The article "Automation and Computerization of (Bio)sensing Systems" by Chamarthi Maheswar Raju, Decibel P. Elpa, and Pawel L. Urban discusses the importance of automation in sensing systems to reduce human effort, increase reproducibility, and enable remote sensing. The authors highlight various types of sensing systems that incorporate automation, including flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. They emphasize the role of computer technology in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from sensor data, supporting the Internet of Things with efficient data management. Key advancements in automation include: 1. **Flow Injection and Sequential Injection Analysis (FIA/SIA)**: These techniques automate liquid handling, increasing throughput and precision while minimizing human errors. 2. **Microfluidics**: Microfluidic devices control small volumes of fluids, enabling rapid and accurate measurements in medical diagnostics, food analysis, and environmental analysis. 3. **Robotics**: Robotic systems automate sample handling, dispensing reagents, mixing, and other tasks, enhancing efficiency and precision. 4. **Other Prototypes**: Sensor prototypes address real-world problems in agriculture, clinical diagnostics, environmental monitoring, and more, leveraging advancements in computer technology and robotics. The article also discusses the integration of computer technology in sensing systems, highlighting how sensors interface with microcontrollers, single-board computers, and conventional computers to convert analog signals into digital data for further processing. Machine learning and AI are used to manipulate and analyze data in real-time, enhancing the capabilities of sensing systems.The article "Automation and Computerization of (Bio)sensing Systems" by Chamarthi Maheswar Raju, Decibel P. Elpa, and Pawel L. Urban discusses the importance of automation in sensing systems to reduce human effort, increase reproducibility, and enable remote sensing. The authors highlight various types of sensing systems that incorporate automation, including flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. They emphasize the role of computer technology in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from sensor data, supporting the Internet of Things with efficient data management. Key advancements in automation include: 1. **Flow Injection and Sequential Injection Analysis (FIA/SIA)**: These techniques automate liquid handling, increasing throughput and precision while minimizing human errors. 2. **Microfluidics**: Microfluidic devices control small volumes of fluids, enabling rapid and accurate measurements in medical diagnostics, food analysis, and environmental analysis. 3. **Robotics**: Robotic systems automate sample handling, dispensing reagents, mixing, and other tasks, enhancing efficiency and precision. 4. **Other Prototypes**: Sensor prototypes address real-world problems in agriculture, clinical diagnostics, environmental monitoring, and more, leveraging advancements in computer technology and robotics. The article also discusses the integration of computer technology in sensing systems, highlighting how sensors interface with microcontrollers, single-board computers, and conventional computers to convert analog signals into digital data for further processing. Machine learning and AI are used to manipulate and analyze data in real-time, enhancing the capabilities of sensing systems.