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QFCOI - Enhancing Objective Quality Assessment for Compressed Omnidirectional Images with Fusion of Measures
(IEEE, 2025-08-08) Šimka, Marek; Polák, Ladislav; Zizien, Adam; Fliegel, Karel
This paper introduces Quality Fusion of Compressed Omnidirectional Images (QFCOI), an enhanced objective quality assessment method for 360° images. QFCOI integrates linear fusion of feature metrics. Established conventional state-of-the-art measures were analyzed to select specific ones for effective fusion and to mitigate eventual overfitting. The feature metrics selection was based on statistical performance and ability to capture key aspects of image quality, including structural preservation, visual information fidelity, and artifact sensitivity. To optimize predictive performance of QFCOI, a genetic algorithm was utilized to determine optimal weight coefficients, maximizing the monotonic correlation with subjective quality scores. The QFCOI performance was validated through correlation coefficients, statistical significance testing, and Receiver Operating Characteristic (ROC) analyses. Results confirmed that QFCOI outperformed other conventional metrics, achieving the highest performance on the OMNIQAD dataset across multiple emerging compression algorithms, including High Efficiency Image File Format (HEIC), Joint Photographic Experts Group XL (JPEG XL), and AV1 Image File Format (AVIF). Further, validation on the relevant public CVIQ dataset for HEIC-compressed images confirmed the robustness of QFCOI, and the method modification on only 25 % of the data, achieved the best performance on the CVIQ dataset. These results highlight the generalization properties and versatility of QFCOI. In contrast to learning-based models, the proposed method offers a transparent and interpretable alternative while achieving high accuracy and statistical reliability in objective quality assessment.
Fractional-Order Equivalent Circuit Representation of Sucrose Solutions Electrical Impedance Measured in Bipolar Configuration
(IEEE, 2025-03-30) Duckworth, Colin; Šotner, Roman; Jeřábek, Jan; Freeborn, Todd
In this work, the alteration in electrical impedance of a liquid (unsweetened FuzeTea) with varying increases in sucrose concentrations (up to 785 mM) was investigated. The aim was to further understand how changes in liquid solutions with adulterants (in this case sucrose) change the electrical impedance as a potential method for food quality monitoring. The electrical impedance was measured from 100 Hz to 1 MHz using gold and platinum electrodes, then numerical optimization was applied to collected datasets to estimate the model parameters of a fractional-order equivalent circuit model to best represent the data. This model demonstrated less than 2% impedance magnitude and less than 6% impedance phase deviation compared to the experimental data. Overall, the model parameter representing the liquid resistance showed large increases for increasing concentrations of sucrose, suggesting that it may be a potential marker for assessing sucrose adulterants in liquid solutions.
A Comprehensive Evaluation of Deep Vision Transformers for Road Extraction from Very-high-resolution Satellite Data
(Elsevier, 2025-01-02) Bolcek, Jan; Gibril, Mohamed Barakat A.; Al-Ruzouq, Rami; Shanableh, Abdallah; Jena, Ratiranjan; Hammouri, Nezar; Sachit, Mourtadha Sarhan; Ghorbanzadeh, Omid
Transformer-based semantic segmentation architectures excel in extracting road networks from very-high-resolution (VHR) satellite images due to their ability to capture global contextual information. Nonetheless, there is a gap in research regarding their comparative effectiveness, efficiency, and performance in extracting road networks from multicity VHR data. This study evaluates 11 transformer-based models on three publicly available datasets (DeepGlobe Road Extraction Dataset, SpaceNet-3 Road Network Detection Dataset, and Massachusetts Road Dataset) to assess their performance, efficiency, and complexity in mapping road networks from multicity VHR satellite images. The evaluated models include Unified Perceptual Parsing for Scene Understanding (UperNet) based on the Swin transformer (UperNet-SwinT), and Multi-path Vision Transformer (UperNet-MpViT), Twins transformer, Segmenter, SegFormer, K-Net based on SwinT, Mask2Former based on SwinT (Mask2Former-SwinT), TopFormer, UniFormer, and PoolFormer. Results showed that the models recorded mean F-scores (mF-score) ranging from 82.22% to 90.70% for the DeepGlobe dataset, 58.98% to 86.95% for the Massachusetts dataset, and 69.02% to 86.14% for the SpaceNet-3 dataset. Mask2Former-SwinT, UperNet-MpViT, and SegFormer were the top performers among the evaluated models. The Mask2Former, based on the SwinT, demonstrated a strong balance of high performance across different satellite image datasets and moderate computational efficiency. This investigation aids in selecting the most suitable model for extracting road networks from remote sensing data.
Differentiator circuits with scalable and electronically adjustable time constant and their application in phase shift evaluation
(Elsevier, 2025-05-03) Šotner, Roman; Polák, Ladislav; Petržela, Jiří; Semenov, Dmitrii; Langhammer, Lukáš; Jaikla, Winai
Two novel scalable and electronically adjustable differentiator designs are presented in this paper. These designs are based on special variable gain amplifiers extending well-known concept of standard single operational amplifier-based differentiators. The key novelty lies in their scalability, which allows for an enhanced time constant value by adjusting the ratio of resistors. Simultaneously, the special form of gain control using a DC voltage offers wide electronic tunability. The solution performs high input and low output impedance, both independent of frequency. Experimental testing demonstrated time constant adjustments in two configurations: from 64 ns to 4.5 mu s (a ratio of maximal and minimal value 70) and from 8.7 mu s to 183 mu s (a ratio of 21). As an application example, the proposed differentiator is utilized in the design of a readout system for an absolute phase shift difference to pulse width ratio converter, suitable for monitoring a very slow phenomenon such biosignals.
Pulsed Electromagnetic Excitation of a Thin Wire-An Approximate Numerical Model Based on the Cagniard-DeHoop Method of Moments
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024-02-01) Štumpf, Martin; Antonini, Giulio; Ekman, Jonas
An approximate computational model of an electromagnetic pulse-excited thin-wire antenna is developed. The presented solution methodology is based on the Cagniard-DeHoop method of moments and Hallen's approximation of the thin-wire model. It is shown that the proposed time-domain solution leads to an inversion-free and efficient updating procedure that mitigates the marching-on-in-time accumulation error. An illustrative numerical example demonstrates the validity of the proposed model.

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