Parameter Tuning Research Articles

Optimizing prediction accuracy in dynamic systems through neural network integration with Kalman and alpha-beta filters.

In the realm of dynamic system analysis, the Kalman filter and the alpha-beta filter are widely recognized for their tracking and prediction capabilities. However, their performance is often limited by static parameters that cannot adapt to changing conditions. Addressing this limitation, this paper introduces innovative neural network-based prediction models that enhance the adaptability and accuracy of these conventional filters. Our approach involves the integration of neural networks within the filtering algorithms, enabling the dynamic augmentation of parameters in response to performance feedback. We present two modified filters: a neural network-based Kalman filter and an alpha-beta filter, each augmented to incorporate neural network-driven parameter tuning. The alpha-beta filter is enhanced with neural network outputs for its α and β parameters, while the Kalman filter employs a neural network to optimize its internal parameter R and noise factor F. We assess these advanced models using the root mean square error (RMSE) metric, where our neural network-based alpha-beta filter demonstrates a significant 38.2% improvement in prediction accuracy, and the neural network-based Kalman filter achieves a 53.4% enhancement. Hence, our novel approach of integrating neural networks into filtering algorithms stands out as an effective strategy to significantly enhance their performance in dynamic environments.

Grid-search method for short-term over long-term average parameter tuning: an application to Stromboli explosion quakes

The collection of a significant catalogue of seismo-volcanic data involves the selection of relevant parts of raw signals, which can be automatised by using the short-term over long-term average (STA/LTA) method. The STA/LTA method employs the “Characteristic Function” to describe a section of a seismic record in terms of trace amplitude and first-time difference. This function is calculated in a short-term and long-term window; the ratio between the two windows defines a quantity that is controlled through threshold values, i.e., trigger on and trigger off. These threshold values indicate whether there is an increase in the energy in the seismic signal compared to the background noise. The common approach to the selection of the STA/LTA values is the adoption of literature-suggested ones. This could be a limitation as there may be cases in which a choice adapted to a specific raw signal may significantly help in the extraction of the relevant parts. To overcome the possible drawbacks of a non-adaptive choice imposed by such standard literature values, in this study, we propose a methodology for the automatic selection of STA/LTA values that can optimise the extraction of explosion quakes (EQs) from a seismo-volcanic raw signal. The values are obtained through a grid search over an index named quality–numerosity index (QNI) that measures the accordance in the automatic cuts and the consequent number of triggered seismo-volcanic events with the ones suggested by a human expert. The method was applied in the volcano domain for the specific application of the explosion quake signal extraction at Stromboli volcano. The experiments were conducted by selecting a subset of the dataset as training where to search for the best values, which were subsequently adopted in a test set. The results prove that the values suggested by our approach significantly improve the quality of the relevant part compared to the one extracted by adopting the values indicated in the literature. The methodology presented in this study can be applied to a wider typology of signals of volcanic, seismic, and other origin, potentially becoming a widely used approach in parameter optimisation processes.

Electronic correlations in parallel-coupled double quantum dot system: An exact analytical approach

Exact eigenstates of the parallel coupled double quantum dots attached to the non-interacting leads taken in the zero-bandwidth limit (effectively quadruple quantum dots) are analytically obtained in each particle and spin subspace. The ground state of the half-filled system is determined within a four-dimensional subspace of the twenty-dimensional Hilbert space. The effects of tunable parameters, such as quantum dot energy levels, interdot tunneling, ondot and interdot Coulomb interactions, on spin-spin correlation and dot occupancies are analyzed. In the parameter space defined by interdot tunneling and ondot Coulomb interaction, the tunnel-coupled dots exhibit both ferromagnetic and antiferromagnetic correlations, suggesting suitability for singlet-triplet qubits in quantum computing. A critical dependency of interdot tunneling on ondot Coulomb interaction leads to a transition from ferromagnetic to antiferromagnetic correlation as interdot tunneling increases. These correlations persist even without interdot tunneling via indirect exchange through the leads, with the interdot Coulomb interaction significantly influencing this dependency.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

Optimal wideband digital fractional-order differentiators using gradient based optimizer

In this paper, we propose a novel optimization approach to designing wideband infinite impulse response (IIR) digital fractional order differentiators (DFODs) with improved accuracy at low frequency bands. In the new method, the objective function is formulated as an optimization problem with two tuning parameters to control the error distribution over frequencies. The gradient based optimizer (GBO) is effectively employed on the proposed objective function. A wide range of design examples are presented to illustrate the effectiveness of the proposed approach. The proposed approximations are compared to those of recent literature in terms magnitude, phase, and group delay errors. The result reveal that our method can attain approximations have a favorable low frequency performance (with about 60% of relative magnitude error reduction) and maintain a comparable accuracy at most of the Nyquist band to those of the existing ones. Thus, our approximations can be attractive for low frequency applications.

Filter‐Based Average Dwell‐Time Tuning Approach for Adaptive Prescribed‐Time Tracking of Uncertain Switched Nonlinear Systems

ABSTRACTThis paper addresses neural‐network‐based adaptive prescribed‐time (PT) tracking for uncertain switched systems with unmatched nonlinearities. A continuously switched adaptive tuning mechanism for neural network learning is developed by applying the average dwell time (ADT). First, a neural‐network‐based PT tracking control design strategy using the ADT‐based adaptive tuning mechanism is established for switched nonlinear systems in strict‐feedback form. A novel adaptive dynamic surface controller is designed recursively using a practical finite‐time scaling function and continuously switched tuning parameters. The switched adaptive tuning laws for neural networks are structured to reduce the conservatism associated with common adaptive laws. Then, a filter‐based tuning approach is employed to ensure the continuity of switched adaptive parameters with ADT in the designed controller. The practical PT stability of the closed‐loop system is demonstrated based on the boundedness of the adaptive parameters. Building upon this foundation, the proposed PT design approach is extended to control switched pure‐feedback nonlinear systems, even in cases where control directions are unspecified. The unknown sign problem encountered with switched virtual and actual control coefficient functions is resolved in the PT control framework. It is shown that the PT performance bound of the tracking error can be reduced by selecting the design parameter of the scaling function. Finally, simulation results illustrate the merits of the proposed theoretical approach.

Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Drip Irrigation Network System Based on the Jaya Algorithm

To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm2, a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks.

Adaptive anti-unwinding attitude stabilisation with predefined time for rigid spacecraft

In this paper, the problem of attitude stabilisation without unwinding is investigated for rigid spacecraft. To guarantee predefined-time convergence and unwinding-free performance of the closed-loop system, a predefined-time sliding mode function with adjustable parameters is proposed. This sliding mode function contains two equilibrium points. When the states of the closed-loop system are on the sliding mode surface, they converge to the equilibrium point near the initial states. Based on this sliding mode function, an adaptive attitude stabilising control law with tuning parameters is designed to compensate the external disturbances and inertia uncertainty. The presented control law guarantees that the settling time of the closed-loop system and the bounds of the system states can be explicitly determined in advance by adjusting parameters of the sliding mode function and the control law. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented control law. The simulation results illustrate that the predefined-time convergence and unwinding-free property of the closed-loop system are guaranteed by adopting the presented control law.

Data-driven predictor of control-affine nonlinear dynamics: Finite discrete-time bilinear approximation of koopman operator

This paper introduces a novel and efficient data-driven approach for approximating a finite discrete-time bilinear model of control affine nonlinear dynamical systems with a tunable parameter that balances model dimension and prediction accuracy. An approximation of the Koopman operator based on the evolutions of the nonlinear system measurements used to lift a control-affine nonlinear system to a higher dimensional model. However, higher dimensional spaces can result in a long learning time and the curse of dimensionality in control analysis. The proposed approach addresses these challenges by introducing a convex optimization which identifies informative observable functions. This technique allows for the adjustment of a parameter to strike a balance between model dimension and accuracy in prediction. The main contribution of this study is to introduce a reduced dimensional bilinear model for a nonlinear complex system. This achievement is made possible by implementing convex sparse optimization, enabling the exploration of informative estimated Koopman eigenfunctions while minimizing the number of system measurements required. The optimization problem is solved using the alternating direction method of multipliers. The effectiveness of the proposed method is evaluated on three different nonlinear systems: a numerical nonlinear system, a Van der Pol oscillator, and a Duffing oscillator. In the last simulation, an estimation of the Koopman linear model is considered as a special case, and the policy iteration algorithm is employed to evaluate optimal control designed for different reduced-dimensional models.

An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters.

This study aimed to predict temporomandibular disorder (TMD) using machine learning (ML) approaches based on measurement parameters that are practically acquired in clinical settings. 125 patients with TMD and 103 individuals without TMD were included in the study. Pain intensity (with visual analog scale), maximum mouth opening (MMO) and lateral excursion movements (with millimeter ruler), cervical range of motion (with goniometer), pressure pain threshold (PPT; with algometer), oral parafunctional behaviors (with Oral Behaviors Checklist), psychological status (with Hospital Anxiety and Depression Scale), and quality of life (with Oral Health Impact Profile) were evaluated. The measurements were analyzed via over 20 ML algorithms, taking into account an extensive parameter tuning and cross-validation process. Results of variable importance were also provided. Bagging algorithm using Multivariate Adaptive Regression Spline (MARS) algorithm (accuracy = 0.8966, area under receiver operating characteristic curve = 0.9387, F1-score = 0.9032) was the best performing model regarding the performance criteria. According to this model, the 5 most important variables for predicting TMD were pain intensity, MMO, lateral excursion and PPT values of masseter and temporalis anterior muscles, respectively. The Bagging algorithm using the MARS algorithm is a robust model that, in combination with clinical parameters, assists in the detection of patients with TMD in settings with limited capabilities. The clinical parameters and ML algorithm proposed in this study may assist clinicians inexperienced in TMD to make a preliminary detection of TMD in clinics where diagnostic imaging tools are limited.

Enhancing Parameters Tuning of Overlay Models with Ridge Regression: Addressing Multicollinearity in High-Dimensional Data

The extreme ultraviolet (EUV) photolithography process is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary least squares (OLS) is the most widely used method for parameters tuning of overlay models, but in most cases it fails to compensate for such phenomena. In this paper, we propose the usage of ridge regression, a widely known machine learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method was applied in perturbed data from a 300 mm wafer fab, and the results show reduced residuals when ridge regression is applied instead of OLS.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models

Effective model evaluation is crucial for robust machine learning, and cross-validation techniques play a significant role. This study compares Repeated k-folds Cross Validation, k-folds Cross Validation, and Leave-One-Out Cross Validation (LOOCV) on imbalanced and balanced datasets across four models: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Bagging, both with and without parameter tuning. On imbalanced data without parameter tuning, Repeated k-folds cross-validation demonstrated strong performance for SVM with a sensitivity of 0.541 and balanced accuracy of 0.764. K-folds Cross Validation showed a higher sensitivity of 0.784 for RF and a balanced accuracy of 0.884. In contrast, LOOCV achieved notable sensitivity for RF and Bagging at 0.787 and 0.784, respectively, but at the cost of lower precision and higher variance, as detailed in Table 1. When parameter tuning was applied to balanced data, the performance metrics improved. Sensitivity for SVM reached 0.893 with LOOCV and balanced accuracy for Bagging increased to 0.895. Stratified k-folds provided enhanced precision and F1-Score for SVM and RF. Notably, processing times varied significantly, with k-folds being the most efficient with SVM taking 21.480 seconds and Repeated k-folds showing higher computational demands where RF took approximately 1986.570 seconds in model processing, as shown in Table 4. This analysis underscores that while k-folds and repeated k-folds are generally efficient, LOOCV and balanced approaches offer enhanced accuracy for specific models but require greater computational resources. The choice of cross-validation technique should thus be tailored to the dataset characteristics and computational constraints to ensure optimal model evaluation.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

A computer algorithm for scoring cow teats based on udder images

ABSTRACT In this work, we aimed to develop a computer algorithm for detecting and scoring cow teats according to udder images. In addition, by assessing three teat traits, i.e. the length, thickness, and placements, we scored them according to international standards. By applying the algorithm to 71 cows, we were able to detect all four teats in 62 of 71 (85%) cow udder images, and differentiate the front and rear teats in 56 of 62 (92%) udder images. Based on the three teat traits, we scored the teats of 56 cows. The final teat score was given by averaging the three scores for each cow. We discuss the limitations of the algorithm, and also the issues with teat conformation and image-taking itself, e.g. teats not facing perpendicular to the camera. In conclusion, we have developed the first computerised teat scoring system based on cow udder images. Further improvements require tuning parameters of the workflow and the image-taking process.

Data-driven room acoustic modeling via differentiable feedback delay networks with learnable delay lines

Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a feedback delay network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics and outperforms existing methods based on genetic algorithms and analytical FDN design.

Deep Learning-Based Segmentation and Prediction of Liver Cancer Using CT Images

Background: Liver cancer presents a significant global health challenge, particularly in Asia, due to high incidence rates and the difficulty of early detection. Despite advances in medical imaging, the accurate segmentation and prediction of liver tumors remain critical for effective treatment planning. Deep-learning is a revolutionary tool in medical imaging, offering promising solutions for automated analysis of liver cancer using CT images. Methods: This study develops a framework based on deep-learning- for the segmentation, and prediction cancer of the liver from Computed Tomography images. The framework leverages advanced convolutional neural network (CNN) architectures, including U-Net and its variants, optimized with class balancing methods. The model is trained and authenticated using a high-quality annotated images, dataset of liver CT images. Key techniques such as transfer learning, data augmentation, and hyper parameter tuning are employed to enhance model performance. Evaluation metrics include accuracy, sensitivity, specificity, Dice-coefficient, and (IoU).Intersection over Union Results: The design of proposed program achieved significant improvements in segmentation, accuracy, with an average Dice, Similarity-Coefficient (DSC) of 0.96 for liver segmentation and 0.84 for the tumor segmentation. The results demonstrate the superior performance of the model's compared to the other traditional methods and other existing deep learning models. Cascaded fully convolutional networks (FCNs) combined with 3D Conditional Random Fields (CRFs) showed robust performance, achieving true value accuracy rates of approximately 99.55% for liver segmentation. Conclusion: This study presents a novel and effective deep learning framework for liver cancer segmentation and prediction, highlighting the clinical applicability and relevance of the developed models in real-world medical settings. The findings highlight the potential in deep-learning for enhancing liver cancer diagnosis, facilitating early detection, timely interventions, and improved patient outcomes. This research contributes in increasing the body of knowledge in medical image analysis, enlighten the way for future advancements in the field. Keywords: Computed Tomography, Deep Learning, Machine Learning, U-Net, 3D, Segmentation, Fully Convolutional Networks, Conditional Random Fields, Dice Similarity Coefficient, Intersection over Union, Conventional Neural Network

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. The experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.

Enhancing circular dichroism with anisotropic heterogeneous-structure based on MQBIC resonance

Metasurfaces based on quasi-bound states in the continuum (QBIC) typically require careful tuning of structural parameters to meet critical coupling condition, thereby maximizing circular dichroism (CD). In this study, we propose an innovative heterogeneous-structure comprising four pairs of symmetry-breaking silicon dielectric rods embedded in an anisotropic polymer. The resonance of magnetic QBIC (MQBIC) within the dielectric rods generates a CD response, while the polymer effectively modulates the chirality of the heterogeneous-structure and enhances the CD. This enhancement occurs because the resonance of the MQBIC in the diagonal direction of the heterogeneous-structure is confined, leading to a break in mirror symmetry. Further investigations reveal that even when critical coupling condition are not satisfied, CD can still be maximized through rotational modulation. Additionally, the critical coupling condition for the resonance of MQBIC in the heterogeneous-structure are more readily achievable, allowing the CD response to remain unconstrained by lattice constants. This research is anticipated to have a significant impact on the fields of biosensing and medical imaging in the future.