Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Accurate and non-destructive assessment of fruit firmness is critical for evaluating quality and ripeness, particularly in postharvest handling and supply chain management. This study presents the development of an image-based vibration analysis system for evaluating the firmness of kiwifruit using computer vision and machine learning. In the proposed setup, 120 kiwifruits were subjected to controlled excitation in the frequency range of 200–300 Hz using a vibration motor. A digital camera captured surface displacement over time (for 20 s), enabling the extraction of key dynamic features, namely, the damping coefficient (damping is a measure of a material’s ability to dissipate energy) and natural frequency (the first peak in the frequency spectrum), through image processing techniques. Results showed that firmer fruits exhibited higher natural frequencies and lower damping, while softer, more ripened fruits showed the opposite trend. These vibration-based features were then used as inputs to a feed-forward backpropagation neural network to predict fruit firmness. The neural network consisted of an input layer with two neurons (damping coefficient and natural frequency), a hidden layer with ten neurons, and an output layer representing firmness. The model demonstrated strong predictive performance, with a correlation coefficient (R2) of 0.9951 and a root mean square error (RMSE) of 0.0185, confirming its high accuracy. This study confirms the feasibility of using vibration-induced image data combined with machine learning for non-destructive firmness evaluation. The proposed method provides a reliable and efficient alternative to traditional firmness testing techniques and offers potential for real-time implementation in automated grading and quality control systems for kiwi and other fruit types....
This article presents a design solution for feeding cylindrical parts with axial orientation. A working algorithm was developed to control and synchronize the main components, which was verified via a simulation. The pneumatic and electrical circuits were designed using a software platform for engineering purposes. Based on the CAD project created, a real prototype was built. The energy consumption of the system was tested and evaluated. The results from the prototype verified the solution. This article emphasizes the use of specific sensors for detecting part orientation and their role in improving process reliability. The system is suitable for industrial implementation due to its functionality, stable operation, low energy consumption, and ability to be integrated into automated production systems....
Sensation seeking represents a significant risk factor for various mental health disorders and maladaptive behaviors, highlighting the need for objective assessment methods that circumvent the limitations of traditional self-report measures. This study introduces an innovative digital phenotyping approach that combines computational gait analysis with machine learning (ML) to quantify sensation-seeking traits and examines its validity. Natural gait sequences (using a Sony camera at 25 FPS) and self-report measures (Brief Sensation-Seeking Scale for Chinese, BSSS-C) were collected from 233 healthy adults. Computer vision processing through OpenPose extracted 25 skeletal keypoints, which were subsequently transformed into a hip-centered coordinate system and denoised using Gaussian filtering. From these kinematic data, 300 temporospatial gait features capturing various aspects of movement dynamics were derived. Using a supervised ML approach with feature selection, three ML models (SMO Regression, Multilayer Perceptron, and Bagging) were developed and compared through 10-fold cross-validation. The SMO Regression model demonstrated superior performance (r = 0.60, MAE = 3.50, RMSE = 4.59, R2 = 0.26), outperforming the other approaches. These results establish proof-of-concept for gait-based digital phenotyping of sensation seeking, offering a scalable, objective assessment paradigm with potential applications in clinical screening and behavioral research. The methodological framework presented here advances the field of behavioral biometrics by demonstrating how computer vision and ML can transform basic movement patterns into meaningful psychological indicators....
Event-based cameras, as a revolutionary class of dynamic vision sensors, offer transformative advantages for capturing transient mechanical phenomena through their asynchronous, per-pixel brightness change detection mechanism. These neuromorphic sensors excel in challenging industrial environments with their microsecond-level temporal resolution, ultra-low power requirements, and exceptional dynamic range that significantly outperform conventional imaging systems. In this way, the event-based camera provides a promising tool for machine vibration sensing and fault diagnosis. However, the dynamic vision data from the event-based cameras have a complex structure, which cannot be directly processed by the mainstream methods. This paper proposes a dynamic vision-based non-contact machine fault diagnosis method. The Eagle Vision Transformer (EViT) architecture is proposed, which incorporates biologically plausible computational mechanisms through its innovative Bi-Fovea Self-Attention and Bi-Fovea Feedforward Network designs. The proposed method introduces an original computational framework that effectively processes asynchronous event streams while preserving their inherent temporal precision and dynamic response characteristics. The proposed methodology demonstrates exceptional fault diagnosis performance across diverse operational scenarios through its unique combination of multi-scale spatiotemporal feature analysis, adaptive learning capabilities, and transparent decision pathways. The effectiveness of the proposed method is extensively validated by the practical condition monitoring data of rotating machines. By successfully bridging cutting-edge bio-inspired vision processing with practical industrial monitoring requirements, this work creates a new paradigm for dynamic vision-based non-contact machinery fault diagnosis that addresses critical limitations of conventional approaches. The proposed method provides new insights for predictive maintenance applications in smart manufacturing environments....
The performance of the lighting system significantly impacts the efficiency and accuracy of the overall defect detection process in additive manufacturing. However, achieving both high optical efficiency and exceptional illuminance uniformity within compact detection areas at typical working distances remains challenging with conventional designs. This paper proposes a novel uniform lighting system design utilizing a freeform optics-based microlens array. Optical performance, focusing on efficiency and uniformity, was optimized across key distances using the Taguchi method. Simulation results demonstrate that the optimized uniform illumination system, featuring a 13 × 13 array with microlens of 2 mm radius positioned 300 mm from the target plane, achieves a high optical efficiency of 93.7% and an outstanding illuminance uniformity of 98.9%. Furthermore, the system maintains good uniformity across different wavelengths, enhancing its versatility. These findings strongly support the feasibility of the proposed freeform optics-based microlens array lighting system for machine vision in laser additive manufacturing defect detection, significantly contributing to improved image contrast....
Loading....