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When unavoidable light, age, expression, and gesture changes occur, the machine vision detection accuracy will be greatly reduced, especially the light change impact on image processing. Combining the reflection equation and frequency domain analysis to construct a light change model based on spherical harmonic function and process the image from four directions, namely, digital image conversion, median filtering, sharpening processing, and image global segmentation. Aiming at the shortcomings of traditional image processing algorithms, the light compensation based on the improved pulse-coupled neural network model is proposed, and the model is used to analyze the impact of light changes on image processing. Despite the changes in light conditions, this algorithm maintains above 0.95 in each subset and has the best overall performance, as shown by the results. When the light intensity is less than 41.88kLux, using the optimal threshold for background segmentation of adult green peppers with respect to a fixed threshold T=0.3 results in a significant reduction of the background segmentation error. When the light intensity is greater than 41.88kLux, the superiority of the optimal threshold is not obvious. The results of this research not only have a significant role in promoting the development of the field of computer vision but also can provide people with convenient and efficient services in all aspects of social life.

A versatile sensing platform employing inorganic MoS2 nanoflowers and organic poly(3,4-ethylene dioxythiophene): poly (styrene sulfonate) (PEDOT:PSS) has been investigated to develop the resistive and capacitive force-sensitive devices. The microstructure of the sensing layer heightens the sensitivity and response time of the dual-mode pressure sensors by augmenting electron pathways and inner stress in response to mechanical stimuli. Consequently, the capacitive and resistive sensors exhibit sensitivities of 0.37 and 0.12 kPa−1, respectively, while demonstrating a remarkable response time of approximately 100 ms. Furthermore, it is noteworthy that the PEDOT:PSS layer exhibits excellent adhesion to polydimethylsiloxane (PDMS) substrates, which contributes to the development of highly robust force sensitive sensors capable of enduring more than 10000 loading/unloading cycles. The combination of MoS2/PEDOT:PSS layers in these dual-mode sensors has shown promising results in detecting human joint movements and subtle physiological signals. Notably, the sensors have achieved a remarkable precision rate of 98% in identifying target objects. These outcomes underscore the significant potential of these sensors for integration into applications such as electronic skin and human-machine interaction. [ABSTRACT FROM AUTHOR]

Timber architectures have arisen as sustainable solutions for high-rise and long-span buildings, assisting in implementing a circular economy. The creep strain dissipation of laminated veneer lumber (LVL) was investigated in this work to understand the inherent creep behaviors of LVL derived from natural wood. The results demonstrated a significant loading regime dependency of the creep behaviors of LVL. Coupled creep strain dissipation that transits/is parallel to the wood–adhesive interface was proven in the creep deformation of flat-wise and edge-wise bent LVL. In contrast, the creep strain dissipated considerably along the wood–adhesive interface when the LVL was subjected to axial compression creep. Further investigation into the morphologies of LVL after creep revealed that direct contact between the loading plane and wood–adhesive interface could be a plausible trigger for the accelerated deformation and the resultant plastic deformation of the LVL after creep. We believe that this work provides essential insights into the creep strain dissipation of LVL. It is thus beneficial for improving creep resistance and assisting in the long-term safe application of LVL-based engineered wood products in timber architectures. [ABSTRACT FROM AUTHOR]