Real-time machine vision FPGA implementation for microfluidic monitoring on Lab-on-Chips

Intelligent optoelectrowetting digital microfluidic system for real-time selective parallel manipulation of biological droplet arrays

Optoelectrowetting technology generates virtual electrodes to manipulate droplets by projecting optical patterns onto the photoconductive layer. This method avoids the complex design of the physical circuitry of dielectricwetting chips, compensating for the inability to reconstruct the electrode. However, the current technology relies on operators to manually position the droplets, draw optical patterns, and preset the droplet movement paths. It lacks real-time feedback on droplet information and the ability for independent droplet control, which can lead to droplet miscontrol and contamination. This paper presents a combination of optoelectrowetting with deep learning algorithms, integrating software and a photoelectric detection platform, and develops an optoelectrowetting intelligent control system. First, a target detection algorithm identifies droplet characteristics in real-time and automatically generate virtual electrodes to control movement. Simultaneously, a tracking algorithm outputs trajectories and ID information for efficient droplet arrays tracking. The results show that the system can automatically control the movement and fusion of multiple droplets in parallel and realize the automatic arrangement and storage of disordered droplet arrays without any additional electrodes and sensing devices. Additionally, through the automated control of the system, the cell suspension can be precisely cultured in the specified medium according to experimental requirements, and the growth trend is consistent with that observed in the well plate, significantly enhancing the experiment's flexibility and accuracy. In this paper, we propose an intelligent method applicable to the automated manipulation of discrete droplets. This method would play a crucial role in advancing the applications of digital microfluidic technology in biomedicine and other fields.

Recent advances in non-optical microfluidic platforms for bioparticle detection

The effective analysis of the basic structure and functional information of bioparticles are of great significance for the early diagnosis of diseases. The synergism between microfluidics and particle manipulation/detection technologies offers enhanced system integration capability and test accuracy for the detection of various bioparticles. Most microfluidic detection platforms are based on optical strategies such as fluorescence, absorbance, and image recognition. Although optical microfluidic platforms have proven their capabilities in the practical clinical detection of bioparticles, shortcomings such as expensive components and whole bulky devices have limited their practicality in the development of point-of-care testing (POCT) systems to be used in remote and underdeveloped areas. Therefore, there is an urgent need to develop cost-effective non-optical microfluidic platforms for bioparticle detection that can act as alternatives to optical counterparts. In this review, we first briefly summarise passive and active methods for bioparticle manipulation in microfluidics. Then, we survey the latest progress in non-optical microfluidic strategies based on electrical, magnetic, and acoustic techniques for bioparticle detection. Finally, a perspective is offered, clarifying challenges faced by current non-optical platforms in developing practical POCT devices and clinical applications.

FPGA Integrated Optofluidic Biosensor for Real-Time Single Biomarker Analysis

Integrated optofluidic biosensors can fill the need for sensitive, amplification-free, multiplex single molecule detection which is relevant for containing the spread of infectious diseases such as COVID-19. Here, we demonstrate a rapid sample-to-answer scheme that uses a field programmable gate array (FPGA) to enable live monitoring of single particle fluorescence analysis on an optofluidic chip. Fluorescent nanobeads flowing through a micro channel are detected with 99% accuracy and particle concentrations in clinically relevant ranges from 3.4×104 to 3.4 × 106/ml are determined within seconds to a few minutes without the need for post-experiment data extraction and analysis. In addition, other extract salient experimental parameters such as dynamic flow rate changes can be monitored in real time. The sensor is validated with real-time fluorescence detection of single bacterial plasmid DNA at attomolar concentrations, showing excellent promise for implementation as a point of care (POC) diagnostic tool.

Machine vision-based driving and feedback scheme for digital microfluidics system

A digital microfluidic system based on electrowetting-on-dielectric is a new technology for controlling microliter-sized droplets on a plane. By applying a voltage signal to an electrode, the droplets can be controlled to move, merge, and split. Due to device design, fabrication, and runtime uncertainties, feedback control schemes are necessary to ensure the reliability and accuracy of a digital microfluidic system for practical application. The premise of feedback is to obtain accurate droplet position information. Therefore, there is a strong need to develop a digital microfluidics system integrated with driving, position, and feedback functions for different areas of study. In this article, we propose a driving and feedback scheme based on machine vision for the digital microfluidics system. A series of experiments including droplet motion, merging, status detection, and self-adaption are performed to evaluate the feasibility and the reliability of the proposed scheme. The experimental results show that the proposed scheme can accurately locate multiple droplets and improve the success rate of different applications. Furthermore, the proposed scheme provides an experimental platform for scientists who focused on the digital microfluidics system.

Development of an Optical Method for the Evaluation of Whole Blood Coagulation

Blood coagulation is a defense mechanism, which is activated in case of blood loss, due to vessel damage, or other injury. Pathological cases arise from malfunctions of the blood coagulation mechanism, and rapid growth of clots results in partially or even fully blocked blood vessel. The aim of this work is to characterize blood coagulation, by analyzing the time-dependent structural properties of whole blood, using an inexpensive design and robust processing approaches. The methods used in this work include brightfield microscopy and image processing techniques, applied on finger-prick blood samples. The blood samples were produced and directly utilized in custom-made glass microchannels. Color images were captured via a microscopy-camera setup for a period of 35 min, utilizing three different magnifications. Statistical information was extracted directly from the color components and the binary conversions of the images. The main advantage in the current work lies on a Boolean classification approach utilized on the binary data, which enabled to identify the interchange between specific structural elements of blood, namely the red blood cells, the plasma and the clotted regions, as a result of the clotting process. Coagulation indices produced included a bulk coagulation index, a plasma-reduction based index and a clot formation index. The results produced with the inexpensive design and the low computational complexity in the current approach, show good agreement with the literature, and a great potential for a robust characterization of blood coagulation.

Hardware Trojans in Chips: A Survey for Detection and Prevention

Diverse and wide-range applications of integrated circuits (ICs) and the development of Cyber Physical System (CPS), more and more third-party manufacturers are involved in the manufacturing of ICs. Unfortunately, like software, hardware can also be subjected to malicious attacks. Untrusted outsourced manufacturing tools and intellectual property (IP) cores may bring enormous risks from highly integrated. Attributed to this manufacturing model, the malicious circuits (known as Hardware Trojans, HTs) can be implanted during the most designing and manufacturing stages of the ICs, causing a change of functionality, leakage of information, even a denial of services (DoS), and so on. In this paper, a survey of HTs is presented, which shows the threatens of chips, and the state-of-the-art preventing and detecting techniques. Starting from the introduction of HT structures, the recent researches in the academic community about HTs is compiled and comprehensive classification of HTs is proposed. The state-of-the-art HT protection techniques with their advantages and disadvantages are further analyzed. Finally, the development trends in hardware security are highlighted.