Laser Actuated Non-Invasive Smart Instrumentation - Enabling Lab-on-Chip

N. Z. Azeemi, N. Ahmed, N. U. Saquib


Non-invasive optical instrumentation provides non-destructive, reliable, and precise control in industrial process regulation, especially when chemical compounds or organic material surfaces are always a point of care. Nanomaterial dynamics intrinsically exhibit higher order of visual scanning complexities, associate wholly or partially to the poor scanning instrumentations. Additionally, growing trends in analytical instrumentation towards smart Lab-On-a-Chip (IoT sensing nodes) have shifted the emphasis on sensitivity and robustness tailoring Product Specific Environment (PSE). This work presents a hybrid laser actuated scanning mechanism, rastered back and forth 3-D imaging technique enabling Microscopy to its widest application in biological and material sciences and hence rose challenge of predicting large missing or incorrect data obtained during experiments. Our Confocal Self Calibrated Interferometry based fabricated Laser Sensor demonstrates its efficacy in non-invasive scanning microscopy to achieve a high-resolution 3D topographical view, eventually an add-on to the analytical model of microorganisms and nanomaterial. In contrast to linear controllers, PI controllers demonstrate better stability in controlling the laser leakage at tip, which consists of two channel tube adjustments and successively in laser reflector lens, Photo Multiplier Tube (PMT), and Data Acquisition Unit (DAU). We expose our results for error propagation across various grid patterns over a 1mm2 section, plotting the intensity of a key band or bands over the PMT grid. We observe that the instrumentation errors can be nullified by modeling the ergodicity of information flow along with the SLM instrumentation.


Smart instrumentation; IoT; non-invasive; laser; confocal; optical microscopy.

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P. Arthasarathy, P. Tamarapu and S. Vivekanandan, “A typical IoT architecture-based regular monitoring of arthritis disease using time wrapping algorithm.” International Journal of Computers and Applications 42 (2020): 222-232.

K. Amanli, A. Seyit, “A 3D imaging and visualization workflow, using confocal microscopy and advanced image processing for brachyuran crab larvae.” Journal of Microscopy 266 (2017): 307–323.

Z. Huang, X. Ling, “Synchronous detection of vascular tension and nitric oxide release in pulmonary artery: A combined application of confocal wire myograph with confocal laser scanning microscopy.” Vascular (2020): 1708538120917555.

K. E. McCracken, J. Yoon, “Recent approaches for optical smartphone sensing in resource limited settings: a brief review,” Anal. Methods, 8(36), pp. 6591-6601, Sep. 2016.

N. Z. Azeemi, "Handling Architecture-Application Dynamic Behavior in Set-top Box Applications," 2006 International Conference on Information and Automation, Shandong, 2006, pp. 195-200.

F. Giancarlo, L. Fotia, F. Messina, D. Rosaci, M. L. Sarné, “A meritocratic trust-based group formation in an IoT environment for smart cities.” (2020).

Z. Wuchao, T. Wang, Y. Gan, J. Yang, H. Zhu, A. Wang, Y. Wang W. Xi, “Effect of micropore/microsphere topography and a silicon-incorporating modified titanium plate surface on the adhesion and osteogenic differentiation of BMSCs.” Artificial cells, nanomedicine, and biotechnology 48 1 (2020), pp. 230-241.

Qualcomm® Snapdragon™ 865 mobile platform scales 5G and leading 5th gen AI to power next generation (Retrieved Mar 2020).

The Qualcomm Snapdragon Tech Summit reveals breakthroughs in 5G, AI, XR, and PCs (Retrieved April 2020).

B. Paul, M. Janssen, P. Herder, “The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations.” Int. J. Inf. Manag. 51 (2020): 101952.

S. Sebastian, R. Louise, J. Meyer, “Confocal microscopy imaging of the biofilm matrix.” Journal of microbiological methods 138 (2017): pp. 50-59.

M. Ghazanfar, S. A. Shah, “IoT-Flock: An Open-source Framework for IoT Traffic Generation.” ArXiv abs/2004.00844 (2020).

N. Z. Azeemi, G. Al-Utaibi, O. Al-Basheer, “Customer-in-Loop Adaptive Supply Chain Migration Model to Enable IoT”, IJITEE, ISSN: 2278-3075, Volume-9 Issue-6, pp. 1755-1762, April 2020.

F. Faezeh, M. Sayad, H. A. Jolfaei, M. Alazab, “Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT.” IEEE Transactions on Industrial Informatics 16 (2020): 2716-2725.

Q. Mei, H. Jing, Y. Li, W. Yisibashaer, J. Chen, B. N. Li, Y. Zhang, “Smartphone based visual and quantitative assays on upconversional paper sensor,” Biosens. Bioelectron, 75, pp. 427-432, Jan. 2016.

G. Wu, X. Di, “Towards Distributed SDN: Mobility Management and Flow Scheduling in Software Defined Urban IoT.” IEEE Transactions on Parallel and Distributed Systems 31 (2020): pp. 1400-1418.

D. Sione, E. Jones, R. J. Harvey, J. M. Skousen, T. J. Schoenherr, “Designing hardware for the boundary condition round Robin Challenge,” Kansas City National Security Campus, Sandia National Laboratories (2017).

J. V. Capella, A. Bonastre, R. Ors, M. Peris, “A wireless sensor network approach for distributed in-line chemical analysis of water,” Talanta, 80(5), pp. 1789–1798, Mar. 2010.

R. Cheikhousman, M. Zude, D. J. R. Bouveresse, C. L. Leger, D. N. Rutledge, and I. B. Aragon, “Fluorescence spectroscopy for monitoring deterioration of extra virgin olive oil during heating,” Anal. Bioanal. Chem., 382(6), pp. 1438–1443, Jul. 2005.

E. L. Johan, J. A. Abuellil, A. C. Reyes, M. Abouzied, S. Yoon and E. S. Sinencio, “A Fully Integrated Maximum Power Tracking Combiner for Energy Harvesting IoT Applications.” IEEE Transactions on Industrial Electronics 67 (2020): 2744-2754.

K. Ojha, T. Ankita. “Chapter 19 Nanomaterials for removal of waterborne pathogens opportunities and challenges.” Waterborne Pathogens (2020).

N. Ramanujam, “Fluorescence spectroscopy in vivo,” in Encyclopedia of Analytical Chemistry, John Wiley and Sons Ltd. Chichester, 2000; pp. 20–56.

N. Z. Azeemi, Z. Hayat, G. Al-Utaibi, O. Al-Basheer, “Hybrid Data Protection Framework to Enhance A2O Functionality in Production Database Virtualization”, IGRTE, Volume-8 Issue-6, pp. 5691-5697, Mar. 2020.

D. Chen, W. Yang, J. Hu, Y. Cai, X. Tang, “Energy-efficient secure transmission design for the internet of things with an untrusted relay.” IEEE Access 6, 11862–11870 (2018).

N. Z. Azeemi, A. Sultan and A. A. Muhammad, "Parameterized Characterization of Bioinfomatics Workload on SIMD Architecture," 2006 International Conference on Information and Automation, Shandong, 2006, pp. 189-194.

P. Deshmukh, S. Solanke, Review paper: sarcasm detection and observing user behavioral. Int. J. Comput. Appl. 166 (2017).

T. Tanwar, S. Tyagi, S. Kumar, “The Role of internet of things and smart grid for the development of a smart city, in Intelligent Communication and Computational Technologies,” LNNS, vol. 19, ed. by Y. Hu, S. Tiwari, K. Mishra, M. Trivedi (Springer, Singapore, 2018), pp. 23–33.

A. Kumar, S. Bharti, “Design and performance analysis of OFDM and FBMC modulation techniques.” Sci. Bull. Electr. Eng. Fac. 17, 30–34 (2017).

N. Z. Azeemi, "Exploiting Parallelism for Energy Efficient Source Code High Performance Computing," 2006 IEEE International Conference on Industrial Technology, Mumbai, 2006, pp. 2741-2746.

N. Khan, M. Alsaqer, H. Shah, G. Badsha, A. Abbasi, S. Salehian, “The 10 Vs, issues and challenges of big data,” in International Conference on Big Data and Education (ACM, New York, 2018), pp. 52–56.

R. Karimian, A. Nashmil, P. Hashemi, A. K. Jamosh, A. Afkhami, H. Bagheri. “The Principles and Recent Applications of Bioelectrocatalysis.” (2020).

W. Z. Khan, Y. Xiang, M. Y Aalsalem, and Q. Arshad, “Mobile phone sensing systems: a survey,” IEEE Comm. Surveys & Tutorials, 15(1), pp. 402-427, Jan. 2013.

S. Yu, W. Xiao, Q. Fu, Z. Wu, C. Yao, H. Shen, and Y. Tang, “A portable chromium ion detection system based on a smartphone readout device,” Anal. Methods, 8(38), pp. 6877-6882, Oct. 2016.

L. Lixiang, Z. Yang, Z. Dang, C. Meng, J. Hao-tian, D. Wang, G. Chen, J. Zhang, H. Peng and Y. Shao. “Propagation analysis and prediction of the COVID-19.” Infectious Disease Modelling 5 (2020): 282-292.

"Coronavirus Disease 2019 (COVID-19)". Centers for Disease Control and Prevention. (Retrieved March 2020).

"Coronavirus disease (COVID-19) technical guidance: Laboratory testing for 2019-nCoV in humans". (Retrieved March 2020).

Ocean Control, VIC, Australia, Smartphone electromagnetic sensor, [Online]. Available: (Retrieved March 2020).

Ocean Control, VIC, Australia, Smartphone UV sensor, [Online]. Available: https://oceancontrols. (Retrieved March 2020).

The Royal Swedish Academy of Sciences, Stockholm, Sweden, “Molecular Machines, Scientific Background on the Nobel Prize in Chemistry.” 2016, https:// (Retrieved Mar 2020).

K. Wak, B. Okeon, J. Bae, “Integrated Design and Fabrication of a Conductive PDMS Sensor and Polypyrrole Actuator Composite.” IEEE Robotics and Automation Letters 5 (2020): 3758-3765.

K. Yang, H. Peretz-Soroka, Y. Liu, and F. Lin, “Novel developments in mobile sensing based on the integration of microfluidic devices and smartphones,” Lab Chip, 16(6), pp. 943-958, Mar. 2016.

Malvern Instruments (2015) Manual: Nano-Sight NS300 user manual MAN0516. Malvern Instruments, Malvern Malvern-Instruments (2014) Nanosight NS300 NTA software guide. Malvern Instruments, Malvern, pp 1–24.

S. Ehsan, B. Ghaemi, R. Sahraei, Z. M. Sabzevari, S. Kharrazi and A. Amani. “Colloidal synthesis of tunably luminescent AgInS-based/ZnS core/shell quantum dots as biocompatible nano-probe for high-contrast fluorescence bioimaging.” Materials science & engineering. C, Materials for biological applications 111 (2020).



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