A company may receive loads of invoices and need to process them to make the payment in time. Staff need to manually extract the information from the invoices and key in the payment details into the company system, which will take a lot of man-hours and be subject to human error. This paper proposes the use of optical character recognition (OCR) and artificial intelligence (AI) to extract semi-structured data from invoice images. The Robotic Process Automation (RPA) bot approach was developed as automation for entering data into the company's system to automate system. The validation result displays how well the system performed in terms of accuracy and processing time for the samples of invoice images. The system shows that accuracy is 100% with less than 30 seconds to complete the process. Hence, RPA with AI and OCR are suitable methods to be used as the invoice processing solution.
Dynamic fault coverages of March test algorithms need to be analyzed to ensure the robustness of Memory BIST for dynamic fault detection. Dynamic faults are proven to be a significant group of faults in Static Random Access Memory (SRAM). However, the manual analysis of dynamic fault coverage is time costly and tedious since it involves a total of 222 dynamic fault primitives. Conventional fault simulator depends on the size of the memory under test. Therefore, this proposed research focuses on the design of an automated fault analyzer for dynamic fault detection analysis by identifying the sensitizers and detectors, the sensitization cases for each test element for different March algorithms and lastly exporting the output of the computed fault coverage to a text file. The performance of the proposed analyzer is tested by using March MD2 and March MD9a and the fault coverages obtained from the analyzer matched with expected dynamic fault coverages and the execution time is around 6ms.
Auscultation that defined as listening to heart sound is the most practical and comfortable method to monitor patient's heart condition. Despite that, the complexity of murmur that exist in the heart sounds often provide misleading in diagnosis. This work proposed an auto-diagnosis system for heart disease by classifying the pathological heart sounds. Mitral valve stenosis and bicuspid aortic valve stenosis are two focused diseases due to the fatal effect. The analysis stage involved the transformation of heart sound signal into envelope signal in order to assist the location of the first and second heart sound. Moreover, power spectrum and Mel Frequency Cepstral Coefficient (MFCC) are extracted to classify the diseases where Fine Gaussian SVM is found to perform the best classifier model with 86.1% of accuracy. Later, the testing stage of the classification process has yielded the 86% of accuracy in detecting the true disease with 93% of average F1-score. Finally, a pre-develop prototype is constructed using GUI system that able to automatically diagnose the type of heart disease. The system, in future, has high potential to provide a better diagnosis system with less time-diagnose and operator-independent.
Due to the nature of soft robot manipulator, the modelling and control of its stiffness is crucial for a successful contact and non-contact tasks. The stiffness of soft robot manipulators generally embodies the manipulator's ability to satisfy the desired position and force commands. The goal of this study is to compare and investigate variable stiffness strategies in improving the distal tip stiffness of two-segment soft continuum manipulator which have in-built stiffness chamber constructed via granular jamming. The first step is to characterize the behaviour of the manipulator with the change in pressure to determine the Young's modulus. Then forces and their resultant length change relationship is established and based on the two characterization work, the stiffness matrix is obtained. The result shows that the stiffness matrix obtained is able to reject disturbances at the distal tip of the manipulator and the activation of the in- built stiffness chamber via granular jamming further enhance the stiffness and robustness of the manipulator, giving multiple strategies in improving tip distal stiffness.
Plug-in electric vehicles (PEVs) have been receiving a high demand among the public since they were introduced due to their benefits from incentives to reduce greenhouse gas emissions. With a number of PEVs on the road, they can be managed to provide ancillary services to power grid and deal with the intermittent resources of renewable energy. This requires an optimization technique to properly schedule the PEV operations that connect to power grid to give minimum power losses. Suitable substations for PEVs either to charge or discharge and appropriate number of their involvement are identified. Binary gravity search algorithm (BGSA) is one of the used optimization technique to solve the non-linear and non-convex scheduling problem. However, a suitable decision function needs to be investigated to give better performance. This work investigates three different decision functions, namely tangent, sigmoid and round functions. A modified 34-node distribution test system is used to showcase the performance in solving the PEVs scheduling problem. The results show that sigmoid function is more suitable for BGSA as compared to the conventional tangent function in solving the PEVs scheduling problem. Power loss reduction can be achieved up to almost 14% when compare to the base case. As a conclusion, a suitable decision function gives better performance in solving an optimization problem.
In this paper, the study of the reliability issue of Negative Bias Temperature Instability (NBTI) on a 15 nm p-channel Junctionless Fin Field Effect Transistor (p-JLFinFET) was analyzed. This work provides inputs on the degradation effect caused by NBTI on the p-JLFinFET which will degrade the electrical parameters of the device. The simulation is done by constructing the p-JLFinFET device structure with a gate length of 15 nm by using Sentaurus TCAD tool, and then applying a stress voltage to the p-JLFinFET's gate terminal to observe the NBTI characteristics. The electrical behavior of the simulated p-JLFinFET was compared with the previous JLFinFET experimental work for device structure validation. Once the device structure is validated, negative stress voltage is applied to the gate terminal of the p-JLFinFET for 10,000 seconds. The electrical parameters before and after stress application were analyzed and compared. Results show under stress application of -1.8V for 10,000s and have been extrapolated up to 10 years by using the power law method, the threshold voltage $(\mathrm{V}_{\text{th}})$ increased by 15.7 % from its initial value thus causing an increase in the power consumption and slower switching speed of the device.
LoRa (short for long-range) refers to the RF modulation under the low-power wide area networks (LPWANs) that offers the potential of smart solution under various environment conditions using the long-range communication capability. Nevertheless, the performance of this technology varies depending on the surrounding conditions as well as the presence of obstructions between the end-to-end transceivers. This paper describes the LoRa project that was developed to foster the local rural agriculture business by implementing smart agriculture applications based on LoRa. The Reyax RYLR890 LoRa transceiver is used to allow bi-directional communications between multiple nodes to operate two different agriculture systems remotely without using the intenet. The communication performance was investigated by studying the changes in the receiving signal strength indicator (RSSI) and signal-to-noise ratio (SNR) against distance in the urban and rural areas. The initial hypotheses stated that LoRa communicates better in rural areas. However, it is highly dependent on the placement of the transceiver.
Modern challenges in electronics engineering include the need for massive scale computation devices that are energy-efficient. Conventional digital memory devices such as static random-access memories (SRAMs) and dynamic random-access memories (DRAMs) are volatile and require continuous power supply to retain its data or interval refreshes even when the cell is not selected. These ‘non-use’ power consumption scenarios massively contribute to power inefficiency in large-scale arrays. In this work, nonvolatility is introduced to the field-programmable gate array (FPGA), an integrated circuit capable of parallel computation. The nonvolatile (NV) FPGA (nvFPGA) is achieved through novel resistive random-access memory (ReRAM)-based designs of three major components in the FPGA architecture, the lookup table (LUT) and the D flip-flop (DFF) inside the configurable logic block (CLB) and the switching blocks (SwBs) responsible for interconnect routing. The nvFPGA successfully demonstrates NV with the NV CLB having a 73.463% higher path delay during WRITE and a 92.206% lower path delay during READ compared to the conventional volatile CLB. The NV SwB shows higher WRITE and READ delays compared to the conventional volatile switching block (SB). However, its function in the FPGA is to store routing configuration bits and there is only a one-time WRITE into the nvSB during initial programming.
Automated Essay Scoring (AES) is a one of the important research areas in educational technology. Research about AES is started in the early 1960s and keep growing in development along with the advances of the computing technology. AES is more widely use by the educational system for assessment and classroom activities, it being use in all layers of education. The potential for Automated Essay Scoring is being used widely becomes a reality as more and more classroom activities, assessment, and test preparation materials are supplied online. These essay questions might include everything from arithmetic, where students are asked to explain how they arrived at their answers, to science, where they might be asked to define words or explain experiments, or history, where they must ask to explain or discuss an event. In this study we try to design the AES using Natural Language Processing (NLP) and machine learning techniques to evaluate the student's answer by comparing it with the answer scheme. It is not simply a string-matching program and need a proper system to process the students answer. We propose the 4 main process to the system that is Spelling Checking, Pre-Processing, Latent Semantic Analysis and Thesaurus Checking. All this process is combined and analyze to become a good AES system. Each of this process mean give somethings important to the system flow on how the essay going to be evaluate. The system has been done some experiment and the system get the average correlation between system scores and human appraiser scores is only 70%. However, there needs to be more research on the future use of this Automatic Essay Scoring system.
Currently, the Chini Lake shores house around 500 indigenous people distributed across six villages with limited access to cellular tower communication coverage. This is mainly due to the challenging terrain profile and dense foliage. Therefore, it is important to establish a reliable line-of-sight (LoS) data transmission via a low-altitude platform (LAP). The high availability but low-cost wireless communication infrastructure such as LoRa is the perfect solution in this scenario. This will allow better coverage due to good propagation characteristics at lower frequency bands as well as the elevated platform. The solution shall also equip a wireless machine-to-machine (M2M) network, sensors technologies and a big data analytic enablement platform. In addition, the characterization of the wireless channel behaviour in Malaysia's tropical rural areas, where the propagated wireless signal suffers from several imperfections, such as attenuation, diffraction, scattering and absorption due to the presence of various surrounding elements is also being investigated. The outcome of this research is expected to offer a new understanding of the propagation behaviour of current and future wireless IoT technologies, thus helping the network engineer to perform accurate planning and deployment in a rural environment. With this solution, the indigenous Orang Asli community in Chini Lake, Pahang, Malaysia will have access to digital content, as well as water level alerts for mitigation of flooding and drought situations, and Internet access for the promotion of local products and services.
There are numerous uses of human body motion analysis, including rehabilitation of patients, training of athletes and others. Use of Inertial Measurement Units (IMUs) is one process for analyzing human movements. Accelerometer, Gyroscope, and Magnetometer make up IMUs, however each sensor has its own limitations. In this context, sensor fusion techniques, such as Madgwick Filter (MAD), Mahony Filter (MAH), Complementary Filter (CF), Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and others are used to address such shortcomings. In this study, WitMotion sensor has been employed for range of motion (ROM) analysis of a proposed 2D rigid body and performance of WitMotion provided sensor fusion algorithm has been verified in comparison with an Electro-Goniometer (EG). The main objective of this work is to identify a reliable and effective sensor fusion technique for measuring upper limb ROM so that experiments can be repeated in various conditions. The results of this study demonstrate that the WitMotion supplied sensor fusion technique achieves sufficient accuracy, while the Madgwick filter (MAD) performs slightly better than all other considered algorithms based on Root Mean Square Error (RMSE).
Power over fiber is an emerging technology that utilizes fiber as an alternative medium for power delivery. The power over fiber application diversifies across sensors, internet of things, medical devices, and 5G cell applications. Nevertheless, collating the system design to achieve an optimum power system efficiency has appeared to be a challenging approach. This research work aims to demonstrate the feasibility of power transmission across 100 m- 300 m of multimode fiber for phone charging applications with 7.23% of power conversion efficiency. Experimental result has also demonstrated the power system efficiency differences across three types of multimode fiber. The testbed development of proof of concept could be further utilized for other power delivery applications.
Controlling the shape memory behavior in four-dimensional (4D) printed actuators allows for great control and accuracy in performance, which can be achieved using the printing parameters or structural preferences. The use of these parameters eliminates the need for traditional programming of the printed materials to induce strain into these materials. This creates parameter-controlled actuators that are printed with pre-strain, allowing their use in applications right off the printer. In this study, 4D printing of strain-induced actuators is carried out by varying printing angles. This is done to assess the effect of this parameter on the twisting abilities of polylactic acid structures. The ability of actuators to twist allows them to perform non-linear movements that might offer advantages in applications of certain structures, such as the use of certain shapes or reaching cylindrical objects. The print angle is varied from 0° to 90° at intervals of 15°, as measured from the length of the actuators. The actuators are assessed based on their helix angle, with the highest angle of 22.63° achieved when printing at 45°. The 45° actuator is used to create a proof-of-concept gripper design that is made to grasp a cylindrical object. The gripper arms twist around and under the cylindrical object, providing a better grip that an arm that would only close on it because of its adapted shape. The full deformation of the gripper arms takes about 12 seconds to fully close. Moreover, this design offers different helix angles by changing the printing direction. The results show that the developed gripper is suitable for various robotics applications.
The COVID-19 pandemic had a tremendous impact on socioeconomics and directly impacted the electrical system. In Malaysia, Grid System Operators (GSOs) were found to lack detailed information to differentiate the total energy demand before and during a pandemic. Working from home during the pandemic has changed the way of life and daily energy management methods for the domestic sector. This paper aims to study the national energy demand during the pandemic and then look into domestic energy management. The study included 3 phases. Phase 1 involved the analysis of data from the GSO to identify differences in energy demand before and during the pandemic. Next, in phase 2, a survey will be conducted on the energy management of the domestic sector. Finally, phase 3 involves household energy-saving proposals through examples of structural improvements. During the 2020 Movement Control Order (MCO) in Malaysia, the average total decrease in energy demand compared to 2019 was 15.82%. This high percentage is due to the closure of several economic sectors, such as trade and industry. From the survey, 88 110 respondents reported that domestic electricity bills increased during the MCO. Statistical analysis using ANOVA indicated no significant link between age range and behavior, knowledge, and total bills paid by respondents. Furthermore, this study also suggested structural upgrades incorporating 5-star air conditioning that can save RM389.47 per year, which will take 4.78 years to repay. This study concluded with suggestions on changes that can be implemented to aid homeowners with energy savings.
In this paper, model order reduction techniques for discrete-time bilinear second order structured systems (SOSSs) over finite and infinite frequency intervals are proposed. Mathematical developments regarding generalized system form, system Gramians, Lyapunov equations and reduction mechanism for bilinear SOSSs for infinite and finite frequency intervals have been discussed. The formulation involves derivative pairs of each state and reduction techniques for such systems require retention of state pairs in a reduced order model to make reduction meaningful/useful. Retention of the state's structure has been achieved by partitioning the system Gramians into position and velocity portions. Balancing Gramians with different combinations yields different second-order balanced truncation techniques (SOBTs). The resulting SOBTs are tested on a bilinear system model for infinite and finite interval applications. Results certify for correct development as per claimed superiority of later proposed techniques in the limited intervals. The proposed developments can be considered for model order reduction of discrete-time bilinear second order structured system application especially over finite and infinite frequency intervals.
Unmanned aerial vehicles (UAVs), commonly referred to as drones, are the added element for the beyond 5G quality of service (QoS), capacity, and reliable connection enhancement. UAVs are an effective tool in bridging gaps between terrestrial base stations by increasing band capacity due to the free mobility features and their reliable line-of-sight communication. The widespread usage of UAV have potential but also new challenges such as the constantly changing network topology, and vertical height variations that caused changes in their geometry. UAVs' energy efficiency, 3D deployment, and security have shown to be bottlenecks in various applications together with the satellite, aerial and terrestrial layers that constitute the vertical Heterogeneous Network (v-HetNet). Scholars have studied the different aspects of UAV's aerial network as an extension of the terrestrial network, but few have developed the management of the various layers together. In this paper, we review works on the v-HetNet as an important enabler of 6G mobile networks. This study reviews works surrounding the concept, future opportunities as well as challenges in the coexistence of terrestrial and aerial layers. This survey aims to provide guidelines and motivations for additional studies for upcoming 6G communications system development and research in v-HetNet.
LED illumination-based multispectral imaging (LEDMSI) is one of the effective methods in the spectral image acquisition system. The availability of high-intensity LEDs with many different colours makes it possible to produce satisfying data for multivariate image analysis, including deep learning multispectral systems. However, the impact of white LED correlated colour temperature (CCT) on data images has received attention in the field. In this paper, we aimed to compare the effect of red-green-blue (RGB) and CCT of an LED light source on honey images. Light and dark honey are considered image samples in the experiment. The honey images were recorded under the influence of RGB LED and three categories of CCT white LED using an 18-megapixel DSLR camera. All the pictures are saved in a JPEG format and uploaded in ImageJ software to acquire the spectra measurement (raw data) and histogram analysis. The result showed that CCT variations indicated a variation histogram as shown in the variation of RGB LED. Moreover, grayscale intensity analysis shows better brightness, contrast, and distinction between the two types of honey.
In recent years, gait analysis has gained prominence among scholars. Gait analysis is used extensively in medical diagnoses, rehabilitation, and biometric identification. Clinical gait analysis is often conducted in a gait lab employing a 3D motion capture system and a pressure sensing walkway. Using wearable inertial sensors, the gait assessment can also be conducted outside the gait lab. However, these methods are limited by expensive equipment and the need for specialized knowledge to conduct a reliable gait evaluation. Thus, a marker-free deep learning based pose estimation method is suggested to assess the lower limb joint kinematics robustly and accurately during gait analysis. This study seeks to determine the pose estimation model that provide reliable and accurate lower limb joint kinematics evaluation in real-world applications. In the conclusion, the average inference speeds for OpenPose, MediaPipe Pose, and MMPose are 17.00, 30.19, and 2.82 frames per second, respectively, with average correlations of 0.896, 0.944, and 0.942 between the calculated lower limb joint kinematics and baseline. Therefore, MediaPipe Pose is the best pose estimation model for assessing the kinematics of the lower limb joints in real-world applications.
Space weather can have a profound impact on trans-ionospheric radio signals. Through quantifying the intensity of a space weather event, from its genesis at the Sun to its effect on the ionosphere, many physical parameters must be extracted from scientific data. Consequently, they are used for the quick conveyance of a streamlined but sufficient space situational awareness. Nevertheless, space weather-driven ionospheric phenomena can impact many consumers in the communication and navigation domains, which are not adequately served by the existing indices. Here we propose and discuss the advantage of the new ionospheric index, known as M index over the Malaysian region in terms of disturbed storm time (Dst) and Total Electron Content (TEC). We took a geomagnetic storm event on 1st and 2nd June 2013. It has been found that the storm started effects over this region with a time delay of approximately 21 hours. The suggested ionospheric disturbance index may overcome several shortcomings of previous ionospheric measurements and may be appropriate as a possible driver for an ionospheric space weather scale.
The rapid population growth in both urban and rural areas has led to an increase in energy consumption. Therefore, each generator must operate to meet the needs of the population. Economic load management (ELD) aims to schedule economical generators in the power system to produce minimum operating costs. At large, each generators have different generation costs due to type of generators and different positions from the load center. Furthermore, generator output which is usually not at the optimum level with power demand will also result in increased generation cost. So, it is important to propose a most effective and cost-effective method in power generation. If the generator efficiency is not achieved, high power loss will occur. In this study, Mutated Flower Pollination Algorithm (MFPA) has been proposed as the ELD solution by determining the generation value for each generator unit to produce the minimum generation cost according to the power demand. MFPA method will be compared with the Evolutionary Programming (EP) method and the Flower Pollination Algorithm (FPA). IEEE 6-bus-3-generator power system was chosen as the test system and simulated using MATLAB. The results show that the MFPA show better performance than FPA and EP by producing total fuel cost as well as minimum amount of power loss in short iterations.
