Highly integrable planar-structured printed circularly polarized antenna for emerging wideband internet of things applications in the millimeter-wave band.

This paper proposes a numerically and experimentally validated printed wideband antenna with a planar geometry for Internet of Things (IoT) applications. This design tackles the challenges associated with deploying IoT sensors in remote areas or across extensive geographical regions. The proposed design exploits a coplanar-waveguide-fed modified microstrip line monopole for excitation of circularly polarized waves radiating in the broadside direction. The primary design is based on perturbations of the microstrip line protracted from a grounded coplanar waveguide. The capacitively coupled short rectangular stubs are periodically inserted alternately and excited asymmetrically on each side of the microstrip line parallel to the direction of the electric field vector. The sequential phase excitation of the periodic stubs generates a rectangular-cascaded electric field, which suppresses the stop band at the open end. As a result, the antenna radiates in the broadside direction. The impedance bandwidth of the antenna exceeds 8 GHz in the 28 GHz mm-wave band, i.e., it ranged from 25 to 33.5 GHz. Additionally, an axial ratio below 3 dB is achieved within the operating band from 26 to 33.5 GHz with the alterations of the surface current using straightforward topological adjustments of the physical parameters. The average in-band realized gain of the antenna is 10 dBic when measured in the broadside direction. These results indicate that the proposed design has the potential to improve the connectivity between IoT devices and the constantly varying orientation of satellites by mitigating the polarization mismatch.

Fast EM-driven nature-inspired optimization of antenna input characteristics using response features and variable-resolution simulation models.

Utilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response features, smoothing the objective function landscape processed by the algorithm. We begin with initial parameter space screening and surrogate model construction using coarse-discretization EM analysis. Subsequently, the surrogate evolves iteratively into a co-kriging model, refining itself using accumulated high-fidelity EM simulation results, with the infill criterion focusing on minimizing the predicted objective function. Employing a particle swarm optimizer (PSO) as the underlying search routine, extensive verification case studies showcase the efficiency and superiority of our procedure over benchmarks. The average optimization cost translates to just around ninety high-fidelity EM antenna analyses, showcasing excellent solution repeatability. Leveraging variable-resolution simulations achieves up to a seventy percent speedup compared to the single-fidelity algorithm.

An improvement of body surface area formulas using the 3D scanning technique.

OBJECTIVES: Body surface area (BSA) is one of the major parameters used in several medical fields. However, there are concerns raised about its usefulness, mostly due to the ambiguity of its estimation. MATERIAL AND METHODS: Authors have conducted a voluntary study to investigate BSA distribution and estimation in a group of 179 adult people of various sex, age, and physique. Here, there is provided an extended analysis of the majority of known BSA formulas. Furthermore, it was supplement with a comparison with the authors' propositions of enhanced formulas coefficients for known formulas models as well as with new power models based on an increased number of anthropometric data. RESULTS: Introduction of the enhanced formulas coefficients cause a reduction of at least 30.5% in mean absolute error and 21.1% in maximum error in comparison with their known counterparts. CONCLUSIONS: In the context of the analysis presented it can be stated that the development of a single universal body surface area formula, based on a small number of state variables, is not possible. Therefore, it is necessary and justified to search for new estimation models that allow for quick and accurate calculation of body surface area for the entire population, regardless of individual body variations. The new formulas presented are such an alternative, which achieves better results than the previously known methods. Int J Occup Med Environ Health. 2024;37(2):205-19.

The change in handgrip strength, after physical exercise, is moderated by digit ratio (2D:4D): A study among the young adults in Poland.

BACKGROUND: The digit ratio (2D:4D), the ratio of the lengths of second (2D) to the fourth (4D) fingers, is a proxy indicator of prenatal androgen exposure. On average, males display lower 2D:4D than females. Previous studies have shown that lower 2D:4D ratios were associated with better sports and physical abilities. AIM: To assess whether a challenge condition, imposed by intense exercise, could increase handgrip strength (HGS) associated with 2D:4D. METHODS: This cross-sectional experimental study included 90 healthy young Polish adults (40 males, 50 females). They underwent intense physical exercise, before (7 days) and after which they were measured for HGS and 2D:4D. Height and weight were also measured. Analyses of Covariance were employed to delineate associations. RESULTS: 2D:4D had significant predictive effects on the differences in HGS (DHGS) measured in two occasions, without and after, physical exercises. The lower was the 2D:4D, the higher the DHGS, particularly, for the left hand. CONCLUSION: The results reconfirmed that the link between prenatal testosterone exposure (indicated by 2D:4D) and physical strength depends on the context, such as a challenged condition.

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning.

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning.

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Age at peak height velocity in Polish adolescents: Effect of socioeconomic factors.

Age at peak height velocity (APHV) is an indicator of maturity timing which is applicable to both sexes, and which is influenced by environmental factors. The objective of this study was to assess variation in APHV associated with several indicators of socioeconomic status (SES) in a longitudinal sample of Polish adolescents. The sample included 739 boys born in 1983 and followed annually from 12 to 16 years, and 597 girls born in 1985 and followed annually from 9 to 13 years. The height records were fitted with the SITAR model to estimate APHV. SES was estimated using principal component analysis of indicators of familial status based on parental education, family size, living conditions and household possessions. Statistical analyses included analysis of variance (one-way for general SES and three-way for parental education and family size) and Tukey post-hoc tests for unequal samples. General SES (p <.001) and family size (p < .05) significantly influenced APHV among boys, while only maternal education (p < .05) significantly influenced APHV among girls. Among youth from families of higher SES, as defined by the respective indicators, APHV was attained significantly earlier, on average, than in peers from families of lower SES. Overall, the results showed a sex-dependent effect of SES on APHV, and highlighted the influence of favorable socioeconomic conditions for optimal growth and maturation during adolescence.

Exploring the Beam Squint Effects on Reflectarray Performance: A Comprehensive Analysis of the Specular and Scattered Reflection of the Unit Cell.

In this article, the phenomena of beam deviation in reflectarray is discussed. The radiation pattern of the unit cell, which plays a vital role in shaping the beam of the reflectarray, is analyzed by considering undesired specular and scattered reflections. These unwanted reflections adversely affect the pattern of the single unit cell, thereby reducing the overall performance of the reflectarray. To conduct our investigations, three cases of reflectarray-i.e., (i) a center-fed with broadside beam (Case-I), (ii) a center-fed with the beam at 30° (Case-II), and (iii) off-center-fed with the beam at 30° reciprocal to feed position with reference to the broadside direction (Case-III)-are simulated. Different degrees of beam deviation are analyzed in each reflectarray by assessing the radiation pattern of a single element. The simulation results shows that maximum of 0°, 3.4°, and 0.54° beam squint across the bandwidth found in Case-I, Case-II, and Case-III, respectively; this leads to aperture efficiencies of 31.2%, 11.9%, and 31.2%, respectively. The significance of specular reflections is further confirmed by half (left half and right half) aperture analysis of Case-II. This involves simulating the half-plane aperture illuminated by horn antenna, resulting in a distinct beam angle at the same frequency. However, deviations of -4.71 to +4.1 for the left half aperture and -1.82 to +1.1 for the right half aperture are noticed. Although the analysis specifically focuses on the three cases of the reflectarray, the proposed methodology is applicable to any type of reflectarray. The study presented in this work provides an important insight into the practical aspects of reflectarray operation, particularly in terms of quantifying undesirable effects that are normally overlooked in the design of this class of arrays. To achieve a good performance, a new design of the dielectric loaded horn feed is proposed. This design approach is both simple and applicable to any reflectarray, with the added benefit of maintaining a low profile for the RA. Moreover, this work holds significant potential for remote sensing satellite systems as beam deviation can adversely impact data collection accuracy and compromise observation precision, resulting in distorted images, reduced data quality, and overall hindrance to the system's performance in capturing reliable information.

Highly Sensitive Microwave Sensors Based on Open Complementary Square Split-Ring Resonator for Sensing Liquid Materials.

This paper presents high-sensitivity sensors based on an open complementary square split-ring resonator and a modified open complementary split-ring resonator operating at 4.5 GHz and 3.4 GHz, respectively. The sensors are designed for the detection of multiple liquid materials, including distilled water, methanol, and ethanol. The liquid under test is filled in a glass container loaded using a pipette. Compared to the conventional OCSSRR, the modified OCSSRR with multiple rings exhibits a higher frequency shift of 1200 MHz, 1270 MHz, and 1520 MHz for ethanol, methanol, and distilled water, respectively. The modified sensor also demonstrates a high sensitivity of 308 MHz/RIU for ethanol concentration which is the highest among the existing microwave sensors. The sensors in this manuscript are suitable for multiple liquid-material-sensing applications.

Machine-learning-based global optimization of microwave passives with variable-fidelity EM models and response features.

Maximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit reliability, but the expense of conducting rudimentary EM-driven global optimization by means of popular bio-inspired algorithms is impractical. Similarly, nonlinear system characteristics pose challenges for surrogate-assisted methods. This paper introduces an innovative technique leveraging variable-fidelity EM simulations and response feature technology within a kriging-based machine-learning framework for cost-effective global parameter tuning of microwave passives. The efficiency of this approach stems from performing most operations at the low-fidelity simulation level and regularizing the objective function landscape through the response feature method. The primary prediction tool is a co-kriging surrogate, while a particle swarm optimizer, guided by predicted objective function improvements, handles the search process. Rigorous validation demonstrates the proposed framework's competitive efficacy in design quality and computational cost, typically requiring only sixty high-fidelity EM analyses, juxtaposed with various state-of-the-art benchmark methods. These benchmarks encompass nature-inspired algorithms, gradient search, and machine learning techniques directly interacting with the circuit's frequency characteristics.

Design and optimization of metamaterial-based highly-isolated MIMO antenna with high gain and beam tilting ability for 5G millimeter wave applications.

This paper presents a wideband multiple-input multiple-output (MIMO) antenna with high gain and isolation, as well as beam tilting capability, for 5G millimeter wave (MMW) applications. A single bow-tie antenna fed by a substrate-integrated waveguide (SIW) is proposed to cover the 28 GHz band (26.5-29.5 GHz) with a maximum gain of 6.35 dB. To enhance the gain, H-shaped metamaterial (MM)-based components are incorporated into the antenna substrate. The trust-region (TR) gradient-based search algorithm is employed to optimize the H-shape dimensions and to achieve a maximum gain of 11.2 dB at 29.2 GHz. The MM structure offers zero index refraction at the desired range. Subsequently, the MIMO system is constructed with two vertically arranged radiators. Another MM, a modified square resonator (MSR), is embedded between the two radiators to reduce the mutual coupling and to tilt the antenna main beam. Herein, the TR algorithm is again used to optimize the MSR dimensions, and to enhance the isolation to a maximum of 75 dB at 28.6 GHz. Further, the MSR can tilt the E-plane radiation by ± 20° with respect to the end-fire direction when alternating between the two ports' excitation. The developed system is validated experimentally with a good matching between the simulated and measured data.

Microfluidically frequency-reconfigurable compact self-quadruplexing tunable antenna with high isolation based on substrate integrated waveguide.

This communication presents a novel concept of microfluidically frequency-reconfigurable self-quadruplexing tunable antenna for quad-band applications. At the initial design stage, a substrate-integrated square cavity is divided into four unequal quarter-mode cavity resonators by inserting an X-shaped slot on the top surface of the cavity. Applying four 50-Ω microstrip feed-lines to these four quarter-mode cavity resonators enables quad-band operation with self-quadruplexing capabilities. The feed lines are organized orthogonally and off-center, which leads to port isolation greater than 32.3 dB. An equivalent network model is developed to validate the proposed antenna. To realize frequency reconfigurability, two microfluidic channels corresponding to each port are created by engraving the bottom surface of the cavity. To create a reconfigurable self-quadruplexing antenna, the channels are either filled with air or dielectric liquids of higher permittivity, so that the design offers independent tunability of the operating frequencies. As a proof of concept, the prototype of a self-quadruplexing tunable antenna is fabricated and validated through measurements. The antenna prototype occupies a footprint area of 0.37λg2. The design exhibits frequency tuning ranges of 350 MHz (8.3%), 500 MHz (10.3%), 610 MHz (11.2%), and 845 MHz (14.1%) for the first, second, third, and fourth operating bands, respectively. In all bands and across the entire tuning range, the realized gains of the designed antenna exceed 4.05 dBi. The electromagnetic modeling responses agree extremely well with the measured characteristics.

wideband high-gain low-profile series-fed antenna integrated with optimized metamaterials for 5G millimeter wave applications.

This paper presents a series-fed four-dipole antenna with a broad bandwidth, high gain, and compact size for 5G millimeter wave (mm-wave) applications. The single dipole antenna provides a maximum gain of 6.2 dBi within its operational bandwidth, which ranges from 25.2 to 32.8 GHz. The proposed approach to enhance both gain and bandwidth involves a series-fed antenna design. It comprises four dipoles with varying lengths, and a truncated ground plane. These dipoles are connected in series on both sides, running in parallel through a microstrip line. The proposed design significantly enhances the bandwidth, which extends from 26.5 to 40 GHz. This frequency range effectively covers the 5G bands of 28 and 38 GHz. The expedited trust-region (TR) gradient-based search algorithm is utilized to optimize the dimensions of the antenna components, resulting in a maximum gain of 11.2 dBi at 38 GHz. To further enhance the gain, modified H-shaped metamaterial (MTM)-based unit cells are integrated into the antenna substrate. The TR algorithm is employed once more to optimize the MTM dimensions, yielding a maximum gain of 15.1 dBi at 38 GHz. The developed system is experimentally validated, showing excellent agreement between the simulated and measured data.

Ages at peak height velocity in male soccer players 11-16 years: relationships with skeletal age and comparisons among longitudinal studies.

Estimated ages at take-off (TO) and at peak height velocity (PHV) based on two models and maturity status based upon age at PHV and skeletal age (SA) were compared in a longitudinal sample of male soccer players. In addition, estimated ages at PHV in 13 longitudinal samples of soccer players were compared. The longitudinal height records of 58 players of European ancestry, measured annually on four or five occasions between 11 and 16 years, were modeled with Superimposition by Translation and Rotation (SITAR) and Functional Principal Component Analysis (FPCA) to estimate ages at TO and PHV. SAs were assessed with the Fels method. Ages at PHV in 13 longitudinal samples of soccer players (Europe 7, Japan 6) were evaluated with meta-analysis. Estimated ages at TO, 11.2 ± 0.8 (SITAR) and 11.0 ± 0.8 (FCPA) years, and at PHV, 13.6 ± 0.9 (SITAR) and 13.7 ± 0.0 (FCPA) years, were similar. An earlier age at PHV was associated with advanced skeletal maturity status (rho = -0.77 at ~14 years). Ages at PHV among European players indicated a north (later) - south (earlier) gradient, and were later than ages at PHV among Japanese players. In summary, ages at TO and PHV were similar with SITAR and FPCA, and ages at PHV were most strongly correlated with SA at ~14 years. Mean ages at PHV showed a north-south gradient among European samples, and were later compared to Japanese samples.

Radiomorphometric indices of the mandible as indicators of decreased bone mineral density and osteoporosis - meta-analysis and systematic review.

This review aims to evaluate the accuracy of various mandibular radiomorphometric indices in comparison with DEXA BMD measurements in the diagnosis of osteopenia and osteoporosis based on a meta-analysis of the sensitivity and specificity of the indices. PRISMA statement was followed. The materials for analysis were collected in August 2023 by searching three databases: PubMed Central, Web of Science, and Scopus. The selection of studies consisted of three selection stages, and 64 articles were finally obtained. Quality assessment was performed with the QUADAS-2 tool, and the general methodological quality of retrieved studies was low. Statistical analysis was performed based on 2 × 2 tables and estimated sensitivity and specificity were obtained using SROC curves. The most used indices were MCI, MCW and PMI. The best results in detecting reduced BMD obtained for MCW ≤ 3 mm, estimated sensitivity and specificity were 0.712 (95% CI, 0.477-0.870) and 0.804 (95% CI, 0.589-0.921), respectively. The most prone to the risk of bias is the MCI due to the examiner's subjectivism. Radiomorphometric indices of the mandible can be useful as a screening tool to identify patients with low BMD, but should not be used as a diagnostic method. Further research needs to focus on analysing the ability of the indices to detect osteoporosis and also in combination the indices with clinical parameters.

New Complementary Resonator for Permittivity- and Thickness-Based Dielectric Characterization.

The design of high-performance complementary meta-resonators for microwave sensors featuring high sensitivity and consistent evaluation of dielectric materials is challenging. This paper presents the design and implementation of a novel complementary resonator with high sensitivity for dielectric substrate characterization based on permittivity and thickness. A complementary crossed arrow resonator (CCAR) is proposed and integrated with a fifty-ohm microstrip transmission line. The CCAR's distinct geometry, which consists of crossed arrow-shaped components, allows for the implementation of a resonator with exceptional sensitivity to changes in permittivity and thickness of the material under test (MUT). The CCAR's geometrical parameters are optimized to resonate at 15 GHz. The CCAR sensor's working principle is explained using a lumped-element equivalent circuit. The optimized CCAR sensor is fabricated using an LPKF protolaser on a 0.762-mm thick dielectric substrate AD250C. The MUTs with dielectric permittivity ranging from 2.5 to 10.2 and thickness ranging from 0.5 mm to 1.9 mm are used to investigate the properties and calibrate the proposed CCAR sensor. A two-dimensional calibration surface is developed using an inverse regression modelling approach to ensure precise and reliable measurements. The proposed CCAR sensor is distinguished by its high sensitivity of 5.74%, low fabrication cost, and enhanced performance compared to state-of-the-art designs, making it a versatile instrument for dielectric characterization.

High-efficacy global optimization of antenna structures by means of simplex-based predictors.

Design of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the accumulated cost of multiple antenna evaluations. This problem is especially pronounced in the case of global search, frequently performed using nature-inspired algorithms, known for poor computational efficiency. At the same time, global optimization is often required, either due to multimodality of the design task or the lack of sufficiently good starting point. A workaround is to combine metaheuristics with surrogate modeling methods, yet a construction of reliable metamodels over broad ranges of antenna parameters is challenging. This work introduces a novel procedure for global optimization of antenna structures. Our methodology involves a simplex-based automated search performed at the level of approximated operating and performance figures of the structure at hand. The presented approach capitalizes on weakly-nonlinear dependence between the operating figures and antenna geometry parameters, as well as computationally cheap design updates, only requiring a single EM analysis per iteration. Formal convergence of the algorithm is guaranteed by implementing the automated decision-making procedure for reducing the simplex size upon detecting the lack of objective function improvement. The global optimization stage is succeeded by gradient-based parameter refinement. The proposed procedure has been validated using four microstrip antenna structures. Multiple independent runs and statistical analysis of the results have been carried out in order to corroborate global search capability. Satisfactory outcome obtained for all instances, and low average computational cost of only 120 EM antenna simulations, demonstrate superior efficacy of our algorithm, also in comparison with both local optimizers and nature-inspired procedures.

Reduced-cost microwave modeling using constrained domains and dimensionality reduction.

Development of modern microwave devices largely exploits full-wave electromagnetic (EM) simulations. Yet, simulation-driven design may be problematic due to the incurred CPU expenses. Addressing the high-cost issues stimulated the development of surrogate modeling methods. Among them, data-driven techniques seem to be the most widespread owing to their flexibility and accessibility. Nonetheless, applicability of approximation-based modeling for real-world microwave components is hindered by a high nonlinearity of the system characteristics, dimensionality issues, and broad ranges of operating parameters the model should cover to make it practically useful. Performance-driven modeling frameworks deliver a partial mitigation of these problems through appropriate spatial orientation of the metamodel domain, which only encapsulates high-quality designs and not the entire space. Unfortunately, the initial model setup cost is high, as defining the domain requires database designs that need to be a priori acquired. This paper introduces a novel approach, where the database designs are replaced by random observables, and dimensionality of the domain is reduced using spectral analysis thereof. The major contributions of the work include implementation of the explicit dimensionality reduction of the confined surrogate model domain and introducing this concept into a complete cost-efficient framework for modeling of microwave components. Comprehensive benchmarking demonstrates excellent performance of the introduced framework, both in terms of predictive power of the rendered surrogates, their scalability properties, as well as low computational overhead associated with the model setup.

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates.

Modern microwave devices are designed to fulfill stringent requirements pertaining to electrical performance, which requires, among others, a meticulous tuning of their geometry parameters. When moving up in frequency, physical dimensions of passive microwave circuits become smaller, making the system performance increasingly susceptible to manufacturing tolerances. In particular, inherent inaccuracy of fabrication processes affect the fundamental operating parameters, such as center frequency or bandwidth, which is especially troublesome for narrow-band structures, including notch filters. The ability to quantify the effects of tolerances, and-even more-to account for these in the design process, are instrumental in making the designs more reliable, and to increase the likelihood that adequate operation is ensured despite manufacturing errors. This paper proposes a simple yet computationally efficient and reliable procedure for statistical analysis and yield optimization of resonator-based notch filters. Our methodology involves feature-based surrogate models that can be established using a handful of training data points, and employed for rapid evaluation of the circuit fabrication yield. Furthermore, a yield optimization procedure is developed, which iteratively sets up a sequence of feature-based models, constructed within local domains relocated along the optimization path, and uses them as predictors to find a robust (maximum yield) design at a low computational cost. The presented approach has been demonstrated using two complementary split ring resonator (CSRR)-based notch filters. The cost of statistical design is about a hundred of EM simulations of the respective filter, with yield evaluation reliability corroborated through EM-based Monte Carlo analysis.