bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Riders Pattern For Automatic License Issuing System
Authors: Harikrishna Kani V , Malarvezhi P. , Sharath R , Jeyaraman S
Published: December 2021

This paper presents about the mechanization of driving permit test framework and informing the aftereffect of the applicant remotely. Normally, in driving test an up-and comer applied for license need to drive over a shut hover path before the trained professionals. The up-and-comer needs to roll over the way with explicit guidelines and on the off chance that he neglects to do so he will be excluded and should attempt next time. These specialists watch the mistakes of the up-and-comers physically. In this paper, the Arduino framework with important sensor modules has been produced for viewing the contender for getting their permit. By utilizing this framework, the applicant who take up the test are checked and the outcome climate the competitor is passed or fizzled is refreshed to the up-and-comer just as the specialists remotely utilizing IOT and GSM modules. This checking of the driving test ground is done autonomously using the Arduino structure. This framework is produced for improving the norms of permit giving instrument to improve street wellbeing
Water Budget Estimation in Water Resources Management in Drought Prone Areas in Rayalaseema Region, South India
Authors: Kiran Kumar T. , Niveditha K.
Published: December 2021

Water is a valuable natural resource, fresh water is vital for health and to the economy, and reliable access to it is becoming increasingly important as the India’s population rises. Yet its availability is limited, per capita accessibility of useable water is depleting, but with increasing living standard of people, all around fast industrial development and expansion, necessity of fresh water is raising high continuously. Water audit is a successful tool for minimizing losses, working on different utilizations and in this way empowering water conservation in irrigation, domestic, power and industrial as well. Water audit controls the measure of water lost from a distribution system because of spillage and different reasons like burglary, unapproved withdrawals from the organizations and the cost of such misfortunes to the utility. Water budget is a bookkeeping of all the water that streams into and out of an undertaking region of project area. Water budget gives the financial aspects on the distribution of quantity of water in Rayalaseema region. The study area this paper to calculate the water budget in the study area of 51 Mandal of Rayalaseema region and based on the water quantity analysis to prepare water, soil conservation structures in study area, for effective water management
Fuzzy Congruence Relations in Generalized Almost Distributive Fuzzy Lattices
Authors: Sangeetha T. , Senthamil Selvi S.
Published: December 2021

This paper defines the fuzzy congruence relation of GADFL (Generalized nearly distributive fuzzy lattices). The ideas of θ - ideal and θ - Prime ideal are introduced in GADFL, and the fuzzy congruence relation is used to explain these ideals.

AMS subject classification: 06D72, 06F15, 08A72.
Analysis of Microgrid System with Photovoltaic Array
Authors: Ramamohan Reddy K. , Siddhik N. , Jyothirmayi A. , Kalyan Kumar K.
Published: December 2021

As the modern power system is advancing, new challenges are coming in to picture. The micro-grid concept along with renewable energy PV systems is emerging as a key factor for the long-term doable solution for future energy sector requirements. Micro-grid can have distributed energy resources like PV panels, wind turbines, Geothermal Tidal energy & power generators that produce power. Controlling and protection are the main problems that need to be handled in microgrid operation. Microgrids need to provide multiple end user needs simultaneously. For example, electricity generation, heating and cooling. This paper accords with the analysis of a Microgrid system connected with a PhotoVoltaic array.
An Efficient Interconnection System for Neural NOC Using Fault Tolerant Routing Method
Authors: Pradeep kumar A. , Devendar Reddy Y. , Srinivas Reddy T. , Jamal K.
Published: December 2021

Large scale Neural Network (NN) accelerators typically have multiple processing nodes that can be implemented as a multi-core chip, and can be organized on a network of chips (noise) corresponding to neurons with heavy traffic. Portions of several NoC-based NN chip-to-chip interconnect networks are linked to further enhance overall nerve amplification capacity. Large volumes of multicast on-chip or cross-chip can further complicate the construction of a cross-link network and create a NN barrier of device capacity and resources. In this paper, this refer to inter-chip and inter-chip communication strategies known as neuron connection for NN accelerators. Interconnect for powerful fault-tolerant routing system neural NoC is implemented in this paper. This recommends crossbar arbitration placement, virtual interrupts, and path-based parallelization strategies in terms of intra-chip communications for the virtual channel routing resulting in higher NoC output at lower hardware costs. A lightweight NoC compatible chip-to-chip interconnection scheme is proposed regarding to inter-chip communication for multicast-based data traffic to enable efficient interconnection for NoC-based NN chips. Moreover, the proposed methods will be tested with four Field Programmable Gate Arrays (FPGAs) on four hard-wired deep neural network (DNN) chips. From the experimental results it can be illustrate that a high throguput can obtained effectively by the proposed interconnection network in handling thedata traffic and low DNN through advanced links.
Publicly Verifiable Vibrant Digital Medical Information Systems
Authors: Anju Shaik Sumi , Sravani BSN , Madala Srinivasa Rao
Published: December 2021

As data processing advances, decentralized media has been widely recognized for its ability to store large amounts of data. By comparing a revisited content to a dispersed repository, a cloud provider may verify the document’s integrity without having to retrieve it. A reconsidered examining strategy is offered to lead the customer to reconsider the significant assessing task to third sector inspector, taking into consideration the important computing cost brought up by the checking process (TPA). TPA may be deterred by the primary revisited evaluating strategy, but the second plan gives the harmful organization the right of inspection over the readdressed data of users, which poses a significant risk to patient privacy. Human Emphasis for reconsidered inspection is presented in this work, which emphasizes that the service user can be overwhelmed by her own data. Based on user-centered design, our suggested methodology not only prevents patient’s data from leaking to TPA without depending on cryptographic algorithms, but can also avoid the use of additional free unpredictable supply that is impossible to fulfill on a daily basis. Also, we start to make our approach work with continuous changes. Our recommended scheme is both verifiably safe and essentially productive, as shown by the privacy analysis and test evaluations.
College students’ Network behavior Using data mining and feature analysis
Authors: Sravani Thatiparthi , Madala Srinivasa Rao , HeenaKauser Sk
Published: December 2021

Teachers may use advanced analytics to rapidly and correctly understand undergraduate behavior trends, especially when it comes to identifying undergraduate groupings that need to be focused on at a later time. This study uses data mining cluster analysis to analyze the constituent behavior of 3,245 undergraduates in a specific level ‘B’ institution’s college network. According to the data, there are four different undergraduate groups with different Web access features, with 350 participants using the accomplishments and other variables of their success have an influence on these students. As a result of this research, we were able to collect data on undergraduate college network activity, which may be used to aid in the development of academic advising management.
Click Fraud Detection Approaches to analyze the Ad Clicks Performed by Malicious Code
Authors: Bathula Mahesh , Tanguturi Rama Chaithanya , Madala Srinivasa Rao
Published: December 2021

Mobile PR is an important component of the mobile app ecosystem. A major threat to this ecosystem’s long-term health is click fraud, which involves clicking on ads while infected with malware or using an automated bot to do it for you. The methods used to identify click fraud now focus on looking at server requests. Although these methods have the potential to produce huge numbers of false negatives, they may easily be avoided if clicks are hidden behind proxies or distributed globally. AdSherlock is a customer-side (inside the app) efficient and deployable click fraud detection system for mobile applications that we provide in this work. AdSherlock separates the computationally expensive click request identification procedures into an offline and online approach. AdSherlock uses URL (Uniform Resource Locator) tokenization in the Offline phase to create accurate and probabilistic patterns. These models are used to identify click requests online, and an ad request tree model is used to detect click fraud after that. In order to develop and evaluate the AdSherlock prototype, we utilise actual applications. It injects the online detector directly into an executable software package using binary instrumentation technology (BIT). The findings show that AdSherlock outperforms current state-of-the-art methods for detecting click fraud with little false positives. Advertisement requests identification, mobile advertising fraud detection are some of the keywords used in this article.
Machine Learning Medical Resource Allocation
Authors: Sailaja M. , Ahad Abdul , Sivaramakrishna K , Hussain Ali
Published: December 2021

In the last decade, machine learning has become very interesting, driven by cheaper computing power and costly storage—so that growing numbers of data can be saved, processed and analysed effectively. Enhanced algorithms are designed and used to identify hidden insights and correlations between non-human data elements in broad datasets. These insights help companies to better decide and optimize key indicators of interest. Machine learning is becoming more common because of the agnostic use of learning algorithms. The paper presents a number of machinery and auxiliary tumour processes to assign health resources, and proposes a number of new ways to use these resources at the time of artificial intelligence in order to make human life part of this process and explore the good conditions which are shared by both the medical and computer industries.
Data analytics for traffic flow prediction in Custom using Long Short Term Memory (LSTM) networks
Authors: Pin Loon Lee , Refaie Elbaraa , Mohd Faudzi Ahmad Athif
Published: December 2021

This paper proposes data analysis for traffic flow prediction of customs to help the officer in Customs, Immigration, and Quarantine (CIQ) Complex to understand more about the traffic situation in CIQ. Currently in CIQ, the traffic behaviour for car is unpredictable; sometimes the traffic is very heavy while there are times where all the lanes are cleared. There is a plan to have installation of cameras for smart traffic management system in the future. Therefore, this research aims to have prediction of traffic flow based on time and visualize the trend of traffic data for the officer. The data consist of traffic flow and the respective timestamp. To analyse it with time-series data, Long Short-Term Memory (LSTM) Recurrent Network is used as deep learning approach for prediction. The data pre-processing and training of model would be done using Python. To organize the data, Tableau Prep Builder is used and integrate with Python to publish the data to Tableau Server for storage. An interactive dashboard would be designed on Tableau and made available online for the usage of the officer.
Visual tracking using multi-layer appearance approach
Authors: Abu Hassan Mohd Fauzi , Pri Azurahisham Sah , Ahmad Zakiah , Azahar Tuan Dir Tengku Mohd
Published: December 2021

This paper investigated single target tracking of arbitrary objects. Tracking is a difficult problem due to a variety of challenges such as scale variation, motion, background clutter, illumination etc. To achieve better tracking performance under these severe conditions, this paper proposed covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of centre location error (in pixels) compared to covariance descriptor with using a fixed bounding box. From this work, it is believed that we have constructed a great solution in choosing best layer for this descriptor. This will be addressed in the next future work such as consider target motion during tracking.
Validation of the Effectiveness of Near-Infrared Spectroscopy for Detecting Impurities in Milk Powder using ANN and SVM
Authors: Syahir A.S Fathinul , Basah S.N. , Yazid H , Aziz Safar M.J. , Ali Hassan M.K.
Published: December 2021

Nowadays, there are so many productions on the market that based on dairy product that include milk powder. The milk powder has different categories which is for children, adult and others. The milk powder involves the local product until the import product. The transparency of the content in the milk powder cannot be identified easily and can cause food poisoning to be happened. Hence, with the use of near-infrared spectroscopy, NIRS it can be use easily to detect the content in the food simply. There are many methods in NIRS which is reflectance and absorbance. This method being uses to detect which milk powder is adulterated. Ocean Optic XR-1 ES, USA device and Ocean View software have been used in this project to take data from the sample. The device use wavelength between 200nm-1025nm. The milk powder obtained from the supermarket and nearby stores. Data that have been taken then transferred into suitable table and will go through two different analysis method which is SVM and ANN. The reflection method is not suitable to detect the adulterated milk powder. From the result ANN shows the higher accuracy to classify the pure and Adulterant milk powder.
Development of aquaculture water quality real-time monitoring using multi-sensory system and internet of things
Authors: Abdullah A H , Saad F S A , Sudin S , Ahmad Z A , Ahmad I , Abu Bakar N , Omar S , Sulaiman S F , Che Mat M H , Umoruddin N A , Johari B H
Published: December 2021

Water quality is an important parameter for the health and growth of aquatic species in aquaculture farming system. The threshold values of the water main parameters should be monitored continuously. Contaminated aquaculture water will affect the health, growth and ability of animals to survive. In addition, it will also affect the harvesting yields based on the number and size of the animals. To overcome this problem, the main water parameters, namely temperature, pH, Dissolved Oxygen and Electrical Conductivity are monitored in real-time using a multi-sensory system and the internet of things. Data is acquired by a developed instrument and transmitted wirelessly via a GPRS / GSM module to a web server database. The data obtained are analyzed and monitored through the website and in real-time. Therefore, corrective action could be taken immediately for contaminated water, indicated by water parameters out of range. The system also provides an early signal to farmers based on a specific range of water quality parameters values. This will help farmers make adjustments to ensure appropriate water quality for the aquaculture system.
A study of extreme learning machine on small sample-sized classification problems
Authors: Ooi Boon Pin , Rahim Norasmadi Abdul , Masnan Maz Jamilah , Zakaria Ammar
Published: December 2021

Extreme learning machine (ELM) is a special type of single hidden layer feedforward neural network that emphasizes training speed and optimal generalization. The ELM model proposes that the weights of hidden neurons need not be tuned, and the weights of output neurons can be calculated by finding the Moore-Penrose generalized inverse method. Thus, the ELM classifier is suitable to use in a homogeneous ensemble model due to the untuned random hidden weights which promote diversity even with the same training data. This paper studies the effectiveness of the ELM ensemble models in solving small sample-sized classification problems. The research involves two variants of the ensemble model: the normal ELM ensemble with majority voting (ELE), and the random subspace method (RS-ELM). To simulate the small sample cases, only 30% of the total data will be used as the training data. Experiment results show that the RS-ELM model can outperform a multi-layer perceptron (MLP) model under the assumptions of a Friedman test. Furthermore, the ELE model has similar performance as an MLP model under the same assumptions.
Design of Remote Warning System for Miniature Circuit Breaker (MCB) Power Shortage via Internet of Things (IOT)
Authors: Quan L H , Hasan M Z , Khalid N S
Published: December 2021

Miniature Circuit Breaker (MCB) is an electro-magnetic device designed to cut off the circuit when an overcurrent occurs. This MCB system is essential in many fields that involved with electric and commonly found in industry, it will protect the electric component in the circuit from short circuit. The Internet of Things (IoT) technology is a new profound technology which will lead to a better and easy lifestyle world. One of the benefits of IoT system is the worker can monitor their work from home. With this new and promising technology, most of the work can be done at home by using mobile phone or a computer. As the title of the project presented, the project developed a microcontroller to monitor the MCB using the technology of the Blynk as the IoT platform. The system will send a message to the user to inform about the trips through a mobile phone by the Blynk application. At the same time, the alternating current (AC) of the MCB is record and graph at the mobile application.
Measurement of Negative Lightning Return Strokes Using a Proposed Small-Scale Parallel Plate Antenna at the Central Region in Peninsular Malaysia
Authors: Haris F Abdul , Kadir M Z A Ab , Sudin S , Johari D , Hamzah M N
Published: December 2021

Lightning can occur between the clouds (intra-clouds), ground-to-cloud (CG), and inside the clouds. The lightning strike hazards can be managed appropriately using a lightning detector system consisting of an antenna, buffer amplifier circuit, and a measurement device. In this study, the development and fabrication of small-scale parallel plate antenna were carried out by reducing the physical height and antenna dimension to measure the generated electric fields. The experimental work was conducted at the rooftop of the College of Engineering, Universiti Tenaga Nasional (UNITEN), Selangor, Malaysia, from August 2019 to March 2020. The total number of 115 return strokes (RS) of negative lightning events were recorded during the measurement period. Characterization of seven types of criteria for negative return stroke has been analysed and compared to the existing parallel plate antenna with a similar climate condition and different countries. Based on the comparative study, the proposed and the existing parallel plate antenna shows a good agreement. Hence, the proposed small-scale parallel plate antenna can be used as a portable, lightweight, and easy to install device for the lightning measurement system.
Intelligent Shopping Trolley (IST) - a social distancing device during pandemic shopping
Authors: Darren Low Chin Sheng , Assyakirin  , Amiruddin Ahmad , Tan Kheng Chia , Razak Sabrina , Kiang Lam Chee
Published: December 2021

Intelligent Shopping Trolley (IST) is a device that was built to help in fighting the Covid-19 pandemic. This Intelligent Shopping Trolley is equipped with a RFID and timer system, Indoor Positioning System (IPS) and Microwave Sensor Detection system to determine the distance and to alert customers as they shop in a supermarket. This Intelligent Shopping Trolley will also utilise the Internet of Things (IoT) to manage its functionality. The Intelligent Shopping Trolley will help customers to determine the distance between other customers while implementing social distancing measures that is recommended by the Ministry of Health Malaysia and manage their shopping time as well. Besides that, it helps supermarkets to track their customers after the time limit given to the customers ends. This paper explains the details on this Intelligent Shopping Trolley project. This project helps in making the community aware of the importance of social distancing.
Development of surveillance drone based internet of things (IoT) for industrial security applications
Authors: Ali H , Hang L Y , Suan T Y , Polaiah V R , Aluwi M I F , Zabidi A A Mohd , Elshaikh M
Published: December 2021

Drones or mini-unmanned aerial vehicles, have becoming an emerging trends due to their boundless applications in surveillance, military and numerous public services. Nowadays, deployment of surveillance drone for monitoring or security application remains challenging and ongoing research. As Internet of Things (IoT) becomes more commercialized, various concept of IoT have been integrated with the drones due to efficient usage. Therefore, this paper proposed the development of surveillance drone system based on IoT for industrial monitoring-security applications. The rationale of integrating IoT with surveillance drone is that it allows authenticated users to login from any device, anywhere, and view video or images from surveillance drones in real-time for security awareness. In this work, the surveillance drone consists of mechanical system, electrical and electronic interfacing and IoT platform (mobile application system). In electronic system, power module, communication module, sensor and actuator as well as user interface module have been adopted and integrated into the systems. Besides, in software development system, user interface configuration was developed through mobile application to serve as IoT platform. A series of experiments shows that the surveillance drone based IoT able to operate with a promising flying distance with surveillance camera as the “eyes” of the drone system.
Check-In Location System Using NFC Technology
Authors: Chze Ooi Nian , Markom Marni Azira , Hui Ong Xin , Chin Wong Tze , Vern Koay Yu , Tan Erdy Sulino Mohd Muslim , Nordin Noradila
Published: December 2021

In COVID 19 pandemic, new norms have been introduced, including, to leave a record when checking-in to a particular place. This new norm is regulated in order to trace locations that have been visited by someone with positive COVID-19. This paper presents a work on development of check-in location system. The system implemented Near Field Communication (NFC) technology which is mainly utilized two NFC compatible devices where an identification card (IC) is used as a smart object (NFC tag) and the NFC detector as an NFC reader to exchange information. Testing has been conducted in order to observe the system performance, and, the results showed that this system is able to collect information of users who were coming to premise. Also, the information can be checked by authority in order to track someone with positive COVID-19. As conclusion, this system can be an alternative to MySejahtera App.
Automotive Mechanical Vehicle Starter
Authors: Kader M M M. A , Razali Z B , Mustafa W A , Saidi S A , Nagoor Gunny A A , Setumin S , Osman M K , Idris M , Akbar M F , Sarbini M A A , Mizam N S S
Published: December 2021

This research is used to crank start automotive vehicle. There are many different system used in order to start-up vehicles using electric starter, in the time of battery low-power or totally drained. The purpose of this research is to help the driver to get out of this difficulty. Nowadays there are many people that have experienced such a bad moment, where they are stranded at road side due to malfunction starter in their car because of battery problem. Most of the vehicle electric starter failure is because of battery corrosion or battery undercharged. The importance of this research is to solve this problem. Starter is a vital part of the vehicle, without it no automotive vehicles able to operate. These starters will rotate an internal-combustion engine to initiate the engine’s operation under its own power. Starters also can be malfunction too due to corroded electrical connections or an undercharged battery. This system can be used to solve this problem. This system used human energy by using mechanical parts in order to produce electrical power. In order to produce electrical current, workforce will be applied by rotating the wheel that already linked by belt and from that rotations will trigger a magnetic force and it will produce an electrical current and supply it into battery. This system is divided into two development; hardware development and software development. The hardware development involved, mechanical device which is used and electrical device such as monitor. For software development, Fritzing is used to construct circuit.
Automated Negative Lightning Return Strokes Classification System
Authors: Haris F. Abdul , Ab Kadir M.Z.A. , Sudin S. , Johari D. , Jasni J. , Mohammad Noor S.Z.
Published: December 2021

Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes.
Robot Face and Its Integration to the Mobile Robot for Wireless Signal Collection in the Fingerprinting-Based Indoor Positioning System
Authors: Sarhan M A H , Ismail A H , Ayob M N , Hashim M S Mohd , Azmi M S M , Basri H H , Othman S M , Safar M J A
Published: December 2021

The wireless data collection for instance the Received Signal Strength (RSS) of the Wireless Fidelity (Wi-Fi) remained unfavourable in the Indoor Positioning System utilizing the signal fingerprinting approach. This is because the enormous sampling time and routines works making it tedious human labour. To alleviate this issue, we propose to use a robot for wireless data collection. The robot, named ‘ICSiBOT’ is a service robot with multiple purpose such as assisting human in daily lives, guest or hospitality robot and man others. This paper mainly describes the ICSiBOT robot face with speech recognition technology and the integration of the robot face to the motion controller. The experimental was conducted to see the correlation between the synthesized instructions from the speech in terms of distance need to be travelled i.e., the location for wireless signal collection and translate them into actual distance travelled. The results showed that the robot is able to travel to the specific distance as instructed to the robot face.
Markov Decision Process approach in the estimation of raw material quality in incoming inspection process
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali
Published: December 2021

The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtime due to defective raw materials. Prediction of the raw material acceptance rate can regulate the raw material supplier selection and improve the manufacturing process by filtering out non-conformities. This paper presents a raw material acceptance prediction model (RMAP) developed based on the Markov analysis. RFID tags are used to track the parts throughout the process. A secondary dataset can be derived from the raw RFID data. In this study, a dataset is simulated to reflect a typical incoming inspection process consisting of six substations (Packaging Inspection, Visual Inspection, Gauge Inspection, Rework1, and Rework2) are considered. The accepted parts are forwarded to the Pack and Store station and stored in the warehouse. The non-conforming parts are returned to the supplier. The proposed RMAP model estimates the probability of the raw material being accepted or rejected at each inspection station. The proposed model is evaluated using three test cases: case A (lower conformities), case B (higher conformities) and case C (equal chances of being accepted and rejected). Based on the outcome of the limiting matrix for the three test cases, the results are discussed. The steady-state matrix forecasts the probability of the raw material in a random state. This prediction and forecasting ability of the proposed model enables the industries to save time and cost.
Categorization of material quality using a model-free reinforcement learning algorithm
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali
Published: December 2021

Reinforcement learning is the most preferred algorithms for optimization problems in industrial automation. Model-free reinforcement learning algorithms optimize for rewards without the knowledge of the environmental dynamics and require less computation. Regulating the quality of the raw materials in the inbound inventory can improve the manufacturing process. In this paper, the raw materials arriving at the incoming inspection process are categorized and labeled based on their quality through the path traveled. A model-free temporal difference learning approach is used to predict the acceptance and rejection path of raw materials in the incoming inspection process. The algorithm presented eight routes paths that the raw materials could travel. Four pathways correspond to material acceptance, while the rest lead to material refusal. The materials are annotated using the total scores acquired in the incoming inspection process. The materials traveling on the ideal path (path A) get the highest total score. The rest of the accepted materials in the acceptance path have a 7.37% lower score in path B, whereas path C and path D get 37.28% and 42.44% lower than the ideal approach.
Development of IoT Real-Time Groundwater Monitoring System
Authors: Hawari H F , Chantar P P
Published: December 2021

Internet of Things (IoT) Real-Time Groundwater Monitoring System is a system built to monitor groundwater extraction and consumption. Groundwater scarcity has become a major threat to the government especially water utility company. Water theft, inaccuracy meter reading & lock out access are some problems contributing to water scarcity. In this research, data obtained from the groundwater consumption using flow sensor will be sent to the server where all this data will be recorded for future analysing by respective authorities. The system has been tested thoroughly using of Long Range (LoRa) communication module together with Thingspeak cloud and mobile application. The results showed promising coverage with Line of Sight (LOS) is tested at 900m maximum while for Non-Line of Sight (NLOS) is at 600m. A very small standard deviation up to 4.93 was observed for received Signal Strength Indicator (RSSI) value for LOS and NLOS. Compared with the existing manual method, the proposed IoT system will water authority to monitor water consumption effectively through real time and better coverage.
Normalized Cross Correlation Template Matching for Oil Palm Tree Counting from UAV image
Authors: Hanapi Sharifah Nurul Husna Syed , Shukor S A A , Johari Jalal
Published: December 2021

Tree crown detection and counting from remote sensing data such as images from Unmanned Aerial Vehicle (UAV) shows significant role in this modern era for vegetation monitoring. Since the data processing would depends on raw data available and for this case the RGB data, thus a suitable method such as template matching is presented. Normalized cross correlation is widely used as an effective measure in similarity between template image and the source or testing images. This paper focuses on six (6) steps involved in the overall process which are: (1) image acquisition, (2) template optimisation, (3) normalized cross correlation, (4) sliding window, (5) matched image and counting, and (6) accuracy assessment. Normalized cross correlation and sliding window techniques proposed for this work resulted in 80% to 89% F-measure values. This result indicates that UAV image data with appropriate image processing method/s have the potential to provide vital information for oil palm tree counting. This would be beneficial in plantation management to estimate yield and productivity. However, there are still rooms for improvement to achieve better results.
Design And Development Of Real-Time Small Scale IoT Based Energy Monitoring System
Authors: MatSaat E.H. , M.A. Majid , Abdul Rahman N.H. , Muhamad Nur Amalina , Othman N.
Published: December 2021

This paper presents the digitization of small-scale energy monitoring systems based on IoT. The proposed energy monitoring system known as EMOSY eliminates the high-cost energy meter. EMOSY is designed to be portable and practical to use without modification of internal or external connection of appliances. EMOSY is developed by using a voltage detector circuit concept by amplifying the existence of electrostatic. This electrostatic reading sends to the database through Wi-Fi module ESP8266 integrated with Arduino NodeMCU. The web page is designed using Adobe Dreamweaver with HTML and PHP coding. In the proposed system, the user able to monitor the energy usage of each appliance and estimated billing time to time. Based on the result, the energy monitoring system successfully can detect the existence of electrostatic, and the webpage database can display the energy usage extended to the estimated electricity bill. The monitoring system is found to be useful to the residential, commercial, and industrial to monitor energy patterns, which is essential to facilitate energy conservation measures for minimizing energy usage.
Face Recognition System Using Local Features Fusion for Multi-Masks
Authors: Al-Dabagh Mustafa Zuhaer Nayef , Ahmad Muhammad Imran
Published: December 2021

Face recognition is a relatively novel research field, and its application is closely related to numerous other areas. Moreover, it is emerging as a critical research theme due to its broad range of applications. Thus, many face recognition methods use a variety of feature extraction approaches. Nonetheless, the issue continues to be challenging, particularly identifying non-biological entities. This paper proposes an extended descriptor for local features of an effectual facial recognition system using a local directional pattern operator. This technique combines the Frei-Chen and Robinson masks’ strengths by fusion of the directional features of LDP for these two masks; this elicits a robust feature extraction method for distinguishing faces. Experimental results using the Yale database show that the extended descriptor considerably improved recognition rate and better performance than traditional local feature descriptors.
Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles
Authors: Nooriman W M , Abdullah A H , Abdul Rahim N , Tan Erdy Sulino Mohd Muslim
Published: December 2021

Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia’s state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree’s demands is critical. The aim of this study is to create for Harumanis mango a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.
Comparative investigations on different types of inductors in single-phase inverter
Authors: Aihsan M. Z. , Yusof A. M. , Rahim Hasliza A , Ismail B. , Mustafa W. A. , Saifizi M. , Fahmi M. I.
Published: December 2021

This article organized in two sections where it compares the performance of single-phase inverters using various types of inductors with differences modulation technique of pulse width modulation (PWM). Not all inductors perform the same function, even the inductance value is the same. The study will investigate the capability of each inductor on its performance to convert the unfiltered AC voltage into filtered sinusoidal AC voltage. The drum core and toroidal core inductors were used in this investigation. For both inductors, the performance will be analyzed based on Bipolar and Unipolar switching schemes in a single unit H-bridge circuit. The validation of results are through experimental assessment only and it will be evaluating the shape of sinusoidal AC voltage and the content of total harmonics distortion in the AC voltage for both inductors.
Ground penetrating radar for buried utilities detection and mapping: a review
Authors: Ali Hasimah , Ideris Nurul Syahirah Mohd , Ahmad Zaidi A F , Zanar Azalan M S , Tengku Amran T S , Ahmad M R , Abdul Rahim N , Abdul Shukor S A
Published: December 2021

This paper presents a review on Ground Penetrating Radar (GPR) detection and mapping of buried utilities which have been widely used as non-destructive investigation and efficiently in terms of usage. The reviews cover on experimental design in GPR data collection and survey, pre-processing, extracting hyperbolic feature using image processing and machine learning techniques. Some of the issues and challenges facing by the GPR interpretation particularly in extracting the hyperbolas pattern of underground utilities have also been highlighted.
Facemask quality measurement using AMG8833 multiarray thermal sensor
Authors: Tan Erdy Sulino Mohd Muslim , Markom Marni Azira , Andrew A.M. , Hamid MHA , Izham Muhammad Aiman Hakim Muhammad , Nizam Muhammad Amir Ridhuan Mohd , Abdullah Aniq Amsyar , Lok Hoo Weng
Published: December 2021

Face masks have become a necessary thing that people need to wear daily. Even though some people might already be vaccinated, there is still a chance that the Covid-19 virus could still infect them. Hence, this paper presents a device developed to help determine the quality of face masks crucial in preventing the spread of the virus. These devices can calculate the temperature outside the face mask at a maximum distance of three meters. The way this device works is by measuring the temperature released out of the face mask. Here, the developments of the device with the ability to help determine the quality of face masks are explained and discussed. In the end, the device is perfectly functioning and definitely would assist in verifying the quality of the face masks being worn by someone. Two types of faces are used as test materials: the KN95 type face mask and the 3ply facemask used in Malaysia. Each facemask collected data from 30 minutes to 300 minutes for ten subjects over ten days. Studies that have been conducted show that the thermal value of the KN95 facemask increased to 30.27°C after 5 hours of use. At the same time, the 3ply type facemask offers a thermal value of up to 34.58oC after 5 hours of use. This shows a thermal value difference of up to 4.3PC for both facemasks after 5 hours of use.
Autonomous robotic fire detection and extinguishing system
Authors: Sathiabalan Nivennesh A/L , Lokimi Anis Farihin Mohamed , Jin Ong Zi , Hasrin Nur Syazwani Mohd , Zain Aini Syuhada Md , Ramli Nuraminah , Zakaria Hasneeza Liza , Ariffin Wan Nur Suryani Firuz Wan , Hashim Nur Baya Mohd , Taib Muhamad Husairi Md
Published: December 2021

Firefighting is a dangerous job with a high death rate. Robotics is the new way to protect the environment and human lives. This work proposes an autonomous robot system that can inevitably discover fire using the flame sensor and extinguish it. This project includes Arduino UNO, flame sensor, servo motor, motor driver, relay module, Bluetooth HC-06 module, and water pump. Besides, using the push Bluetooth app at the transmitting end, commands are sent to the receiver to control the robot’s movement. The motors are connected to the microcontroller and used to move the robot and sprinkle water on the fire. A water tank and a water pump are mounted on the robot body and automatically detected by the infrared. An ATMEGA328 series microcontroller controls the flame sensor and the whole operation. A motor driver IC, L298N, is interfaced to the microcontroller through which the controller drives the motors. As a result, the robot can detect fire from a distance. The average length for detecting flame is approximately 5.11cm, and Bluetooth transmission is about 300cm. It has the potential to reduce human error and limitations associated with fire extinguishing tasks.
Position Tracking Performance with Fine Tune Ziegler-Nichols PID Controller for Electro-Hydraulic Actuator in Aerospace Vehicle Model
Authors: Othman S.M. , Sunar Noorhazirah , H.B Hassrizal , Ismail A.H. , Ayob M.N. , Muhamad Azmi M.S. , Hashim M.S.M.
Published: December 2021

Electro-Hydraulic Actuator (EHA) system is a third order non-linear system which is highly suffer from system uncertainties such as Coulomb friction, viscous friction and pump leakage coefficient which makes this system more complicated for the designing of the controller. The Proportional-Integral-Derivative (PID) controller has proposed in this paper to control EHA system and main problem in its application is to tune the parameter to its optimum value. Two different methods are used to tune the PID controller which are trial and error and Ziegler-Nichols method. MATLAB Simulink is used to simulate the system. In order to determine the performance of EHA system for the position tracking. 3 different of external disturbance such as 0N, 5000N and 10000N has been injected into the system. Simulation results show that the Ziegler-Nichols fine tuning method provides the better tracking performance when compared to the trial and error method for every specific disturbance setting. The Ziegler Nichols method provides better disturbance rejection as the performances indexes such as percentage overshoot, settling time and steady state error are not affected by the varying of disturbance.
Harumanis Mango Leaves Image Segmentation based on Wavelet Transformation with Phansalkar and Sauvola Thresholding
Authors: Chin Ong Boon , Nasir Aimi Salihah Abdul , Herng Ooi Wei , Tan Erdy Sulino Mohd Muslim
Published: December 2021

Harumanis mango is one of the economic sources of the Perlis state. It has a sweeter taste compared to other mangoes. However, the Harumanis mango tree required specific weather, soil nutrient contents and pH level. This makes the farmer does not know the health condition of their Harumanis mango tree. Therefore, this project aims to provide the best method of leaves detection to the farmer. The leaves image samples are collecting from the orchard and undergo pre-processing. Then the input image was converted into grayscale with principal component analysis (PCA). Wavelet transformation was implemented to increase the discriminability of the segmentation technique for separating the leaf and background. The leaf segmentation is done by using Phansalkar and Sauvola thresholding techniques. After that, fill hole and area opening techniques are implementing to reduce noise in the image. These two thresholding techniques are comparing and discuss with their segmentation performance. Overall, Phansalkar thresholding has produced better performance in segmenting healthy and unhealthy Harumanis mango leaves with sensitivity, specificity and accuracy of 92.05%, 81.37% and 83.51%, respectively.
Risk Assessment for Alternating Current / Direct Current (AC/DC) Hybrid Systems with Renewables Penetration
Authors: Chen Ye , Yang Qiongqian , Li Zhenting , Liu Mengmeng , Zhang Jianfeng , Pan Yang
Published: December 2021

In this paper, a risk assessment method is proposed for AC/DC hybrid systems with renewables penetration, considering the effect of renewables penetration and the application of DC transmission. The sequential Monte Carlo method is introduced to simulate the output of renewables generators, and the unified iterative method is used to solve the problem of AC/DC hybrid system power flow calculation. By establishing the quantized risk assessment indices, the risk of AC/DC hybrid systems with renewables penetration can be analysed. The results of EPRI of China 6-machine-22-bus Test case show that the proposed method can effectively evaluate the risk level of AC/DC hybrid systems with renewable energy penetration and provide reference for power grid planning and the actual operation in advance.
Measuerment of CO2-crude oil minimum miscible pressure in YC Oilfield by core displacement method
Authors: Liu Li , Liu Jinxin , Pi Yanfu , Guo Xuan , Dai Zhipeng , Li Zhihao
Published: December 2021

Aiming at the defect of measuring the CO2-crude oil MMP(minimum miscible pressure) by the slim tube test, the core displacement method is established based on indoor physical simulation and homogeneous rectangular core in the low permeability block of YC oilfield. For comparison, the MMP is measured by the slim tube test in the same block. Experimental results show that the method has good repeatability and can simulate porous media and reservoir water content, which is more consistent with the actual reservoir conditions. The MMP in the target block of YC oilfield was 19.85MPa, which was 1.87MPa lower than that measured by traditional slim tube test.
Fabrication Strategy to Promote Performance of Perovskite Solar Cells
Authors: Wang YanZhi , Hou Ruixiang
Published: December 2021

Improvements in process of perovskites, materials of auxiliary layers and encapsulation have significantly enhanced the performance of perovskite solar cells (PSCs). However, unified fabrication of PSCs has not been completely settled till now. Whether it is harmful to the perovskite should be concerned when selecting function layer materials and encapsulation materials of PSCs. Encapsulation is the main way to enhance the stability of PSCs. Besides, to cope with the emerging environment issues, the function layer materials can be modified to adsorb lead, preventing the leakage of lead from PSCs. To integrate the advantages of each part of PSCs, interactions between constituent materials are needed to be studied.