex electronic circuits.