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In this article, we presented the development of fully modular microfluidic flow cells for an electrochemical using 3D printing. The proposed devices are potential for electrochemical measurements using a small sample volume on a fully portable, reusable, simply fabricated, low-cost, PDMS-free, and leakage-free flow cell. This concept offers a simple, controllable sample over the conventional electrochemical platform with a three-electrode system, which requires a considerable volume of samples or a non-controllable drop cast method for sequential protocols. We demonstrated an easy alignment and lock, namely, click-and-fit modular microfluidics, for quick and easy assembly and disassembly of flow cell modules using magnetic force instead of the screw, polymer glue, or resin. Two microfluidic modules were presented using tube- and syringe-flow cells (TFC and SFC) to integrate the screen-printed carbon electrodes (SPCE) in the electrochemical sensor. The proof-of-concept of the integrated sensor-microfluidic platforms was conducted under cyclic voltammetry using a tiny volume of a ferricyanide redox probe at only ~50 µL, differential pulse voltammetry, and square wave voltammetry. Implementing the proposed click-and-fit microfluidic modules in electrochemical detection achieves higher current peaks than droplet measurements. These flow cell modules are promising for biosensing applications using a small volume of physiological fluid samples.

Dopamine (DA) is an important neurotransmitter in the kidney, cardiovascular system, and central nervous system, which abnormality is associated with many diseases. In this work, we synthesized a functionalized multi-walled carbon nanotube/silver nanoparticle (f-MWCNT/AgNP) nanocomposites as the biosensing material to detect DA. The SEM, EDS, and TEM characterizations indicated the success of the functionalization process with MWCNT as the base material. The values of the linear range, the limit of detection (LOD), and the selectivity of the nanocomposite were all obtained from the Differential Pulse Voltammetry (DPV) measurements. The obtained LOD value was 0.2778 µM in the linear range of 0–8 µM, which is lower than the required concentration value for detecting DA in human urine (0.3–3 µM). The biosensor’s high selectivity on DA with the presence of other human-related biofluids was also reported. These results show that f-MWCNT/AgNP nanocomposites are a promising biosensor material for the detection of DA.

Metal-organic framework (MOF) nanomaterials offer a wide range of promising applications due to their unique properties, including open micro- and mesopores and richness of functionalization. Herein, a facile synthesis via a solvothermal method was successfully employed to prepare amine-functionalized Cu-MOF nanospheres. Moreover, the growth and the morphology of the nanospheres were optimized by the addition of PVP and TEA. By functionalization with an amine group, the immobilization of a bioreceptor towards the detection of hepatitis B infection biomarker, i.e., hepatitis B surface antigen (HBsAg), could be realized. The immobilization of the bioreceptor/antibody to Cu-MOF nanospheres was achieved through a covalent interaction between the carboxyl group of the antibodies and the amino-functional ligand in Cu-MOF via EDC/NHS coupling. The amine-functionalized Cu-MOF nanospheres act not only as a nanocarrier for antibody immobilization, but also as an electroactive material to generate the electrochemical signal. The electrochemical sensing performance was characterized using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and differential pulse voltammetry (DPV). The results showed that the current response proportionally decreased with the increase of HBsAg concentration. More importantly, the sensing performance of the amine-functionalized Cu-MOF nanospheres towards HBsAg detection was found to be consistent in real human serum media. This strategy successfully resulted in wide linear range detection of HBsAg from 1 ng mL-1 to 500 ng mL-1 with a limit of detection (LOD) of 730 pg mL-1. Thus, our approach provides a facile and low-cost synthesis process of an electrochemical immunosensor and paves the way to potentially utilize MOF-based nanomaterials for clinical use.

This work reports on sonochemical synthesis of carbon paste electrodes modified with magnetic Fe3O4/graphene nanocomposites as a label-free electrochemical biosensor for prostate-specific antigen detection. To optimize their properties as a biosensor, the compositions of both Fe3O4 and graphene were varied. The as-synthesized nanocomposites possess good crystallinity and purity with Fe3O4 nanoparticles attached onto the graphene surface. From voltammetry measurements, the modified electrode shows an increase of reduction peaks as compared to the bare electrode which promisingly led to a more sensitive biosensing detection. The modified electrode was then used for biomarker detection through the immobilization of the anti-prostate-specific antigen (anti-PSA) antibodies onto it. The results showed that the electrochemical signal increased linearly against PSA concentration, from 1 to 150 ng/mL, with a detection limit of 0.38 ng/mL. Having all these characteristics, the Fe3O4/graphene nanocomposites offer a promising path towards the development of highly sensitive prostate biosensors, especially for cancer detection.

Heart diseases still contribute as the highest cause of death globally. It is also well-known that hypertension and high blood cholesterol are the key risk factors for heart diseases. This emphasizes that more preventive efforts are urgently needed. One common preventive method is to conduct a 24-hour ambulatory setting for blood pressure (BP) monitoring. With this monitoring, the doctors can give the patients a more precise treatment/medication at the earliest stage before the disease develops into a much-worsening state. However, the established methods use cuff-based devices. This is indeed very inconvenient and painful for the patients. Hence, a cuff-less BP monitoring device and system becomes the ideal solution. Furthermore, our daily activities including habits and food/drink intake can also trigger the increase/decrease of BP. This where the use of wearable and flexible devices for BP monitoring plays an important role. Thus, we provide a minireview on how the BP can be estimated using cuff-less methods and also how the flexible materials crucially support the realization of wearable cuff-less BP monitoring devices and systems by providing more comfort to wear and more cardiovascular health assessment as well as more electronics functionalities.

Contactless smart card has been applied in various applications, including electronic transaction and personal identification. These applications particularly require a big data transfer, as compared to other applications. Hence, it is very important that the card supports a high-speed data transfer rate. In this paper, we propose a design of a fully integrated transceiver module for contactless smart card that supports a high bit rate data communication, up to 848 kbps. Furthermore, to increase the stability and security of the card, the module is also designed to compensate the applied temperature variation, ranging from −25 to 125 °C.

The rise of wearable technology has gradually shifted modern health monitoring from clinical to personal use. Smart wearables can collect physiological signals and show them directly on a smartphone. In contemporary healthcare scenarios, this big data could aid medical doctors in online health analysis. Most currently available wearables are designed to monitor specific health parameters, while the combination of many devices is practically not convenient and not cost-effective. Therefore, a strong trend is towards the development of multifunctional devices. This demands, however, alternative sources of power other than conventional batteries. The concept of human-body-powered biosensing textiles (HBBTs) addresses this challenge. By harvesting energy produced from the human body such as motion, pressure, vibration, heat, and metabolites and converting them into electricity, HBBTs could potentially work without a battery. Additionally, the textiles themselves provide a suitable substrate for interconnects and biosensors, such that a system based on HBBTs could provide multifunctional health monitoring. This review explains the fundamental theories, the classification, the energy-conversion efficiency assessment, and the possible biomonitoring applications of HBBTs. Furthermore, we discuss the challenges for technology maturity and the perspectives of HBBTs in shaping the future of health monitoring.

Surface plasmon resonance (SPR) is a versatile optical bio-sensing technique which has an ability to detect antibody-antigen molecular binding. In this work, we present a data processing algorithm that can process and analyze the data output from SPR equipment. The SPR data output is typically a non-periodic square wave, an indicator that a biological substance is captured, with continuous noises. To remove the outliers and smoothen the data, moving average and Savitzky-Golay Filter were employed. Then, a change point detection method and polynomial regression were used to isolate the buffer data as baseline and give baseline prediction which is later used to calculate and quantify the response. From this study, the algorithm is expected to give an accurate baseline prediction and response calculation. Based on the results, the algorithm was able to detect the SPR signal response (change point detection) with an error below 15%. Thus, this algorithm would enable the researcher to analyze and interpret the SPR data much faster and simpler.

The rapid spread of COVID-19 disturbs the availability of the diagnostic tests for detecting COVID-19. Recently, a new test method is being developed by the cooperation between BPPT, ITB, Unpad and PT. TMC. The method is called as the Surface Plasmon Resonance (SPR). However, the data output of the SPR device must be further processed to obtain a powerful information. Hence, we propose a new approach for interactively visualizing SPR data by designing a web app platform. By this platform, user/operator can directly upload the SPR data output file in the web app. In the current design, the imported data are stored in the cloud storage. Moreover, user can also enter additional data for the purpose of labelling the graph that will be automatically generated. Bokeh was chosen as a Python library for visualizing data into an interactive graph. The graph features are label, tooltips, and interactive legend. A SPR data can have various signal responses. But, the labels can be as the help to distinguish those responses. The tooltips allow the user to obtain detailed information from a data point. Meanwhile, the interactive legend is used to distinguish SPR data plot and baseline plot. Therefore, by the use of this web app, the SPR sample analysis could be done just within in minutes. It also only requires a web browser and Python to works.

In order to increase user experience in using near field communication smartcard, analog front-end (AFE) module is required to provide a sufficient and a well-regulated voltage regardless the distance between the card and the reader. A highly stable AFE design for energy harvesting purpose is introduced in this paper. The design consists of antenna, rectifier, voltage limiter, bandgap reference, and low-dropout (LDO) voltage regulator circuit. The antenna is designed to resonate at 13.56 MHz as regulated by ISO/IEC 14443-2. In order to simplify the implementation using 0.18 μm CMOS process, a full-wave rectifier circuit is built of all low-threshold-voltage diode-connected PMOS transistors. To protect the system from undesired excessive input voltages, a voltage limiter circuit is included in the module. Moreover, control and maintain a stable supply voltage for the whole system, a robust LDO voltage regulator and bandgap circuits are specially designed for this purpose. The LDO is able to provide a stable 1.8 V of supply voltage with a sub-1% ripple factor even under a low input current as low as 20 mA.

The big idea of this research is to build node design for IoT system which will integrates all required sensor and communication protocol in single chip (SoC) so that designing, integrating and also testing node system can be done in easy way. ZigBee offers many advantages to be used in IoT system, therefore in this research ZigBee chosen as communication protocol for this node design. In this paper we present ZigBee baseband transceiver design implemented in an APSoC (All Programmable System on Chip) architecture. ZigBee transceiver is firstly designed in RTL level using VHDL code, then is implemented in APSoC platform. ZigBee-APSoC system runs well with clock frequency source of 50 MHz. Verification test is carried out by comparing the result of HDL simulation in ModelSim simulator software and the output of APSoC system. Final test is conducted by firstly implementing transceiver design in Xilinx® Zynq®−7000 development board and then performing the functional test in some amount of iteration cycles. Performance of APSoC design is then analyzed for several aspects i.e. total bit error, area consumption and power consumption. Based on the final simulation, no error is found. Furthermore, the power consumption of the transmitter is less than 7 mW, while for the receiver is less than 4 mW.

The MNIST database was derived from a larger dataset known as the NIST Special Database. In this paper, colorectal histology CSV data is multi-classified with several method: Convolutional Neural Network (CNN), K-Nearest Neighbour (KNN), Logistic Regression, and Random Forest. The aim of this paper is to compare the performance of each method and define the optimum algorithm; the best method is obtained with CNN with 82.2% accuracy.

Early detection for Parkinson's Disease (PD) can be realized by investigating the speech abnormalities of the patient. Utilizing machine learning approach, PD can be well diagnosed by investigating its speech features. Oxford Parkinson's Disease (OPD) dataset, containing pieces of PD patients' speech and normal speech was used in this study. The investigated algorithms that were tested are Support Vector Machine, K-Nearest Neighbor, Linear Discriminant Analysis, Gradient Boost, Multi-layer Perceptron, and Decision Tree. The performance evaluation of all these methods is based on accuracy, precision, recall, and F1 score. Based on the evaluation, the most suitable algorithm for PD case is Multilayer Perceptron with the accuracy of 95.92% without data scaling.

This paper presents the application of machine learning on gene expression datasets in order to classify cancer cells. Several analytical methods, including Principal Component Analysis (PCA), Support Vector Machine (SVM), Gradient Boosting, and XGBoost are performed to find the best model for processing the datasets. Additionally, classification with hyperparameter tuning using GridSearch and RandomSearch are also performed. The dataset is obtained from the study published by Golub et al [1]. They reported how new cases of cancer could be classified by gene expression monitoring via DNA microarray and thereby provided a general approach in identifying new classes of cancer and assigning tumors to the existing and known classes. The datasets were used to classify patients diagnosed with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). These datasets contain measurements in correspond to ALL and AML data samples from Bone Marrow and Peripheral Blood. Based on the simulation results, PCA with K-Nearest Neighbor shows the best result by providing 82% of classification accuracy.

Microfluidic chamber or channel is one important tool in the lab-on-a-chip (LoC) technology, which usually serves as a platform to contain liquid analytes. The conventional method to fabricate microfluidic chamber is based on photolithography technique. However, it is a high-cost process and requires special equipment, such as mask aligners and expensive photosensitive resist material. In this work, we fabricated the microfluidic chamber and channel using a much simpler and lower-cost technique that is using PDMS as the main material and polyimide tape as the mold. Using this method, the diameter of the microfluidic pattern (channel/chamber) could be made up to as small as 1 mm and the height of the flow channel was down to tens or hundreds of μm scale-depending on the thickness of the tape. The flow test results showed that low cost and simple microfluidic fabrication method had a good performance and could potentially be further used for real analyte flow test.

In lab-on-chip development, screen printed electrode (SPE) method is usually utilized as an electrochemical sensor. As a basic conductive material, carbon has several advantages compared to other conductive materials. SPE performance can be enhanced by using a nanomaterial due to its unique properties, such as its small size particle and large surface area that can accelerate the electron transfer on the surface of the electrode. Graphene as a carbon-based nanomaterial is an extraordinary material to work with because of its good electrical conductivity and large specific surface area. In this work, we developed a graphene paste from the water-based graphene ink with the addition of polyurethane binder material to realize a nanocarbon conductive paste, which insoluble in water and other electrolytes. Our graphene paste was deposited on the working electrode area of SPE and the performance was tested using cyclic voltammetry method. The result showed that the optimal ratio for the graphene carbon paste, polyurethane to graphene ink, was 1:15 %vol. With this ratio, the performance of the modified SPE could successfully be increased and it also showed a stable sensing performance by having a low error value, below 3%, for 7 times of repeated measurements.

Electrochemical biosensors have attracted a tremendous attention for many researchers recently due to its facile synthesis process, tunability easiness by tailoring the material properties or composition, and wide range of biological analyte types detection. To obtain an excellent electrochemical biosensor performance, a material that facilitates fast electron transfer, large surface area, excellent electrocatalytic activity, and abundant available sites for bioconjugation is immensely needed. Metal-organic frameworks in the two-dimensional form (2D MOFs) provide all of the criteria needed as the sensing material for electrochemical biosensors application. However, the design and preparation of 2D MOFs, which have high stability and sensitivity as well as good selectivity for biological analyte detection, is still quite challenging. This review provides the recent studies and development of 2D MOFs as electrochemical biosensor. A detailed discussion about 2D MOFs structures, their synthesis strategy and control, 2D MOFs materials in electrochemical biosensor application, and the future challenges is thoroughly explained in this review. Hopefully, this review will also provide a new inspiration to advance future studies of 2D MOFs materials development as electrochemical biosensor.

This paper presents the hardware design of optical character recognition for handwritten mathematical expressions using field programmable gate array (FPGA). The OCR is based on feedforward neural networks. The purpose of this research is to increase the speed and reduce the energy consumption of the learning process of neural networks by performing forward and backpropagation in the hardware, instead of software. To optimize the speed and the hardware area of the design, we proposed a parallel architecture and hardware sharing design. The simulation result of our system indicated that our proposed approach achieved an accuracy of 72.5% on a 25 MHz FPGA. Based on simulation measurement, it significantly consumes a very low power, while being as fast as the baseline method in terms of wall clock time.

In this paper, an implementation of a neural network model using systolic arrays, programmed in Verilog Code, is presented. The neural network model is mapped in a three-layer perceptron in forward mode. Dependency graph is also provided to illustrate the operations in each phases of the neural network model. Afterwards, the operations in a linear directional of systolic array is realized using a recursive iterative algorithm. The modelling in Verilog code is later confirmed with the MATLAB code for 9-input-output structure. The result proofs that the neural network architecture based on systolic array is successfully implemented in Verilog code.

This paper presents a full custom implementation of net function as a building block of neuron in an Artificial Neural Network system. The net function is implemented using radix-4 booth multiplier architecture. The main challenge or bottle neck in a booth multiplier architecture is designing an efficient adder. In this work, we propose a flipped logic parallel prefix adder to optimize the performance of the multiplier. By using this adder, the processing stages are able to be reduced more than a half, which resulting in a significant improvement of the propagation delay. The system has a total layout area of 0.027 mm2 with maximum operating frequency of 3.4 MHz. The circuit and layout design are implemented on 130nm CMOS technology.

With the advancement of technology nowadays, the number of electrical home appliances increases, especially for the Internet of Things (IoT) based smart home. Therefore, human becomes immensely dependent with electricity. However, many of us do not aware with the use of electricity. Hence, a lot of energy are just wasted away and thus we are potentially subjected to an expensive electricity bill. In this paper, we propose a mobile application which is able to be run on Android based smartphone. The app is designed to have a high security by implementing a double encryption method, using both RSA and AES algorithm, to encrypt the communication data. Besides, it also has other security features, including privilege and autolock. Moreover, the app can also intelligently control the appliances automatically using the scenario and scheduling feature.

The asymmetric RSA cryptosystem requires modulo operations in its encryption and decryption process, which is often realized with Montgomery modular multiplication. In this paper, we proposed a Montgomery multiplier hardware design using only primitive gates, adders, shifters, multiplexers, and registers. Our algorithm is also adaptable, which means that it can be reconfigured for applications with any arbitrary bits. The algorithm involves iteration, and to achieve less transistor count, we realized the iteration by feeding back the calculation results at the output back to the input, instead of connecting the gates in series. These considerations are made to allow us to create a compact custom ASIC design. The design was made with 130nm standard CMOS technology with NMOS and PMOS base width of 0.5|jm and 1 urn respectively. With the algorithm, our 8-bit multiplier ASIC design occupies an area of 0.0266mm2. The design is created and verified with Mentor Graphics™ EDA tools.

The hardware prototyping of an IrDA-based remote infrared (IR) device has been presented. The designed IR remote capable of controlling the smart home devices that use IR to interact with them such as the generic air conditioner (AC), Televisions (TV), and projector, in which the mentioned electronics appliances haven't an encryption system embedded. Hence, we can read the installed source-code of these devices, then stored in our IR remote. We used STM32L100 as a microcontroller. According to the test results, our prototype able to perform well as expected. The TV and AC can be controlled ”on” as well as ”off” at a certain maximum distance depends on power level used.

The demand of lower power and smaller dimension of flash memory requires size down scaling of the device. To meet this requirements, charge trapping flash using high-k dielectric is considered as one of the future technologies. In this paper, the design and simulation of HfO2 based charge trapping flash (CTF) is investigated. The CTF is based on a typical 160 nm NMOS structure with hafnium dioxide layer and ONO embedded in the gate stack. The program and erase (P/E) states of the device are simulated for several cycles to achieve stead-state charge density in trapping layer. Current-voltage characteristics are also run to extract threshold voltages of the P/E states. The threshold voltage shift is 0.82 V. A 10-year simulation is also taken to characterize the long-term charge retention capabilities of the structure. The device shows improved barrier tunnelling current and high retention ability for electron as well as their high density.

Recently, the implementation of IoT technology development has been growing rapidly. One of IoT applications is smart home system. In this system, in-home hardwares are connected to the internet. Thus, residents can access them using any internet-connected devices, such as mobile phones. Since the main concern of the IoT system is communication, the design of an efficient and secure communication protocol and database becomes very important. In this paper, we design an AMQP communication protocol with the existence of queuing data queuing scheme. For ensuring security, the entire communication process is encrypted using RSA and AES encryption algorithms, while the database is stored in MySQL RDBMS.

Design of a temperature compensated and a highly stable bandgap voltage reference (BGR) circuit is presented in this paper. The design is used for power harvester in a 13.56 MHz smart card tag IC. In order to provide a high-power supply rejection ratio (PSRR) and wide range input supply, the bandgap reference circuit is enhanced by a pre-regulator circuit. The pre-regulator circuit is designed using differential amplifier to form a Low-dropout (LDO) regulator with a feedback to BGR core circuit. The pre-regulator circuit mainly improves the stability of output voltage when the input voltage changes. In bandgap core circuit, the reference voltage is obtained by compensating the negative temperature coefficient (CTAT) with a positive temperature coefficient (PTAT). Therefore, the output voltage can be kept stable when the temperature changes. Additionally, the start-up circuit is designed to overcome zero current condition in BGR circuit during the power starts on and this circuit turns off after the whole circuit reaches the desired operating point. This paper provides both the schematic and layout design implemented in 180nm CMOS technology. From the simulation result, the output voltage has a temperature coefficient of −0.26 mV/°C with temperature range from −50 °C to 125 °C and PSRR of −116 dB at DC with input voltage supply range of 1.8–6 V. The output voltage reference is stable around 1.143 V at the operating point region.

As saving the energy inside a house is an important issue to address, more home appliances are now implementing IoT technology. Here, we propose an IoT based smart home software application that are able to intelligently control the home appliances automatically based on our instruction set or behavior. The smart home uses the software to receive input from the user and forward the input to the server or host. In addition to receiving input from the user, the smart home software is also used to display all information about the house condition to the user. The smart home system uses AMQP as the communication protocol between software, server, and host. To ensure the security, all of the communication data is encrypted by the system using either RSA or AES algorithm.

In rapidly growing e-commerce businesses, searching purchased items in the warehouse and delivering them within a very short time becomes a crucial problem. In order to solve this problem, we propose a complete RFID based goods locator system which consists of specially designed RFID reader and an integrated cloud based Warehouse Management System (WMS). The RFID reader is able to detect and sense the distance to the location of the purchased item which is attached with a RFID tag based on its RSSI information. The RFID reader is connected with the WMS application installed in a smartphone, through a Bluetooth connection. To update the inventory item information in real time, the Android based WMS application is connected to cloud based logistic and commerce database. Hence, by using this system, the time needed to search the purchased item and track the item delivery status can be shortened. As the result, it increases the efficiency of WMS system.