bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Analysis of 32-Bit Signed and Unsigned Multiplier Using Booth, Vedic and Wallace Architecture
Authors: Ming Yong Kun , Hussin Razaidi , Kamarudin Afzan , Che Ismail Rizalafande , Nazrin Md Isa Mohd , Zarina Md Naziri Siti 
Published: March 2021

Abstract
This paper presents the implementation and performance comparison of the Booth encoding technique and Wallace Tree reduction scheme on Vedic architecture. The radix-4 Booth encoder is widely used to enhance the multiplication speed as it has the capability to reduce the number of partial products generated by half. Vedic multiplier partitions the inputs into two blocks to speed up the partial product generation and Wallace Tree reduction scheme speed up the partial product addition process by eliminating the carry chain of the addition. Radix-4 Booth encoding scheme Vedic multiplier with and without Wallace Tree partial product reduction scheme for signed and unsigned multiplication was designed and synthesized in Synopsys 130 nm technology. For unsigned multiplier, the Booth-Vedic multiplier is 37.29% faster and 26.13% smaller while the Booth-Vedic-Wallace multiplier is 39.79% faster and 28.81% smaller when compared with Vedic multiplier. The performance of both multipliers was dropped when used in signed multiplication due to signed extension during the partial product addition process. All the multiplier is functionally verified using modified testbench that is based on the concept of UVM testbench.
Mutual Coupling Suppression in Wearable MIMO Antenna for On/Off-Body WBAN Applications
Authors: Adam Ismahayati , Rahim Hasliza A , Najib Mohd Yasin Mohd , Nazrin Mohd Nasrol Muhammad 
Published: March 2021

Abstract
A 2x1 wearable multiple-input-multiple-output (MIMO) antenna with line patch as mutual coupling suppression is presented. The proposed MIMO antenna is based on a simple rectangular structure that works at 2.45 GHz frequency. The patches are designed using ShieldIt textiles and are placed to a very closed (0.1λ) gap. The results show that the suppression in the mutual coupling of 5 dB (from -20 dB to -25dB), reduced by 25% after the line patch is inserted in between patches. On a positive note, an improvement in the antenna gain, from 4.4 dB to 4.7dB is achieved. On top of that, the performance of the reflection coefficient (S11) and the antenna impedance bandwidth is preserved. Important diversity performance parameter for MIMO antenna such as ECC, diversity gain and MEG results are found to be in an acceptable limit
Design and Development of Scene Recognition and Classification Model Based on Human Pre-attentive Visual Attention
Authors: Kudus A R , Teh C S 
Published: March 2021

Abstract
Recent works on scene classification still utilize the advantages of generic feature of Convolutional Neural Network while applying object-ontology technique that generates limited amount of object regions. Human can successfully recognize and classify scene effortlessly within short period of time. By utilizing this idea, we present a novel approach of scene classification model that built based on human pre-attentive visual attention. We firstly utilize saliency model to generate a set of high-quality regions that potentially contain salient objects. Then we apply a pre-trained Convolutional Neural Network model on these regions to extract deep features. Extracted features of every region are then concatenated to a final features vector and feed into one-vs-all linear Support Vector Machines. We evaluate our model on MIT Indoor 67 dataset. The result proved that saliency model used in this work is capable to generate high-quality informative salient regions that lead to good classification output. Our model achieves a better average accuracy rate than a standard approach that classifies as one whole image.
Classification of Multiple Visual Field Defects using Deep Learning
Authors: Abu M , Zahri N A H , Amir A , Ismail M I , Kamarudin L M , Nishizaki H 
Published: March 2021

Abstract
In this work, a custom deep learning method is proposed to develop a detection of visual fields defects which are the markers for serious optic pathway disease. Convolutional Neural Networks (CNN) is a deep learning method that is mostly used in images processing. Therefore, a custom 10 layers of CNN algorithm is built to detect the visual field defect. In this work, 1200 visual field defect images acquired from the Humphrey Field Analyzer 24–2 collected from Google Image have been used to classify 6 types of visual field defect. The defect patterns are including defects at central scotoma, right/left/upper/lower quadratopia, right/left hemianopia, vision tunnel, superior/inferior field defect and normal as baseline. The custom designed CNN is trained to discriminate between defect patterns in visual field images. In the proposed method, a mechanism of pre-processing is included to improve the classification of visual field defects. Then, the 6 visual field defect patterns are detected using a convolutional neural network. The dataset is evaluated using 5-fold cross-validation. The results of this work have shown that the proposed algorithm achieved a high classification rate with 96%. As comparison, traditional machine learning Support Vector Machine (SVM) and Classical Neural Network (NN) is chose and obtained classification rate at 74.54% and 90.72%.
Accuracy and Stability for Optimum Performance of Digital Fringe Profilometry Method
Authors: Chua Shanyu , Chin Lim Chee , Kheng Eng Swee , Tai Loh Chiun , Fook Chong Yen 
Published: March 2021

Abstract
Various method was developed for the acquisition of the three-dimensional surface of an object. One of the more popular methods used is the structured light profilometry method. It can capture a high-resolution three-dimensional object in real-time. Not only that, but this method is also non-invasive, which is very suitable for the measurement of fragile samples. This paper discusses the accuracy and stability of a structured light profilometry method that is used to obtain a 3D measurement. The experiment is done by using a calibrated camera and projector. A light pattern is then projected onto the sample and captured by the camera. The accuracy of the system is investigated by capturing a flat plate with an increment of 50 µm from 0 µm to 1000 µm. The result has shown a maximum percentage error of this system is 15.76% which is 9.3511 µm, and the minimum percentage error is 0.15% which is 0.7339 µm. For the stability test, the plate was captured thirty times at the same location, and the data obtained shows the consistency of the system has a minimum and maximum standard deviation of 2.4991 µm and 6.8886 µm, which is within 7 µm. The test on the feeler gauge shows a maximum percentage error of 2.12% which is 2.1671 µm.
Dyes extracted from Hibiscus Sabdariffa flower and Pandannus amaryllifolius leaf as natural dye sensitizer by using an alcohol-based solvent
Authors: Rosli N. , Sabani N. , Shahimin M.M. , Juhari N. , Shaari S. , Ahmad M.F. , Zakaria N. 
Published: March 2021

Abstract
The efficiency improvement of Dye sensitized solar cells (DSSC), the fabrication of DSSC by using metal-free organic natural dyes with a different type of solvents for the extraction of dye sensitizer was investigated. The metal-free organic Dye which comes from anthocyanin and chlorophyll dyes were dissolved by using different solvents, which are ethanol, methanol and mixture of ethanol and methanol. Anthocyanin dye was extracted from the petals of Hibiscus Sabdariffa (Roselle), and chlorophyll dye was extracted from the epidermal leaves of Pandannus amaryllifolius (Pandan). The purpose of using different solvents from alcohol-based was to determine which solvents that produce the highest effect in term of efficiency for DSSC. To confirm which solvents that produce the highest efficiency to the DSSC, the photovoltaic measurement was conducted, and the data was collected. From the photovoltaic analysis, the J-V characteristics under illumination are recorded. The sample that used anthocyanin dye extracted from Roselle flower diluted with methanol solvent gave the highest efficiency which is 0.0005% with the following parameters – Voc = 0.419 V, Jsc = 0.0057 mA/cm2 and FF = 0.24. Meanwhile, the sample that used chlorophyll dye extracted from Pandan leaves diluted with a mix of ethanol and methanol solvents gave the highest efficiency which is 0.00014% with the following parameters - Voc = 0.347 V, Jsc = 0.0016 mA/cm2 and FF = 0.25.
Wideband slotted microstrip patch antenna for UHF-RFID reader
Authors: Faudzi N M , Ibrahim A , Mozi A M , Hanaffi W N A 
Published: March 2021

Abstract
This paper is focused on the development of wideband microstrip patch antenna for Ultra High Frequency (UHF) Radio Frequency Identification (RFID) Reader application. This RFID reader can read information from tags and allow the detection of objects. Usually, the RFID Reader has narrow bandwidth and can be used in certain country only. Therefore, this proposed antenna is designed with wideband frequency, low cost and simple structure which can be used worldwide. The operating frequency range for worldwide operation is between 860 MHz to 960 MHz. The structure of the antenna is composed of a Z-shaped feedline on the upper side of FR-4 substrate and a slotted patch on the lower side. The cutting slot method has been used in this antenna design in order to improve the antenna bandwidth as well as the size of the antenna. This RFID Reader was designed and simulated using CST Microwave Studio. The overall size of the proposed UHF RFID antenna is 100 × 100 × 1.6 mm3. The proposed RFID Reader antenna has the reflection coefficient (S11) below than -10 dB and has wideband frequency from 845 MHz to 1.09 GHz in simulation result. The measured and simulated result was compared in order to analyse the performance of the designed antenna. Other antenna parameters such as radiation pattern, bandwidth and gain has also been evaluated and analysed.
T-way Test Suite Generation Strategy based on Ant Colony Algorithm to Support T-way Variable Strength
Authors: Ramli N , Othman R R , Hendradi R , Iszaidy I 
Published: March 2021

Abstract
T-way test suite generation strategy based on Ant Colony algorithm (TTSGA) has been developed to support t-way variable strength testing which tackles exhaustive testing issues. It employs the ant colony optimization algorithm to generate near-optimal number of test suite size. Even though the test suite size is smaller than exhaustive testing, the strategy covers every possible combination of interacting parameters. The strategy has been evaluated by using benchmarked experiments. Results obtained were compared with other existing strategies that support variable strength. It was found that TTSGA produces comparable results with other existing strategies especially for higher strength configurations. Two non-parametric tests, which are Wilcoxon Rank and Friedman test, have been conducted to analyze the results statistically between TTSGA and HSS as only both strategies have complete experiments results. Although the results shows that there is no significant difference of test suite size among them, TTSGA is in the first rank in the Friedman test.
Narrowband to Narrowband Frequency Tunable Slotted Dipole Antenna
Authors: Rodi R. I. M. , Osman M. N. , Rahim M. K. A. , Jusoh M. , Sabapathy T. , Abdullah Al-Hadi A. , Soh P. J. 
Published: March 2021

Abstract
In this paper, the slotted dipole antenna structure is proposed to have four narrowband frequencies with ability to be reconfigured. Narrowband reconfiguration can be achieved by controlling the length of slotted dipole by using switches. The narrowband frequency is to be at 1.8 GHz (GSM), 2.4 GHz (WLAN), 2.6 GHz (4G), and 3.5 GHz (5G) respectively. The longest dipole length produced 1.8 GHz, the second-longest dipole length produced 2.4 GHz, the third-longest dipole length produced 2.6 GHz, while the smallest dipole length produced 3.5 GHz. To maintain the stability of the RF current in the system, sixteen capacitors were used. As well eight inductors were used to isolate the RF current and power supply. The PIN diode was used as a switch to allow the induced current to stop and pass into the slot which means that it can be used to select the desired frequency. Depending on switching configuration, the operating frequency is tuned. Good matching is achieved for all configurations. The simulated result of gain for the four operating is higher than 2 dB while the omnidirectional radiation pattern has been obtained thus makes the proposed antenna suitable for wireless application.
Inhalation and Exhalation Detection for Sleep and Awake Activities Using Non-Contact Ultra-Wideband (UWB) Radar Signal
Authors: Fatihah Shamsul Ariffin Fatin , Munirah Kamarudin Latifah , Ghazali Najah , Nishizaki Hiromitsu , Zakaria Ammar , Muhammad Mamduh bin Syed Zakaria Syed 
Published: March 2021

Abstract
Respiratory is one of the vital signs used to monitor the progression of the illness that are important for clinical and health care fields. From home rehabilitation to intensive care monitoring, the rate of respiration must be constantly monitored as it offers a proactive approach for early detection of patient deterioration that can be used to trigger therapeutic procedures alarms. The use of invasive procedures based on contact transducers is typically necessary to measure the quantity. Nevertheless, these procedures might be troublesome due to the inconvenience and sensitivity of physical contact. Therefore, non-contact human breathing monitoring as a non-invasive procedure is important in long term intensive-care and home healthcare applications. In this paper, respiratory signals from two type of resting activities had been acquired and proposed a Deep Neural Network (DNN) model that can classify the respiratory signal into inhalation and exhalation signal. Several pre-processing techniques has been done onto the signal before it is implemented into the proposed model. The average recognition rate of the respiratory signal using the proposed method was 84.1% when the subject was sleeping and 83.8% when awake.
Ensemble method based architecture using random forest importance to predict employee’s turn over
Authors: Anwar Hossen Md. , Hossain Emran , Zahereel Ishwar Abdul Khalib , Siddika Fatema 
Published: March 2021

Abstract
The departure of a skilled employee can create a problem for a company and this incident is increasing globally. Employee turnover has become an important issue these days due to the heavy workload, low pay, low job satisfaction, poor working environment. Companies face problems as their budget will increase, losing skilled manpower and employees’ trust. It’s taking time to adjust for a new employee and bring risk and increase the cost for the company. It is necessary to bring appropriate solutions to the problem. The main purpose of this paper is to predict the turnover of employees with the help of state of the art machine learning classifier. We have determined employee turnover selection factors using some prediction models. We first pre-processed the dataset by removing correlative attributes. Then, we have scaled the attributes. Secondly, a Sequential selection algorithm (SBS) has been using to reduce features from a high number to a relatively small signal-canton. Then use Chi-square and Random Forest important algorithms to determine the most significant shared key features. Then we get average_montly_hours, satisfaction_level, time_spend_company are responsible for the employee’s departure. Then, we have applied different state of the art machine learning algorithm to measure the accuracy. We have achieved the highest accuracy of 99.4% using the reduced feature with 10-Fold Cross-validation by applied the Random Forest classifier and which is higher than the mentioned reference work.
Compact 2-Way Power Divider for IoT Application
Authors: Masrakin Khuzairi , Ikram Zulkepli Muhammad , Zuraidah Ibrahim Siti , Rahim Hasliza A. , Dewani Aliya A. , Karim M. N. A. 
Published: March 2021

Abstract
The utilization of power dividers covers a vast range of applications such as the Internet of Things (IoT). For this purpose, the need for having a robust and ergonomic electronic device is crucial. Bulky size could barely integrate onto the human body due to its weight that could be very difficult to implement. Hence, this paper presents the design of a compact power divider for IoT applications. It was designed on a substrate Rogers 4350B, εr=3.48, tan δ= 0.0037 and h=0.17mm. The design was meant to be operated at 2.4GHz frequency with the output of equally 3dB power division on the output port. Wilkinson design was implemented to perform the design of power divider which will be simulated using Computer Simulation Technology (CST) microwave studio simulation tools. Parameter sweep was done to its width of transmission line at 0.21mm and 0.31mm to optimize the performance of the design. The simulated result was then compared against measured results in which S-parameter was evaluated in terms of input return loss, isolation and power division. Simulated and measured results showed that 0.21mm width of the transmission line performs better as compared to 0.31mm width of the transmission line.
Multi-Target Detection and Tracking (MTDT) Algorithm Based on Probabilistic Model for Smart Cities
Authors: Jamal Jadaa Khalid , Munirah Kamarudin Latifah , Noori Hussein Waleed , Zakaria Ammar , Muhammad Mamduh Syed Zakaria Syed 
Published: March 2021

Abstract
Wireless Sensor Network (WSNs) provides promising solutions for monitoring in several domains including industrial monitoring and control, home automation and smart cities, etc. There are numerous restrictions on the current development of target detection and tracking algorithms which makes them unsuitable or effective for indoor use. Such constraints include changes in the direction and changing target speeds, missing a target, and target dynamics. These issues come with difficulty in detection and tracking multiple targets. Moreover, the majority of the target tracking algorithms were presented on the conditions that the target is typically smooth with no unexpected changes that are difficult absolutely. Moreover, sensing coverage considers the crucial issue in a wireless sensor network. This paper implies an algorithm for detection and tracking of moving targets (intruders) for an indoor environment based on the probabilistic model utilizing WSN for safety and security. A mathematical model is presented to determine the optimum number of sensor nodes needed. The findings of the simulation showed that the MTDT algorithm provides a low missing target rate of less than 0.7 % for worst-case and can be utilized for different kinds of environment scenarios.
Detection of Crack on Asphalt Pavement using Deep Convolutional Neural Network
Authors: Ahmad Faudzi M. J. A. , Osman M. K. , Muhamed Yusof N. A. , Ahmad K. A. , Ahmad F. , Idris M. , Raof R. A. A. , Nor Hazlyna H. 
Published: March 2021

Abstract
Detection of crack on asphalt pavement is an essential task of monitoring and regulatory inspection. Currently, this task is conducted manually by surveyor or human inspectors for further maintenance works. Manual practice would lead some drawback such as time-consuming, labour intensive, hazardous and also subjective valuation for different individual. To overcome this deficit circumstances an automated technique is implemented. The objective of this study is to develop an intelligent system to detect pavement crack using Deep Convolutional Neural Network (DCNN). This study consists of several procedures which is started with collecting pavement crack images using online and from own developed dataset. The images are pre-processed by resizing the image into desire dimensions. Next, small patches are extracted as inputs to ease of detection and reduce classifier burden. The images further be labelled into two (2) types which is crack and non-crack. In this study, it is utilized Python environment and Keras framework to establish DCNN model. 80% of dataset is used for training set to train, while another 20% is used for testing set to test the model in order to evaluate the performance in terms of accuracy, precision and recall and F1 score. This proposed model is compared on different patch sizes, training algorithms and architectures to get the best classification. Thus, an automated system that able to accurately detect the present of crack in pavement images within speedy computation is successfully developed. To conclude, the system can be used to assist the surveyor or human operator in task of crack detection, so that the process of detection can be done faster and more efficient. This will help in reducing cost of maintenance and enhancing safety of road users.
Segmentation Technique for Nucleus Detection in Blood Images for Chronic Leukaemia
Authors: Daud N. H. M. , Raof R. A. A. , Osman M. K. , Harun N. H. 
Published: March 2021

Abstract
leukaemia is a disease which develops in the bone marrow, causing a large formation of abnormal cells and usually affected adults. The process of inspecting visually on the microscopic images is time consuming and a tiring process. The developed technique is aimed at assisting the haematologists upon identifying the presence of nucleus in blood cell images. Therefore, this technique is hoped to aid the haematologists in early and fast identification of leukaemia. This paper will be focusing on Acute Myeloid Leukaemia (AML). This research work proposed a combination of methods for the detection of Leukaemia using image processing techniques such as L*A*B colour-based thresholding algorithm, Sobel edge detection algorithm, and watershed distance transform to identify the nucleus blasts of leukaemia cells from blood cells image. The developed technique shows that it is able to produce the image of segmented nucleus blasts.
Utilization of semi-aerobic bamboo lid to decrease organic matter
Authors: Arfiati D , Kumala D K W , Lailiyah S , Dina K F 
Published: March 2021

AbstractCatfish cultivation will produce wastewater with a high content of organic matter from feces and uneaten feed. If the wastewater is discharged directly into common waters, it will disturb the aquatic ecosystem. So, it is necessary to treat wastewater before it is discharged into common waters. The purpose of this study is to determine the difference in the percentage of experimental media using wiker bamboo lid towards the organic matter in the wastewater of catfish farming. The research was conducted from September to October 2019 at the Laboratorium Hidrobiologi Divisi Sumberdaya Ikan, Universitas Brawijaya. The research method used was an experimental method with a 2-factor factorial completely randomized design. The first factor was the percentage of bamboo wicker lid (25%, 50%, 75%, 100% and 0% (control)). The second factor was the observation time (12, 36, 48, 60 and 72 hour). The results indicated that organic matter could decrease to 82.19% (73.31 mg / L to 13.06 mg / L) in the 75% lid treatment at 48 hours. The results of water quality measurements except carbondioxide are in accordance with water quality standards. The presence of bamboo cover can reduce organic matter in wastewater. However, it needs treatment for carbondioxide.