Elena Serea

Smartphone-based colorimetric biosensor readout is strongly influenced by illumination, yet lighting conditions are often characterized mainly by illuminance rather than by their spectral composition. This study investigates the spectral, photometric, colorimetric, and energetic effects of programmable LED illumination on smartphone colorimetric readout under controlled acquisition conditions. Twenty lighting scenarios were generated using three independently controlled color-capable LED luminaires and characterized by spectral irradiance, illuminance, chromaticity, color rendering, and electrical power. A Samsung Galaxy S20 FE was used to acquire JPEG and RAW images of a standardized 24-patch ColorChecker target used as a controlled colorimetric reference for evaluating the optical readout methodology, and colorimetric deviation was quantified using CIEDE2000. Mean ΔE₀₀ ranged from 11.73 to 42.33 across the investigated scenarios. Two conditions differing by less than 1% in illuminance produced a 37% difference in mean colorimetric error, demonstrating that illuminance alone does not determine readout accuracy. An illuminance-only regression model yielded R² = 0.819, whereas inclusion of a relative spectral descriptor increased R² to 0.919. High-output conditions also revealed substantial JPEG clipping despite negligible RAW saturation. These results show that reliable smartphone colorimetric readout requires joint consideration of illumination intensity, spectral composition, electrical demand and image-saturation constraints.

Industrial lighting audits may combine heterogeneous measurements into performance scores without demonstrating whether the underlying evidence is sufficiently complete, representative, and traceable. This study proposes an evidence-aware multicriteria assessment framework that separates performance evaluation from evidence coverage. Visual, energy, and operational indicators are evaluated only when supporting evidence is available and an applicable criterion has been declared, while evidence quality is reported separately through an evidence-coverage measure. The methodology is illustrated using field records from an industrial research hall under daylight-only and mixed daylight/electric-light conditions. Because no representative whole-installation operating state, applicable task-plane grid, or circuit-level electrical campaign was available, the case is treated as a methodological illustration rather than a compliance assessment or full validation of the installation. The framework prevents non-equivalent records from being combined into a single performance criterion, distinguishes missing evidence from undeclared targets, and converts evidence gaps into a prioritized measurement-completion plan. In the case study, weighted evidence coverage is 36.0%, indicating that measurement completion rather than global performance classification is the immediate audit priority. The proposed approach improves the transparency, traceability, and defensibility of industrial lighting audits conducted under incomplete evidence.

This paper proposes a frequency-estimation method based on an iterative midpoint search applied to the discrete Fourier transform (MS-DFT). The method is designed to reduce the sensitivity of classical interpolation-based estimators to fractional-bin offsets and noise variations. The proposed approach exploits the unimodal structure of the spectral magnitude and performs iterative interval contraction using midpoint evaluation and magnitude ordering. This mechanism enables consistent estimation behavior across different spectral alignments without relying on interpolation formulas. Extensive simulations show that the method achieves a nearly constant ratio between the root mean square error (RMSE) and the Cramér–Rao lower bound (CRLB) over a wide range of signal-to-noise ratios (−7.5 dB to 65 dB). This behavior indicates stable relative efficiency with respect to the theoretical limit, rather than optimization at isolated operating points. In addition, the method reaches near-optimal accuracy within a small number of iterations (typically 8–9), resulting in low and predictable computational complexity. These properties make the MS-DFT estimator suitable for real-time and resource-constrained applications such as embedded sensing and biosignal processing.

Digital Twin (DT) implementations in electrical and industrial systems are governed by fragmented ISO/IEC and IEC standards spanning terminology, architecture, interoperability, lifecycle management, and cybersecurity. This paper proposes a mathematical framework that integrates these standards into a unified compliance model. A layered DT architecture is defined as a finite set of functional abstractions, and standards are linked to layers through a multivalued mapping and an incidence matrix. Traceability, interoperability, fidelity, and security/governance indicators are normalized and aggregated through a bounded weighted functional to obtain a deterministic compliance score. The model is then extended by treating selected indicators as random variables, which enables probabilistic maturity classification and Monte Carlo-based robustness analysis. The resulting functional is bounded, monotone, and stable under bounded perturbations. Numerical experiments on a synthetic portfolio illustrate deterministic scoring and uncertainty effects. The framework provides a proof-of-concept basis for structured DT compliance assessment across heterogeneous electrical systems; however, broader empirical validation is still required before operational deployment.

This study presents a site-specific calibration of the ratio between S-wave and P-wave peak ground acceleration (PGA) for use in low-cost, on-site earthquake early warning (EEWS) systems in Iași, Romania. A dataset of 25 intermediate-depth Vrancea earthquakes (Mw 4.1–5.7; epicentral distances 150–210 km) was analyzed. PGA values were extracted for the P- and S-wave windows on both horizontal components and combined using geometric means. The resulting S/P amplitude ratios yield a median value of k_S/P = 6.19 and a logarithmic standard deviation of σlog₁₀ = 0.31, corresponding to a multiplicative uncertainty factor of approximately ×2. These results indicate that S-wave amplitudes are typically six times larger than P-wave amplitudes at this site, consistent with soft-soil amplification observed in comparable stations in Japan and Italy. The calibrated ratio can be used as a site-specific input for future MEMS-based on-site EEW implementations to estimate the expected S-wave PGA immediately after P-wave detection, with the observed S–P delays in Iași indicating a typical available warning window of 20–22 s.

Earthquake Early Warning (EEW) systems based on on-site measurements enable ultra-rapid alerts by exploiting the time gap between the arrival of P-waves and the subsequent damaging S-waves. A central challenge is the reliable estimation of impending ground motion using only the earliest portion of the signal. This study investigates a site-specific methodology based on the S/P amplitude ratio for near-real-time seismic acceleration estimation at the Iași stations, Romania, in a region dominated by Vrancea intermediate-depth seismicity. Using 50 strong-motion records from the European Strong-Motion (ESM) database, a local calibration coefficient of k = PGA_S/PGA_P = 6.2 was derived for the Iași area, consistent with its soft-soil conditions and with values reported for comparable sedimentary environments worldwide. A regional analysis confirms that the S/P ratio is primarily governed by local site effects, requiring station-level calibration. The methodology was experimentally validated through shaking-table tests using real P-wave recordings. Predicted S-wave peak ground accelerations exhibit no systematic bias, with a median relative error of +2.0% and dispersion consistent with the intrinsic log-normal variability of the S/P ratio. The results demonstrate that a locally calibrated S/P ratio provides a robust and physically grounded basis for rapid seismic acceleration estimation in on-site EEW systems.

This paper presents a novel and efficient method for accurate phase difference shift estimation between sinusoidal signals, based on a variable-resolution Discrete Fourier Transform (DFTv). Traditional FFT-based approaches, which rely on frequency-domain oversampling via extensive zero-padding, often incur high computational cost while remaining sensitive to spectral leakage and frequency bin misalignment.In contrast, the proposed DFTv method dynamically adjusts the frequency resolution during analysis by using the amplitude of the previously computed spectral component as a control parameter. This amplitudedependent resolution scheme allows the algorithm to apply finer frequency steps near dominant spectral peaks and coarser steps in low-energy regions, significantly reducing the number of computations without sacrificing estimation accuracy.The effectiveness of the method is demonstrated through comparative tests against the classical zeropadded FFT technique. Additional experiments were conducted in the presence of additive white noise, showing that DFTv maintains its accuracy and robustness under noisy conditions.Due to its adaptability, low computational complexity, and resilience to noise, the proposed method is particularly well-suited for real-time phase shift estimation in resource-constrained embedded systems requiring precise sinusoidal signal analysis.

The accurate frequency estimation of sinusoidal signals remains a key requirement in precision instrumentation and signal analysis, particularly in applications where noise and spectral leakage affect the measurement accuracy. This paper introduces a new frequency-domain technique, called IAE-DFT (Iterative Amplitude Equalization in the Frequency Domain), which estimates the true frequency of a sinusoidal component by iteratively adjusting two dominant spectral points until their amplitudes become balanced. Both spectral components are shifted together in the same direction according to amplitude dominance, and the step size is halved each time the amplitude relationship reverses, ensuring smooth and deterministic convergence. Experimental results demonstrate that IAE-DFT achieves superior performance at 0 dB SNR, outperforming the state-of-the-art methods, while maintaining comparable accuracy at 20 dB and 40 dB. Its precision and robustness make it a promising candidate for frequency-output biosensors and resonant sensing applications, where accurate tracking of small frequency shifts is critical. Future work will focus on optimizing the iteration control strategy, particularly the selection of the initial step size and the adaptive adjustment rate, to further enhance convergence speed and accuracy.

The Fast Fourier Transform (FFT) is the most powerful and widely used method for transforming signals from the time domain to the frequency domain, being employed in cutting-edge research fields. Achieving high-accuracy FFT involves increasing the number of frequency points through frequency oversampling, typically by extending the time-domain signal with zeros (zero-padding) and applying the FFT to the extended signal. While increasing the number of zeros enhances the precision of the results, it also substantially raises the computational load. To address the high computational demands associated with traditional frequency oversampling, this paper proposes a direct oversampling method. By employing a non-integer number of cycles within the FFT kernel, the method achieves spectral analysis results identical to those obtained with zero-padded oversampling but with a significantly lower computational cost. Specifically, it reduces the complexity from O(Mlog₂M) to O(Mlog₂N), where N is the original signal length and M is the oversampled frequency resolution. This approach eliminates zero-padding altogether, providing a mathematically equivalent and more efficient alternative for applications requiring high spectral resolution.

Lighting measurements and calculation is an old and widespread process, evolving with the variety of technologies that use light or operate efficiently depending on the natural or artificial light conditions in the ambient environment. The complexity of human activities gives rise to different techniques and approaches to lighting effect analysis, and this paper aims to clarify which type of units, photometric or radiometric, are appropriate, and which light measurement and calculation techniques are optimal for evaluating the environmental microclimate intended for an activity. Quantitative lighting analysis is common and accessible through the measuring devices, calculation formulas, and simulation software available. In contrast, qualitative analysis remains less prevalent, partly due to its complexity and the need to consider human perception as a central component in assessing lighting impact, as emphasized by the human-centric lighting paradigm. Current evaluation frameworks distinguish between the quantitative and qualitative approaches, with actinic calculations addressing biologically relevant aspects of lighting in specific environmental contexts.