Along with the information technology rising, Computer Aided Design activities are becoming more modern and more complex. But learning how to operation these new design tools has become the main problem lying in front of each designer. This study was purpose on finding problems encountered during...

Slow sensors, widely used in industrial automation, environmental monitoring, and smart agriculture, face significant challenges in real-time data processing due to limitations such as bandwidth constraints, delayed response times, and low sampling rates. These issues, including data latency, sparsity, and noise, can hinder system performance and decision-making. However, integrating adaptive learning techniques, such as incremental and real-time learning algorithms, holds promise in enhancing the efficiency and reliability of slow sensor systems. Recent advancements in sensor technology, supported by machine learning and computational resources, have facilitated the development of intelligent sensor designs that optimize data acquisition and reduce computational burdens. This study explores the application of TinyML technology to slow sensors, enabling machine learning models to run efficiently on resource-constrained devices. Through a hybrid sensor design, combining hardware and senseware layers, the research aims to achieve real-time behavior perception using deep learning techniques. Two key experimental outcomes include the successful assembly of the slow sensor prototype and the deployment of a trained neural network within the system. Additionally, the study addresses challenges such as small sample sizes and data purification, both of which were critical to improving model performance and training efficiency. The findings underscore the potential of intelligent slow sensors and highlight the need for further exploration of their application in real-world scenarios, particularly in the realm of artificial intelligence integration. Future research will focus on refining sensor designs and evaluating the performance of slow sensors in specific application contexts.

Cantonese morning tea drinking is a cherished intangible cultural heritage in the Guangdong–Hong Kong–Macao Greater Bay Area in China and embodies Guangdong’s culinary tradition. In the digital era, innovative design techniques and digital technology have been applied to preserve and promote this tradition. To enhance heritage education, we designed an augmented reality device for Cantonese morning tea using Pepper’s Ghost technique. Employing field investigations, literature research, and experiments, we showcased Cantonese morning tea drinking. For further developments, affordable materials and professional photos are essential for constructing the model.

Design knowledge is the basis for designers to solve design problems. In product design, sketches assist designers in thinking as an important step. Novice designers need to use different types of design knowledge in sketching to solve design problems and find product solutions. In this study, we determined the novice designers’ knowledge needs and processes in sketching and discovered their sketch ideas to understand how they solve design problems.

Guangdong puppetry, a 700-year-old intangible cultural heritage, faces declining popularity due to economic influences and evolving youth interests. The shrinking audience and a lack of successors to inherit this tradition further compound the issue. This research aims to revitalize the puppet show by preserving its traditional roots, exploring new possibilities, and engaging the younger generation. Divided into three stages—preliminary investigation, role modeling, and holographic projection device design—the study utilizes field research, literature review, questionnaires, and experiments. Information is gathered through venue visits, which is followed by questionnaire distribution and 3D model creation based on survey results. The final stage involves designing and testing a holographic projection booth.