Abstract
In modern industrial environments, ensuring the quality of manufactured components is critical, particularly when dealing with reflective surfaces that hinder conventional inspection techniques. Although deep learning-based methods offer robust solutions for visual defect detection, their performance often hinges on the availability of substantial annotated datasets. In industrial scenarios, labeling such datasets is costly and time-consuming. This study investigates applying sample selection techniques to reduce annotation efforts for porosity detection on machined aluminium parts. Several selection strategies were evaluated using a real-world dataset composed of high-resolution images, including uncertainty, diversity, random-based criteria, and hybrid combinations. The best-performing strategy, which combined entropy-based uncertainty, spatial diversity, and random-based, achieved an F1-score of 86.70% and a recall of 82.99% after ten iterations using only 2,400 annotated images, corresponding to 66.67% of the active learning pool. Although the fully supervised model achieved an F1-score of 88.84% and a recall of 86.30%, the proposed approach proved a competitive alternative. These results demonstrate that selective data annotation can significantly reduce labeling effort while maintaining reliable performance in defect detection, even under the challenging conditions posed by reflective industrial parts.

Abstract

Abstract
Stroke affects over 100 million people worldwide, and over two-thirds of survivors experience lasting upper-limb impairments, which significantly impact their quality of life. The global shortage of rehabilitation providers, who cannot attend to all patients who need it, creates an urgent, not yet answered, need for reliable and accessible rehabilitation innovations. Robotic rehabilitation has been emerging as an effective alternative to traditional physical therapy. This paper presents the development and evaluation of 2 degree-of-freedom exoskeleton, coupled to a collaborative robotic manipulator, which performs upper-limb rehabilitation. The system targets elbow flexion/extension and forearm pronation/supination, using two direct current brushless actuators. To accommodate a wide range of users, the mechanical design is modular and adjustable, allowing the rehabilitation of a broad range of arm lengths, while mechanical barriers prevent unsafe joint motions. Furthermore, limit switches ensure the movements are performed within safe values and an emergency button is also available for emergency stop. Safety assessment confirmed the actuators' performance and the integrity of the physical barriers. Three different rehabilitation modes were implemented: passive assist, active assist and active resist. Passive assistance tests achieved consistent trajectory tracking with a root mean square error of 4.85(o)& strns; for pronation/supination and 0.87 & strns;(o) for elbow flexion/extension, while maintaining smooth motion profiles with spectral arc length values of-1.603 and-1.56, respectively. Active resistance generated stable bidirectional torque across the full range of motion, reaching up to 1 Nm for forearm pronation/supination and 7 Nm for elbow flexion/extension. The adaptive active assistance strategy modified the assistance torque in real time according to the detected user performance. These findings establish a foundation for future clinical evaluation and real-world applications, with the system's modular design and multiple therapy modes showing potential to support diverse rehabilitation needs.

Abstract
The textile industry is experiencing rapid advancement, reflected in the adoption of innovative and efficient manufacturing techniques. The automation of clothing sewing systems has the potential to reduce the allocation of repetitive tasks to operators, freeing them for more value-added operations. There are several machines on the market that automatically sew the bottom hem of T-shirts, a key component of the garment that fulfills both functional and aesthetic purposes. However, most of them require the fabric to be positioned manually by an operator. To address this issue, this work presents a solution to automate the process of feeding a T-shirt into a SiRUBA sewing machine using a YuMi dual-arm robot. In this scenario, the T-shirt arrives at the workstation with the main front and back pieces of cloth sewn together, seams facing out, and with no sleeves yet. This setup starts by turning the garment inside out with the aid of an automated hanger, ensuring that the seams are facing inward (as the machine requires), and then using the dual-arm robot to feed the garment into the sewing machine. With our approach, the feeding and hemming process took less than 35 seconds, with a feeding success rate of 98%. Therefore, this work can serve as a steppingstone towards more efficient automated sewing systems within the garment production industry.

Abstract
Stroke is a leading cause of long-term disability worldwide, with survivors often facing significant challenges in regaining upper-limb functionality. In response, robotic rehabilitation systems have emerged as promising tools to enhance post-stroke recovery by delivering precise, adaptable, and patient-specific therapy. This paper presents a review of robotic interfaces developed specifically for upper-limb rehabilitation. It analyses existing exoskeleton- and end-effector-based systems, with respect to three core design pillars: assistance types, control philosophies, and actuation methods. The review highlights that most solutions favor electrically actuated exoskeletons, which use impedance- or electromyography-driven control, with active assistance being the predominant rehabilitation mode. Resistance-providing systems remain underutilized. Furthermore, no hybrid approaches featuring the combination of robotic manipulators with actuated interfaces were found. This paper also identifies a recent trend towards lightweight, modular, and portable solutions and discusses the challenges in bridging research prototypes with clinical adoption. By focusing exclusively on upper-limb applications, this work provides a targeted reference for researchers and engineers developing next-generation rehabilitation technologies.