Rectangular Window Research Articles

Line Clipping in 2D: Overview, Techniques and Algorithms.

Clipping, as a fundamental process in computer graphics, displays only the part of a scene which is needed to be displayed and rejects all others. In two dimensions, the clipping process can be applied to a variety of geometric primitives such as points, lines, polygons or curves. A line-clipping algorithm processes each line in a scene through a series of tests and intersection calculations to determine whether the entire line or any part of it is to be saved. It also calculates the intersection position of a line with the window edges so its major goal is to minimize these calculations. This article surveys important techniques and algorithms for line-clipping in 2D but it also includes some of the latest research made by the authors. The survey criteria include evaluation of all line-clipping algorithms against a rectangular window, line clipping versus polygon clipping, and our line clipping against a convex polygon, as well as all line-clipping algorithms against a convex polygon algorithm.

Полосно-пропускающий фильтр на прямоугольном волноводе с плоскопродольными тонкими диафрагмами

In modern microwave systems, a wide variety of frequency-selective devices are used, which are performed according to certain technologies. Waveguide microwave filters are currently a rapidly developing class of functional devices. The variety of manufacturing technologies, calculation methods and designs of such filters is great. In this paper, we consider a band-pass filter on a rectangular waveguide with a cross section of 35×15 mm, in which, instead of the classic thin plane-transverse diaphragms, plane-longitudinal diaphragms are used. Four classical rectangular windows are used as a resonant window, which makes it possible to conduct electrodynamic analysis and show good convergence of the calculation and simulation results. Calculations were performed using the computer-aided design environment CST Studio.

Evidence for prolate-oblate shape coexistence in the odd- ABr383573 nucleus

The excited states in $^{73}\mathrm{Br}$ nucleus have been investigated through the fusion evaporation reaction $^{50}\mathrm{Cr}(^{28}\mathrm{Si}$, $\ensuremath{\alpha}p)^{73}\mathrm{Br}$ at a beam energy of 90 MeV using the Indian National Gamma Array. The $\ensuremath{\gamma}\text{\ensuremath{-}}\ensuremath{\gamma}$ coincidence technique has been used to add eight new $\ensuremath{\gamma}$-ray transitions in the level scheme. The mixing ratio of $\mathrm{\ensuremath{\Delta}}I=0$ (mixed with $E2$ and $M1$) transitions have been determined using angular distribution and ${R}_{\mathrm{DCO}}$-polarization measurement. The half-life of the ${9/2}^{+}$ isomeric state has been measured to be ${\ensuremath{\tau}}_{1/2}=52(2)$ ns from the variation in the intensity of delayed $\ensuremath{\gamma}$-ray transition as a function of coincidence time window. The two state mixing model calculations were performed to obtain the mixing amplitude, and mixing interaction of two different configurations of $^{73}\mathrm{Br}$. The calculated mixing amplitudes along with the deformations of two different configurations provide the monopole transition strength ${\ensuremath{\rho}}^{2}(E0)$ for Se, Br, and Kr isotopes in a semiempirical approach. These results support a prolate-oblate shape coexistence in the odd-$A\phantom{\rule{4pt}{0ex}}^{73}\mathrm{Br}$ nucleus. The observed structural properties have been discussed in terms of projected shell model calculations.

Дослідження характеристик резонансної системи на основі прямокутного хвилеводу з двома діафрагмами

Microwave methods for the study and control of the dielectric properties of substances using regular and irregular rectangular metal waveguides have many applications and are of great practical interest. The dielectric properties of materials are assessed based on the determination of the reflection and transmission coefficients in a resonant system. This article considers resonant phenomena of electromagnetic oscillations in a prismatic cavity based on a length of a rectangular waveguide with two diaphragms and with a dielectric specimen in the form of a cylinder centrally positioned between them. The goal of this work is to evaluate the resonant properties of a prismatic cavity based on a length of a rectangular waveguide with two diaphragms as a function of the geometrical dimensions of the rectangular diaphragm windows, the distance between the diaphragms, and the dimensions and dielectric properties of the specimen located at the center of the waveguide length. By computer simulation, the resonant properties of the system were determined as a function of the dielectric constant and diameter of the specimen and the geometrical dimensions of the diaphragm windows. It was found that for each value of the specimen diameter there exists a maximum allowable value of the dielectric constant at which the resonant properties of the system are kept in a selected operating frequency range. The effect of the height of the diaphragm windows on the resonant frequency of a prismatic cavity and the reflection and transmission coefficients was studied, and the behavior of the intrinsic Q factor was assessed qualitatively. A minimum possible diaphragm window height was found such that both the required coupling between the rectangular cavity and the external microwave line is provided and the maximum value of the loaded Q factor in the absence of a dielectric specimen is kept. The results of this work may be used in the design of high-frequency sensors for controlling the dielectric properties of materials in various industries.

When is a firearm suppressor like a hearing protector?

High-level impulse noise exposure from small caliber firearms presents a significant risk of noise induced hearing loss (NIHL) for an unprotected ear. The most common method to reduce the level of noise is to provide hearing protection to both the shooter(s) and potential observers such as firing range instructors or bystanders. Not all hunters and shooters consistently use correctly fit hearing protection. Firearm suppressors provide an engineering noise control that can mitigate a significant portion of the NIHL risk. In this paper, potential analyses for the noise reduction of a firearm suppressor will be presented. The impulse insertion loss as a function of the measurement analysis time window for A-weighted, C-weighted, and unweighted sound pressure levels will be presented. This approach will be contrasted with the ANSI S12-42 standard for measuring impulse peak insertion loss for hearing protection devices.

Asymptotic approximation of the likelihood of stationary determinantal point processes

AbstractContinuous determinantal point processes (DPPs) are a class of repulsive point processes on with many statistical applications. Although an explicit expression of their density is known, it is too complicated to be used directly for maximum likelihood estimation. In the stationary case, an approximation using Fourier series has been suggested, but it is limited to rectangular observation windows and no theoretical results support it. In this contribution, we investigate a different way to approximate the likelihood by looking at its asymptotic behavior when the observation window grows toward . This new approximation is not limited to rectangular windows, is faster to compute than the previous one, does not require any tuning parameter, and some theoretical justifications are provided. It moreover provides an explicit formula for estimating the asymptotic variance of the associated estimator. The performances are assessed in a simulation study on standard parametric models on and compare favorably to common alternative estimation methods for continuous DPPs.

Accurate Frequency Estimator for Real Sinusoid Based on DFT

An accurate frequency estimator for real sinusoid based on Discrete Fourier Transform (DFT) is proposed. The proposed estimator is based on the interpolation of the maximum DFT spectral line and two Discrete-Time Fourier Transform (DTFT) spectral lines and can operate with both the rectangular window and the maximum sidelobe decay (MSD) window. In the coarse estimation step, the proposed estimator with the MSD window is used. According to the value of the coarse estimate, the negative frequency spectral component is removed. In the fine estimation step, the proposed estimator with the rectangular window is utilized to achieve the Cramer–Rao lower bound (CRLB). Simulation results show that the performance of this algorithm is better than that of the AM algorithm, Candan algorithm, Djukanovic algorithm, and FDIAM algorithm.

A solution for the greedy approximation of a step function with a waveform dictionary

In this paper we consider a step function characterized by an arbitrary sequence of real-valued scalars and approximate it with a matching pursuit (MP) algorithm. We utilize a waveform dictionary with rectangular window functions as part of this algorithm. We show that the waveform dictionary is not necessary when all of the scalars are either non positive or non negative and the parameters of a wavelet dictionary on an integer lattice yields a closed-form solution for the initial optimization problem as part of the MP. Additionally, for any real-valued scalar sequence, we provide a solution with a related wavelet dictionary at each iteration of the algorithm. This allows for practical calculation of the approximating function, which we use to provide examples on simulated and real univariate time series data that display discontinuities in its underlying structure where the step function can be thought of as a sample from a signal of interest.

Motion Video Image Based on Sensor and Multiprocessing Technology in Track and Field Sports

In order to use motion video image processing technology to better guide and evaluate athletes’ technical and tactical ability, this paper first constructed a motion video image acquisition system. Secondly, a fuzzy kernel estimation method for uniform linear motion based on cepstrum property is proposed. Then, the motion blur areas to be processed are selected, and only these areas are deblurred. Finally, the effective removal of local motion blur is realized, and a clear scene image is obtained. Then, the maximum value of the total entropy of the image is calculated by using the information entropy theory, and the particle swarm optimization algorithm is introduced to find the maximum threshold of image segmentation. Finally, small wave optical flow estimation algorithm and rectangular window scanning algorithm across scales of motion image target detection algorithm are proposed, to not only solve the traditional optical flow estimation for fast moving object detection accuracy but also improve the efficiency of the optical flow computation. Compared to many kinds of algorithms, this paper proposed an algorithm that can improve the accuracy of moving target detection and measurement accuracy. And, the detection accuracy of the proposed algorithm is up to 86.5%. The estimated accuracy was as high as 65%. The segmentation accuracy is up to 95%.

Design of Portable Wireless Fall Monitoring and Warning System

Aiming at preventing the old man falling down in the field of study, this article studies the status quo, focusing on sitting down research and analyze the behavior characteristic, this paper proposes a portable wireless fall monitoring and alarm system scheme design and manufacture, the conventional time-domain motion data multilevel threshold detection algorithm combined with combined with frequency domain characteristics of machine learning classification has realized the real-time identification of fall behavior. The time domain data used rectangular window filter to preserve the critical time series of behavior occurrence, fully considering the time characteristics of data changes, and completed the detection of lying posture and falling and the exclusion of daily low-intensity activities of the elderly. In the frequency domain, Discrete Fourier Transform (DFT) was applied to the combined acceleration data of sitting posture fall and moderate intensity activity, and the Support Vector Machine (SVM) classifier model was established by using frequency domain features. Complete the recognition and detection of sitting posture fall, and complement the detection of lying posture fall. After the decision of falling is made, the information of falling will be sent to the guardian of the elderly in time through wireless communication module, and the on-site alarm will be made at the same time, so that the elderly can get help from others in time when there are people nearby.

Side lobe level reduction of metasurface transmit array

This paper presents a study on the side-lobe level (SLL) reduction with transmission type metasurface (MTS) using the concept of non-uniform array technique and transmit array antenna. According to the non-uniform antenna array technique, the SLL can be controlled by controlling the amplitude and phase distribution simultaneously. Initially, transmit array concept is used to design the MTS. The radiation characteristics of the MTS antenna are analyzed by exciting with a rectangular patch antenna at a fixed feed position. The simulation results show a gain of 17.6 dB with SLL of −18.3 and −19 dB in ϕ = 0° and 90°-planes, respectively. Later, non-uniform array techniques are used to reduce the SLL by introducing various amplitude distribution functions on the MTS for a fixed focal distance. The MTS incorporates the amplitude tapering functions such as Kaiser, Hamming, Hanning, and Blackman distribution functions. The resulting SLLs are analyzed, and the achieved SLLs are far from the theoretical values. In order to further reduce and control the SLL, the Gaussian distribution function is used, which minimizes the SLL by optimizing the standard deviation(σ). A MTS with 16 × 16 unitcells is fabricated and measured with an optimized σ = 0.31. The measured SLL are −25.1 and −26.3 dB in ϕ = 0° and 90°-planes, respectively.

Credibility evaluation system for The Internet of Things: Impact and Implications for Health Care Delivery

Due to the imperfection of the technology of speech recognition embedded in the Internet of things devices, the evaluation results of the credibility evaluation system for speech recognition control of the Internet of things devices have errors, so a credibility evaluation system for the Internet of things devices based on speech recognition technology is designed. First of all, the hardware structure of the system is designed, the MCU main control chip is developed, and the system hardware is optimized with the help of MCU. In the software design, the digital filter is used for speech signal preprocessing, the rectangular window and Hamming window are used to frame and add windows to the speech, the function related gate is introduced to divide the fault events and basic events, and the credibility evaluation system of Internet of things equipment based on speech recognition technology is completed. The performance test results show that compared with the traditional system, the average recognition rate of the designed system for different lengths of instructions is improved.

Machine learning advances the classification and prediction of responding from psychophysiological reactions

Psychological responding to stressful situations such as experiential avoidance (EA) is central to the development of several psychopathologic conditions, yet it tends to be assessed solely via retrospective self-report. The purpose of this study is to utilize machine-learning algorithms to delineate and predict individuals classified as high vs. low in EA across stress-inducing situations based on their psychophysiological signals. Three samples (total N = 212) arising from previous research included 43 smokers, 86 females at high-risk for eating disorders, and 83 anxious individuals. Each of the studies involved a stress induction laboratory experiment. Supervised algorithms were used to compare the ability of physiological signals (skin conductance (SCR), heart rate variability (HRV), and facial electromyography (fEMG)) in classifying both individuals presenting with high vs. low EA (using common self-report measures) and situations. Overall accuracy of the Rectangular Window Methodology (RWM) model was 77.8%, with prediction of 88.9% accuracy of participants being high in EA. The features of SCR, fEMG and HRV accurately and stably classified individuals characterized by dispositional high EA as they navigated stressful situations. This study is the first to use machine learning to delineate and predict high vs. low EA in individuals and situations from psychophysiological signals. These parameters can be used for future real-time assessments and predictions.

Design and implementation of two-dimensional digital finite impulse response filter using very high speed integrated circuit hardware description language

<span>The main purpose of this paper is to design a two-dimensional digital finite impulse response (FIR) filter using data broadcast and non-broadcast structure. The implementation of two-dimensional digital FIR filter is done using very high speed integrated circuit hardware description language (VHDL). Rectangular window method is used for calculating 2D digital FIR filter coefficients for data broadcast and non-broadcast structure. The coefficients of the one-dimensional digital FIR filter are obtained using the MATLAB filter design and analysis (FDA) tool for two different cut-off frequencies and are multiplied to get the necessary coefficient for the two-dimensional FIR filter to be designed; the simulation is done on Artix-7 series field programmable gate array (FPGA), target device (xc7a35t-cpg236) using Vivadov.2015.2. The proposed design reduces the area utilization and the power consumption when compared with the existing literature. The experimental result shows that the power consumption is improved by 97% and there is an improvement of 24% in area utilization for the two-dimensional with and without data broadcast one dimensional FIR filter structures.</span>

Kernel-Windowed SDFT Based Frequency-Locked Loop for Capacitance Sensing on an FPGA

Sliding discrete Fourier transform with kernel windowing technique is proposed for frequency estimation of periodic signals that can be applied to capacitance sensing. SDFT is an efficient technique to compute <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -point DFT of a signal by sliding the rectangular window one sample at a time. In the proposed work Hanning and Blackman windows replace rectangular window of SDFT to reduce the spectral leakage and increase the mainlobe width of the spectra. The concept of increased mainlobe width of the SDFT spectra has been exploited to increase the operating range of the frequency-locked loop. Kernels of these windows have been computed with certain coefficients and incorporated in the FLL to increase the operating range which increases the estimation input signal frequency. Designed FLL with Hanning window having bin index <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k} = 1$ </tex-math></inline-formula> estimates frequency from 4 to 56 kHz while with Blackman window estimates frequency from 6 to 75 kHz. The proposed scheme with kernel windowing has been implemented in FPGA and verified for enhanced performance. The experimental investigation on the proposed technique is validated for discrete capacitance sensing with maximum frequency estimation error of 0.054 and liquid level measurement of 0 - 13.5 cm with a sensitivity of 4.435 pF/mm. This method provides wide range of frequency estimation which results in wide range of capacitance measurement.

Implementation of a System for Assessing the Quality of Spoken English Pronunciation Based on Cognitive Heuristic Computing.

This paper analyzes and investigates the quality assessment of spoken English pronunciation using a cognitive heuristic computing approach and designs a corresponding spoken pronunciation quality assessment system for practical training. Using the general Goodness of Pronunciation assessment algorithm as a benchmark, the shortcomings of the traditional Goodness of Pronunciation method are explored through statistical experiments, and the validity of the overall posterior probability output from the speech model for pronunciation quality assessment is verified. For the analysis of rhythm, there is no common algorithm framework, but in this paper, the F0 similarity algorithm based on dynamic time regularization and the stop similarity algorithm based on forced alignment is proposed for the two main factors of rhythm, intonation, and pause, respectively. After framing, the Hamming window processing is used to make the signal smoother, reduce the side lobe size after fast Fourier transform processing, and solve the problem of spectrum leakage. Compared with the ordinary rectangular window function, the Hamming window can obtain a higher quality spectrum. And combined with CTC for speech recognition modeling, the recognition rates are comparable in the case of using BLSTM and bidirectional threshold cyclic unit BGRU as the hidden layer unit, respectively, and the training time is 23% less than BLSTM using BGRU; in addition, the BGRU-CTC model is improved by using a 2-BGRU-CTC model with 256 hidden layer nodes, so that the error rate of phoneme recognition is reduced to 33%. The effectiveness of the algorithm framework is also verified through experiments, which further proves the effectiveness of our proposed phoneme segment feature and rhyme similarity algorithm.

Depth-resolved dispersion compensation method for optical coherence tomography imaging based on rectangular window function optimization

In the optical coherence tomography system, the mismatch in dispersion between the two interference arms will reduce the axial resolution of the system. Therefore, dispersion compensation is required. However, the current depth-resolved dispersion compensation method uses a fixed window function to extract signals at different depths, which has phase calculation error and reduces the accuracy of dispersion compensation. In this paper, we optimized the rectangular window function for the signal interception. According to the functional relationship between the phase and the wave number in the presence of dispersion, the second-order fit to the obtained phase is performed and the standard error of the fit result is used as the criterion. The optimized rectangular window is used to intercept the signal and perform FFT, thereby effectively reducing the phase error caused by the window function. And we accurately extracted the dispersion coefficients of samples to improve the accuracy of dispersion compensation.

Signal detection with spectrum windows

Spectrum sensing is needed in frequency agile cognitive radio systems, cognitive radar systems as well as in (cognitive) electronic warfare systems. This paper studies window based detectors where window shapes can be matched for the expected spectrum. The provided analytical results for the probabilities of false alarm and detection are valid for arbitrary windows. Based on the window shape mismatch analysis, it was concluded that the simple rectangular window is often sufficient if its bandwidth is properly set. Furthermore, based on the window bandwidth mismatch analysis, it was discussed how many different rectangular windows are needed to cover the instantaneous analysis bandwidth of the receiver without compromising too much on sensitivity. Finally, suitable constant false alarm rate threshold setting schemes were compared. It was observed that almost ideal setting schemes exist.

A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand.

The signals from electromyography (EMG) have been used for volitional control of robotic assistive devices with the challenges of performance improvement. Currently, the most common method of EMG signal processing for robot control is RMS (root mean square)-based algorithm, but system performance accuracy can be affected by noise or artifacts. This study hypothesized that the frequency bandwidths of noise and artifacts are beyond the main EMG signal frequency bandwidth, hence the fixed-bandwidth frequency-domain signal processing methods can filter off the noise and artifacts only by processing the main frequency bandwidth of EMG signals for robot control. The purpose of this study was to develop a cost-effective embedded system and short-time Fourier transform (STFT) method for an EMG-controlled robotic hand. Healthy volunteers were recruited in this study to identify the optimal myoelectric signal frequency bandwidth of muscle contractions. The STFT embedded system was developed using the STM32 microcontroller unit (MCU). The performance of the STFT embedded system was compared with RMS embedded system. The results showed that the optimal myoelectric signal frequency band responding to muscle contractions was between 60 and 80 Hz. The STFT embedded system was more stable than the RMS embedded system in detecting muscle contraction. Onsite calibration was required for RMS embedded system. The average accuracy of the STFT embedded system is 91.55%. This study presents a novel approach for developing a cost-effective and less complex embedded myoelectric signal processing system for robot control.

Optimum Extrapolation Techniques for Two-Dimensional Antenna Array Tapered Beamforming

Optimizing antenna arrays is essential for achieving efficient beamforming with very low sidelobe level (SLL) where adopting tapered window functions is one of the straightforward efficient techniques for achieving this goal. Recently, two-dimensional (2D) beamforming has been extensively required for many applications; therefore, this paper proposes two extrapolation techniques applied to one-dimensional (1D) tapered functions to efficiently feed 2D antenna arrays using cross-linear and adaptive radial tapering techniques. The first proposed 2D cross-linear tapering technique determines the 2D tapering coefficients by Hadamard multiplication of two right-angled grids of repeated 1D functions, while the second proposed adaptive radial tapering technique locates the antenna element in the 2D array in terms of its radial distance with respect to the array center, then converts this distance to an element index in a virtual 1D tapering window to determine the element weighting value. The adaptive radial tapering technique is optimized for achieving the minimum SLLs. The two proposed techniques are analyzed and discussed, where it is found that the adaptive radial tapering provides deeper SLLs compared to the cross-linear tapering technique. The two extrapolation techniques are examined for four window functions including triangular (Bartlett), Hamming, cosine-square, and Blackman windows, and the simulation results show that for extrapolating the Blackman window using adaptive radial tapering, a –50 dB SLL can be achieved which is independent on the array size, while cross-linear tapering provides –35 dB and –41 dB SLLs for and antenna arrays, respectively.