The goal of the research project is to design, fabricate, and characterize an extremely sensitive biosensor for use in healthcare. Using AutoCAD software, a novel IDE pattern with a 5 μm finger gap was created. Conventional photolithography and regular CMOS technology were used in the fabrication process. A 3D nano profiler, scanning electron microscopy (SEM), high-power microscopy (HPM), and low-power microscopy (LPM) were used to physically characterize the manufactured IDE. Chemical testing was done using several pH buffer solutions, and electrical validation was performed using I-V measurements. The Al IDE was produced, with a tolerance of 0.1 μm between the fabricated IDEs and the design mask. Electrical measurements verified the flawless fabrication of the IDE, and the device's repeatability was validated by the outcomes of comparable IDE samples. For each pH buffer solution, a modest additional volume of 2 μl was used to quantitatively detect slight current fluctuations in the microampere range. Through pH calibration for advanced applications in the realm of chemical sensors using an amperometric method, this research study has verified the chemical behavior of the IDE.

The number of applications for wireless Bluetooth devices is on the rise this decade. However, power consumption has become a major concern as the demand for more functionality on a single chip increases. Furthermore, since the device has a limited battery capacity, high power consumption limits the chip's long-term operation. Therefore, low-power architecture becomes crucial in these devices. To minimize the power consumption, this work implements a clock gating and a Gray code state encoding in a Bluetooth microcontroller system-on-chip (SoC). Four operation state is being tested to measure switching activity: Bluetooth transmission test, sleep test, timer test, and UART test. This design targets 180 nm Silterra CMOS technology with the system working at 16 MHz. Synopsys System on Chip EDA tools is used to perform the gate-level simulation and power analysis. The experimental result showed that the system's power consumption decreased by 86% when applying clock gating. Implementing a Gray code state encoding on the bridge reduces the power consumption further by up to 42 µW.

Agricultural settings present unique challenges for the transmission of huge amounts of images over long-range wireless networks. It is challenging to remotely gather data for transmission over a wireless network in research areas due to a lack of basic amenities like internet connections, especially in distant agricultural areas. In this research, the Fast Fourier Transform (FFT) method was used in conjunction with the Discrete Cosine Transform (DCT) method of image compression to achieve a higher compression ratio. In order for a Wireless Sensor Network (WSN) to provide compressed image data to a wireless based station, a LoRaWAN network has been identified. Low-power LoRaWAN networks may regularly transmit compressed images from an agricultural region to a monitoring system up to 15 km away. Images of golden apple snails were collected for this study from a variety of sources. The procedure was coded in MATLAB so that it could be run with input images being judged by the created algorithm. The input images can be compressed with a range of compression ratios (CR) from 3.00 to 50.00, as shown by the simulation results. Compressed image quality is measured not only by the above-mentioned criteria, but also by Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). According to the numbers, the best achievable compression ratio is 49.04, with an MSE of 172.72 and a PSNR of 25.75 at its highest.

Wireless microcontrollers have become widely used in domestic and industrial applications, where Bluetooth is one of the most popular wireless communication mediums. This paper discusses the design and implementation of a wireless Bluetooth microcontroller System-on-Chip (SoC) using Silterra 180nm CMOS technology. It incorporates Cortex-M0 as the main processor and other essential peripherals for a microcontroller, such as a timer, watchdog, UART, and RTC. This paper demonstrates the gate-level simulation result of the integrated system where several firmware tests are loaded into the RAM and operate those peripherals to verify the overall system functionality. The simulation results show that the system is able to perform the data transmit and receive successfully.

Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

The integrated circuit had been developed rapidly from microprocessor to microcontroller and System-on-Chip (SoC) just happen within a few decade. The development is continuing as the demand for the functionality increase. The Real Time Clock (RTC) is one of the peripherals most widely used inside the microcontroller. Besides, when Moore’s law is still applied, the number of transistor will increase, so does the temperature. Thus, the temperature digital signal processing (TDSP) unit had become an important peripheral to monitor the system-on-chip temperature. In this paper, these two Intellectual Property which are RTC and TDSP are designed and integrated into ARM system. The system architecture and implementation strategies are discussed. The results were run and simulated in the Synopsys software.

A deoxyribonucleic acid (DNA) microarray image requires a three-stage process to enhance and preserve the image’s important information. These are gridding, segmentation, and intensity extraction. Of these three processes, segmentation is considered the most difficult, as its function is to differentiate between features in the foreground and background. The elements in the foreground form the object or the vital information of the image, while the background features less critical information for DNA microarray image analysis. This paper presents a study that utilises the Markov random field (MRF) segmentation algorithm on a DNA microarray image. The MRF algorithm evaluates the current pixel depends on its neighbouring pixels. The experimental results show that the MRF algorithm works effectively in the segmentation process for a DNA microarray image.

Digital image processing is important for image information extraction. One of the image processing methods is morphological image processing. This technique uses erosion and dilation operations to enhance and improve the image quality by shrinking and enlarging the image foreground. However, morphological image processing performance depends on the characteristics of structuring elements and their foreground image that need to be extracted. This paper studies how the structuring elements affect the performance of morphological erosion and dilation on binary images. The experimental result shows that choosing the right structuring element for morphological erosion and dilation can significantly influence the foreground and background structure of the output image.

Wireless sensor nodes play an important role for Internet of Things (IoT) applications. However, these devices often come with limited memory sizes and battery life. Thus, to overcome these problems, this work focuses on studying the data compression algorithm suitable for wireless sensor nodes. In this work, run-length encoding (RLE) compression algorithm performance is studied, especially when compressing various climate datasets. This dataset includes temperature, sea-level pressure, air pollution index, and water level. In our experiment, the RLE algorithm gives the best compression ratio for temperature and sea-level pressure, with 0.62 and 0.63 compression ratios, respectively. These are equivalent to around 40% data saving. For air pollution index and water level dataset, our experiment gives 0.96 and 0.93 compression ratios, respectively. Since this data has a low number of repetitive values, the RLE achieves around 10% saving for this kind of data.

Generally, two electrode based amperometric biosensors show extremely low current signal output around pico ampere (pA) to micro ampere (µA) range. This paper describes the development of electronic reader to capture and amplify four different range of current as mili, micro, nano and pico ampere and convert it to detectable voltage range as an output signal to the microcontroller. The MAX4238 op-amp IC was used to amplify micro voltage to mili voltage. NodeMCU was act as the process and control circuit to read the output voltage from the amplifier circuit. The entire system is comprised of a voltage amplifier circuit, filter circuit, microcontroller and power supply unit. The range of the current measurement of the system was from 1 pA to 650 mA. The amplifier operation was measured with a high impedance current source and has been compared with the theoretical measurement. The Design Spark PCB software was used to design the voltage amplifier circuit. Arduino software was used to create a programming code to upload in NodeMCU microcontroller.

Amplification of nano and mircoampere electrical signal to the detectable range is essential in the biosensor field. This research is mainly focused on design an amplifier circuit to capture and amplify three different range of current as nano, micro and mili ampere and convert it to detectable voltage range as an output signal to the processing circuit. The Proteus 8 Pro software was used to design, simulate and calibrate the amplifier circuit. Firstly, current input as mili, micro and nano current were flown through 0.1 mΩ, 10 Ω and 10 KΩ resistors, respectively to convert different current inputs to the similar range in micro voltage. The MAX 4238 opamp IC was used to amplify micro voltage to mili voltage. LM 358 dual operational amplifier was used to supply virtual ground to MAX 4238 amplifier. The amplified output voltage of three different current inputs as nano, micro and mili were nearly equal to theoretical outputs.

The aim of the research study to design high sensitive biosensor for medical applications. IDE pattern was designed using AutoCAD software with 5 µm ginger gap. The fabrication process was done using a conventional photolithography process and standard CMOS process. The fabricated electrode was physically characterized using a low power microscope (LPM) and a high power microscope (HPM). The electrically validated through I-V measurements and chemically tested with different pH buffer solutions. Al IDE was well fabricated with 0.1 µm tolerance between the design mask and fabricated IDEs. Electrical measurements confirmed that IDE was well fabricated without any shortage and results of similar IDE samples were confirmed that the repeatability of the device. The extremely small current variations in nano ampere range were quantitatively detected using an extra small volume of 2 µl for different pH buffer solutions. It is confirmed that IDEs are sensitive in both alkali and hydroxyl ions medium.

Contamination of various food samples became one of the critical issues in food pathogen infection. Food pathogen can be detected by using digital polymerase chain reaction (PCR) and sequencing. These methods were reliable but consuming and take a longer time for detection. The present work describes the innovation to develop a technology to extract double-stranded deoxyribonucleic acid (dsDNA) from food samples and then denatured dsDNA into and single-strand DNA (ssDNA) for further use on the chip using microfluidic device. Microfluidic device is a lab-on-chip device that consist of microfluidic channels that provide paths for biomolecules to flow to individual point of care. DNA extraction is the process by which DNA is separated from proteins, membranes, and other cellular material contained in the cell from which it is recovered. Lysis solution is used in the process of extraction the DNA to break up the cells containing DNA from protein and other cellular materials. This extraction firstly be done in the most labour-intensive in obtaining the DNA biomolecules. Extraction methods may require an overnight incubation, may be a protocol that can be completed in minutes or a couple of hours by using a commercial kit. The disadvantages of the laboratory and commercial kit is due to time-consuming, poor cost-effectiveness, the need to use big laboratory and a complicated process which need an expertise to conduct the experiment and interpret the data. This research is proposed to design and fabricate a microfluidic device that has DNA extraction capabilities. In this research DNA extraction using a commercial kit will be used as a comparison for the quality of the result. The microfluidic device can be used in health care delivery system and will help the doctors in diagnostic process to identify disease of a patient rapidly. Other than that, the output extracted from microfluidic device will be used for DNA probe target interaction for diagnostic kit. The major advantage of microfluidic device is that it consumes less time compared to the conventional chemical methods.

There are limited number of electrical based two type electrode electronic readers for biosensors are commercial available because of the noise issues and amplification at nano to pico ampere current range. This research is mainly focused on designing an active low pass filter circuit of electronic reader for biosensors. The entire circuits are comprised of a voltage converter circuit, active low pass filter circuit, voltage amplifier, microcontroller and display unit. The circuit capture, filter and amplify nano and pico ampere current convert it to detectable voltage range as an output signal to the processing circuit. NodeMCU was act as the process and control circuit to read the output voltage from the amplifier circuit. The signal generator will act as a replacement for the biosensor input current and oscilloscope will display the input and output signal. The Design Spark PCB software was used to design the voltage amplifier circuit. Arduino software was used to create a programming code to upload in NodeMCU microcontroller.

This paper is about an experiment for performing foodborne pathogens electronic reader using wireless sensing Internet of Thing (IoT). There are limited number of electronic readers for biosensors application with wireless internet connection. This research is to overcome the problem of commercial available electronic reader based on biosensor application method that only can be perform in offline or standalone device. This paper shows a complete system on how the data from electronic reader can be collected, easily understand by user and transfer data through the wireless internet connection via platform of IoT. There are three stages that is coding modification, android application development and transmit data to cloud storage. The NodeMCU microcontroller was used as a transfer medium for transfer data to internet. The Android Studio software was used for mobile application development. While, Arduino software was used to create a programming code to upload in NodeMCU microcontroller.

Laser micromachining provide significant effect in thin film solar industrial field especially in determining cell efficiency of each panels. However, there is an issue in determining scribing failure or defect on solar module. This research aims to investigate the defects of laser micromachining process in thin film solar module in manufacturing fields. Machine vision inspection system is used as inspection tools and to investigate the defect of laser micromachining in thin film solar cells. As a result, two major defects is define which is scribe line quality and scribe line position defects in every scribe line. By identifying the defect cause by laser micromachining through machine vision, quality control plan can be taken together to prevent reoccurrence.

Nowadays there are many alternative methods that have been discovered and developed for the rapid detection of foodborne pathogens that can cause food poisoning. Unfortunately, majority of them still requires improvement in sensitivity and selectivity issues to be of any practical use daily. In this research, biosensors was prepared from 5 µm gap Aluminium interdigitated electrode (Al IDE) to detect Salmonella enterica typhi (S. typhi). The IDE sensors in the biosensor field is extremely interest in these days due to the high number of finger electrodes as comb structure which can gain high sensitivity through electrical measurements. S. typhi is a serious food borne pathogen, makes typhoid disease which causes many deaths annually in worldwide. Functionalization steps of the Al IDE to create biosensor was based on silanization by APTES, immobilization with carboxylic functionalized S. typhi ssDNA probes and blocking agent with tween-20 were the major functionalization steps. The functionalized steps were electrically characterized using current voltage measurements. The selectivity measurement was performed with specific target was identified electrically using complementary, non-complementary and single base mismatch ssDNA target.

Titanium dioxide (TiO2) nanoparticles based Aluminium Interdigitated Electrode (Al IDE) was tested as pH electrodes and measured quantitatively. TiO2 nanoparticle was synthesized using sol-gel method with monoethanolamine (MEA) as a catalyst. The mixing of titanium butoxide as a precursor, ethanol as a solvent and MEA as stabilizer were stirred using magnetic stirrer under ambient temperature. Al IDE were fabricated by conventional photolithography method. TiO2 solution prepared then was deposited on Al IDE using spin-coater and the coated device were annealed at 400°C. Deposition of TiO2 solution on the fabricated Al IDE forms a sensor that promising for development of TiO2 nanoparticle based biosensors. The surface morphologies structural properties were studied using Scanning Electron Microscopy (SEM). The small amount of current measurement of this device towards hydrogen and hydroxide ions was measured by Keithley 2450 pico ammeter.

Abstract Nowadays interdigitated electrode (IDE) based sensor have stimulated increasing interest in the application of biosensor filed. A large number of finger electrodes as comb structure gain high sensitivity through electrical measurements. In this paper, we have investigated Listeria bacteria detection through the electrical based IDE. Listeria monocytogenes is a food borne pathogen-based bacterium that can cause dangerous disease to human, some infection may result in death. The AutoCAD software was used to design the chrome mask of IDE sensor and the fabrication process was done using conventional photolithography method. The fabricated Al IDE morphologically analyzed using a low power microscope (LPM), a high-power microscope (HPM) and 3D profiler. Functionalization step of the Al IDE, silanization process was done using (3-Aminopropyl) triethoxysilane (APTES), immobilization process was done using carboxylic probe Listeria and Tween-20 as a blocking agent for nonspecific binding on the non-immobilized area of the biosensor surface. The biosensor was validated with complementary, non-complementary and single base mismatch ssDNA targets. Different concentration of complementary ssDNA target from 1 fM to 1 µM was done for the sensitivity detection.

Abstract Amperometric electrical biosensors have small current variations at nano to micro range. There are limited number of electrical based two electrode electronic readers for biosensors are commercial available because of the amplification and noise issues at nano to micro ampere current range. The electronic reader focused on design a voltage amplifier circuit to capture and amplify three different range of current as nano, micro and mili ampere and convert it to detectable voltage range as an output voltage signal. Current input as nano, micro and mili current were flown through 10 K Ω, 10 Ω and 10 m Ω resistors, respectively to convert different current inputs to the similar range in micro voltage. Then, MAX 4238 op-amp IC was used to amplify micro voltage to mili voltage. Arduino Uno circuit was act as the process and control circuit to read the output voltage from the amplifier circuit. Arduino Uno circuit will convert analog signal to digital signal and then the output voltage value is display in the LCD screen. The Proteus 8 Pro software was used to design, simulate and calibrate the amplifier circuit and Arduino Uno circuit. While, Arduino software was used to create a programming code and to upload in Arduino Uno circuit. Start your abstract here.