bluuebunny/crossref_metadata_2025 huggingface dataset:
Australian primary health care nurses’ knowledge, practice and attitudes relating to preconception care: learnings for service implementation
Authors: Dorney E. , Millard J. , Hammarberg K. , Griffin K. , Gordon A. , McGeechan K. , Black K. I.
Published: December 2021

Preconception care (PCC) entails counselling and interventions to optimise health before pregnancy. Barriers to this service delivery include access and time. Primary healthcare nurses (PHCNs) are uniquely placed to deliver PCC. The aim of this study was to understand PHCNs’ knowledge, practice and attitudes to PCC. A cross-sectional study was performed of a convenience sample of PHCNs in Australia who were seeing people of reproductive age. Recruitment was via the Australian Primary Health Care Nurses Association (APNA) electronic communication platforms. The 18-item, online, anonymous survey captured demographics, as well as PCC knowledge, practices and attitudes. Descriptive statistics were used to describe our findings. In all, 152 completed surveys were received. Of all respondents, 74% stated they discuss PCC in their practice, although only 13% do so routinely. Of these, more preconception discussions are held with women than with men. In total, 95% of respondents identified at least one barrier to delivery of PCC, with lack of time and knowledge being the most common. The findings of this study can inform targeted strategies, including education programs and resources, and consideration of incentives to support PHCNs deliver PCC. This study identifies areas for improvement at the individual, organisational and health system levels to enhance the role of PHCNs in PCC
Facial Emotions over Static Facial Images Using Deep Learning Techniques with Hysterical Interpretation
Authors: ViswanathReddy Dr A , Aswini Reddy A , Bindyashree C A
Published: December 2021

Recognition of facial expression has many potential applications that have attracted the researcher’s attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger, and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. Face Detection, Extraction, Classification, and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determine which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial-and-error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results.
Design of Decision Based Recursive Weighted Median Filter With Exponential Weights
Authors: Ganesh Motepalli Siva Rama , Kadali Kalyan Sagar , Bhukya Ramu , Palleswari Y.T.R. , Siva Asapu , Pragaspathy S
Published: December 2021

The prescribed algorithm for removing impulse noise effectively even under high noise densities without causing any loss of image details. Hence a cascaded section of median filters that, involves an Decision-based Median Filter followed by a Recursive Weighted Median (RWM) Filter employing exponential weights are used. The median controlled algorithm is employed to calculate the exponential weights. In the algorithms that where proposed in earlier which involves a cascaded section of the median with the RWM filters provided lesser Peak Signal/Noise Ratio (PSNR) and greater Mean Square Error(MSE) values. Hence the output appeared to be distorted for higher noise levels. These drawbacks have been eliminated in this proposed algorithm.
A Peer Review of Hybrid Electric Vehicle Based on Step-Up Multi-input Dc-Dc Converter and renewable energy source
Authors: Babu Jarapala Ramesh , Nayak Manas Ranjan , Mangu B.
Published: December 2021

Due to the rapid increase of environmental pollution caused by automobiles. To decrease pollution and to save our resources, there is an alternator to use an electric vehicle instead of a gasoline engine. The main drawback of a gasoline engine of compared to the electric vehicle can polluter noise efficiency durability. When it comes to durability, efficiency, and acceleration capabilities of electric vehicles, they are more impressive. The electric vehicles involve HEVs and BEVs. Generally, ultra-capacitor, solar Photovoltaic (PV) system, batters, regenerative braking systems and flywheel are utilized in HEVs as energy storage devices. All energy storage devices are linked to this distinct dc-dc converter scheme for raising input sources’ voltage. In past few decades, most HEVs have incorporated multi-input converters in order to enhance their reliability and efficiency. There are several distinct multi-input dc-dc converter schemas utilized in HEVs. This research discusses their current and future trends as well as energy storage devices.
Analysis and implementation of realtime stock prediction using reinforcement frameworks
Authors: Pragathi DrYVS Sai , Phani Narasimham M V S , Ramana Murthy B V
Published: December 2021

Real time stock prediction is interesting research topic due to the risk involved with volatile scenarios. Modelling of the stocks by reducing the overestimation in ANN model, due to rapid fluctuations in the market guide fund managers risky decisions while building stock portfolio. This paper builds real time framework for stock prediction using deep reinforcement learning to buy, sell or hold the stocks. This paper models the transformed stock tick data and technical indicators using Transformed Deep-Q Learning. Our framework is cost reduced and transaction time optimized to get real time stock prediction using GPU and Memory containers. Stock predictor is architected using GRPC based clean architecture which has the benefits of easy updates, addition of new services with reduced integration costs. Data archive features of the cloud will give benefit of reduced cost of the new stock predictor framework.
RETRACTED: Artificial Neural Network Based shafts surface pressures Analysis
Authors: Murthy B V Ramana , Padmakar Vuppu , Chandrika B N S M , Lanka Satya Prasad
Published: December 2021

This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.
Recovery of residual public spaces to improve the quality of life of the inhabitants of San Borja, Lima
Authors: Facho O , Cama T , Esenarro D , Livia J , Cuetoand C , Ramos D
Published: December 2021

The present research aims to propose a model for the recovery of residual public spaces to improve the quality of life of the district of San Borja’s inhabitants. San Borja is in the process of densification and requires a more significant number of public spaces that offer, in addition to vegetation, public places for active and passive recreation, such as spaces for sports and games, walking pets, and relaxation. These needs have increased due to the confinement caused by the COVID-19 pandemic. Also, it was noted that the median strip, or central reservation of the avenue, can be recovered for people to use. Therefore, a four-phase study was carried out that included reviewing the literature and observing two cases. In conclusion, a model was proposed to recover the public space of the median strips of San Borja Norte Avenue and San Borja Sur Avenue to improve the quality of life of the inhabitants of San Borja, which can be replicated in other avenues with residual spaces with similar characteristics.
Voice Controlled Guided System for Wheelchair with Collision Detection and Avoidance
Authors: Chhowara Purvi , GhansyamRathod
Published: December 2021

Many people are suffering from temporary or permanent disabilities due to accidents or any pre-illness that may introduce a wheelchair for the person as essential. The use of a wheelchair independently can be derived from the severity of the disability. But in the case of severe situations when a person relies on somebody else to handle the movement of a wheelchair. This paper aims to provide a solution to this problem by using voice command to guide the wheelchair eliminating the reliability of another person.
Weather Prediction using Advanced Machine Learning Techniques
Authors: Hemalatha G. , Srinivasa Rao K. , Arun Kumar D.
Published: December 2021

Prediction of weather condition is important to take efficient decisions. In general, the relationship between the input weather parameters and the output weather condition is non linear and predicting the weather conditions in non linear relationship posses challenging task. The traditional methods of weather prediction sometimes deviate in predicting the weather conditions due to non linear relationship between the input features and output condition. Motivated with this factor, we propose a neural networks based model for weather prediction. The superiority of the proposed model is tested with the weather data collected from Indian metrological Department (IMD). The performance of model is tested with various metrics..
Curvelet Transform based Denoising of Multispectral Remote Sensing Images
Authors: Lokeshwara Reddy P. , Pawar Santosh , Prathapa Reddy S.L.
Published: December 2021

With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..
A Real-time Implementation for the Speech Steganography using Short-Time Fourier Transformior Secured Mobile Communication
Authors: Shrivastava Rajeev , Singh Mangal , RakhiThakur  , Teja Kalluri Saidatta Subrahmanya Ravi
Published: December 2021

Steganography can be described as approach of masking an undisclosed message with a normal message which is known as the Carrier message signal. DSP techniques, such as LSB encoding, have historically been implemented for secret information hiding. Utilization ofsteganography functions of deep neural networks for voice data is something this paper will present. This paper also demonstrate that the steganography techniques suggested for vision are less suitable for speech signals this paper present a implementation technique that involves the use of ISTFT and STFT as differentiablelayers in the network. Empirically, the efficacy of the proposed methods based on multiple datasets of speech should be demonstrated and the outcome are examined quantitatively and qualitatively. Using of multiple decoders or a single conditional decoder helps to hide multiple signals in a single carrier signal. Finally, under various channel distortion situations, this model Qualitative studies indicate that human listeners cannot detect changes made to the carrier and hence the decoded messages are highly intelligible.
Design of High Speed and Area Efficient Finite Field Multiplier Using Factoring Technique for Communication
Authors: Baba Fariddin S , Mishra Rahul
Published: December 2021

In this paper, design of high speed and area efficient finite field multiplier using factoring technique for communication is implemented. Data security plays very important role in present generation. Therefore, initially inputs and key are given to S-Box. The main intent of S-Box is to substitute the input data and key. After that input data and key are merged using S-Box merge. This data will be multiplied using finite field multiplier and to improve the performance along with that mix column technique is applied. Factoring technique will increase the speed of operation. After the data performs shift row operation. At last rounding is performed to the obtained data. At last simulation results shows that effective outcome in terms of delay, memory and security.
Artificial Intelligence and Deep Learning for Weapon Identification in Security Systems
Authors: Brahmaiah Makkena , Madala Srinivasa Rao , Mastan Chowdary Ch
Published: December 2021

As crime rates rise at large events and possibly lonely places, security is always a top concern in every field. A wide range of issues may be solved with the use of computer vision, including anomalous detection and monitoring. Intelligence monitoring is becoming more dependent on video surveillance systems that can recognise and analyse scene and anomaly occurrences. Using SSD and Faster RCNN techniques, this paper provides automated gun (or weapon) identification. Use of two different kinds of datasets is included in the proposed approach. As opposed to the first dataset, the second one comprises pictures that have been manually tagged. However, the trade-off between speed and precision in real-world situations determines whether or not each method will be useful.
IMPLEMENTATION OF ELECTRONIC DEVICES OF CMOS FULL ADDER IN LOW POWER VLSI CIRCUITS
Authors: Durgaprasadarao P. , Daya Sagar K.V.
Published: December 2021

Battery-powered devices (for example, mobile phones, digital personal aids, etc) are increasing on the mobile electronic systems market by developing microelectronic circuits with low energy dissipation. The problem of dissipating power could limit the flexibility of the computer system, as the chip’s density and complexity keep on increasing. The power supply consumes approximately 35% of the chip power, particularly at the nanometer level. The purpose of this project is to investigate the efficiency of one of the most reliable low power concepts called Power Gating. It is only nanometer-scale CMOS devices that are the most common technology in existing VLSI systems. Leakage power has become an integral component of total power in the nanometer technology regime. The ALU’s basic feature unit is Full Adder. The electricity consumption of an ALU is decreased by decreasing the energy consumption of an ALU, and an ALU will reduce the power consumption by decreasing the total power consumption. So these days, the complete adder designs are becoming more common with low power characteristics. The proposed project shows the concept of the micro wind tool for low power less transistors.
Medical Analysis for Surgeryon Patients Complaining of Sciatic Pain
Authors: Nakkella Aruna Kumari , Mohan Surapaneni Krishna , Pasupuleti Sri Rama Murthy , Ananda Vayaravel C.
Published: December 2021

A prospective researchwas performed on 50 patients submitted for radiological screening in Samarra general hospital complaining of chronic backache and sciatica. The study was completed for patients recommended by senior neurosurgeons, orthopedicians, &rheumatologist, to the radiological unit in Samarra public hospital from October 2016 to October 2017. Radiological studies were analyzed by senior radiologists of Samarra public hospital in conjunction with the recommending senior doctors. Patients who are operated on were all proven to have chronic Lateral Recess Syndrome both radiological and surgically.
Performance analysis of different classifiers in segmenting point cloud data
Authors: Boslim N I , Abdul Shukor S A , Mohd Isa S N , Wong R
Published: December 2021

3D point clouds are a set of point coordinates that can be obtained by using sensing device such as the Terrestrial Laser Scanner (TLS). Due to its high capability in collecting data and produce a strong density point cloud surrounding it, segmentation is needed to extract information from the massive point cloud containing different types of objects, apart from the object of interest. Bell Tower of Tawau, Sabah has been chosen as the object of interest to study the performance of different types of classifiers in segmenting the point cloud data. A state-of-the-art TLS was used to collect the data. This research’s aim is to segment the point cloud data of the historical building from its scene by using two different types of classifier and to study their performances. Two main classifiers commonly used in segmenting point cloud data of interest like building are tested here, which is Random Forest (RF) and k-Nearest Neighbour (kNN). As a result, it is found out that Random Forest classifier performs better in segmenting the existing point cloud data that represent the historic building compared to k-Nearest Neighbour classifier.
Defects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction
Authors: Abu Bakar M N , Abdullah A H , Abdul Rahim N , Yazid H , Zakaria N S , Omar S , Wan Nik W M F , Abu Bakar N , Sulaiman S F , Ahmad M I , Ahmad K , Maliki N M , Romle S R
Published: December 2021

Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.
Research on effects and influence by presenting information on priority order and oncoming vehicle to operators for teleoperation of multiple autonomous vehicles
Authors: Yokota Mizuki , Tsutsumi Shigeyoshi , Hayakawa Soichiro , Ikeura Ryojun
Published: December 2021

With self-driving vehicles, it is possible to manage multiple vehicles from a remote location even if one observer does not have a driver in the driver’s seat. Therefore, demonstration experiments are being conducted in various places to remotely monitor two autonomous vehicles and operate them as needed. However, when one observer manages multiple vehicles, the amount of information that can be processed is limited. If we can assist with an appropriate amount of information, we may be able to manage more vehicles. In this study, we perform an experiment in which the priority and the type of assist information are changed and presented to the observer in the overtaking scene of a parked vehicle using a simulator. Focusing on the burden on the observer during remote management of multiple units, the purpose is to identify the information required for monitoring and reduce the burden from changes depending on the type of information to be assisted.
Smart Classroom for Electricity-Saving with Integrated IoT System
Authors: Nasrudin Mohd Wafi , Nordin Nur Asyikin , Ismail Iszaidy , Ilman Jais Mohd , Abdul Rahim Amir Nazren , Azani Mustafa Wan
Published: December 2021

Electricity-saving can be achieved through the efficient use of energy such as turning off lights and electrical appliances when not in use. Therefore this work proposed the smart classroom for electricity-saving with an integrated IoT System to prevent wasting electricity in the classroom. Smart Classroom means that it will detect and count the number of students entering and exiting the classroom by using a sensor system automatically. The main objective of this work is to control the lighting systems and fans by using the IoT application and sensor system. This means that when the sensor is triggered the sensor will send data to the Blynk application software using IoT to display the status of the classroom. This proposed work is also able to detect whether a classroom is available to use or not based on the presence of people. If the classroom is being used the Blynk application software will show the lamp and fan are ON. Otherwise the lamps and fans are OFF if there are no people in the classroom. The result successfully shows that if the first student entering the classroom all the lamps and fans are ON. While if the last student exiting the classroom all the lamps and fans are OFF. This result also indicates that electricity can be saved if all appliances in the classroom are switch OFF at the right time.
Development of Solar Powered Sprinkler Robot Based on Watering Plant Using IOT Approach
Authors: You Lim Xin , Shariffudin Nordiana , Zamri Hasan Mohd
Published: December 2021

Nowadays, solar energy’s popularity is growing consistently every year, along with the growth of amazing solar technologies, which is considered to be one of the most popular. Non-renewable energy like petrol and gasoline is being replaced with solar energy, which is renewable energy. The main objective of this project is to design and simulate a robot solar system. The robot is developed using Arduino Mega 2560 as the main brain of the system. This system is equipped with a solar tracking system to track the movement of the sun and LDR is used to detect the presence of sunlight. The solar tracker is used to get the maximum efficiency of solar energy and reduce power losses. In addition, the solar tracker can rotate from 0° - 180°, which is the best angle for the solar panel to reach the sunlight. This robot will be attached to the sprinkler system to perform the watering process. This robot is developed for use in the agriculture field to reduce the manpower and cost of the watering process. Three analyses will be conducted in this project such as solar panel analysis, Wi-Fi connectivity analysis and sprinkler system analysis. The result shows the solar panel will gain the highest intensity of the sunlight at 12.00 pm and a sunny day compared to the other time and a cloudy day. The maximum range of Wi-Fi connectivity and the water pump, time used to finish the watering process and watering area will be discussed.
Comparison of optimized Markov Decision Process using Dynamic Programming and Temporal Differencing – A reinforcement learning approach
Authors: Mani Annapoorni , Bakar Shahriman Abu , Krishnan Pranesh , Yaacob Sazali
Published: December 2021

Reinforcement learning is one of the promising approaches for operations research problems. The incoming inspection process in any manufacturing plant aims to control quality, reduce manufacturing costs, eliminate scrap, and process failure downtimes due to non-conforming 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 Markov model developed to estimate the probability of the raw material being accepted or rejected in an incoming inspection environment. The proposed forecasting model is further optimized for efficiency using the two reinforcement learning algorithms (dynamic programming and temporal differencing). The results of the two optimized models are compared, and the findings are discussed.
DEVELOPING AR INTERFACE FOR INTERIOR MODELLING FROM 3D SENSOR
Authors: Sanusi Muhammad Syahmi Bin Ahmad , Shukor S A A , Johari Jalal
Published: December 2021

This paper aims to show the process of Augmented Reality (AR) development representing existing building interior from a 3D sensor. These days, AR is becoming well known and widely used in various type of areas such as in gaming industry, medical training, and education. Due to its advantages, AR development for building interior is also necessity, specifically to assist people in understanding the scene more, in Architectural, Engineering, Construction / Facility Management (AEC/FM) applications as well as businesses like interior designing and household marketing. With respect to current situation of COVID-19 pandemic that has restricted travelling makes the usage of AR very beneficial and it could assist people to visualize the interior that needs to be visited. With the aid of suitable sensors like 3D sensor, the development of AR representing existing building interior which is usually full of clutter and occlusion could be eased.
An IoT Agricultural System For Harumanis Farm
Authors: Ismail F. A. , Ali Hassan M. K. , Ahmad Saad F. S. , Yazid H. , Aziz Safar M. J. , Basaruddin K.S. , Ismail A.H. , Ayob M.N.
Published: December 2021

Internet of Things (IoT) is a revolutionary technology that represents the future of communication and computing. The field of IoT implementation is vast and can be applied in every field. This project is about to develop an IoT system for Harumanis Farm as agriculture is becoming an essential growing sector throughout the world due to the increasing population. The major challenge in the Harumanis sector is to improve the productivity and quality of Harumanis without continuous manual monitoring. IoT improves crop management, cost-effectiveness, crop monitoring and also improves the quality and quantity of the crop. This IoT system completes with several sensors to monitor the Harumanis farm, such as temperature and humidity sensor, pH level sensor, soil moisture sensor, also nitrogen, phosphorous, and potassium (NPK) sensor. The system is a simple IoT architecture where sensors collect information and send it over the Wi-Fi network to the mobile applications.
A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning
Authors: Che Ali M A A , Ilias B , Abdul Rahim N , Abdul Shukor S A , Adom A H , Saad M A H
Published: December 2021

One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.
An Aggressiveness Level Analysis Based On Buss Perry Questionnaire (BPQ) And Brain Signal (EEG)
Authors: Munian Lugieshwaran , Wan Ahmad Wan Khairunizam , Xu Tung Kai , Mustafa Wan Azani , Rahim Muhajir Ab.
Published: December 2021

Aggression is the most important human aspects that make daily things possible for individuals, to succeed and have a better level of behaviour. Aggression is feelings of anger or antipathy resulting in hostile or violent behaviour. The importance of aggression is to increase an individual dominance of the subject in their social environment. Traditionally, the subject’s aggression is usually measured by using a survey through Buss-Perry Questionnaire (BPQ). Considering the variability of the aggressiveness level, this study proposes investigation of aggression by using BPQ and Electroencephalography (EEG) to evaluate the aggressiveness level of the subjects. The results of the BPQ are analysed based on the final score that are responded by the subjects. In EEG experiment, the evaluation of subject’s aggressiveness while playing a smart phone game “Subway Surfers”, a basic method has been employed, namely correlation coefficient method. The EEG signals are recorded while the subject playing the game. The number of subjects involves in the experiment is 9 and they are the UniMAP’s male students at the age of 21-25 years old. In the analysis, the induced aggression is compared between BPQ with Net Aggressiveness Index (NAI), which is obtained from brain signals (EEG). The BPQ obtains the subjects #2 and subject #8 are the highest Buss-Perry Aggresinenes Index (BPAI) scores, which are 0.32244 and 0.32223 respectively. Meanwhile in EEG analysis the subject #8 only achieves the highest score of 0.34713. From the results of the investigation, it could be concluded that the use of EEG to identify the aggressiveness level will overcome the disadvantage of the conventional methods.
Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming-Firefly Algorithm
Authors: Husni Noor Najwa Husnaini Mohammad , Rahim Siti Rafidah Abdul , Adzman Mohd Rafi , Hussain Muhammad Hatta , Musirin Ismail
Published: December 2021

The cost of energy losses analysis for distributed generation (DG) is presented in this paper using a Hybrid Evolutionary Programming-Firefly Algorithm (EPFA). The proposed method was created to determine the optimal DG sizing in the distribution system while accounting for the system’s energy losses. This study presents an investigation into hybrid optimization techniques for DG capabilities and optimal operating strategies in distribution systems. The objectives of this study were to reduce the cost of energy losses while increasing the voltage profile and minimize distribution system losses. In this study, the analysis was done by consider DG type I which is DG-PV. The suggested methodology was tested using the IEEE 69-bus test system, and the simulation was written in the MATLAB programming language. Power system planners can use appropriate location and sizing from the results obtained for utility planning in terms of economic considerations. From the simulation, the result shows the proposed method can identify the suitable sizing of DG while reduce cost of energy losses and total losses in the system.
Thermal Comfort: A Review on Methods of AC Control in a Small Indoor Space
Authors: Ibrahim I.I. , Abdul Shukor S.A. , Kohlhof K.
Published: December 2021

This paper discusses the comparison of two methods to achieve thermal comfort utilising air conditioning (AC) system in a small indoor space – adaptive control and fuzzy control. Thermal comfort indoors is performed to provide comfortability individually or for a group of people. Due to the small indoor space which usually a bit cramped, crowded and less airy, the ambience can be very uncomfortable either for doing sedentary or active work, thus the AC system can be very useful to provide thermal comfort. Both methods can be utilised depending on how thermal comfort is viewed and how the level of thermal comfort is decided. Every method has its own advantage and limitations, and will be covered in this paper as well.
CFD analysis on the effect of temperature on gas distribution in indoor environment
Authors: Mohd Juffry Z H , Kamarudin K , Adom A H , Abdullah A N , Ismail I N
Published: December 2021

There are times when people are required to spend time indoors, especially due to unhealthy outdoor air quality as well during a pandemic when lockdowns are imposed. However, spending time indoors can also at times be dangerous due to the release of harmful gasses if left unchecked. This has very much to do with many parameters, among which is the indoor environment and its ventilation. The latter is affected by the way gases distribute inside the building. It is influenced by many factors such as temperature, wind, air circulation, and also ventilation system itself. The knowledge on how the gases spread in different conditions within the indoor environment can be utilized in many applications such as improving the smoke detector safety system and identifying as well as predicting the potential risks. This paper presents the investigation of the effect of different temperatures on gas distribution in an indoor environment. A three-dimensional simulation was performed of different temperature gas released in a closed room that has different ambient temperatures. The effect of temperature on the gas dispersion was observed. The results revealed that there is a significant effect of temperature on the way gas spread in the indoor environment support by the theoretical knowledge on the relationship between temperature and gas in the gas law.
Design and Fabrication of Low-Cost Thermal Imaging Device Prototype to Detect Heat Energy Loss in Electrical Equipment
Authors: Andrew A. M. , Tan Erdy Sulino Mohd Muslim , Markom Marni Azira , Zawi Mohammad Alif Hakimi Mohd
Published: December 2021

Ability to detect heat energy loss from electrical equipment can be proven crucial to avoid overheating and unexpected fire incident. The thermal remote camera was invented to prevent power outages or electrical machinery failures. Thermal imaging cameras are device which converts thermal energy (heat) into infrared spectrum to inspect a specific object or scene. Imagery that reflects the spatial variability of temperature differences in a scene observed by a thermal camera are called thermal images. Due to the complexity of the device, it can be cost and not a solution sought for. This research presents the designing and fabrication of low-cost thermal imaging cameras that plays major role in detecting heat energy losses in various applications. This prototype of thermal imaging cameras is tested to monitor and diagnose the heat health, especially in electrical installations and components in non-contact mode. Based on the test result presented, the prototype able to detect heat energy spectrum up to 80°C. The 8x8 thermal array able to calculate average temperature of the tested items.
Detection of building fixtures in 3D point cloud data
Authors: Mansor H , Abdul Shukor S A , Wong R
Published: December 2021

Building architectural and civil engineering are constantly changing, causes the increases of building spaces as well as renovation works which includes structures such as walls, ceilings and floors, and building fixtures. Building fixtures are objects which is secured to the building, such as lighting fixtures, plug and socket, ceiling fan and so on. It is considered as one of the complex structures in building as the size of the fixtures are small and sometimes are hardly seen immediately. When a certain building changes, the building information need to be updated along with the changes of the building. The process to update the changes has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, to recognize the fixtures in building environment before renovation, an object recognition method is applied. This investigation focused on the recognition of lighting fixtures in the environments. By using MATLAB, an algorithm is developed to detect the point cloud data that belongs to the lighting fixtures. The investigation shows that the lighting fixtures can be identified by using Region of Interest (ROI) method within an environment. From the results, the accuracy of the dimensions of the lighting fixtures detected in point cloud data compared to the real one in the environment is 75% and 72% match, which is good but still need an improvement to be closely match with the real dimensions. The finding is hoped to simplify the tasks of determining the fixtures in the building before any changes is done.
Underground object reconstruction from Ground Penetrating Radar (GPR) data – An investigative study of feature extraction
Authors: Mahmud Nurush Syamimie , Shukor S.A. Abdul , Ali H. , Ahmad Zaidi A.F. , Zanar Azalan M.S. , Amran T.S. Tengku , Ahmad M.R.
Published: December 2021

Ground Penetrating Radar (GPR) is very useful for underground object detection as its signal able to penetrate surfaces in obtaining the underneath information. However, its radargram output in hyperbolic signal are very challenging to be analyzed. This work investigates the suitability of selected data processing methods in extracting important features of the signal in order to understand and reconstruct it to make it more beneficial. Results show that with suitable combination of data processing, it able to extract the peak of the hyperbolic signal accordingly and further reconstruction can be made.
Muscle Activity and Gait Analysis of Assistive Device for Rehabilitation Gait Abnormalities Patient
Authors: Hambali Ruzy Haryati , Akmal Suriati , Komaruddin Nurul Hamizan
Published: December 2021

Gait abnormality is a muscle disorder that disabling the patient to walk properly. This is caused by several factors including genetic influence, accident history, health issue, and others. Some suffered from this illness could be cured but some cases only could be helped by rehabilitation. This work is an original initiative in developing an assistive device as part of patient’s recovery and rehabilitation in helping the patient to regain muscle, as well assisting patient in performing their activities. However, the effectiveness on the device’s performance to serve its purpose has not yet been confirmed. Therefore, an electrical tool known as surface Electromyography (EMG) is being used to obtain the information required. This research is analysing the patient’s nerves system with and without the assistive device and demonstrate the effectiveness of the assistive device in reducing the muscle contraction, as well to increase the time-to-fatigue of the muscle. The findings of this research showed that the reduction in percent of average Root Mean Square (RMS) value of patient’s contraction muscle when using the device has increase the time-to-fatigue of the muscle. In conclusion, this assistive device assists the patient, minimise the muscle fatigue, and ease the patient in their daily chores.
Vision-Based Edge Detection System for Fruit Recognition
Authors: Tan Sean Huey , Lam Chee Kiang , Kamarudin Kamarulzaman , Ismail Abdul Halim , Rahim Norasmadi Abdul , Azmi Muhamad Safwan Muhamad , Yahya Wan Mohd Nooriman Wan , Sneah Goh Kheng , Seng Moey Lip , Hai Teoh Phaik , Lye Ong Thean
Published: December 2021

There are variety of fruits around the world, different types of fruits contain different types of nutrients and vitamins which could benefits our health. In order to understand which fruit can provide specific type of nutrients, we need to identify the types of fruits. However, fruits grow in a different shape, colour and texture based on the country they were planted and the environment of the land. Implementing a machine vision-based recognition on the fruits can help people recognize them easily. In this paper, an edge detection method is applied using computer vision approach to recognize different types of fruits. The fruits are classified based on the features extracted from their images. In the experiment, a total of 450 images of three types of fruit are used, which are apples, lemons and mangoes. Pre-processing steps are applied on the captured image to improve the quality of fruit details and the edge features are extracted using Canny Edge Detection method. Classification of the fruits is accomplished using two different types of learning model, the deep leaning model, Convolution Neural Network (CNN) and machine learning model, Support Vector Machines (SVM). The performance of both classifiers is compared and the model with the best performance, SVM is chosen as the model for the system. The system can achieve 86% classification accuracy with the SVM model, which is good enough for fruit recognition.
Harumanis Mango Leaves Image Segmentation on RGB and HSV Colour Spaces using Fast k-Means Clustering
Authors: Herng Ooi Wei , Nasir Aimi Salihah Abdul , Chin Ong Boon , Tan Erdy Sulino Mohd Muslim
Published: December 2021

Harumanis mango is the signature fruit in Perlis due to its delicious taste and its sweet-smelling. A good quality Harumanis tree requires rich in nutrition (healthy), and the tree will grow lots of fruits compared to the trees which are poor in nutrition (unhealthy). The health condition of a tree can be observed through the leaves in term of shape of leaves. For a healthy Harumanis tree, the leaves grow in scattering shapes. Meanwhile, an unhealthy Harumanis tree grows in gathered shapes. Therefore, this research is focusing on Harumanis mango leaves image segmentation by comparing between RGB and HSV colour spaces in order to obtain the best segmentation performance. 100 of Harumanis mango tree leaves images are used in this research. These images have undergo through image pre-processing such as modified linear contrast stretching and colour components extraction based on RGB and HSV colour spaces. Then, the colour component images have been segmented by using fast k-means clustering in order to obtain the leaves segmented images. Finally, quantitative analyses have been performed to measure the segmentation performance based on sensitivity, specificity and accuracy. Overall, the results show that S component of HSV colour space archives the highest accuracy with 85.81%.
A Comprehensive Experiment on the Synthesis and Performance of Carbon Nitride Photocatalyst Modified by Copolymerization
Authors: Zheng Dandan , Li Yixin
Published: December 2021

Based on cutting-edge topics and research foundation, a comprehensive experiment on the preparation and performance of a copolymerization modified carbon nitride (g-CN) photocatalyst was designed. This experiment involved the preparation of g-CN photocatalytic materials, the basic experimental operation of photocatalytic water decomposition, the use of material characterization instruments such as FT-IR, DRS and PL, as well as the processing and analysis of origin data. It covers knowledge of physical chemistry, analytical chemistry, instrumental analysis and materials chemistry. The comprehensive innovation experiment is beneficial to expand students’ knowledge and cultivate students’ innovation consciousness, exploration spirit, and scientific literacy.
Hybrid van der Waals heterojunction based on two-dimensional materials
Authors: Sun Cuicui , Qi Meili
Published: December 2021

Since the discovery of graphene, two-dimensional (2D) layered materials have always been the focus of material research. The layers of 2D materials are covalent bonds, and the layers are weakly bonded to adjacent layers through van der Waals (vdW) interactions. Since any dangling-bond-free surface could be combined with another material through vdW forces, the concept can be extended. This can refer to the integration of 2D materials with any other non-2D materials through non-covalent interactions. The emerging mixed-dimensional (2D+nD, where n is 0, 1 or 3) heterostructure devices has been studied and represents a wider range of vdW heterostructures. New electronic devices and optoelectronic devices based on such heterojunctions have unique functions. Therefore, this article depicts the research progress of (2D+nD, where n is 0, 1 or 3) vdW heterojunctions based on 2D materials.
Permissible Region Extraction Strategies for XLCT: A Comparative Study
Authors: Liu Xiaohang , Ma Sihao , Zhong Sheng , Su Aocheng , Huang Zhiwei , Yi Huangjian
Published: December 2021

Permissible region (PR) strategy has been used successfully to alleviate the ill-posedness of the X-ray luminescence computed tomography (XLCT) reconstruction problem. In the previous researches on the permissible region strategy, it is obvious that permissible region strategy can solve the reconstruction problem efficiently. This paper aims to research the performances of four types of permissible region extraction strategies, including a permissible region manually extraction strategy, a permissible region extraction strategy with a priori information of the surface nanophosphors distribution, a permissible region extraction strategy based on the first-time reconstruction result and a precise permissible region extraction strategy. In addition, some heuristic conclusions are provided for the future study in this paper. Fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct in this paper. The numerical simulation experiments and physical phantom experiments are setup to evaluate and illustrate the performances of the four different types of permissible region strategies.
Computation on Infrared Radiation of Side Exhaust Plume under Spraying Water
Authors: Sun Zhongke , Chen Zhongwei , Ma Xiao
Published: December 2021

The high temperature plume of ships has obvious infrared radiation feature. Spraying water-liquid droplets in side exhaust system can effectively reduce the high temperature of the tail gas to reduce the infrared radiation of the exhaust plume. In this paper, ANSYS Fluent is used to establish the concentration field and temperature field of the side exhaust plume atter spraying water-liquid droplets. And the statistic narrow band model (Malkmus model) and the C-G approximation method are used to calculate the infrared radiation intensity of the exhaust plume in the normal direction of the exhaust outlet from 3 to 5 μm on this basis. The final results show that spraying water-liquid droplets in side exhaust pipe can reduce the infrared radiation of the side exhaust plume from 3 to 5um by 88.9%% compared with the initial intensity; when the water flow reaches 0.7kg/s, infrared radiation intensity remains unchanged.