Ideal Filter Research Articles

Blind image counterwatermarking \u2013 hidden data filter

Watermarking is a dynamically developing method of copyright protection used for media (sounds, images, films, or 3D objects) that employs signal processing in order to hide additional, invisible information about the owner or author. However, studies until now have not widely considered the problem of attempting to blind removal of hidden data in a manner that allows the watermarked signal to be returned to the original signal. Current considerations of authors of leading articles in the watemarking field focus on the robustness of the method – security system against intentional attacks of removal of the additional information, without taking into account the aspect of simultaneous degradation of the quality and form of the watermarked picture. As has been shown in the article, it is possible to design a perfect filter (same as reversible operations) that allows the removal of additional information from the watermarked picture in a way that makes it possible to return to the form of the host image. This paper describes an ideal filter used for removal of additional, invisible information in forms of an eliminating function and a signal masking function. Their effectiveness has been demonstrated for practical implementations of this type of eliminating and masking filters for watermarking methods in the cepstrum domain.

Two‐dimensional spatiochromatic signal processing using concept of phasors in sequency domain

Spatiochromatic information of colour images is analysed in sequency domain using CIE La*b* colour space. RGB image is processed in transform domain using spatiochromatic-conjugate symmetric sequency ordered complex Hadamard transform (SC-CS-SCHT). Spatiochromatic vectors (a*, b* and a*b*) are considered as real, imaginary and complex inputs to the SC-CS-SCHT. This transform has symmetry properties that are similar to the discrete Fourier transform. Resultant phasor paths are observed with respect to different input signals along the unit complex plane. In sequency domain opponent colour paths are discussed and applied on two-dimensional signals. Peak signal-to-noise ratio is measured for ideal lowpass filter.

Narrowband Notch Filter Using Feedback Structure Tips & Tricks

The design of a notch filter poses challenges in managing the tradeoff between distortion in the passband and the notch band. The ideal notch filter has a 3-dB notch bandwidth (BW) equal to zero in the notch band and the unity gain in the passband. Therefore, the design objective is to shrink the BW as small as possible while preserving the gain as close to one as possible. The task of a notch filter is to eliminate interference while preserving the target broadband signal. These filters have many applications for narrowband interference removal in biomedical engineering [1], speech processing [2], image processing [3], [4], and communication [5].

Improving FIR Filters by Using Cascade Techniques Tips & Tricks

High-performance filtering is the holy grail for all digital signal processing (DSP) practitioners. However, highperformance filtering is almost synonymous with high implementation complexity and, thus, high cost. This article focuses on designing high-performance finite impulse response (FIR) filters with less complexity. We present novel methods for improving the frequency response of an FIR filter by cascading it with complementary comb filters (CCFs). In particular, we redesign a low-order FIR filter by intentionally inducing a ripple at the passband edge and using CCFs of various lengths to compensate for the ripple. We use the zeros of CCFs to reshape the frequency response of the low-order FIR filter, subsequently called the prototype filter. Thus, the composite filter approaches an ideal lowpass filter (LPF) more closely in both the transition band and stopband. Because a CCF contains only one adder and a few storage units, the cost is minimal. The composite filter also maintains a linear phase response because all CCFs have linear phase responses. Through shifting the LPF response to high-frequency bands, we obtain highpass filters and bandpass filters (BPFs) by using the same methodology.

Analysis of Digital Image Filters in Frequency Domain

Digital image filtering in an important approach towards image enhancement. It involves the manipulation and interpretation of digital images. Images may get noisy due to various factors then filtering of images is become an important operation to de-noise the noisy images. Image Smoothing and image sharpening must be performed to achieve it. In our paper we work on Ideal, Gaussian and Butterworth filters to apply high pass and low pass filtering on images. We implement and simulate these filters on MATLAB platform and analyze their performance for equal cutoff frequency. This work gives great experience to understand differences among various above define filtering techniques through keen analysis of their respective results.

Supersampling method for efficient grid-based electronic structure calculations.

The egg-box effect, the spurious variation of energy and force due to the discretization of continuous space, is an inherent vexing problem in grid-based electronic structure calculations. Its effective suppression allowing for large grid spacing is thus crucial for accurate and efficient computations. We here report that the supersampling method drastically alleviates it by eliminating the rapidly varying part of a target function along both radial and angular directions. In particular, the use of the sinc filtering function performs best because as an ideal low pass filter it clearly cuts out the high frequency region beyond allowed by a given grid spacing.

A New Image Quality Index for Objectively Evaluating Despeckling Filtering in SAR Images

Synthetic aperture radar (SAR) images are corrupted with a multiplicative granular-like noise pattern known as speckle. The goal for a despeckling filter consists of suppressing the speckle while preserving all the scene features such as texture, point-type targets, and, especially, edges. There exist several speckle filtering techniques and a relevant number of image quality indexes to evaluate the performances of a filtering operation on an SAR image. However, assessing the superiority of a filter over other is not a trivial issue. In this work, we present a new referenceless estimator ( $\upalpha\upbeta$ -ratio estimator) based on the ratio edge detector which allows helping in objectively evaluating a filter realization on SAR images. The proposed estimator operates on the ratio image obtained as the point-to-point ratio between the original image (noisy image) and the filtered image. An ideal filter operation on an image implies that, in areas where speckle is fully developed, the ratio image should have the features of pure speckle and no geometric content. The new estimator measures the remaining geometric content within the ratio image. This new estimator is easy to compute and it provides an excellent metric to rank a filtering operation on real SAR images.

Maximum Margin Learning of t-SPNs for Cell Classification With Filtered Input

An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructed t-SPN architecture is learned by maximizing the margin, which is the difference in the conditional probability between the true and the most competitive false label. To enhance the generalization ability of the architecture, L2-regularization (REG) is considered along with the maximum margin (MM) criterion in the learning process. To highlight cell features, this paper investigates the effectiveness of two generic high-pass filters: ideal high-pass filtering and the Laplacian of Gaussian (LOG) filtering. On both HEp-2 and Feulgen benchmark datasets, the t-SPN architecture learned based on the max-margin criterion with regularization produced the highest accuracy rate compared to other state-of-the-art algorithms that include convolutional neural network (CNN) based algorithms. The ideal high-pass filter was more effective on the HEp-2 dataset, which is based on immunofluorescence staining, while the LOG was more effective on the Feulgen dataset, which is based on Feulgen staining.

Spatio-temporal filtering of thermal video sequences for heart rate estimation

In this paper, a novel method for non-contact measurement of heart rate using thermal imaging was proposed. Thermal videos were recorded from subjects’ faces. The measurements are performed on three different areas: the whole face, the upper half of the face and the supraorbital region. A tracker was used to track these regions to make the algorithm invulnerable to the subject's motion. After tracking, the videos were spatially filtered using a full Laplacian pyramid decomposition to increase the signal to noise ratio; next, the video frames were successively temporally filtered using an ideal bandpass filter for extracting the thermal variations caused by blood circulation. Finally, the heart rate was calculated by using two methods including zero crossing and Fast Fourier Transform. For evaluating the results, the complement of absolute normalized difference (CAND) index was used which was introduced by Pavlidis. This index was 99.42% in the best case and 92.472% in average for 22 subjects. These results showed a growth in CAND index in comparison with previous work. Zerocrossing outperformed FFT because of the nonstationary nature of thermal signals. Another benefit of our method is that, the videos are taken from the face unlike most of the studies that take it from the neck and Carotid. Neck and carotid are less accessible than faces. Finally, the optimum ROI for estimating the heart rate from face was identified.

A Parametric Investigation of a Concentrating Photovoltaic/Thermal System With Spectral Filtering Utilizing a Two-Dimensional Heat Transfer Model

A 2D heat transfer model of a hybrid photovoltaic/thermal (PV/T) system has been created. This paper investigates the impact of ideal filters to best accommodate for a nonuniform PV temperature along the length of the receiver. The proposed configuration consists of a GaAs cell laminated to an aluminum extrusion. The working fluid, a transparent high-temperature heat transfer fluid with suspended nanoparticles, flows through the hollow extrusion where it cools the PV cell before it is redirected in front of the cell acting as an optical filter. The model accounts for PV cell efficiency, temperature, and bandgap dependence, the details often neglected in prior works.

On derivations and their fixed point sets in residuated lattices

The main goal of this paper is to investigate derivations in residuated lattices and characterize some special types of residuated lattices in terms of derivations. In the paper, we discuss related properties of some particular derivations and give some characterizations of (good) ideal derivations. Then we obtain that the fixed point set of good ideal derivations is still a residuated lattice. Also, we prove that the set of all ideal derivation filters on residuated lattices with good ideal derivations can form a complete Heyting algebra, which is isomorphic to the lattice of all filters in the fixed point set for good ideal derivations. Moreover, we study principal ideal derivations and their adjoint derivations. Finally, we get that the fixed point set of principal ideal derivations and that of their adjoint derivations are order isomorphism. In particular, using the fixed point set of principal ideal derivations, we give characterizations of Heyting algebras and linearly ordered Heyting algebras.

Influence of Solvents in HF Solutions on Anodic Formation of Mesoporous Silicon, Revealed by the Characterization of Mesoporous Silicon Rugate Filters

Formation mechanism of mesoporous silicon by electrochemical dissolution is studied. HF solutions containing a high amount of alcohol were often used for the preparation of mesoporous silicon. The present study focuses the role of alcohol in the HF electrolyte solutions during the anodic formation of mesoporous silicon. To obtain the dynamic information of mesoporous silicon formation, mesoporous silicon rugate filters that is formed by applying a sinusoidal current density is studied in detail. By analyzing the thickness of multilayers as well as the optical spectra of mesoporous silicon rugate filters, gradual change of the local dissolution condition in the vicinity of pore tips is investigated. Deviation of microscopic thickness of rugate filters is prominent when tert-butanol is used as solvent for HF solution. The deviation becomes less remarkable using methanol or ethanol as solvent. However, a nearly ideal porous silicon rugate filters are obtained in an aqueous HF solution containing a surfactant. This study shows that roles of alcohols are not limited to the reduction of surface tension. Their conductivities also have an impact on the dissolution kinetics.

Trends cycles and seasons: Econometric methods of signal extraction

ABSTRACTAlternative methods of trend extraction and of seasonal adjustment are described that operate in the time domain and in the frequency domain.The time-domain methods that are implemented in the TRAMO–SEATS and the STAMP programs are compared. An abbreviated time-domain method of seasonal adjustment that is implemented in the IDEOLOG program is also presented. Finite-sample versions of the Wiener–Kolmogorov filter are described that can be used to implement the methods in a common way.The frequency-domain method, which is also implemented in the IDEOLOG program, employs an ideal frequency selective filter that depends on identifying the ordinates of the Fourier transform of a detrended data sequence that should lie in the pass band of the filter and those that should lie in its stop band. Filters of this nature can be used both for extracting a low-frequency cyclical component of the data and for extracting the seasonal component.

PLL With MAF-Based Prefiltering Stage: Small-Signal Modeling and Performance Enhancement

In three-phase applications, the synchronous reference frame phase-locked loop (SRF-PLL) is a standard PLL, which benefits from a simple structure and satisfactory performance under symmetrical and undistorted grid conditions. Under unbalanced and harmonically distorted conditions, however, it suffers from a very poor performance in the detection of grid voltage parameters. To deal with this challenge, incorporating different filters inside its control loop or before its input has been proposed. Recently, using the moving average filter (MAF) as the SRF-PLL prefiltering stage has been suggested in several works. The MAF is a linear-phase filter that can behave like an ideal low-pass filter under certain conditions. The main aim of this letter is to derive the small-signal model of the SRF-PLL with MAF-based prefiltering stage (briefly called the PMAF-PLL), which has not been presented before. This model enables the designer to simply analyze the stability condition and dynamic behavior of the PMAF-PLL. After developing the model, a simple yet effective modification to enhance the PMAF-PLL performance under frequency varying environments is presented. Finally, the equivalence of the PMAF-PLL and the space-vector Fourier transform-based PLL (SVFT-PLL), which is a well-known PLL in the three-phase applications, is proved. This equivalence implies that the small-signal model of the PMAF-PLL and the method presented to enhance its performance are valid for the SVFT-PLL.

High-Order Moments and Gaussianity of Single-Carrier and OFDM Signals

This paper analyzes the high-order moments of linearly modulated single-carrier as well as orthogonal frequency-division multiplexing (OFDM) signals assuming that the modulated symbols that form resulting continuous signal waveform are statistically independent. For a single-carrier case with the ideal low-pass filter used for pulse shaping, closed-form expressions of the moments with up to the eighth-order are derived. The proposed approach is then extended to Gaussianity assessment of OFDM signals; we show how the moments of OFDM signals can approach to those of Gaussian distribution as the number of subcarriers increases for a given modulation constraint on each subcarrier. Also reported is the peculiar nonstationary behavior of the fourth-order moments in the case of BPSK modulated OFDM signals.

Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM)

Gaussian network model (GNM) and anisotropic network model (ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM (gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method. With this connection, we further introduce two multiscale elastic network models, namely, multiscale GNM (mGNM) and multiscale ANM (mANM), which are able to incorporate different scales into the generalized Kirchhoff matrices or generalized Hessian matrices. We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins. We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. More importantly, we reveal intrinsic multiscale behavior in protein structures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate the benefits of our mGNM through the B-factor predictions of many proteins that fail the original GNM method. We show that the proposed mGNM can also be used to analyze protein domain separations. Finally, we showcase the ability of our mANM for the analysis of protein collective motions.

Ab initio optimization of phonon drag effect for lower-temperature thermoelectric energy conversion

Although the thermoelectric figure of merit zT above 300 K has seen significant improvement recently, the progress at lower temperatures has been slow, mainly limited by the relatively low Seebeck coefficient and high thermal conductivity. Here we report, for the first time to our knowledge, success in first-principles computation of the phonon drag effect--a coupling phenomenon between electrons and nonequilibrium phonons--in heavily doped region and its optimization to enhance the Seebeck coefficient while reducing the phonon thermal conductivity by nanostructuring. Our simulation quantitatively identifies the major phonons contributing to the phonon drag, which are spectrally distinct from those carrying heat, and further reveals that although the phonon drag is reduced in heavily doped samples, a significant contribution to Seebeck coefficient still exists. An ideal phonon filter is proposed to enhance zT of silicon at room temperature by a factor of 20 to ∼ 0.25, and the enhancement can reach 70 times at 100 K. This work opens up a new venue toward better thermoelectrics by harnessing nonequilibrium phonons.

Determination of tailored filter sets to create rayfiles including spatial and angular resolved spectral information

To simulate and optimize optical designs regarding perceived color and homogeneity in commercial ray tracing software, realistic light source models are needed. Spectral rayfiles provide angular and spatial varying spectral information. We propose a spectral reconstruction method with a minimum of time consuming goniophotometric near field measurements with optical filters for the purpose of creating spectral rayfiles. Our discussion focuses on the selection of the ideal optical filter combination for any arbitrary spectrum out of a given filter set by considering measurement uncertainties with Monte Carlo simulations. We minimize the simulation time by a preselection of all filter combinations, which bases on factorial design.

Influence of photoresist layer on unetched guided mode resonance filter

During guided mode resonance (GMR) filter fabrication by ion beam etching in a vacuum chamber, the most difficult aspect of the process is accurate control of the groove depth. Because the resonant wavelength of the GMR filter is susceptible to variations in the fabrication parameters, such as the groove depth and period of the grating, it is very difficult to fabricate an ideal GMR filter that has the same parameters as the designed filter. Therefore, to simplify this fabrication process, a GMR filter that can be fabricated without etching is presented in this paper. However, the proposed filter will bring new problems in that the lack of etching may lead to the presence of a residual photoresist layer during the holographic interferometry process. In this paper, we focus on the influence of the residual photoresist layer on the unetched GMR filter. The effects of different photoresist layer thicknesses and different development times are investigated. Using numerical methods based on rigorous coupled wave analysis (RCWA), the spectral curve shows a blue shift with increasing development time. Moreover, with a constant development time and exposure time, there is a red shift with the increase of the photoresist layer thickness. It is found experimentally that this spectral shift trend agrees well with the results of the theoretical analysis.

GPU-based image method for room impulse response calculation

Room impulse response (RIR) simulation based on the image-source method is widely used in room acoustic research. The calculation of the RIR in computer has to digitalize sound propagation delay into discrete samples. To carefully consider the digitalization error greatly increases the massive computational load of the image-source method. Therefore many real-time audio applications simply round-off the propagation delay to its nearest sample. This approximation, however, especially when the sampling frequency is low, degrades the phase precision that is required by applications such as microphone array. In this paper, by involving a Hanning-windowed ideal low-pass filter to reduce the digitalization error, a more precise image-source model is studied. We analyze its parallel calculation procedure and propose to use Graphics Processing Unit (GPU) to accelerate the calculation speed. The calculation procedure is divided into many parallel threads and arranged according the GPU architecture and its optimization criteria. We evaluate the calculation speeds of different RIRs using a general 5-core CPU, an ordinary GPU (GTX750) and an advanced GPU (K20C). The results show that, with similar precise RIR results, the speedup ratios of GTX750 and K20C over the general CPU can achieve 20 and 120 respectively.