Transfer Function Research Articles

Design of a double-telecentric machine vision lens with continuously adjustable magnification and low distortion

This paper proposes a design method for a double-telecentric machine vision lens with continuously adjustable magnification and low distortion. This applies the zoom principle, the optical focal power distribution condition, and the primary aberration theory of a paraxial optical system to study the solving method for the first-order optical parameters of the initial structure of the optical system, and then optimizes the aberration of the optical system. Finally, a double-telecentric machine vision lens with continuously adjustable magnification and low distortion is obtained, and the optical surfaces of all lenses are designed with a spherical surface. The working wavelength of the lens system is 400–700 nm, the working field of view of the object space is 27.5–18.34 mm, and a magnification ratio of −0.4× to −0.6× is obtained with the photo-detector of a 2/3 in. CCD. The radius of the root mean square of spot diagrams in the full field of view is less than 5.463 µm, and distortion is less than 0.18% in the variation range of the magnification ratio; and the value of the modulation transfer function is greater than 0.28 at the Nyquist frequency of 50 lp/mm, and it is essentially consistent with the diffraction limitation one. The double-telecentric machine vision lens with continuously adjustable magnification designed in this paper has good imaging quality and a reasonable structural design, which can meet the overall and local needs of observing objects at the same time and has good application value for the online detection of small-size defects in industrial production.

Estimating Various Response Spectra from a Fourier Amplitude Spectrum

ABSTRACT Response spectra, including spectral displacement (SD), spectral velocity (SV), and spectral acceleration (SA) are important ground-motion intensity measures. To estimate ground motions in regions lacking earthquake data, a modeling approach based on random vibration theory (RVT) that combines a model of the Fourier amplitude spectrum (FAS) and a ground-motion duration model is an attractive tool. However, this RVT-based approach is currently limited to estimating SD or pseudospectral acceleration. This study aimed to develop a new approach capable of estimating various response spectra from the FAS, including not only SD but also SV and SA, based on the RVT framework. First, equations for estimating SV and SA from the FAS were derived by replacing the oscillator transfer function for SD in the traditional RVT framework with those for SV and SA, respectively. Moreover, two simple modification factors for the duration of the root mean square oscillator response were introduced to ensure that the SV and SA from the RVT approach matched those from the time-series analysis. The proposed approach maintains the use of the RVT framework for estimating SD without introducing any additional concepts; however, it can readily provide three types of response spectra simultaneously.

An Improved Fast Prediction Method for Full-Space Bistatic Acoustic Scattering of Underwater Vehicles

This paper presents an improved rapid prediction method for solving the full-space bistatic scattering sound field of underwater vehicles. The scattering sound field is represented as the product of the acoustic scattering transfer function and the sound source density function. By utilizing target surface mesh information and partial scattered sound pressure data as known inputs, the method predicts other bistatic scattering sound fields through numerical integration, matrix theory, and the least squares method. To reduce the data input required for predicting the scattering field, the monostatic to bistatic equivalence theorem is incorporated into the algorithm. A comparison with simulation results demonstrates that the proposed approach achieves favorable computational efficiency and reliability. Experimental tests on a double-layered ribbed cylindrical shell further validate the method’s performance.

Simplified Model Characterization and Control of an Unmanned Surface Vehicle

This study presents the modeling and control of the unmanned surface vehicle (USV) SABALO. Two models were built, one based on a transfer function matrix and another based on state variables, and from these models, two control strategies were developed. The first strategy is based on independent Proportional-Integral/Proportional-Derivative (PI/PD) controllers complemented by a decoupling system, and the second strategy is based on state variable feedback. The two control strategies were evaluated and contrasted. Results demonstrated that the decoupler effectively eliminated variable interaction, enhancing stability in straight trajectories and directional changes. Meanwhile, state feedback control demonstrated markedly faster response times and superior precision, accompanied by higher energy consumption. The study concludes that both strategies are effective, but their suitability depends on the mission. The decoupler could be ideal for energy-efficient, long-duration operations, while state feedback could be appropriate for dynamic environments requiring rapid maneuvers.

Dual‐Polarized Broadband Laplace Differentiator via Quasi‐Bound States in the Continuum Empowered by Nonlocal Metasurfaces

AbstractDirectly performing optical analog computations and image processing in space, such as optical differential operations and image edge detection, is a burgeoning area. To avoid the bulkiness and low efficiency of traditional 4f filtering systems, one can utilize Green's function and metasurfaces for advanced wavefront control. However, some metasurface differentiators can be hindered by issues like polarization sensitivity, restricted bandwidth, low resolution, and the need for additional polarization devices or digital post‐processing, potentially degrading their performance and operation efficiency. In this work, a dual‐polarization Laplace differentiator is engineered to address these issues based on nonlocal hollow metasurface. The optical transfer function (OTF) required by the Laplace operation can be obtained by exciting different quasi‐bound states in the continuum (Q‐BIC) modes with distinct angular dispersion capabilities under p‐ and s‐polarized illumination, respectively. This Laplace differentiator not only directly realizes 2D second‐order edge detection in a dual‐polarization channel but also features a numerical aperture (NA) with an upper limit close to 0.42 and a broadband range reaching 165 nm. Such an efficient, high‐quality dual‐polarization and bandwidth image edge detection approach offers powerful imaging techniques for applications in machine vision, microscopic imaging, and image processing.

Impact of intraocular lens decentration and tilt on higher-order aberrations in patients with high and super-high myopia following cataract surgery.

To evaluate how intraocular lens (IOL) positioning affects visual quality in patients with high myopia after cataract surgery. This prospective, randomized study was conducted on 50 eyes from 50 patients with high myopia (axial length [AL] >26 mm) or super-high myopia (AL >30 mm) who underwent cataract surgery between October 2022 and October 2023. We assessed the IOL position using optical coherence tomography and measured higher-order aberrations (HOAs) with the iTrace system. Correlations between IOL positioning and visual quality markers were then examined. Follow-up rates were 100% (50 eyes) at 1 month and 84% (42 eyes) at 3 months post-surgery. At 1 month, IOL tilt showed a positive correlation with total HOAs in both the high and super-high myopia groups (rs = 0.449, P = 0.010 and rs = 0.570, P = 0.017). IOL decentration was associated with specific aberrations: coma in the high myopia group (rs = 0.492, P = 0.004) and trefoil in the super-high myopia group (rs = 0.539, P = 0.026). At 3 months, IOL decentration negatively correlated with overall visual quality, measured by the modulation transfer function, in the high myopia group (rs = -0.558, P = 0.001), but no significant correlation was observed in the super-high myopia group. IOL positioning significantly affects visual quality in patients with high myopia after cataract surgery. The effects vary based on the degree of myopia and type of IOL misalignment. These findings emphasize the importance of precise IOL placement to optimize visual outcomes in this patient population.

A Method to Design Efficient Noise Shaping IIR Filters for Delta-Sigma Modulators using Modified Jacobian Chain Functions

An optimized Infinite Impulse Response (IIR) Noise Transfer Function (NTF) with reduced in-band quantization noise particularly for audio applications complying with the realizability and stability criteria required by DSM is realized. A modified Jacobian Chain function (mJCF) polynomial is proposed to shape the passband of a continuous time high pass filter, as it yields Almost Maximally Flat (AMF) passband characteristics but not maximally flat characteristics. The reduced flatness enhances the stopband attenuation of the analog high pass filter and once it is bilinear transformed it is reflected as reduced in-band quantization noise in the NTF. For a fourth-order IIR NTF with a signal bandwidth of 22.05[Formula: see text]KHz and OSR of 32, the proposed polynomial outperforms the existing methods with an SQNR of 62.9302[Formula: see text]dB and achieves a better SNR for a wide range of input amplitude. The SQNR for the psycho-acoustically optimal A-weighted NTF for the proposed polynomial is found to be 68.22[Formula: see text]dB.

Electron transfer function of biochar-EPS complex enhances fungal resistance to copper toxicity: mechanisms of oxidative detoxification and mineralization acceleration.

Electron transfer function of biochar-EPS complex enhances fungal resistance to copper toxicity: mechanisms of oxidative detoxification and mineralization acceleration.

Global Admittance: A New Modeling Approach to Dynamic Performance Analysis of Dynamic Vibration Absorbers

The vibration control in structural design has long been a critical area of study, particularly in mitigating undesirable resonant vibrations using dynamic vibration absorbers (DVAs). Traditional approaches to tuning DVAs have relied on complex mathematical models based on Newtonian or Euler–Lagrange equations, often leading to intricate systems requiring simplification of the analysis of multi-degree-of-freedom structures. This paper introduces a novel modeling approach for analyzing DVAs based on the concept of global admittance, which stems from mechanical admittance and network simplifications. This model streamlines the representation of structures with DVAs as one-degree-of-freedom systems coupled with a global admittance function, which emulates additional damping coupled to the primary structure. In this work, global admittance functions are determined by the independent analysis of the mechanical networks of the DVA, restructuring the process of obtaining the system’s transfer function. The model was validated using different classical DVA configurations, demonstrating total accuracy in its applicability across designs concerning conventional modeling. Our most remarkable finding was that the dimensionless function, γgΩ, resulting from the global admittance, partially decouples the dynamics of the DVAs from the primary structure, facilitating the implementation of passive vibration control strategies in more realistic structural models. Additionally, this work establishes a significant advancement in vibration control analysis, providing a flexible tool for control strategies in real-world structural systems.

BINARY HONEY BADGER ALGORITHM ENHANCED WITH TIME-VARYING SIGMOID TRANSFER FUNCTION AND CROSSOVER STRATEGY

Modeling the foraging behavior of honey badgers, the Honey Badger Algorithm (HBA) is a recently proposed metaheuristic algorithm. In this study, a binary version of this algorithm that was proposed for solving continuous optimization problems was developed. The S-shaped transfer function and crossover strategy were used to transform the continuous algorithm into a binary algorithm. Eight S-shaped transfer functions with constant and time-varying features were used, and the most successful function was determined. Additionally, the effect of time-varying transfer functions was examined. Three strategies, single-point, two-point, and uniform, were applied as crossover strategies, and the uniform strategy, which was more successful than others, was integrated into the algorithm. The binary algorithm (BinHBA) developed in this way was tested on a total of twenty-seven knapsack problems, fifteen small-scale and twelve large-scale. Statistical tests were employed to analyze the results and compare them with methods found in the existing literature. The results showed that the proposed BinHBA for binary optimization problems is effective and preferable.

Global SAR Spectral Analysis of Intermediate Ocean Waves: Statistics and Derived Real Aperture Radar Modulation

Spaceborne synthetic aperture radar (SAR) has been proven capable of observing the directional ocean wave spectrum across the global ocean. Most of the efforts focus on the integrated wave parameters to characterize the imaged ocean wave properties. The newly proposed spectrum-based radar parameter mean cross-spectrum (MACS) is investigated using SAR image spectral properties of range-traveling waves at a wavelength of 20 m, based on Sentinel-1 wave mode acquisition of high spatial resolution (5 m). The magnitude of MACS is documented relative to environmental conditions (wind speed and direction) in terms of its variation for two polarizations at two incidence angles. This parameter exhibits distinct upwind–downwind asymmetry and polarization ratio at two incidence angles (23.8° and 36.8°). In addition, by comparing the SAR measurements with simulated MACS, we derive an improved real aperture radar modulation transfer function. Results obtained in this study shall help obtain a more accurate ocean wave spectrum based on the improved RAR modulations.

Super-resolution imaging using surface plasmon resonance cavity lithography

Surface plasmonic lithography (SPL) utilizes the lateral propagation of light on the surface of metals, which generates transverse excitation, resulting in evanescent waves that participate in imaging to break the diffraction limit. However, a challenge is the difficulty in achieving stable evanescent wave imaging. This paper proposes a surface plasmon resonance cavity lithography (SPRCL) technique, which achieves super-resolution imaging and explores the effects of various factors on this technique. The general dispersion relation for surface plasmon polaritons in a double-resonator cavity structure is derived using traditional electromagnetic field theory. Through optical transfer function (OTF) analysis, the essence of the super-resolution imaging phenomenon is revealed—the OTF of the resonator-based super-resolution imaging system exhibits ultra-high transfer efficiency in the high-frequency evanescent wave region. In the numerical simulation, illumination light with a wavelength of 436 nm and a mask with a period of 740 nm were used to successfully obtain a feature size of 26.4 nm (∼1/17 light wavelength) and period of 52.8 nm. This result is smaller than the resolution of a conventional 193 nm immersion lithography machine. Additionally, at wavelengths of 532 nm and 633 nm, this technique achieved a stripe resolution of less than 1/11 light wavelength, with a lithography contrast greater than 0.9 and a normalized image log-slope (NILS) greater than 1.6. Analysis indicates that parameters including the mask duty cycle, incident angle, SiO2 thickness of the photoresist (PR) upper layer, and PR layer thickness significantly influence super-resolution imaging performance. Notably, the PR layer thickness enables optical resolution tuning without altering metal grating dimensions. Compared with traditional plasmonic lithography, this approach demonstrates enhanced pattern uniformity and contrast while exhibiting improved process robustness against parameter variations.

Modulation transfer function degradation of thermal imaging systems due to platform-induced blurs

The resolution of an infrared system is described by its modulation transfer function (MTF). This metric is traditionally measured in the lab but can also be measured in the field. For sensor systems designed for deployment in the field, it is best to measure the MTF in the field under the conditions it is expected to perform. As it becomes more common to mount infrared sensors on aerial platforms, assessing performance becomes a more complex problem. When the sensor is mounted on a moving airborne platform, the performance of the system is expected to change and degrade. Factors such as mechanical vibrations, flight dynamics, and flight path motions contribute to this degradation. Each platform type, whether a single-rotor helicopter or a multi-rotor UAV, exhibits unique dynamics influencing MTF degradation differently. Special attention must also be placed on the mounting technique to avoid introducing additional vibrations and to dampen vibrations when necessary. The comparison of static and dynamic MTFs aids in the optimization of system design for specific aerial platforms and direct-mounted sensors, ensuring peak performance in operational settings.

Modeling and measurement of lead tip heating in implanted wires with loops.


In MRI, conductive lead tip heating caused by radiofrequency (RF) power deposition is an important safety issue for patients with implanted devices. In this work we investigate lead tip heating in different wire configurations that contain loop(s) through theoretical models and experimental measurements.
Approach:
We have previously proposed analytical transfer function models to predict relative heating of implanted, straight metallic leads. Here we extend the models' application to leads containing loops, that are widely used in the clinic. Maximum temperature rise caused by RF heating was measured at 1.5T on twenty (20) insulated, capped wires with various loop and straight segment configurations. The experimental results were compared with predictions from the previously reported simple exponential and adapted transmission line models, as well as with a long-wavelength approximation.
Main Results:
Both models effectively predicted the trends in lead tip temperature rise for all the wire configurations, with the adapted transmission line model showing superior accuracy. In a typical MRI configuration where the RF electric field is predominantly in the superior/inferior (S/I) direction, wires oriented in the same direction showed decreased heating as the number of loops increased. However, when wires were oriented right/left (R/L) where the corresponding component of the electric field is negligible, additional loops increased the overall heating.
Significance:
The simple exponential and the adapted transmission line models previously developed for, and tested on, straight wires require no additional terms or further modification to account for RF heating in a variety of loop configurations. These results extend the models' usefulness to manage implanted device lead tip heating and provide theoretical insight regarding the role of loops and electrical lengths in managing RF safety of implanted devices.&#xD.

Sinewave-based measurement of Modulation Transfer Function for noisy and distorted images

Sinewave-based measurement of Modulation Transfer Function for noisy and distorted images

Study of average short exposure modulation transfer function in presence of atmospheric turbulence

Study of average short exposure modulation transfer function in presence of atmospheric turbulence

Event‐triggered quantized filtering of discrete descriptor hybrid systems with incomplete information

AbstractThis paper concentrates on the event‐triggered (ET) quantized filter analysis and design of discrete‐time descriptor hybrid network systems with incomplete information. The main contributions of this paper are that quantized parameters and ET filter ones are decoupled, and further, ET filter and ET gain matrices are co‐designed based on filter transfer function equivalent and convex function schemes. Utilizing ET communication and quantized techniques, a singular hybrid model with network‐induced delays is initially derived. According to ET strategy and stochastic Lyapunov function approach, sufficient criteria are then established such that the resulting augmented stochastic model is stochastically admissible with an index. Besides, the derived conditions could be solved in terms of convex optimization method. Ultimately, effectiveness of the proposed approach combined quantization strategy with ET mechanism is verified by two practical examples.

Frequency-band specific directed connectivity networks reveal functional disruptions and pathogenic patterns in temporal lobe epilepsy: a MEG study

This study investigates the network mechanisms of temporal lobe epilepsy (TLE) using MEG data, focusing on directed connectivity networks across different frequency bands. Unlike previous studies that primarily localize epileptogenic zones, this research aims to explore whole-brain network differences between left TLE (lTLE), right TLE (rTLE), and healthy controls (HCs). MEG data from 13 lTLE patients, 21 rTLE patients, and 14 HCs were source-reconstructed to 116 brain regions (AAL116). Directed Transfer Function (DTF) was used to construct directed connectivity networks, followed by networks and graph-theoretical analyses. The results indicate that, compared to HCs, TLE subjects exhibited a significant increase in average connectivity strength in the Low Gamma band. The connectivity patterns across frequency bands in TLE patients were found to be unstable. Both HC and TLE subjects demonstrated left hemisphere lateralization. In the mid-to-low frequency bands, TLE subjects showed increases in global clustering coefficient (GCC), global characteristic path length (GCPL), and local efficiency (LE) compared to HCs, which is attributed to enhanced synchronization between local brain regions in TLE subjects.

A Power-Efficient 50 MHz-BW 76.8 dB Signal-to-Noise-and-Distortion Ratio Continuous-Time 2-2 MASH Delta-Sigma Analog-to-Digital Converter with Digital Calibration

Continuous-time Sigma-Delta (CTSD) Analog-to-Digital Converter (ADC) is widely used in wireless receivers due to its built-in anti-aliasing and resistive input. In order to achieve a wide bandwidth while ensuring low power consumption, this paper proposes a CT 2-2 Multi-stAge Noise-sHaping (MASH) ADC for wireless communication. In order to reduce power consumption, the loop filter adopts a feedforward structure, and the operational amplifier uses complementary differential input pairs and feedforward compensation. The pseudo-random sequence injection and Least Mean Squares (LMS) algorithm are adopted to calibrate the digital noise cancelation filter to match the analog transfer function. The simulation results obtained in 40 nm CMOS show that the presented 2-2 CT MASH ADC achieves a 76.8 dB signal-to-noise-and-distortion ratio (SNDR) at a 50MHz bandwidth (BW) with a 1.6 GHz sampling rate and consumes 29.7 mW power under 1.2/0.9 V supply, corresponding to an excellent figure of merit (FoM) of 169.1 dB.

Wireless propagation parameter estimation with convolutional neural networks

Abstract Wireless channel propagation parameter estimation forms the foundation of channel sounding, estimation, modeling, and sensing. This paper introduces a deep learning approach for joint delay and Doppler estimation from frequency and time samples of a radio channel transfer function. Our work estimates the 2D path parameters from a channel impulse response containing an unknown number of paths. Compared to existing deep learning-based methods, the parameters are not estimated via classification but in a quasi-grid-free manner. We employ a deterministic preprocessing scheme that incorporates a multichannel windowing to increase the estimator’s robustness and enables the use of a convolutional neural network (CNN) architecture. The proposed architecture then jointly estimates the number of paths along with the respective delay and Doppler shift parameters of the paths. Hence, it jointly solves the model order selection and parameter estimation task. We also integrate the CNN into an existing maximum-likelihood estimator framework for efficient initialization of a gradient-based iteration, to provide more accurate estimates. In the analysis, we compare our approach to other methods in terms of estimate accuracy and model order error on synthetic data. Finally, we demonstrate its applicability to real-world measurement data from a anechoic bistatic RADAR emulation measurement.