This paper presents a novel approach for predicting the simulation-to-real-world (sim2real) transferability of synthetic-trained vision models by analyzing foreground image quality. Image quality assessment (IQA) techniques are applied to paired synthetic and real-world image subsets to quantify visual similarity and identify feature-level limitations that correlate with transfer performance. Among the evaluated metrics, the Structural Similarity (SSIM) index and Complex Wavelet SSIM (CW-SSIM) index showed consistent trends, where higher IQA scores corresponded to improved sim2real detection accuracy–most notably with increases of approximately 0.10 (SSIM) and 0.15 (CW-SSIM). These metrics effectively captured contrast, structure, and luminance similarities, offering a practical proxy for assessing digital twin fidelity. To support reproducibility and broader use, we introduce the Synthetic Image Quality Analysis Calculator (SIQAC), an open-source tool for automated IQA evaluation and sim2real potential prediction across classifiers and object detectors. Additional experiments demonstrated that the IQA-based approach generalizes to real2sim scenarios using zero-shot object detectors. This work bridges concepts from the human visual system and compression analysis to provide a lightweight, interpretable method for early-stage validation of virtual autonomy pipelines.

The genesis of clutch noise, encompassing squeaks, chatter, shudders, or judders, predominantly arises from friction-induced vibrations. As time progresses, the degradation of the clutch friction lining manifests due to diverse factors, such as the smearing of surface irregularities, elevated transmission fluid temperatures, fluctuating axial pressure, or aggressive shifting. Consequently, the slope of the friction curves tends to assume a negative gradient, giving rise to adverse damping effects like self-excited oscillations or stick-slip oscillations within the clutch pack. A lesser-discussed phenomenon, known as squawking, occurs through analogous mechanisms discussed in this paper. This paper delves into investigating the occurrence of squawking noise observed in automatic transmission multi-disc clutches during low-speed up-shifts. The study discerns friction-induced vibrations as the primary contributor to squawk during the inertia phase of the clutch engagement cycle, a facet previously unidentified. During this phase, high-frequency weakly damped oscillating modes become self-excited due to the negative slope of the coefficient of friction versus slip speed curve. The coefficient of friction functions as negative damping during the inertia phase, where the energy dissipation from damping is insufficient to completely halt the oscillations, allowing them to persist approximately at the natural frequency until clutch lock-up. Experimental data validate the proposed model and hypothesis, with results closely aligning with numerical simulations. The paper concludes by offering practical suggestions to prevent and mitigate squawk in automatic transmission wet clutches, with the aim of enhancing overall performance and reducing undesirable noise.

Monitoring growth behavior of maize plants such as the development of ears can give key insights into the plant's health and development. Traditionally, the measurement of the angle of ears is performed manually, which can be time-consuming and prone to human error. To address these challenges, this paper presents a computer vision-based system for detecting and tracking ears of corn in an image sequence. The proposed system could accurately detect, track, and predict the ear's orientation, which can be useful in monitoring their growth behavior. This can significantly save time compared to manual measurement and enables additional areas of ear orientation research and potential increase in efficiencies for maize production. Using an object detector with keypoint detection, the algorithm proposed could detect 90 percent of all ears. The cardinal estimation had a mean absolute error (MAE) of 18 degrees, compared to a mean 15 degree difference between two people measuring by hand. These results demonstrate the feasibility of using computer vision techniques for monitoring maize growth and can lead to further research in this area.