Jeng-Dao Lee

This brief introduces an innovative recursive discrete cosine transform (DCT) algorithm characterized by its exceptional precision and minimal multiplication requirements. Through the strategic implementation of data reordering and “q” value adjustment schemes, the proposed algorithm entails only a single constant-multiplication operation featuring a fixed cosine coefficient within the iterative phase. By judiciously selecting an appropriate “q” value (q =41), it achieves outstanding results, reaching peak signal-to-noise ratios (PSNRs) of 94.9 and 100.9 dB under 18-bit and 20-bit word length (WL) conditions, respectively, in terms of decimal places. Notably, the proposed algorithm substantially diminishes the number of multiplications by 86.08%, offset by an increase of 2688 additions. The proposed design has a simpler structure and utilizes fewer hardware resources. In field programmable gate array (FPGA) implementation, the device is composed of 43 combinational adaptive look-up tables (ALUTs) specifically allocated for constant multiplication (CM). Overall, the proposed accelerator totally takes 158 combinational ALUTs, 65 registers, a 960-bit read-only memory (ROM), and a 1024-bit random access memory (RAM) in hardware realization and can be operated at a maximum frequency of 156.62 MHz. Therefore, it is particularly well-suited for VLSI implementation in a parallel calculation of Mel-scale frequency cepstral coefficients (MFCCs).

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.

In the global wave of automation, logistics and manufacturing are indispensable and important industries. Among them, the related automatic warehousing system is even more urgently needed. There are quite a few cases of using robotic arms in the current industry cargo stacking operations. Traditional operations require engineers to plan the stacking path for the robotic arm. If the size of the object changes, it will take extra time to re-plan the work path. Therefore, in recent years, quite a lot of automatic palletizing software has been developed; however, none of it has a detection mechanism for stacking correctness and personnel safety. As a result, in this research, an intelligent robotic palletizer system is developed based on a self-developed symmetrical algorithm to stack the goods in a staggered arrangement to ensure the overall structure. Innovatively, it is proposed to check the arrangement status and warnings during the visual stack inspection to ensure the correctness of the stacking process. Besides, an AI algorithm is imported to ensure that personnel cannot enter the set dangerous area during the work of the robotic arm to improve safety during stacking. In addition to uploading the relevant data to the cloud database in real time, the stacking process combined database and vision system also provide users with real-time monitoring of system information.

Owing to the different consumption habits of people, the market has rapidly changed, and customized demands have increased in recent years. This issue has led to the need for manufacturing industry to improve their production-process automation system control to become faster and more flexible. An intelligent automated production-line control system (IAPLCS) is proposed in this study, in which a graphic control system (GCS) is designed to integrate equipment communications in automated production. This system runs complex packaged programs for function blocks, which enable users to easily build function blocks in GCS. Users can plan the control flow of the system to control the machine operation. Not only is a web interface set in the IAPLCS, which makes it easier for users to operate and monitor, but also Internet-of-Things (IoT) sensors can be added for data collection and analysis based on the characteristics of the production line and equipment. Finally, the proposed control system was implemented in an actual production line to verify its feasibility.