Category: ☆☆☆

Miniaturized ELISA Platform

Posted on | by andrewlai61616

Studying in a cross-disciplinary department meant having the freedom to choose what to explore. I entered the intelligence bio-sensing lab (previously named bio-molecular device lab) hosted by professor Lin-Chi Chen when I was a junior. There I was trained how to put into practice the engineering skills that I have learned during college, and implementing them on biosensing. I became interested in manufacturing devices that can realize automation, assist research, or help reduce the cost for lab experiments (e.g. Real-time Impedance Detection Systems, Surface Plasmon Resonance Platform, Automated Microfluidic Controlling Platform). This miniaturized ELISA platform serves as the first one among those devices and systems I had created.

The enzyme-linked immunosorbent assay (ELISA) is a commonly used analytical biochemical assay that uses antibodies against the protein to be tested to detect the presence of ligands (usually proteins) in the liquid sample. However, the traditional method for performing this assay is costly and time-consuming. Therefore, I decided to construct a miniaturized ELISA platform that can help reduce sample usage, and thus make it cheaper.

Small circular holes are cut on a thin acrylic board are by laser cut, a holder for assisting supporting the microwell is fabricated using 3D printing, and PVDF films are used as the base material for protein immobilization (Fig. 1).

Figure 1. Materials used for the miniaturized ELISA platform

The acrylic board with holes and another board with no holes are used to clip the PVDF film tight, wrapped with tape, making microwells with a volume capacity of ~10μL (Fig. 2 left). The microwell is put on the 3D-printed holder, and the right picture of Fig. 2 shows the microwell platform with each well containing 10μL deionized water.

Figure 2. PVDF clipped with an acrylic board with holes and another board without holes (left), and the microwell platform with every well containing 10μL deionized water.

Streptavidin-HRP is diluted using PBS buffer, and 5μL of the solution is added in each of the microwell. Then 5μL TMB is added for validation of the colorimetric detection method. Fig. 3 shows the experiment result using the platform for qualitative analyzing different concentrations of streptavidin-HRP. It can be seen that different concentrations yield different intensities of absorbed light signals (λ = 450nm), thus this platform can be further improved for real experimental use.

Figure 3. Different concentrations of streptavidin-HRP with TMB for colorimetric detection using the miniaturized ELISA platform.

This project is the first one for me to implement simple skills that I have learned during the first three years in university on real bio-detection research issues, which motivated me to start thinking of practical methods to use engineering techniques for solving problems in an interdisciplinary way.

Linear Algebra Calculation using Integrated Circuits

Posted on | by andrewlai61616

Even the simplest thing we recognize may seem increasingly difficult in another point of view. Take a simple arithmetic operation for example, if one wants to calculate the function y = ax + b with given a and b, he simply multiplies any number x with a, then adds b, and gets the answer. What if no multiplication and addition can be used? How can the calculation even be possible?

Computers can actually finish the task by implementing three fundamental logic operations: AND, OR, and NOT. Most of them can do these operations within a nanosecond. In this project, I constructed a circuit for performing a simple linear algebra calculation (Fig. 1) using only basic logic and storage circuits (Fig. 2) that can be realized using standard cells.

Figure 1. Formula to be calculated. (x0, x1, x2 are all 6 bit 2’s complementary integers)

Figure 2. Basic logic and storage circuits. (Note that other circuits (e.g. NAND, XOR) are also used in this project)

Here, x0, x1 and x2 equal the three 6-bit integer inputs (2’s complementary), so there are a total of 18 Boolean input values. The output is stored in a 16-bit integer. Therefore, the goal for this project is to construct a circuit that connects all of the 18 inputs and the 16 outputs, and perform the calculation.

To make it harder, three stages of pipelines are carried out. This means that calculations are divided into three parts, and the most time-consuming part contains the critical path of the whole circuit. Fig. 3 shows an illustration of the designed circuit.

Figure 3. Logic circuit diagram for realizing the arithmetic operation (Fig. 1) of this project.

Verilog is used for simulating the results, and the circuit is written as a spice sub-circuit model. Because the D flip-flop is used, the critical time is defined as the clock cycle of the D flip-flop. Moreover, the number of transistors are defined for every basic logic circuit, so the total number of transistors can be calculated, and is named the “area” of the whole circuit.

Fig. 4 shows the simulation results of the circuit. It can be seen that only 1.3305 nanosecond is used for a half clock cycle of the circuit. This means that the circuit can continuously output calculation results every 2.661 nanosecond, which is really fast!

Figure 4. Simulation results using Verilog.

Having the experience of using absolutely no arithmetic operations for calculating a linear algebra problem really significantly broadened my insight towards digital IC design. This project inspired me to understand that even the most insignificant elements possess the potential to be combined and make up the world that we live in.

[Verilog Source Code for Logic Circuit]

Robot Arm Control

Posted on | by andrewlai61616

It’s easy for us to point at a certain coordinate in space. That’s mainly because we simply locate the point with our eyes, and continuously check if our finger is pointing at that very spot. It surely will be more difficult without using eyes, and this is the case for robot arm control with no image feedback.

Think of a two arm robot (Fig. 1). We usually want to reach a certain point on the x-y plane. The problem is only the angle of the joints can be controlled. How can we correlate the joint angles of a robot with its tip coordinate? Things get harder when it comes to 3D space, and even harder considering its rotation.

In this project, I created a program that can calculate the every joint angle of the 6-arm robot IRB140 for positioning it at a given (x, y, z) coordinate and rotation.

Figure 1. Dimensions of the IRB140 robot (unit: mm) [1].

The problem for reversing an operation from the specified coordinate and rotation to every rotation angle of an arm joint lies in the field of inverse manipulator kinematics. There may be multiple solutions that lead to the same result. Thus, I implemented the Pieper’s solution [2] for solving the joint angles for the IRB140 robot.

Here’s a video demonstration for precision control of the IRB140 by only giving the joint angles as the input. The robot follows a trail surrounding a paper box with the tip of the last arm always pointing at the center of the box.

[Source code for robot arm control program]

1. ABB, IRB140 product specification, 2019, https://library.e.abb.com/public/2893a5756d204e19aba0d37c2a2cadc6/3HAC041346%20PS%20IRB%20140-en.pdf
2. Craig, J.J., Introduction to Robotics: Mechanics & Control. 1986: Addison-Wesley Publishing Company.

Guess the Number AI

Posted on | by andrewlai61616

After completing the first two projects of the Guess the Number series (Guess the Number (Windows) and Guess the Number (iOS)), I made an AI that can play this game at a high-human level.

It has been proven that at most 7 turns are needed to guess the answer, with a best average game length of 5.21 turns. For this game, all the possible combinations (e.g. “0123”, “7381” …) can be saved into a 1D array. After each guess, the possible combinations for the answer will be reduced. Therefore, the algorithm of the program is written for finding a number that will minimize the maximum possible combinations left. The time complexity for each turn is O(n3), and an average of 5 turns of guessing if needed for an arbitrarily chosen number.

For using the program, the user must first choose a 4-digit answer (e.g. “0123”), and input the two numbers [A] and [B] according to the game rules and the numbers guessed by the program. For instance, if the answer is “1357”, and the AI guesses “3127”, the user must input 1 2 ([A] = 1, [B] = 2).

Here’s a demonstration of the AI program guessing the answer “8192” in 5 guesses:

[Download Guess the Number AI program]

[Source code for the Guess the Number AI program]

Nonogram AI

Posted on | by andrewlai61616

Nonograms (also known as Picross, Griddlers, Pic-a-Pix, and various other names) are picture logic puzzles, in which the cells in the grid must be colored or left blank according to the numbers on the side of the grid to display hidden pictures.

Figure 1. The initial and complete state of a 25×25 grid Nonogram.
(Source: https://www.nonograms.org/nonograms/i/32344 )

For a classical type of game, the numbers are a form of discrete tomography that measures how many unbroken lines of filled-in squares there are in any given row or column. For example, a clue of “1 2 3” would mean there are sets of one, two, and three filled squares, in that order, with at least one blank square between successive sets.

Solving Nonograms can be very time-consuming, and can be tremendously brain-twisting as the grid size increases, thus I created an AI program for automatically solving these puzzles.

The program utilizes the depth-first search algorithm that runs recursively from the top to bottom row. For the jth column of the ith row, the black or blank spaces must satisfy the tomographic rules formed by the numbers of the current row and column. This is a relatively simple approach for calculating the answer. However, because the algorithm does not solve the game using a more intuitive method for solving interrelated constraints, it will use a lot of time for big grids (over 40×40).

For the input format of this program, two numbers indicating the number of rows and columns are given first (n and m). Afterwards, there are n rows of data, with each row k starting with a number nk indicating how many numbers are given in that row, and followed by nk numbers representing the discrete tomography of black spaces of that very row; then there are m rows of data, and also starting with a number mk for the kth row, and followed by mk numbers representing the discrete tomography of black spaces for the kth column.

Here’s an example input for the Nonogram in Fig. 1:

25 25
1 8
2 7 3
1 16
2 11 4
2 13 2
2 14 2
1 18
2 8 4
2 6 4
2 5 5
3 4 2 2
3 4 3 1
3 3 2 1
2 3 2
2 3 2
3 2 1 4
3 2 1 4
3 2 1 4
3 3 1 4
2 5 4
1 11
1 10
1 5
1 5
1 6
1 1
1 2
1 2
1 4
1 11
1 13
1 15
2 9 3
2 8 3
3 7 5 2
3 7 3 4
3 6 2 3
3 8 3 2
2 13 2
2 10 2
3 5 5 3
4 4 1 4 6
4 1 2 2 8
3 3 1 10
4 3 1 4 4
4 2 1 2 3
3 2 1 2
2 2 1
2 2 1
1 1

A demonstration for using the AI program for solving a 39×50 Nonogram in about 22 seconds is shown below. The result displays an owl sitting on a branch (Fig. 2).

Figure 2. The complete state of a 39×50 Nonogram displaying an owl on a branch.

[Download program for the Nonogram solver AI]

[Source code for the Nonogram solver AI]

Othello AI

Posted on | by andrewlai61616

Othello (a variant of the traditional game Reversi) is a two-player strategy board game played on an 8×8 square field, where each player takes turns placing black or white pieces and capturing the other player’s pieces.

Figure 1. Othello board game.

The Othello AI program is the second board game AI that I have written in junior high school (the first is the Tic-Tac-Toe AI). Just like the programming strategy for the tic-tac-toe AI, I used the logic thinking experiences that I had learned while playing this game, and hard-coded some summarized strategies into this program.

Compared with the tic-tac-toe AI, which has a game-tree complexity of 9! = 362880, Othello is much more complex, yielding a stunning complexity of approximately 1058. This makes the game still mathematically unsolved up to this very day. Therefore, instead of calculating the definite winning strategy, this Othello AI rather tries to find relative advantageous points (e.g. the corners), and moves that will temporarily maximize the number of pieces which it occupies by the coded algorithm, making it a beginner ~ intermediate level AI.

Here is a demonstration of using the Othello AI program:

[Download Othello AI program]

[Source code for the Othello AI program (Visual Basic 6.0 form file)]