Development of STM8 and Kalman Filter Based Heater Controller for Automatic Tissue Processor

Abstract:

A tissue processor is a device that automatically processes tissue into several steps for histological studies in medical or biological laboratories. One of the stages of this device is embedding, where the tissue is inserted into a vessel that is heated to a stable temperature of 60 to 80°C so that the paraffin remains liquid during this process. The handling of tissue specimens is a crucial aspect of ensuring an accurate diagnosis of the tissue. However, due to its high cost, it can affect scientific development that requires tissue processing. Reverse engineering is one of the methods commonly used to develop a tool at a lower price; one way is to change the essential components used while still maintaining the device's primary function. This research aims to develop a heater controller for a tissue processor using STM8 and LM35 analog sensors equipped with a Kalman filter. The design of the Kalman filter will be determined based on its component equations. The measurement and variance constant values will be simulated and analysed in MATLAB for the most suitable parameter value. Afterwards, the proposed design will be conducted using the Arduino Integrated Development Environment (IDE) to reduce the noise in the LM35 sensor reading and then saved using ArduSpreadsheet. The simulation and implementation results provide evidence that the proposed Kalman filter effectively filters signals contaminated with noise, even in the presence of high data variations that may occur in sensor readings. In testing, it is necessary to pay attention to the ratio that can reduce noise while keeping the characteristics of the sensor itself. Numerous investigations have explored various ratios, recommending an appropriate ratio of 100. Based on the implementation of this ratio with measurement constant (R) 10 and variance constant (Q) 0.1, resulted in a 0.29°C mean error and stable sensor readings.

• Periodical: Engineering Headway (Volume 27)
• Pages: 304-317
• DOI: https://doi.org/10.4028/p-1IWrkP
• Online since: October 2025
• Authors: Naufal Rashad Aryaputra*, Jans Hendry, Hidayat Nur Isnianto, Ahmad Nugraha Bayu Mukti
• Keywords: Automatic Tissue Processor, Heater Controller, Kalman Filter, Reverse Engineering, STM8

References

[1] S. Bansode, Challenges to Cure Cancer Review Article, J. Stem Cell Res. (2015).

[2] D. Wolfe, Bancroft's Theory and Practice of Histological Techniques, 8th ed., Elsevier, 2019, pp.73-83.

[3] R.S. Khandpur, Compendium of Biomedical Instrumentation, John Wiley & Sons, 2020.

[4] D.G. Baskin, Fixation and Tissue Processing in Immunohistochemistry, Pathobiology of Human Disease: A Dynamic Encyclopedia of Disease Mechanisms, 2014, p.3797–3806.

DOI: 10.1016/b978-0-12-386456-7.07402-5

[5] J.D. Bancroft, M. Gamble, Theory and Practice of Histological Techniques, 5th ed., Churchill Livingstone, London, 2002.

[6] S. Ezhilarasan, K.M. Vijayan, M.P. Jeyanthi, Wax embedding technique - innovative and cost-effective method of preservation of human cadaveric specimens, Indian J. Clin. Anat. Physiol. 3 (2016) 423-426.

DOI: 10.5958/2394-2126.2016.00096.7

[7] M. Sholihul Hadi, S.H. Huda, A.E. Zaeni, A.M. Mizar, I. Irvan, IoT Embedded System for Automatic Tissue Processor Machine, in: Proc. 4th Int. Conf. Vocational Education and Training, ICOVET 2020, IEEE, 2020, p.1–6.

DOI: 10.1109/icovet50258.2020.9230048

[8] P.L. Yong, R.S. Saunders, L. Olsen, The healthcare imperative: lowering costs and improving outcomes, Workshop Series Summary, Roundtable on Value & Science-Driven Health Care, Learning Health System Series, 2010.

DOI: 10.17226/12750

[9] A. Jayadev, J. Stiglitz, Perspective: Two ideas to increase innovation and reduce pharmaceutical costs and prices, Health Aff. 28 (2009).

[10] W. Wang, Reverse Engineering: Technology of Reinvention, 1st ed., CRC Press, Boca Raton, 2010.

[11] H.A. Khalili, A. Maleki, S. Ayatollahi, Using combination of Reverse Engineering and Value Engineering for improvement in designs, construction projects and manufacturing industries, in: Proc. 41st Int. Conf. Computers and Industrial Engineering, 2011.

[12] A. Kumar, P.K. Jain, P.M. Pathak, Reverse Engineering in Product Manufacturing: An Overview, 2013.

[13] M. Arman, B. Prasetyo, G.P. Darmawan, Perbandingan Karakteristik Sensor Temperatur LM35 dan DS18B20 Pada Simulator Cerobong Tata Udara, Proc. 13th Ind. Res. Workshop and Natl. Seminar Bandung 13(1) (2022).

DOI: 10.35313/irwns.v13i01.4188

[14] R.C. Burgess, Filters, Analog/Digital, in: Encyclopedia of the Neurological Sciences, 2014, pp.299-307.

[15] I.B. Setyawan, A.K. Huda, F.H. Nashrullah, I.D. Kurniawan, S.I. Frans, J. Hendry, Noise Removal in the IMU Sensor Using Exponential Moving Average with Parameter Selection in Remotely Operated Vehicle (ROV), in: Proc. 8th Int. Conf. Sci. Technol., ICST 2022, 2022.

DOI: 10.1109/icst56971.2022.10136259

[16] R.E. Kalman, A new approach to linear filtering and prediction problems, J. Fluids Eng. Trans. ASME 82(1) (1960).

[17] H. Wang, J. Leng, A brief review on the development of Kalman filter, in: Proc. 30th Chinese Control and Decision Conf., CCDC, 2018.

[18] M.Y. Baihaqi, W. Wijaya, Penerapan Filter Kalman untuk Meningkatkan Akurasi dan Presisi Sensor Suhu LM35, Konstelasi: Konvergensi Teknologi dan Sistem Informasi, 1(1) (2021).

DOI: 10.24002/konstelasi.v1i1.4282

[19] Texas Instrument Team, LM35 Precision Centigrade Temperature Sensors, Texas Instruments, 2017.

[20] A. Ma'arif, I. Iswanto, A.A. Nuryono, R.I. Alfian, Kalman Filter for Noise Reducer on Sensor Readings, Signal Image Process. Lett. 1(2) (2019).

DOI: 10.31763/simple.v1i2.2

[21] M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice with MATLAB, 4th ed., John Wiley & Sons, New York, 2014.